Update parquet files (step 92 of 249)

spaces/101-5/gpt4free/g4f/.v1/gpt4free/forefront/__init__.py

@@ -1,214 +0,0 @@
-import hashlib
-from base64 import b64encode
-from json import loads
-from re import findall
-from time import time, sleep
-from typing import Generator, Optional
-from uuid import uuid4
-
-from Crypto.Cipher import AES
-from Crypto.Random import get_random_bytes
-from fake_useragent import UserAgent
-from mailgw_temporary_email import Email
-from requests import post
-from tls_client import Session
-
-from .typing import ForeFrontResponse, AccountData
-
-
-class Account:
-    @staticmethod
-    def create(proxy: Optional[str] = None, logging: bool = False) -> AccountData:
-        proxies = {'http': 'http://' + proxy, 'https': 'http://' + proxy} if proxy else False
-
-        start = time()
-
-        mail_client = Email()
-        mail_client.register()
-        mail_address = mail_client.address
-
-        client = Session(client_identifier='chrome110')
-        client.proxies = proxies
-        client.headers = {
-            'origin': 'https://accounts.forefront.ai',
-            'user-agent': UserAgent().random,
-        }
-
-        response = client.post(
-            'https://clerk.forefront.ai/v1/client/sign_ups?_clerk_js_version=4.38.4',
-            data={'email_address': mail_address},
-        )
-
-        try:
-            trace_token = response.json()['response']['id']
-            if logging:
-                print(trace_token)
-        except KeyError:
-            raise RuntimeError('Failed to create account!')
-
-        response = client.post(
-            f'https://clerk.forefront.ai/v1/client/sign_ups/{trace_token}/prepare_verification?_clerk_js_version=4.38.4',
-            data={
-                'strategy': 'email_link',
-                'redirect_url': 'https://accounts.forefront.ai/sign-up/verify'
-            },
-        )
-
-        if logging:
-            print(response.text)
-
-        if 'sign_up_attempt' not in response.text:
-            raise RuntimeError('Failed to create account!')
-
-        while True:
-            sleep(5)
-            message_id = mail_client.message_list()[0]['id']
-            message = mail_client.message(message_id)
-            verification_url = findall(r'https:\/\/clerk\.forefront\.ai\/v1\/verify\?token=\w.+', message["text"])[0]
-            if verification_url:
-                break
-
-        if logging:
-            print(verification_url)
-        client.get(verification_url)
-
-        response = client.get('https://clerk.forefront.ai/v1/client?_clerk_js_version=4.38.4').json()
-        session_data = response['response']['sessions'][0]
-
-        user_id = session_data['user']['id']
-        session_id = session_data['id']
-        token = session_data['last_active_token']['jwt']
-
-        with open('accounts.txt', 'a') as f:
-            f.write(f'{mail_address}:{token}\n')
-
-        if logging:
-            print(time() - start)
-
-        return AccountData(token=token, user_id=user_id, session_id=session_id)
-
-
-class StreamingCompletion:
-    @staticmethod
-    def create(
-        prompt: str,
-        account_data: AccountData,
-        chat_id=None,
-        action_type='new',
-        default_persona='607e41fe-95be-497e-8e97-010a59b2e2c0',  # default
-        model='gpt-4',
-        proxy=None
-    ) -> Generator[ForeFrontResponse, None, None]:
-        token = account_data.token
-        if not chat_id:
-            chat_id = str(uuid4())
-
-        proxies = {'http': 'http://' + proxy, 'https': 'http://' + proxy} if proxy else None
-        base64_data = b64encode((account_data.user_id + default_persona + chat_id).encode()).decode()
-        encrypted_signature = StreamingCompletion.__encrypt(base64_data, account_data.session_id)
-
-        headers = {
-            'authority': 'chat-server.tenant-forefront-default.knative.chi.coreweave.com',
-            'accept': '*/*',
-            'accept-language': 'en,fr-FR;q=0.9,fr;q=0.8,es-ES;q=0.7,es;q=0.6,en-US;q=0.5,am;q=0.4,de;q=0.3',
-            'cache-control': 'no-cache',
-            'content-type': 'application/json',
-            'origin': 'https://chat.forefront.ai',
-            'pragma': 'no-cache',
-            'referer': 'https://chat.forefront.ai/',
-            'sec-ch-ua': '"Chromium";v="112", "Google Chrome";v="112", "Not:A-Brand";v="99"',
-            'sec-ch-ua-mobile': '?0',
-            'sec-ch-ua-platform': '"macOS"',
-            'sec-fetch-dest': 'empty',
-            'sec-fetch-mode': 'cors',
-            'sec-fetch-site': 'cross-site',
-            'authorization': f"Bearer {token}",
-            'X-Signature': encrypted_signature,
-            'user-agent': UserAgent().random,
-        }
-
-        json_data = {
-            'text': prompt,
-            'action': action_type,
-            'parentId': chat_id,
-            'workspaceId': chat_id,
-            'messagePersona': default_persona,
-            'model': model,
-        }
-
-        for chunk in post(
-            'https://streaming.tenant-forefront-default.knative.chi.coreweave.com/chat',
-            headers=headers,
-            proxies=proxies,
-            json=json_data,
-            stream=True,
-        ).iter_lines():
-            if b'finish_reason":null' in chunk:
-                data = loads(chunk.decode('utf-8').split('data: ')[1])
-                token = data['choices'][0]['delta'].get('content')
-
-                if token is not None:
-                    yield ForeFrontResponse(
-                        **{
-                            'id': chat_id,
-                            'object': 'text_completion',
-                            'created': int(time()),
-                            'text': token,
-                            'model': model,
-                            'choices': [{'text': token, 'index': 0, 'logprobs': None, 'finish_reason': 'stop'}],
-                            'usage': {
-                                'prompt_tokens': len(prompt),
-                                'completion_tokens': len(token),
-                                'total_tokens': len(prompt) + len(token),
-                            },
-                        }
-                    )
-
-    @staticmethod
-    def __encrypt(data: str, key: str) -> str:
-        hash_key = hashlib.sha256(key.encode()).digest()
-        iv = get_random_bytes(16)
-        cipher = AES.new(hash_key, AES.MODE_CBC, iv)
-        encrypted_data = cipher.encrypt(StreamingCompletion.__pad_data(data.encode()))
-        return iv.hex() + encrypted_data.hex()
-
-    @staticmethod
-    def __pad_data(data: bytes) -> bytes:
-        block_size = AES.block_size
-        padding_size = block_size - len(data) % block_size
-        padding = bytes([padding_size] * padding_size)
-        return data + padding
-
-
-class Completion:
-    @staticmethod
-    def create(
-        prompt: str,
-        account_data: AccountData,
-        chat_id=None,
-        action_type='new',
-        default_persona='607e41fe-95be-497e-8e97-010a59b2e2c0',  # default
-        model='gpt-4',
-        proxy=None
-    ) -> ForeFrontResponse:
-        text = ''
-        final_response = None
-        for response in StreamingCompletion.create(
-            account_data=account_data,
-            chat_id=chat_id,
-            prompt=prompt,
-            action_type=action_type,
-            default_persona=default_persona,
-            model=model,
-            proxy=proxy
-        ):
-            if response:
-                final_response = response
-                text += response.text
-
-        if final_response:
-            final_response.text = text
-        else:
-            raise RuntimeError('Unable to get the response, Please try again')
-
-        return final_response

spaces/101-5/gpt4free/g4f/.v1/testing/hpgptai_test.py

@@ -1,41 +0,0 @@
-import hpgptai
-
-#single completion
-res = hpgptai.Completion.create("你是谁","127.0.0.1:7890")
-print(res["reply"])
-
-
-#chat completion
-messages = [
-    {
-        "content": "你是谁",
-        "html": "你是谁",
-        "id": hpgptai.ChatCompletion.randomStr(),
-        "role": "user",
-        "who": "User: ",
-    },
-    {
-        "content": "我是一位AI助手，专门为您提供各种服务和支持。我可以回答您的问题，帮助您解决问题，提供相关信息，并执行一些任务。请随时告诉我您需要什么帮助。",
-        "html": "我是一位AI助手，专门为您提供各种服务和支持。我可以回答您的问题，帮助您解决问题，提供相关信息，并执行一些任务。请随时告诉我您需要什么帮助。",
-        "id": hpgptai.ChatCompletion.randomStr(),
-        "role": "assistant",
-        "who": "AI: ",
-    },
-    {
-        "content": "我上一句问的是什么？",
-        "html": "我上一句问的是什么？",
-        "id": hpgptai.ChatCompletion.randomStr(),
-        "role": "user",
-        "who": "User: ",
-    },
-]
-res = hpgptai.ChatCompletion.create(messages,proxy="127.0.0.1:7890")
-print(res["reply"])
-
-
-
-
-
-
-
-

spaces/1acneusushi/gradio-2dmoleculeeditor/data/CorelDRAW Graphics Suite 2020 Crack Free Download Pros and Cons.md

@@ -1,25 +0,0 @@
-
-<h1>How to Download and Install CorelDRAW Graphics Suite 2020 Crack for Free</h1>
-<p>If you are looking for a powerful and versatile graphic design software, you may want to try CorelDRAW Graphics Suite 2020. This software offers a comprehensive set of tools for creating vector illustrations, layouts, photo editing, typography, and more. You can also access thousands of clip art, digital images, fonts, templates, and fills to enhance your projects.</p>
-<p>However, CorelDRAW Graphics Suite 2020 is not a free software. You need to purchase a subscription or a perpetual license to use it. The subscription costs $249 per year or $20.75 per month, while the perpetual license costs $499. If you want to save money and still enjoy the features of CorelDRAW Graphics Suite 2020, you may be tempted to download a cracked version from the internet. But is it safe and legal to do so?</p>
-<h2>coreldraw 2020 crack only free download</h2><br /><p><b><b>DOWNLOAD</b> &#9999; &#9999; &#9999; <a href="https://byltly.com/2uKxFw">https://byltly.com/2uKxFw</a></b></p><br /><br />
-<h2>The Risks of Using CorelDRAW Graphics Suite 2020 Crack</h2>
-<p>A cracked version of CorelDRAW Graphics Suite 2020 is a modified version that bypasses the activation process and allows you to use the software without paying for it. However, using a cracked version comes with many risks and disadvantages. Here are some of them:</p>
-<ul>
-<li><b>It is illegal.</b> Downloading and using a cracked version of CorelDRAW Graphics Suite 2020 is a violation of the software's license agreement and copyright laws. You may face legal consequences such as fines or lawsuits if you are caught using it.</li>
-<li><b>It is unsafe.</b> Downloading and installing a cracked version of CorelDRAW Graphics Suite 2020 may expose your computer to malware, viruses, spyware, ransomware, or other malicious programs that can harm your system or steal your personal information. You may also lose your data or damage your hardware if the crack is faulty or incompatible.</li>
-<li><b>It is unreliable.</b> A cracked version of CorelDRAW Graphics Suite 2020 may not work properly or have missing or corrupted features. You may also experience crashes, errors, bugs, or performance issues that can affect your work quality and productivity. You may not be able to update the software or access online services such as cloud storage, collaboration tools, or customer support.</li>
-<li><b>It is unethical.</b> Downloading and using a cracked version of CorelDRAW Graphics Suite 2020 is unfair to the developers who spent time and resources to create the software. You are also depriving yourself of the opportunity to learn and improve your skills with a legitimate and professional software.</li>
-</ul>
-<h2>The Benefits of Using CorelDRAW Graphics Suite 2020 Official Version</h2>
-<p>If you want to avoid the risks and disadvantages of using a cracked version of CorelDRAW Graphics Suite 2020, you should consider using the official version instead. Here are some of the benefits of using the official version:</p>
-<ul>
-<li><b>It is legal.</b> Using the official version of CorelDRAW Graphics Suite 2020 means that you are complying with the software's license agreement and copyright laws. You can use the software without worrying about legal issues or penalties.</li>
-<li><b>It is safe.</b> Using the official version of CorelDRAW Graphics Suite 2020 means that you are downloading and installing the software from a trusted source. You can be sure that the software is free from malware, viruses, spyware, ransomware, or other malicious programs that can harm your computer or compromise your security.</li>
-<li><b>It is reliable.</b> Using the official version of CorelDRAW Graphics Suite 2020 means that you are getting the full and latest version of the software with all its features and functions. You can also enjoy updates, bug fixes, enhancements, and new features that can improve your work quality and productivity. You can also access online services such as cloud storage, collaboration tools, customer support, and more.</li>
-<li><b>It is ethical.</b> Using the official version of CorelDRAW Graphics Suite 2020 means that you are supporting the developers who created the software. You are also investing in your own learning and development with a legitimate and professional software.</li>
-</ul>
-<h2>How to Download and Install CorelDRAW Graphics Suite 2020 Official Version</h2>
-<p>If you are convinced that using the official version of Corel</p> ddb901b051<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Creatink V1.0.6 ? Multi-Concept Responsive WordPress Theme ((EXCLUSIVE)).md

@@ -1,67 +0,0 @@
-<br />
-<h1>Creatink v1.0.6 – Multi-Concept Responsive WordPress Theme Review</h1>
-<p>If you are looking for a powerful, versatile, and creative WordPress theme for your website, you might want to check out <strong>Creatink</strong>. Creatink is an awesome looking, multipurpose WordPress theme that comes with various UI elements and countless features. You can create a unique and productive website with the help of tons of options and tools. This theme will be a great solution for business owners, artists, photographers, creative agencies, digital studios, personal freelancers, and anyone who wants to showcase their work in a stunning way.</p>
-<p>In this article, we will review Creatink v1.0.6 – Multi-Concept Responsive WordPress Theme and show you how it can help you create an amazing website that stands out from the crowd. We will cover the following topics:</p>
-<h2>Creatink v1.0.6 – Multi-Concept Responsive WordPress Theme</h2><br /><p><b><b>Download</b> &rarr; <a href="https://byltly.com/2uKxEh">https://byltly.com/2uKxEh</a></b></p><br /><br />
-<ul>
-<li>How to install and customize Creatink?</li>
-<li>What are the different demos and elements of Creatink?</li>
-<li>How to optimize your website with Creatink?</li>
-</ul>
-<p>By the end of this article, you will have a clear idea of what Creatink can do for you and why you should choose it for your website. So let's get started!</p>
- <h2>How to install and customize Creatink?</h2>
-<p>One of the best things about Creatink is that it is very easy to install and customize. You don't need any coding skills or technical knowledge to set up your website with this theme. Here are the steps you need to follow:</p>
- <h3>How to download and activate Creatink?</h3>
-<p>To download Creatink, you need to purchase it from <a href="(^1^)">ThemeForest</a>, where it costs $59 for a regular license. This license includes 6 months of support from the developer team, future updates, quality checks, and access to all features and plugins. You can also extend your support to 12 months for an extra $17.63.</p>
-<p>After purchasing the theme, you will receive a zip file containing the theme files and documentation. You need to unzip the file and upload the creatink.zip file to your WordPress dashboard. Go to Appearance > Themes > Add New > Upload Theme and select the file. Then click on Install Now and Activate the theme.</p>
- <h3>How to use WordPress Live Customizer and Visual Composer?</h3>
-<p>Creatink comes with two powerful tools that will help you customize your website with ease: WordPress Live Customizer and Visual Composer. WordPress Live Customizer allows you to change various aspects of your website, such as colors, fonts, logos, menus, widgets, and more. You can preview the changes in real-time and save them when you are satisfied. To access the Live Customizer, go to Appearance > Customize and explore the options.</p>
-<p>Visual Composer is a drag-and-drop page builder that lets you create stunning pages with no coding required. You can choose from hundreds of elements and templates, and arrange them according to your preferences. You can also edit the content, style, and animation of each element with a few clicks. To use Visual Composer, go to Pages > Add New and click on Backend Editor or Frontend Editor. Then start building your page with the available elements.</p>
- <h3>How to change color schemes, fonts, headers, footers, and layouts?</h3>
-<p>Creatink gives you complete control over the appearance of your website. You can change the color schemes, fonts, headers, footers, and layouts of your website with a few simple steps. Here is how:</p>
-<ul>
-<li>To change the color schemes, go to Appearance > Customize > Colors and choose from the predefined colors or use the color picker to select your own colors.</li>
-<li>To change the fonts, go to Appearance > Customize > Typography and choose from hundreds of Google Fonts for your body text, headings, menus, and buttons.</li>
-<li>To change the headers, go to Appearance > Customize > Header Options and choose from 10 different header styles. You can also customize the logo, menu, social icons, search bar, and sticky header.</li>
-<li>To change the footers, go to Appearance > Customize > Footer Options and choose from 6 different footer styles. You can also customize the widgets, columns, background image, and copyright text.</li>
-<li>To change the layouts, go to Appearance > Customize > Layout Options and choose from 4 different layout styles: wide, boxed, framed, or bordered. You can also customize the background image, color, or pattern for each layout.</li>
-</ul> <h2>What are the different demos and elements of Creatink?</h2>
-<p>Another great thing about Creatink is that it comes with a huge collection of demos and elements that you can use to create your website. You can choose from 40+ different demos for various niches and purposes, such as agency, business, portfolio, blog, shop, photography, and more. You can also use the amazing slider revolution and social feed plugins to add some dynamic and interactive features to your website. Moreover, you can create stunning portfolios and blogs with various options and styles. Let's take a look at some of the demos and elements of Creatink:</p>
-<p></p>
- <h3>How to choose from 40+ different demos for various niches and purposes?</h3>
-<p>Creatink offers you a wide range of demos that you can import with one click and customize according to your needs. You can browse the demos from the <a href="">Creatink website</a> and see how they look on different devices. You can also preview the demos from your WordPress dashboard by going to Appearance > Import Demo Data and clicking on the Preview button. To import a demo, simply click on the Import button and wait for the process to complete. You can then edit the content, images, and settings of the demo as you wish.</p>
- <h3>How to use the amazing slider revolution and social feed plugins?</h3>
-<p>Creatink comes with two premium plugins that will enhance your website: Slider Revolution and Social Feed. Slider Revolution is a powerful plugin that lets you create beautiful sliders, carousels, hero scenes, and animations with ease. You can choose from hundreds of templates or create your own from scratch. You can also add layers, effects, transitions, and parallax to your sliders. To use Slider Revolution, go to Slider Revolution > New Slider and start creating your slider with the available options.</p>
-<p>Social Feed is a plugin that lets you display your social media posts from various platforms, such as Facebook, Twitter, Instagram, YouTube, Pinterest, and more. You can create a grid, list, or carousel layout for your social feed and customize the appearance and behavior of each element. You can also filter, sort, and search your social feed by keywords, hashtags, or usernames. To use Social Feed, go to Social Feed > Add New Feed and start creating your feed with the available options.</p>
- <h3>How to create stunning portfolios and blogs with various options and styles?</h3>
-<p>Creatink also allows you to showcase your work and share your stories with stunning portfolios and blogs. You can create unlimited portfolios and blogs with various options and styles, such as grid, masonry, metro, carousel, slider, lightbox, video, audio, gallery, and more. You can also customize the columns, gaps, filters, pagination, hover effects, and animations of each portfolio or blog. To create a portfolio or blog, go to Portfolio > Add New or Posts > Add New and start creating your portfolio or blog with the available options.</p> <h2>How to optimize your website with Creatink?</h2>
-<p>The last thing we want to talk about is how Creatink can help you optimize your website for better performance and user experience. Creatink is designed to make your website SEO-friendly and fast-loading, as well as responsive and compatible with any device. You can also get top-notch support and free updates with Creatink. Here is how:</p>
- <h3>How to make your website SEO-friendly and fast-loading with Creatink?</h3>
-<p>Creatink is built with SEO in mind, meaning that it follows the best practices and standards for search engine optimization. It has clean and semantic code, proper heading tags, schema markup, breadcrumbs, and social media integration. It also supports Yoast SEO plugin, which is one of the most popular and powerful plugins for SEO. You can use Yoast SEO to optimize your titles, meta descriptions, keywords, sitemaps, and more.</p>
-<p>Creatink is also optimized for speed, meaning that it loads faster and consumes less resources. It has a lightweight and modular framework, minified CSS and JS files, lazy loading images, and cache plugins compatibility. It also supports WP Rocket plugin, which is one of the best plugins for speed optimization. You can use WP Rocket to improve your page loading time, caching, compression, minification, and more.</p>
- <h3>How to ensure your website is responsive and compatible with any device?</h3>
-<p>Creatink is fully responsive and mobile-friendly, meaning that it adapts to any screen size and resolution. It has a fluid and flexible layout, retina-ready graphics, touch-enabled sliders, and responsive menus. It also supports WPBakery Page Builder plugin, which is one of the best plugins for creating responsive websites. You can use WPBakery Page Builder to create custom layouts for different devices and breakpoints.</p>
-<p>Creatink is also compatible with any browser and platform, meaning that it works flawlessly on any device and operating system. It has cross-browser compatibility, RTL support, translation readiness, and WPML plugin compatibility. It also supports WooCommerce plugin, which is one of the best plugins for creating online shops. You can use WooCommerce to sell your products or services on your website with ease.</p>
- <h3>How to get top-notch support and free updates with Creatink?</h3>
-<p>Creatink comes with a dedicated and professional support team that will help you with any issues or questions you might have with the theme. You can contact them via email or through the <a href="">support forum</a>. They will respond to you within 24 hours and provide you with solutions or guidance. You can also access the <a href="">online documentation</a> that covers all the aspects of the theme in detail.</p>
-<p>Creatink also comes with free lifetime updates that will keep your theme up to date with the latest features and improvements. You can update your theme automatically from your WordPress dashboard or manually by downloading the latest version from ThemeForest. You will also receive notifications whenever a new update is available.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Creatink is a fantastic WordPress theme that can help you create a stunning website that suits your needs and goals. It has a lot of features and options that will make your website unique, creative, and productive. You can easily install and customize Creatink with no coding required. You can also choose from 40+ different demos and elements that will give you a head start on your website creation. Moreover, you can optimize your website with Creatink for better performance and user experience. You can also get top-notch support and free updates with Creatink.</p>
-<p>If you are looking for a powerful, versatile, and creative WordPress theme for your website, you should definitely give Creatink a try. You will not regret it!</p>
-<p>To buy Creatink v1.0.6 – Multi-Concept Responsive WordPress Theme, click on the button below:</p>
-<a href="" >Buy Creatink Now</a>
- <h2>FAQs</h2>
-<ul>
-<li><strong>What are the requirements for using Creatink?</strong></li>
-<p>To use Creatink, you need to have WordPress 4.7 or higher installed on your website. You also need to have PHP 5.6 or higher and MySQL 5.6 or higher on your server. You also need to have enough memory limit and max execution time for your website.</p>
-<li><strong>How much does Creatink cost and what are the payment options?</strong></li>
-<p>Creatink costs $59 for a regular license that includes 6 months of support from the developer team, future updates, quality checks, and access to all features and plugins. You can also extend your support to 12 months for an extra $17.63. You can pay with any major credit card, PayPal, or Skrill. You can also use Envato Credits or Envato Tokens to pay for your purchase.</p>
-<li><strong>How can I get help if I have any issues or questions with Creatink?</strong></li>
-<p>If you have any issues or questions with Creatink, you can contact the support team via email at [email protected] or through the <a href="">support forum</a>. They will respond to you within 24 hours and provide you with solutions or guidance. You can also access the <a href="">online documentation</a> that covers all the aspects of the theme in detail. You can also check the <a href="">video tutorials</a> that show you how to use the theme step by step.</p>
-<li><strong>Can I use Creatink for multiple websites or projects?</strong></li>
-<p>Yes, you can use Creatink for multiple websites or projects, as long as you have a valid license for each website or project. You can buy multiple licenses from ThemeForest or extend your existing license to cover more websites or projects. You can also transfer your license to another website or project, as long as you deactivate the theme from the previous one.</p>
-<li><strong>Can I customize Creatink to suit my brand identity and preferences?</strong></li>
-<p>Yes, you can customize Creatink to suit your brand identity and preferences. You can change the color schemes, fonts, headers, footers, layouts, and more with the WordPress Live Customizer and Visual Composer. You can also create your own custom elements and templates with Visual Composer. You can also use child themes and custom CSS to make further changes to the theme.</p>
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spaces/1gistliPinn/ChatGPT4/Examples/Adobe Photoshop Elements 10 (serial Crack) Crack.md

@@ -1,7 +0,0 @@
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-<dd>A: You can update eBuddy Java Facebook to the latest version by downloading it again from PHONEKY or from the official website of eBuddy. You can also check for updates within the app by going to Menu > Settings > About > Check for updates.</dd>
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spaces/1phancelerku/anime-remove-background/Download Epic Race 3D and Compete with Other Players Online.md

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-<p>Do you love parkour games? Do you want to experience the thrill of running, jumping, sliding, and dodging obstacles in a 3D environment? If yes, then you should try Epic Race 3D, a fun and challenging game that will test your skills and reflexes. In this article, we will show you how to download and play Epic Race 3D on your PC, as well as some tips and tricks to help you win the races.</p>
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-<p>Epic Race 3D is a game developed by Good Job Games, the creators of Run Race 3D and Fun Race 3D. It is a parkour game that lets you compete with other players in various levels with different obstacles. You have to complete the parkours as fast as possible, while avoiding falling or crashing into anything. You can also collect coins and unlock new characters and skins.</p>
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-<p>From the list of results, click on the one that says "Epic Race 3D" by Good Job Games. You will be taken to the app's page on the Google Play Store. Here, you can see more information about the game, such as its description, screenshots, reviews, and ratings. To download and install the game, just click on the green "Install" button. The game will start downloading and installing automatically.</p>
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-<p>Congratulations! You have successfully downloaded and installed Epic Race 3D on your PC. You can now enjoy the game on your bigger screen, with better performance and easier controls. To launch the game, just click on its icon on the emulator's home screen or app drawer. You can also create a shortcut on your desktop for easier access.</p>
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-<p>Epic Race 3D is a fun and addictive game that will keep you entertained for hours. However, it can also be challenging and frustrating at times, especially when you face tough opponents or tricky obstacles. To help you improve your skills and win more races, here are some tips and tricks that you can use:</p>
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spaces/1phancelerku/anime-remove-background/Download J Image for PC A Step-by-Step Guide.md

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-<p>If you have j image files on your computer or device, you may wonder how to open them and view their contents. There are two main ways to do this:</p>
- <h3>Using ImageJ Software</h3>
-<p>The best way to open j image files is to use ImageJ software, which is the program that created them in the first place. ImageJ is a free and open source software that can run on Windows, Mac OS X, Linux, and other platforms. You can download it from <a href="(^1^)">https://imagej.net/</a>. To open a j image file with ImageJ, follow these steps:</p>
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-<ol>
-<li>Launch ImageJ on your computer or device.</li>
-<li>Go to File > Open and browse to the location of the j image file you want to open.</li>
-<li>Select the file and click Open. The file will be displayed in a new window.</li>
-<li>You can use the toolbar, menus, and plugins of ImageJ to manipulate, analyze, or export the image as you wish.</li>
-</ol>
- <h3>Using Online Converters</h3>
-<p>If you don't have ImageJ software installed on your computer or device, or if you want to convert j image files to other formats, you can use online converters. These are websites that allow you to upload j image files and convert them to common formats such as JPEG, PNG, GIF, BMP, etc. Some examples of online converters are:</p>
-<ul>
-<li><a href="(^2^)">https://www.freefileconvert.com/jimage-converter</a></li>
-<li><a href="(^3^)">https://www.zamzar.com/convert/jimage-to-jpg/</a></li>
-<li><a href="(^4^)">https://onlineconvertfree.com/convert-format/jimage-to-jpg/</a></li>
-</ul>
-<p>To use an online converter, follow these steps:</p>
-<ol>
-<li>Go to the website of the online converter you want to use.</li>
-<li>Click on Choose File or Browse and select the j image file you want to convert.</li>
-<li>Select the output format you want from the drop-down menu or list.</li>
-<li>Click on Convert or Start Conversion and wait for the process to finish.</li>
-<li>Download the converted file to your computer or device, or share it via email or social media.</li>
-</ol>
- <h2>How to Download J Image Files from the Web</h2>
-<p>If you want to download j image files from the web, you have two options:</p>
- <h3>Using a Web Browser</h3>
-<p>You can use any web browser, such as Chrome, Firefox, Safari, or Edge, to download j image files from the web. To do this, follow these steps:</p>
-<ol>
-<li>Go to the website that contains the j image file you want to download.</li>
-<li>Right-click on the j image file and select Save Image As or Save Link As.</li>
-<li>Choose a location and a name for the file and click Save.</li>
-<li>The file will be downloaded to your computer or device.</li>
-</ol>
- <h3>Using a Download Manager</h3>
-<p>If you want to download multiple j image files at once, or if you want to resume interrupted downloads, you can use a download manager. A download manager is a software that can speed up and manage your downloads. Some examples of download managers are:</p>
-<ul>
-<li><a href="">https://www.internetdownloadmanager.com/</a></li>
-<li><a href="">https://www.freedownloadmanager.org/</a></li>
-<li><a href="">https://www.flashget.com/en/index.htm</a></li>
-</ul>
-<p>To use a download manager, follow these steps:</p>
-<ol>
-<li>Download and install the download manager of your choice on your computer or device.</li>
-<li>Launch the download manager and go to its settings or options.</li>
-<li>Enable the integration with your web browser, or copy and paste the URLs of the j image files you want to download.</li>
-<li>Start the download and wait for it to finish.</li>
-<li>The files will be downloaded to your computer or device.</li>
-</ol>
- <h2>Conclusion</h2>
-<p>J image is a powerful and versatile image format that is used by ImageJ software for image processing and analysis. It has many features and benefits that make it suitable for scientific and medical applications. You can open j image files using ImageJ software or online converters, and you can download them from the web using a web browser or a download manager. We hope this article has helped you understand how to download j image files easily and efficiently.</p>
- <h2>FAQs</h2>
-<ul>
-<li><b>What is the difference between j image and TIFF?</b><br>J image is based on TIFF, but it adds some extra features that are specific to ImageJ, such as support for multiple images in a single file, metadata, plugins, etc.</li>
-<li><b>Can I edit j image files?</b><br>Yes, you can edit j image files using ImageJ software, which offers a variety of tools and features for image manipulation, enhancement, measurement, segmentation, etc.</li>
-<li><b>Can I view j image files on my smartphone or tablet?</b><br>Yes, you can view j image files on your smartphone or tablet using ImageJ for Android or iOS apps, which are available on Google Play Store and App Store respectively.</li>
-<li><b>How can I convert j image files to PDF?</b><br>You can convert j image files to PDF using online converters such as <a href="">https://www.zamzar.com/convert/jimage-to-pdf/</a> or <a href="">https://onlineconvertfree.com/convert-format/jimage-to-pdf/</a>.</li>
-<li><b>Where can I find more information about j image format?</b><br>You can find more information about j image format on the official website of ImageJ at <a href="">https://imagej.net/JImage_Format</a>.</li>
-</ul></p> 197e85843d<br />
-<br />
-<br />

spaces/1phancelerku/anime-remove-background/Download and Install Word 2017 for Windows 7 Without Paying a Cent.md

@@ -1,232 +0,0 @@
-<br />
-<h1>How to Download Word 2017 Free for Windows 7</h1>
-<p>Word 2017 is a word processor developed by Microsoft that allows you to create, edit, and share professional-looking documents. Whether you need to write a report, a resume, a letter, or a blog post, Word 2017 can help you with its powerful features and tools.</p>
-<h2>download word 2017 free for windows 7</h2><br /><p><b><b>Download</b> &mdash; <a href="https://jinyurl.com/2uNUzv">https://jinyurl.com/2uNUzv</a></b></p><br /><br />
-<p>In this article, we will show you how to download Word 2017 free for Windows 7, what are the features and system requirements of Word 2017, how to install and use Word 2017 on Windows 7, and answer some frequently asked questions.</p>
- <h2>Features of Word 2017</h2>
-<p>Word 2017 is more than just a simple word processor. It offers a range of features that make it easy and convenient to work with documents.</p>
- <h3>Editing and formatting options</h3>
-<p>With Word 2017, you can edit and format text, images, tables, charts, shapes, and more with ease. You can use the ribbon menu or the context menu to access various commands and options. You can also use keyboard shortcuts or voice commands to perform actions faster.</p>
-<p>Some of the editing and formatting options in Word 2017 include:</p>
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-<ul>
-<li>Cut, copy, paste, undo, redo</li>
-<li>Find and replace</li>
-<li>Font, size, color, style, alignment</li>
-<li>Bullets, numbers, indents</li>
-<li>Headers, footers, page numbers</li>
-<li>Styles, themes, templates</li>
-<li>Insert pictures, shapes, icons, stickers</li>
-<li>Add hyperlinks, bookmarks, cross-references</li>
-<li>Create tables, charts, SmartArt</li>
-<li>Add footnotes, endnotes, citations</li>
-<li>Insert captions, table of contents</li>
-<li>Use track changes, comments</li>
-<li>Check spelling, grammar</li>
-<li>Show word count</li>
-<li>Use Smart Lookup</li>
-</ul>
- <h3>Collaboration and sharing features</h3>
-<p>Word 2017 also enables you to collaborate and share your documents with others online Office 365 apps and services, such as Outlook, Excel, PowerPoint, OneNote, and Teams</li>
-<li>Use Microsoft Editor, Translator, Researcher, or Designer to enhance your documents</li>
-<li>Open and edit documents from older versions of Word or other word processors</li>
-<li>Use accessibility features, such as Read Aloud, Dictate, or Immersive Reader</li>
-</ul>
- <h2>System Requirements for Word 2017</h2>
-<p>Before you download Word 2017 for Windows 7, you need to make sure that your computer meets the minimum and recommended system requirements for running the software.</p>
- <h3>Hardware requirements</h3>
-<p>The hardware requirements for Word 2017 are as follows:</p>
-<table>
-<tr>
-<th>Component</th>
-<th>Minimum</th>
-<th>Recommended</th>
-</tr>
-<tr>
-<td>CPU</td>
-<td>1 GHz or faster processor with SSE2 instruction set</td>
-<td>2 GHz or faster processor with SSE2 instruction set</td>
-</tr>
-<tr>
-<td>RAM</td>
-<td>2 GB</td>
-<td>4 GB or more</td>
-</tr>
-<tr>
-<td>HDD</td>
-<td>3 GB of available disk space</td>
-<td>4 GB or more of available disk space</td>
-</tr>
-<tr>
-<td>Display</td>
-<td>1024 x 768 resolution</td>
-<td>1280 x 800 resolution or higher</td>
-</tr>
-<tr>
-<td>Graphics card</td>
-<td>DirectX 9 or later, with WDDM 2.0 or higher for Windows 10 (or WDDM 1.3 or higher for Windows 10 Fall Creators Update)</td>
-<td>DirectX 10 or later, with WDDM 2.0 or higher for Windows 10 (or WDDM 1.3 or higher for Windows 10 Fall Creators Update)</td>
-</tr>
-<tr>
-<td>Sound card</td <td>N/A</td>
-<td>N/A</td>
-</tr>
-</table>
- <h3>Software requirements</h3>
-<p>The software requirements for Word 2017 are as follows:</p>
-<ul>
-<li>Operating system: Windows 7 Service Pack 1 or later</li>
-<li>Browser: Microsoft Edge, Internet Explorer 11, Mozilla Firefox, or Google Chrome</li>
-<li>.NET Framework: 3.5 or 4.6 and higher</li>
-<li>Other software: Some features may require additional or advanced hardware or server connectivity, such as Skype for Business, OneDrive, Exchange Server, SharePoint Server, or Microsoft 365.</li>
-</ul>
- <h2>Download Options for Word 2017</h2>
-<p>There are three main ways to download Word 2017 for Windows 7: through a Microsoft 365 subscription, through a standalone Office Home & Business or Home & Student edition, or through Word Online.</p>
- <h3>Microsoft 365 subscription</h3>
-<p>A Microsoft 365 subscription is the best way to get Word 2017 and other Office apps, such as Excel, PowerPoint, Outlook, OneNote, and more. You also get access to online services, such as OneDrive, Skype, Teams, and SharePoint. You can choose from different plans and prices depending on your needs and preferences.</p>
-<p>Some of the benefits of a Microsoft 365 subscription include:</p>
-<ul>
-<li>Always have the latest version of Word and other Office apps</li>
-<li>Install Word on up to five PCs or Macs, five tablets, and five phones per user</li>
-<li>Get 1 TB of cloud storage per user with OneDrive</li>
-<li>Get 60 minutes of Skype calls per month per user</li>
-<li>Get premium support from Microsoft experts</li>
-<li>Get access to exclusive features and updates</li>
-</ul>
-<p>To download Word 2017 with a Microsoft 365 subscription, you need to:</p>
-<ol>
-<li>Go to the <a href="">Microsoft 365 website</a> and choose a plan that suits you.</li>
-<li>Sign in with your Microsoft account or create one if you don't have one.</li>
-<li>Enter your payment details and confirm your purchase.</li <li>Go to the <a href="">Microsoft 365 portal</a> and sign in with your account.</li>
-<li>Select Install Office and follow the instructions to download and install Word 2017 on your Windows 7 computer.</li>
-</ol>
- <h3>Office Home & Business or Home & Student edition</h3>
-<p>If you don't want to pay for a monthly or yearly subscription, you can also buy a standalone version of Word 2017 that comes with a perpetual license. This means that you can use Word 2017 as long as you want, but you won't get any updates or online services.</p>
-<p>You can choose between two editions: Office Home & Business or Office Home & Student. The main difference is that Office Home & Business includes Outlook, while Office Home & Student does not. Both editions include Word, Excel, PowerPoint, and OneNote.</p>
-<p>Some of the benefits of buying a standalone version of Word 2017 include:</p>
-<ul>
-<li>Pay once and use Word 2017 forever</li>
-<li>Install Word on one PC or Mac per license</li>
-<li>Get basic support from Microsoft</li>
-<li>Get access to some features and updates</li>
-</ul>
-<p>To download Word 2017 with a standalone version, you need to:</p>
-<ol>
-<li>Go to the <a href="">Microsoft Store website</a> and choose the edition that suits you.</li>
-<li>Sign in with your Microsoft account or create one if you don't have one.</li>
-<li>Enter your payment details and confirm your purchase.</li>
-<li>Go to the <a href="">Microsoft Store portal</a> and sign in with your account.</li>
-<li>Select Install Office and follow the instructions to download and install Word 2017 on your Windows 7 computer.</li>
-</ol>
- <h3>Word Online</h3>
-<p>If you don't want to pay or install anything, you can also use Word for free in your web browser. This is called Word Online, and it allows you to create, edit, and share documents online with limited features and functionality.</p>
-<p>Some of the benefits of using Word Online include:</p>
-<ul>
-<li>No installation or payment required</li>
-<li>Access Word from any device or platform</li>
-<li>Save and open documents from OneDrive or other cloud services</li>
-<li>Co-author documents with others online</li>
-<li>Export documents to PDF or other formats</li>
-</ul>
-<p>To use Word Online, you need to:</p>
-<ol>
-<li>Go to the <a href="">Word Online website</a>.</li <li>Sign in with your Microsoft account or create one if you don't have one.</li>
-<li>Create a new document or open an existing one from OneDrive or other cloud services.</li>
-<li>Edit and format your document as you wish, using the available features and tools.</li>
-<li>Save and share your document as you wish, using the available options and commands.</li>
-</ol>
- <h2>How to Install Word 2017 on Windows 7</h2>
-<p>Once you have downloaded Word 2017 for Windows 7, you need to install it on your computer. The installation process may vary depending on the download option you chose, but generally, it involves the following steps:</p>
-<ol>
-<li>Run the setup file that you downloaded or received from Microsoft.</li>
-<li>Follow the instructions on the screen to complete the installation.</li>
-<li>Activate Word 2017 with your Microsoft account or product key, if required.</li>
-<li>Launch Word 2017 from the Start menu or the desktop shortcut.</li>
-</ol>
- <h2>How to Use Word 2017 on Windows 7</h2>
-<p>After you have installed Word 2017 on Windows 7, you can start using it to create, edit, and share documents. Here are some basic tips and tricks to help you get started:</p>
-<ul>
-<li>To create a new document, click File > New and choose a blank document or a template.</li>
-<li>To open an existing document, click File > Open and browse to the location of your document, or use the Recent list to access your recent documents.</li>
-<li>To edit your document, use the ribbon menu or the context menu to access various commands and options, or use keyboard shortcuts or voice commands to perform actions faster.</li>
-<li>To format your document, use the ribbon menu or the context menu to access various commands and options, or use keyboard shortcuts or voice commands to perform actions faster.</li>
-<li>To save your document, click File > Save or Save As and choose a location and a format for your document, or use keyboard shortcuts or voice commands to perform actions faster.</li <li>To share your document, click File > Share and choose an option to send your document as an email attachment or a link, or to co-author your document with others online.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Word 2017 is a powerful and versatile word processor that can help you create, edit, and share professional-looking documents. You can download Word 2017 for Windows 7 in three ways: through a Microsoft 365 subscription, through a standalone Office Home & Business or Home & Student edition, or through Word Online. You can also install and use Word 2017 on Windows 7 easily and conveniently, with the help of the features and tools that Word 2017 offers.</p>
-<p>We hope that this article has helped you learn how to download Word 2017 free for Windows 7, and that you enjoy using Word 2017 for your word processing needs. If you have any questions or feedback, please feel free to contact us or leave a comment below.</p>
- <h2>FAQs</h2>
-<p>Here are some common questions and answers about downloading and using Word 2017 on Windows 7.</p>
- <h3>Q: Is Word 2017 compatible with Windows 7?</h3>
-<p>A: Yes, Word 2017 is compatible with Windows 7 Service Pack 1 or later. However, you may need to install some updates or patches to ensure the best performance and security of Word 2017 on Windows 7.</p>
- <h3>Q: How can I update Word 2017 on Windows 7?</h3>
-<p>A: If you have a Microsoft 365 subscription, you can update Word 2017 automatically or manually through the Microsoft 365 portal. If you have a standalone version of Word 2017, you can update Word 2017 manually through the Microsoft Update website or the Windows Update service.</p>
- <h3>Q: How can I uninstall Word 2017 from Windows 7?</h3>
-<p>A: If you want to uninstall Word 2017 from Windows 7, you can do so through the Control Panel or the Settings app. You can choose to uninstall only Word 2017 or the entire Office suite that includes Word 2017.</p>
- <h3>Q: How can I get help with Word 2017 on Windows 7?</h3>
-<p>A: If you need help with Word 2017 on Windows 7, you can use the following resources:</p>
-<ul>
-<li>The built-in Help feature in Word 2017</li>
-<li>The online support website for Word</li>
-<li>The online community forum for Word</li>
-<li>The online learning center for Word</li>
-<li>The premium support service from Microsoft (for Microsoft 365 subscribers)</li>
-</ul>
- <h3>Q: How can I get more features and functionality with Word 2017 on Windows 7?</h3>
-<p>A: If you want to get more features and functionality with Word 2017 on Windows 7, you can do the following:</p>
-<ul>
-<li>Upgrade to a Microsoft 365 subscription that includes Word 2017 and other Office apps and services</li>
-<li>Install add-ins or extensions for Word 2017 that enhance its capabilities and integration with other apps and services</li>
-<li>Use macros or VBA scripts to automate tasks and customize Word 2017</li>
-<li>Use third-party tools or software that work with Word 2017</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />

spaces/1vash/demo-flask-docker-template/api_server.py

@@ -1,164 +0,0 @@
-# official fastapi HF example https://huggingface.co/docs/hub/spaces-sdks-docker-examples#docker-spaces-examples
-
-##################
-# Flask API usages:
-# 1. Just a wrapper over OpenAI API
-# 2. You can use Chain calls of OpenAI API
-# 3. Using your own ML model in combination with openAPI functionality
-# 4. ...
-##################
-
-import os
-import time
-import numpy as np
-from PIL import Image
-
-from pathlib import Path
-
-# Disable tensorflow warnings
-os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
-from tensorflow import keras
-from flask import Flask, jsonify, request, render_template
-
-load_type = 'remote_hub_from_pretrained'
-"""
-local;
-remote_hub_download; 
-remote_hub_from_pretrained;
-remote_hub_pipeline; - needs config.json and this is not easy to grasp how to do it with custom models
-https://discuss.huggingface.co/t/how-to-create-a-config-json-after-saving-a-model/10459/4
-"""
-
-REPO_ID = "1vash/mnist_demo_model"
-MODEL_DIR = "./artifacts/models"
-
-# Load the saved model into memory
-if load_type == 'local':
-    model = keras.models.load_model(f'{MODEL_DIR}/mnist_model.h5')
-elif load_type == 'remote_hub_download':
-    from huggingface_hub import hf_hub_download
-
-    model = keras.models.load_model(hf_hub_download(repo_id=REPO_ID, filename="saved_model.pb"))
-elif load_type == 'remote_hub_from_pretrained':
-    # https://huggingface.co/docs/hub/keras
-    os.environ['TRANSFORMERS_CACHE'] = str(Path(MODEL_DIR).absolute())
-    from huggingface_hub import from_pretrained_keras
-    model = from_pretrained_keras(REPO_ID, cache_dir=MODEL_DIR)
-elif load_type == 'remote_hub_pipeline':
-    from transformers import pipeline
-
-    model = pipeline("image-classification", model=REPO_ID)
-else:
-    raise AssertionError('No load type is specified!')
-
-# Initialize the Flask application
-app = Flask(__name__)
-
-
-# API route for prediction
-@app.route('/predict', methods=['POST'])
-def predict():
-    """
-    Predicts the class label of an input image.
-
-    Request format:
-    {
-        "image": [[pixel_values_gray]]
-    }
-
-    Response format:
-    {
-        "label": predicted_label,
-        "pred_proba" prediction class probability
-        "ml-latency-ms": latency_in_milliseconds
-            (Measures time only for ML operations preprocessing with predict)
-    }
-    """
-    if 'image' not in request.files:
-        # Handle if no file is selected
-        return 'No file selected'
-
-    start_time = time.time()
-
-    file = request.files['image']
-
-    # Get pixels out of file
-    image_data = Image.open(file)
-
-    # Check image shape
-    if image_data.size != (28, 28):
-        return "Invalid image shape. Expected (28, 28), take from 'demo images' folder."
-
-    # Preprocess the image
-    processed_image = preprocess_image(image_data)
-
-    # Make a prediction, verbose=0 to disable progress bar in logs
-    prediction = model.predict(processed_image, verbose=0)
-
-    # Get the predicted class label
-    predicted_label = np.argmax(prediction)
-    proba = prediction[0][predicted_label]
-
-    # Calculate latency in milliseconds
-    latency_ms = (time.time() - start_time) * 1000
-
-    # Return the prediction result and latency as dictionary response
-    response = {
-        'label': int(predicted_label),
-        'pred_proba': float(proba),
-        'ml-latency-ms': round(latency_ms, 4)
-    }
-
-    # dictionary is not a JSON: https://www.quora.com/What-is-the-difference-between-JSON-and-a-dictionary
-    # flask.jsonify vs json.dumps https://sentry.io/answers/difference-between-json-dumps-and-flask-jsonify/
-    # The flask.jsonify() function returns a Response object with Serializable JSON and content_type=application/json.
-    return jsonify(response)
-
-
-# Helper function to preprocess the image
-def preprocess_image(image_data):
-    """Preprocess image for Model Inference
-
-    :param image_data: Raw image
-    :return: image: Preprocessed Image
-    """
-    # Resize the image to match the input shape of the model
-    image = np.array(image_data).reshape(1, 28, 28)
-
-    # Normalize the pixel values
-    image = image.astype('float32') / 255.0
-
-    return image
-
-
-# API route for health check
-@app.route('/health', methods=['GET'])
-def health():
-    """
-    Health check API to ensure the application is running.
-    Returns "OK" if the application is healthy.
-    Demo Usage: "curl http://localhost:5000/health" or using alias "curl http://127.0.0.1:5000/health"
-    """
-    return 'OK'
-
-
-# API route for version
-@app.route('/version', methods=['GET'])
-def version():
-    """
-    Returns the version of the application.
-    Demo Usage: "curl http://127.0.0.1:5000/version" or using alias "curl http://127.0.0.1:5000/version"
-    """
-    return '1.0'
-
-
-@app.route("/")
-def hello_world():
-    return render_template("index.html")
-    # return "<p>Hello, Team!</p>"
-
-
-# Start the Flask application
-if __name__ == '__main__':
-    app.run(debug=True)

spaces/2ndelement/voicevox/voicevox_engine/synthesis_engine/make_synthesis_engines.py

@@ -1,122 +0,0 @@
-import json
-import sys
-from pathlib import Path
-from typing import Dict, List, Optional
-
-from ..utility import engine_root, get_save_dir
-from .core_wrapper import CoreWrapper, load_runtime_lib
-from .synthesis_engine import SynthesisEngine, SynthesisEngineBase
-
-
-def make_synthesis_engines(
-    use_gpu: bool,
-    voicelib_dirs: Optional[List[Path]] = None,
-    voicevox_dir: Optional[Path] = None,
-    runtime_dirs: Optional[List[Path]] = None,
-    cpu_num_threads: Optional[int] = None,
-    enable_mock: bool = True,
-    load_all_models: bool = False,
-) -> Dict[str, SynthesisEngineBase]:
-    """
-    音声ライブラリをロードして、音声合成エンジンを生成
-
-    Parameters
-    ----------
-    use_gpu: bool
-        音声ライブラリに GPU を使わせるか否か
-    voicelib_dirs: List[Path], optional, default=None
-        音声ライブラリ自体があるディレクトリのリスト
-    voicevox_dir: Path, optional, default=None
-        コンパイル済みのvoicevox、またはvoicevox_engineがあるディレクトリ
-    runtime_dirs: List[Path], optional, default=None
-        コアで使用するライブラリのあるディレクトリのリスト
-        None のとき、voicevox_dir、カレントディレクトリになる
-    cpu_num_threads: int, optional, default=None
-        音声ライブラリが、推論に用いるCPUスレッド数を設定する
-        Noneのとき、ライブラリ側の挙動により論理コア数の半分か、物理コア数が指定される
-    enable_mock: bool, optional, default=True
-        コア読み込みに失敗したとき、代わりにmockを使用するかどうか
-    load_all_models: bool, optional, default=False
-        起動時に全てのモデルを読み込むかどうか
-    """
-    if cpu_num_threads == 0 or cpu_num_threads is None:
-        print(
-            "Warning: cpu_num_threads is set to 0. "
-            + "( The library leaves the decision to the synthesis runtime )",
-            file=sys.stderr,
-        )
-        cpu_num_threads = 0
-
-    if voicevox_dir is not None:
-        if voicelib_dirs is not None:
-            voicelib_dirs.append(voicevox_dir)
-        else:
-            voicelib_dirs = [voicevox_dir]
-        if runtime_dirs is not None:
-            runtime_dirs.append(voicevox_dir)
-        else:
-            runtime_dirs = [voicevox_dir]
-    else:
-        root_dir = engine_root()
-        if voicelib_dirs is None:
-            voicelib_dirs = [root_dir]
-        if runtime_dirs is None:
-            runtime_dirs = [root_dir]
-
-    voicelib_dirs = [p.expanduser() for p in voicelib_dirs]
-    runtime_dirs = [p.expanduser() for p in runtime_dirs]
-
-    load_runtime_lib(runtime_dirs)
-
-    synthesis_engines = {}
-
-    if not enable_mock:
-
-        def load_core_library(core_dir: Path, suppress_error: bool = False):
-            """
-            指定されたディレクトリにあるコアを読み込む。
-            ユーザーディレクトリの場合は存在しないこともあるので、エラーを抑制すると良い。
-            """
-            try:
-                core = CoreWrapper(use_gpu, core_dir, cpu_num_threads, load_all_models)
-                metas = json.loads(core.metas())
-                core_version = metas[0]["version"]
-                if core_version in synthesis_engines:
-                    print(
-                        "Warning: Core loading is skipped because of version duplication.",
-                        file=sys.stderr,
-                    )
-                else:
-                    synthesis_engines[core_version] = SynthesisEngine(core=core)
-            except Exception:
-                if not suppress_error:
-                    raise
-
-        for core_dir in voicelib_dirs:
-            load_core_library(core_dir)
-
-        # ユーザーディレクトリにあるコアを読み込む
-        user_voicelib_dirs = []
-        core_libraries_dir = get_save_dir() / "core_libraries"
-        core_libraries_dir.mkdir(exist_ok=True)
-        user_voicelib_dirs.append(core_libraries_dir)
-        for path in core_libraries_dir.glob("*"):
-            if not path.is_dir():
-                continue
-            user_voicelib_dirs.append(path)
-
-        for core_dir in user_voicelib_dirs:
-            load_core_library(core_dir, suppress_error=True)
-
-    else:
-        # モック追加
-        from ..dev.core import metas as mock_metas
-        from ..dev.core import supported_devices as mock_supported_devices
-        from ..dev.synthesis_engine import MockSynthesisEngine
-
-        if "0.0.0" not in synthesis_engines:
-            synthesis_engines["0.0.0"] = MockSynthesisEngine(
-                speakers=mock_metas(), supported_devices=mock_supported_devices()
-            )
-
-    return synthesis_engines

spaces/7hao/bingo/src/components/user-menu.tsx

@@ -1,113 +0,0 @@
-'use client'
-
-import { useEffect, useState } from 'react'
-import Image from 'next/image'
-import { toast } from 'react-hot-toast'
-import { Button } from '@/components/ui/button'
-import pkg from '../../package.json'
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger
-} from '@/components/ui/dropdown-menu'
-import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons'
-import SettingIcon from '@/assets/images/settings.svg'
-import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard'
-
-export function UserMenu() {
-  const [host, setHost] = useState('')
-  const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 })
-  useEffect(() => {
-    setHost(location.host)
-  }, [])
-
-  useEffect(() => {
-    if (isCopied) {
-      toast.success('复制成功')
-    }
-  }, [isCopied])
-  return (
-    <div className="flex items-center justify-between">
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <Button className="pl-0">
-            <div className="flex items-center justify-center text-xs font-medium uppercase rounded-full select-none h-7 w-7 shrink-0 bg-muted/50 text-muted-foreground">
-              <Image alt="settings" src={SettingIcon} width={20} />
-            </div>
-            <span className="ml-2">设置</span>
-          </Button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent sideOffset={8} align="start" className="w-[180px] bg-background">
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="settings"'
-            }
-            className="cursor-pointer"
-          >
-            设置用户
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={() =>
-              location.href='#dialog="voice"'
-            }
-            className="cursor-pointer"
-          >
-            语音设置
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://github.com/weaigc/bingo/"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              开源地址
-              <IconGitHub />
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/spaces/hf4all/bingo"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              托管地址
-              🤗
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem asChild>
-            <a
-              href="https://huggingface.co/login?next=%2Fspaces%2Fhf4all%2Fbingo%3Fduplicate%3Dtrue%26visibility%3Dpublic"
-              target="_blank"
-              rel="noopener noreferrer"
-              className="inline-flex items-center justify-between w-full gap-2 cursor-pointer"
-            >
-              复制站点
-              <IconExternalLink className="w-3 h-3 ml-auto" />
-            </a>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">版本信息 {pkg.version}</div>
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem className="flex-col items-start">
-            <div className="font-medium">站点域名</div>
-            <div onClick={() => copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer">
-              {host} <IconCopy />
-            </div>
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-    </div>
-  )
-}

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

@@ -1,253 +0,0 @@
-import os
-import subprocess
-import shutil
-from concurrent.futures import ThreadPoolExecutor, as_completed
-from tqdm.notebook import tqdm
-from pathlib import Path
-import requests
-
-def run_script():
-    def run_cmd(cmd):
-        process = subprocess.run(cmd, shell=True, check=True, text=True)
-        return process.stdout
-
-    # Change the current directory to /content/
-    os.chdir('/content/')
-    print("Changing dir to /content/")
-
-    # Your function to edit the file
-    def edit_file(file_path):
-        temp_file_path = "/tmp/temp_file.py"
-        changes_made = False
-        with open(file_path, "r") as file, open(temp_file_path, "w") as temp_file:
-            previous_line = ""
-            second_previous_line = ""
-            for line in file:
-                new_line = line.replace("value=160", "value=128")
-                if new_line != line:
-                    print("Replaced 'value=160' with 'value=128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("crepe hop length: 160", "crepe hop length: 128")
-                if new_line != line:
-                    print("Replaced 'crepe hop length: 160' with 'crepe hop length: 128'")
-                    changes_made = True
-                line = new_line
-
-                new_line = line.replace("value=0.88", "value=0.75")
-                if new_line != line:
-                    print("Replaced 'value=0.88' with 'value=0.75'")
-                    changes_made = True
-                line = new_line
-
-                if "label=i18n(\"输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络\")" in previous_line and "value=1," in line:
-                    new_line = line.replace("value=1,", "value=0.25,")
-                    if new_line != line:
-                        print("Replaced 'value=1,' with 'value=0.25,' based on the condition")
-                        changes_made = True
-                    line = new_line
-
-                if "label=i18n(\"总训练轮数total_epoch\")" in previous_line and "value=20," in line:
-                    new_line = line.replace("value=20,", "value=500,")
-                    if new_line != line:
-                        print("Replaced 'value=20,' with 'value=500,' based on the condition for DEFAULT EPOCH")
-                        changes_made = True
-                    line = new_line
-
-                if 'choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny"], # Fork Feature. Add Crepe-Tiny' in previous_line:
-                    if 'value="pm",' in line:
-                        new_line = line.replace('value="pm",', 'value="mangio-crepe",')
-                        if new_line != line:
-                            print("Replaced 'value=\"pm\",' with 'value=\"mangio-crepe\",' based on the condition")
-                            changes_made = True
-                        line = new_line
-
-                new_line = line.replace('label=i18n("输入训练文件夹路径"), value="E:\\\\语音音频+标注\\\\米津玄师\\\\src"', 'label=i18n("输入训练文件夹路径"), value="/content/dataset/"')
-                if new_line != line:
-                    print("Replaced 'label=i18n(\"输入训练文件夹路径\"), value=\"E:\\\\语音音频+标注\\\\米津玄师\\\\src\"' with 'label=i18n(\"输入训练文件夹路径\"), value=\"/content/dataset/\"'")
-                    changes_made = True
-                line = new_line
-
-                if 'label=i18n("是否仅保存最新的ckpt文件以节省硬盘空间"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE ONLY LATEST")
-                            changes_made = True
-                        line = new_line
-
-                if 'label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),' in second_previous_line:
-                    if 'value=i18n("否"),' in line:
-                        new_line = line.replace('value=i18n("否"),', 'value=i18n("是"),')
-                        if new_line != line:
-                            print("Replaced 'value=i18n(\"否\"),' with 'value=i18n(\"是\"),' based on the condition for SAVE SMALL WEIGHTS")
-                            changes_made = True
-                        line = new_line
-
-                temp_file.write(line)
-                second_previous_line = previous_line
-                previous_line = line
-
-        # After finished, we replace the original file with the temp one
-        import shutil
-        shutil.move(temp_file_path, file_path)
-
-        if changes_made:
-            print("Changes made and file saved successfully.")
-        else:
-            print("No changes were needed.")
-
-    # Define the repo path
-    repo_path = '/content/Applio-RVC-Fork'
-
-    def copy_all_files_in_directory(src_dir, dest_dir):
-        # Iterate over all files in source directory
-        for item in Path(src_dir).glob('*'):
-            if item.is_file():
-                # Copy each file to destination directory
-                shutil.copy(item, dest_dir)
-            else:
-                # If it's a directory, make a new directory in the destination and copy the files recursively
-                new_dest = Path(dest_dir) / item.name
-                new_dest.mkdir(exist_ok=True)
-                copy_all_files_in_directory(str(item), str(new_dest))
-
-    def clone_and_copy_repo(repo_path):
-        # New repository link
-        new_repo_link = "https://github.com/IAHispano/Applio-RVC-Fork/"
-        # Temporary path to clone the repository
-        temp_repo_path = "/content/temp_Applio-RVC-Fork"
-        # New folder name
-        new_folder_name = "Applio-RVC-Fork"
-
-        # Clone the latest code from the new repository to a temporary location
-        run_cmd(f"git clone {new_repo_link} {temp_repo_path}")
-        os.chdir(temp_repo_path)
-
-        run_cmd(f"git checkout 3fa4dad3d8961e5ca2522e9e12c0b4ddb71ad402")
-        run_cmd(f"git checkout f9e606c279cb49420597519b0a83b92be81e42e4")
-        run_cmd(f"git checkout 9e305588844c5442d58add1061b29beeca89d679")
-        run_cmd(f"git checkout bf92dc1eb54b4f28d6396a4d1820a25896cc9af8")
-        run_cmd(f"git checkout c3810e197d3cb98039973b2f723edf967ecd9e61")
-        run_cmd(f"git checkout a33159efd134c2413b0afe26a76b7dc87926d2de")
-        run_cmd(f"git checkout 24e251fb62c662e39ac5cf9253cc65deb9be94ec")
-        run_cmd(f"git checkout ad5667d3017e93232dba85969cddac1322ba2902")
-        run_cmd(f"git checkout ce9715392cf52dd5a0e18e00d1b5e408f08dbf27")
-        run_cmd(f"git checkout 7c7da3f2ac68f3bd8f3ad5ca5c700f18ab9f90eb")
-        run_cmd(f"git checkout 4ac395eab101955e8960b50d772c26f592161764")
-        run_cmd(f"git checkout b15b358702294c7375761584e5276c811ffab5e8")
-        run_cmd(f"git checkout 1501793dc490982db9aca84a50647764caa66e51")
-        run_cmd(f"git checkout 21f7faf57219c75e6ba837062350391a803e9ae2")
-        run_cmd(f"git checkout b5eb689fbc409b49f065a431817f822f554cebe7")
-        run_cmd(f"git checkout 7e02fae1ebf24cb151bf6cbe787d06734aa65862")
-        run_cmd(f"git checkout 6aea5ea18ed0b9a1e03fa5d268d6bc3c616672a9")
-        run_cmd(f"git checkout f0f9b25717e59116473fb42bd7f9252cfc32b398")
-        run_cmd(f"git checkout b394de424088a81fc081224bc27338a8651ad3b2")
-        run_cmd(f"git checkout f1999406a88b80c965d2082340f5ea2bfa9ab67a")
-        run_cmd(f"git checkout d98a0fa8dc715308dfc73eac5c553b69c6ee072b")
-        run_cmd(f"git checkout d73267a415fb0eba98477afa43ef71ffd82a7157")
-        run_cmd(f"git checkout 1a03d01356ae79179e1fb8d8915dc9cc79925742")
-        run_cmd(f"git checkout 81497bb3115e92c754300c9b3992df428886a3e9")
-        run_cmd(f"git checkout c5af1f8edcf79cb70f065c0110e279e78e48caf9")
-        run_cmd(f"git checkout cdb3c90109387fa4dfa92f53c3864c71170ffc77")
-
-        # Edit the file here, before copying
-        #edit_file(f"{temp_repo_path}/infer-web.py")
-
-        # Copy all files from the cloned repository to the existing path
-        copy_all_files_in_directory(temp_repo_path, repo_path)
-        print(f"Copying all {new_folder_name} files from GitHub.")
-
-        # Change working directory back to /content/
-        os.chdir('/content/')
-        print("Changed path back to /content/")
-        
-        # Remove the temporary cloned repository
-        shutil.rmtree(temp_repo_path)
-
-    # Call the function
-    clone_and_copy_repo(repo_path)
-
-    # Download the credentials file for RVC archive sheet
-    os.makedirs('/content/Applio-RVC-Fork/stats/', exist_ok=True)
-    run_cmd("wget -q https://cdn.discordapp.com/attachments/945486970883285045/1114717554481569802/peppy-generator-388800-07722f17a188.json -O /content/Applio-RVC-Fork/stats/peppy-generator-388800-07722f17a188.json")
-
-    # Forcefully delete any existing torchcrepe dependencies downloaded from an earlier run just in case
-    shutil.rmtree('/content/Applio-RVC-Fork/torchcrepe', ignore_errors=True)
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)
-
-    # Download the torchcrepe folder from the maxrmorrison/torchcrepe repository
-    run_cmd("git clone https://github.com/maxrmorrison/torchcrepe.git")
-    shutil.move('/content/torchcrepe/torchcrepe', '/content/Applio-RVC-Fork/')
-    shutil.rmtree('/content/torchcrepe', ignore_errors=True)  # Delete the torchcrepe repository folder
-
-    # Change the current directory to /content/Applio-RVC-Fork
-    os.chdir('/content/Applio-RVC-Fork')
-    os.makedirs('pretrained', exist_ok=True)
-    os.makedirs('uvr5_weights', exist_ok=True)
-
-def download_file(url, filepath):
-    response = requests.get(url, stream=True)
-    response.raise_for_status()
-
-    with open(filepath, "wb") as file:
-        for chunk in response.iter_content(chunk_size=8192):
-            if chunk:
-                file.write(chunk)
-
-def download_pretrained_models():
-    pretrained_models = {
-        "pretrained": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth"
-        ],
-        "pretrained_v2": [
-            "D40k.pth",
-            "G40k.pth",
-            "f0D40k.pth",
-            "f0G40k.pth",
-            "f0G48k.pth",
-            "f0D48k.pth"
-        ],
-        "uvr5_weights": [
-            "HP2-人声vocals+非人声instrumentals.pth",
-            "HP5-主旋律人声vocals+其他instrumentals.pth",
-            "VR-DeEchoNormal.pth",
-            "VR-DeEchoDeReverb.pth",
-            "VR-DeEchoAggressive.pth",
-            "HP5_only_main_vocal.pth",
-            "HP3_all_vocals.pth",
-            "HP2_all_vocals.pth"
-        ]
-    }
-    part2 = "I"
-    base_url = "https://huggingface.co/lj1995/VoiceConversionWebU" + part2 + "/resolve/main/"
-    base_path = "/content/Applio-RVC-Fork/"
-    base_pathm = base_path
-
-    # Calculate total number of files to download
-    total_files = sum(len(files) for files in pretrained_models.values()) + 1  # +1 for hubert_base.pt
-
-    with tqdm(total=total_files, desc="Downloading files") as pbar:
-        for folder, models in pretrained_models.items():
-            folder_path = os.path.join(base_path, folder)
-            os.makedirs(folder_path, exist_ok=True)
-            for model in models:
-                url = base_url + folder + "/" + model
-                filepath = os.path.join(folder_path, model)
-                download_file(url, filepath)
-                pbar.update()
-
-        # Download hubert_base.pt to the base path
-        hubert_url = base_url + "hubert_base.pt"
-        hubert_filepath = os.path.join(base_pathm, "hubert_base.pt")
-        download_file(hubert_url, hubert_filepath)
-        pbar.update()
-def clone_repository(run_download):
-    with ThreadPoolExecutor(max_workers=2) as executor:
-        executor.submit(run_script)
-        if run_download:
-            executor.submit(download_pretrained_models)

spaces/AI-Dashboards/Memory-Chat-Story-Generator-ChatGPT/app.py

@@ -1,217 +0,0 @@
-import gradio as gr
-import os 
-import json 
-import requests
-import pandas as pd
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-OPENAI_API_KEY= os.environ["HF_TOKEN"] # Add a token to this space .  Then copy it to the repository secret in this spaces settings panel.  os.environ reads from there.
-# Keys for Open AI ChatGPT API usage are created from here: https://platform.openai.com/account/api-keys
-
-def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
-
-    # 1. Set up a payload
-    payload = {
-    "model": "gpt-3.5-turbo",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    # 2. Define your headers and add a key from https://platform.openai.com/account/api-keys
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
-
-    # 3. Create a chat counter loop that feeds [Predict next best anything based on last input and attention with memory defined by introspective attention over time]
-    print(f"chat_counter - {chat_counter}")
-    if chat_counter != 0 :
-        messages=[]
-        for data in chatbot:
-          temp1 = {}
-          temp1["role"] = "user" 
-          temp1["content"] = data[0] 
-          temp2 = {}
-          temp2["role"] = "assistant" 
-          temp2["content"] = data[1]
-          messages.append(temp1)
-          messages.append(temp2)
-        temp3 = {}
-        temp3["role"] = "user" 
-        temp3["content"] = inputs
-        messages.append(temp3)
-        payload = {
-        "model": "gpt-3.5-turbo",
-        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
-    chat_counter+=1
-
-    # 4. POST it to OPENAI API
-    history.append(inputs)
-    print(f"payload is - {payload}")
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    token_counter = 0 
-    partial_words = "" 
-
-    # 5. Iterate through response lines and structure readable response
-    counter=0
-    for chunk in response.iter_lines():
-        if counter == 0:
-          counter+=1
-          continue
-        if chunk.decode() :
-          chunk = chunk.decode()
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter 
-                   
-
-def reset_textbox():
-    return gr.update(value='')
-
-
-    
-
-# Episodic and Semantic IO
-def list_files(file_path):
-    import os
-    icon_csv = "📄 "
-    icon_txt = "📑 "
-    current_directory = os.getcwd()
-    file_list = []
-    for filename in os.listdir(current_directory):
-        if filename.endswith(".csv"):
-            file_list.append(icon_csv + filename)
-        elif filename.endswith(".txt"):
-            file_list.append(icon_txt + filename)
-    if file_list:
-        return "\n".join(file_list)
-    else:
-        return "No .csv or .txt files found in the current directory."
-
-# Function to read a file
-def read_file(file_path):
-    try:
-        with open(file_path, "r") as file:
-            contents = file.read()
-            return f"{contents}"
-            #return f"Contents of {file_path}:\n{contents}"
-    except FileNotFoundError:
-        return "File not found."
-
-# Function to delete a file
-def delete_file(file_path):
-    try:
-        import os
-        os.remove(file_path)
-        return f"{file_path} has been deleted."
-    except FileNotFoundError:
-        return "File not found."
-
-# Function to write to a file
-def write_file(file_path, content):
-    try:
-        with open(file_path, "w") as file:
-            file.write(content)
-        return f"Successfully written to {file_path}."
-    except:
-        return "Error occurred while writing to file."
-
-# Function to append to a file
-def append_file(file_path, content):
-    try:
-        with open(file_path, "a") as file:
-            file.write(content)
-        return f"Successfully appended to {file_path}."
-    except:
-        return "Error occurred while appending to file."
-
-def download_csv(file_path):
-    df = pd.read_csv(file_path)
-    csv = df.to_csv(index=False)
-    b64 = base64.b64encode(csv.encode()).decode()
-    href = f'<a href="data:application/octet-stream;base64,{b64}" download="{file_path}">Download</a>'
-    return href
-
-title = """<h1 align="center">Memory Chat Story Generator ChatGPT</h1>"""
-description = """
-## ChatGPT Datasets 📚
-- WebText
-- Common Crawl
-- BooksCorpus
-- English Wikipedia
-- Toronto Books Corpus
-- OpenWebText
-## ChatGPT Datasets - Details 📚
-- **WebText:** A dataset of web pages crawled from domains on the Alexa top 5,000 list. This dataset was used to pretrain GPT-2.
-  - [WebText: A Large-Scale Unsupervised Text Corpus by Radford et al.](https://paperswithcode.com/dataset/webtext)
-- **Common Crawl:** A dataset of web pages from a variety of domains, which is updated regularly. This dataset was used to pretrain GPT-3.
-  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/common-crawl) by Brown et al.
-- **BooksCorpus:** A dataset of over 11,000 books from a variety of genres.
-  - [Scalable Methods for 8 Billion Token Language Modeling](https://paperswithcode.com/dataset/bookcorpus) by Zhu et al.
-- **English Wikipedia:** A dump of the English-language Wikipedia as of 2018, with articles from 2001-2017.
-  - [Improving Language Understanding by Generative Pre-Training](https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch?logs=build) Space for Wikipedia Search
-- **Toronto Books Corpus:** A dataset of over 7,000 books from a variety of genres, collected by the University of Toronto.
-  - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://paperswithcode.com/dataset/bookcorpus) by Schwenk and Douze.
-- **OpenWebText:** A dataset of web pages that were filtered to remove content that was likely to be low-quality or spammy. This dataset was used to pretrain GPT-3.
-  - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.  
-  """
-
-# 6. Use Gradio to pull it all together
-with gr.Blocks(css = """#col_container {width: 1400px; margin-left: auto; margin-right: auto;} #chatbot {height: 600px; overflow: auto;}""") as demo:
-    gr.HTML(title)
-    with gr.Column(elem_id = "col_container"):
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter")
-        chatbot = gr.Chatbot(elem_id='chatbot')
-        state = gr.State([])
-        b1 = gr.Button()
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
-            chat_counter = gr.Number(value=0, visible=True, precision=0)
-
-            
-    # Episodic/Semantic IO
-    fileName = gr.Textbox(label="Filename")
-    fileContent = gr.TextArea(label="File Content")
-    completedMessage = gr.Textbox(label="Completed")
-    label = gr.Label()
-    with gr.Row():
-        listFiles = gr.Button("📄 List File(s)")
-        readFile = gr.Button("📖 Read File")
-        saveFile = gr.Button("💾 Save File")
-        deleteFile = gr.Button("🗑️ Delete File")
-        appendFile = gr.Button("➕ Append File")
-        downloadCSV = gr.Button("📥 Download File")
-    listFiles.click(list_files, inputs=fileName, outputs=fileContent)
-    readFile.click(read_file, inputs=fileName, outputs=fileContent)
-    saveFile.click(write_file, inputs=[fileName, fileContent], outputs=completedMessage)
-    deleteFile.click(delete_file, inputs=fileName, outputs=completedMessage)
-    appendFile.click(append_file, inputs=[fileName, fileContent], outputs=completedMessage )
-    downloadCSV.click(download_csv, inputs=fileName, outputs=fileContent)
-
-    inputs.submit(predict, [inputs, top_p, temperature,chat_counter, chatbot, state], [chatbot, state, chat_counter])
-    b1.click(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter])
-    b1.click(reset_textbox, [], [inputs])
-    inputs.submit(reset_textbox, [], [inputs])
-    gr.Markdown(description)
-    
-    demo.queue().launch(debug=True)

spaces/AIFILMS/StyleGANEX/models/stylegan2/op/__init__.py

@@ -1,2 +0,0 @@
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnetv1d101_8xb32_in1k.py

@@ -1,5 +0,0 @@
-_base_ = [
-    '../_base_/models/resnetv1d101.py',
-    '../_base_/datasets/imagenet_bs32_pil_resize.py',
-    '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py'
-]

spaces/Abdllh/poetry202/app.py

@@ -1,53 +0,0 @@
-import gc 
-import gradio as gr
-from transformers import pipeline, set_seed
-
-pipe = pipeline('text-generation', framework='pt', model='akhooli/ap2023', tokenizer='akhooli/ap2023')
-#gc.collect()
-samples = [['أنت'
-           ,1.0, 50, 1.0, 1.0, 114],['هل غادر'
-            ,1.0, 50, 1.0, 1.0, 114 ],['ألا ليت'
-              ,1.0, 50, 1.0, 1.0, 114 ],['يا قدس'
-                 ,1.0, 50, 1.0, 1.0, 114],['عيد بأية حال'
-                   ,1.0, 50, 1.0, 1.0, 114],['لكل شيء إذا ما'
-                    ,1.0, 50, 1.0, 1.0, 114 ],['.'
-                       ,1.0, 50, 1.0, 1.0, 114]]
-                     
-notes = """
-- Enter a short prompt or select (click) one of the examples and click SEND 
-- Adjust parameters (temperture, top k, top p and penalty) through the slider (keep close to default values).
-- For the same seed (randomness), the same output is regenerated if other parameters are fixed 
-- Clear and enter new prompt or select another example and SEND to regenerate
-- The '.' means start a new line from no prompt (your prompt need not be long) 
-- Be patient: this runs on CPU (free tier)
-- Feedback (Twitter): @akhooli (https://twitter.com/akhooli/status/1611025232201977859) 
-- Note/Disclaimer: may generate unaccepted or inappropriate content. Use at your own risk.
-"""
-def sayPoetry(prompt, temp=1.0, topk = 50, topp = 1.0, penalty=1.0, seed=114):
-    if not int(seed) >= 0: seed=114
-    set_seed(seed)
-    gen = pipe(prompt, max_length=96, do_sample=True, temperature=temp, top_k=topk, top_p=topp, repetition_penalty=penalty,
-              min_length = 64, no_repeat_ngram_size = 3, return_full_text=True,
-               num_beams=5, num_return_sequences=1)[0]["generated_text"]
-    poetry =""
-    for line in gen.split('.')[:-1]:
-        poetry += line #+ "\n"
-    return poetry
-poetry = gr.Interface(fn=sayPoetry, 
-                    inputs=[
-                        gr.Textbox(label="Enter short prompt or select from examples:"), 
-                        gr.Slider(0.70, 1.2, step=0.01,value=1.0, label='control  temperature'),
-                        gr.Slider(25, 100, step=1,value=50, label='control  top k'),
-                        gr.Slider(0.80, 1.0, step=0.01,value=1.0, label='control  top p'),
-                        gr.Slider(0.90, 1.50, step=0.01,value=1.0, label='control  penalty'),
-                        gr.Number(value=139750, precision=0, label='Seed'),
-                    ], 
-                outputs=[gr.Textbox(label="Generated Poetry:")], 
-                    
-                allow_flagging='never',
-                title='Arabic Poetry Generation Demo (updated Jan. 2023)',
-                description = "A simple demo of AI generated poetry based on 1M poems fine-tuned using AraGPT2 (be patient, runs on cpu)",
-                examples=samples,
-                cache_examples=False,
-                article = notes)
-poetry.launch() # show_error = True, debug=True

spaces/AchyuthGamer/OpenGPT/g4f/__init__.py

@@ -1,110 +0,0 @@
-from __future__ import annotations
-from requests import get
-#from g4f.models import Model, ModelUtils
-from .models     import Model, ModelUtils
-from .Provider import BaseProvider
-from .typing import CreateResult, Union
-from .debug import logging
-
-version = '0.1.5.6'
-version_check = False
-
-def check_pypi_version() -> None:
-    try:
-        response = get("https://pypi.org/pypi/g4f/json").json()
-        latest_version = response["info"]["version"]
-
-        if version != latest_version:
-            print(f'New pypi version: {latest_version} (current: {version}) | pip install -U g4f')
-
-    except Exception as e:
-        print(f'Failed to check g4f pypi version: {e}')
-
-def get_model_and_provider(model    : Union[Model, str], 
-                           provider : Union[type[BaseProvider], None], 
-                           stream   : bool) -> tuple[Model, type[BaseProvider]]:
-    
-    if isinstance(model, str):
-        if model in ModelUtils.convert:
-            model = ModelUtils.convert[model]
-        else:
-            raise Exception(f'The model: {model} does not exist')
-
-    if not provider:
-        provider = model.best_provider
-
-    if not provider:
-        raise Exception(f'No provider found for model: {model}')
-
-    if not provider.working:
-        raise Exception(f'{provider.__name__} is not working')
-
-    if not provider.supports_stream and stream:
-        raise Exception(f'ValueError: {provider.__name__} does not support "stream" argument')
-
-    if logging:
-        print(f'Using {provider.__name__} provider')
-
-    return model, provider
-
-class ChatCompletion:
-    @staticmethod
-    def create(model: Union[Model, str],
-        messages : list[dict[str, str]],
-        provider : Union[type[BaseProvider], None] = None,
-        stream   : bool = False,
-        auth     : Union[str, None] = None, **kwargs) -> Union[CreateResult, str]:
-
-        model, provider = get_model_and_provider(model, provider, stream)
-
-        if provider.needs_auth and not auth:
-            raise Exception(
-                f'ValueError: {provider.__name__} requires authentication (use auth=\'cookie or token or jwt ...\' param)')
-
-        if provider.needs_auth:
-            kwargs['auth'] = auth
-
-        result = provider.create_completion(model.name, messages, stream, **kwargs)
-        return result if stream else ''.join(result)
-
-    @staticmethod
-    async def create_async(
-        model: Union[Model, str],
-        messages: list[dict[str, str]],
-        provider: Union[type[BaseProvider], None] = None,
-        **kwargs
-    ) -> str:
-        model, provider = get_model_and_provider(model, provider, False)
-
-        return await provider.create_async(model.name, messages, **kwargs)
-
-class Completion:
-    @staticmethod
-    def create(
-        model: str,
-        prompt: str,
-        provider: Union[type[BaseProvider], None] = None,
-        stream: bool = False,
-        **kwargs
-    ) -> Union[CreateResult, str]:
-
-        allowed_models = [
-            'code-davinci-002',
-            'text-ada-001',
-            'text-babbage-001',
-            'text-curie-001',
-            'text-davinci-002',
-            'text-davinci-003'
-        ]
-
-        if model not in allowed_models:
-            raise Exception(f'ValueError: Can\'t use {model} with Completion.create()')
-
-        model, provider = get_model_and_provider(model, provider, stream)
-
-        result = provider.create_completion(model.name, [{"role": "user", "content": prompt}], stream, **kwargs)
-
-        return result if stream else ''.join(result)
-    
-if version_check:
-    check_pypi_version()

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/simpledropdownlist/SimpleDropDownList.d.ts

@@ -1,20 +0,0 @@
-import DropDownList from '../dropdownlist/DropDownList';
-import BuildListConfig from '../utils/build/BuildListConfig';
-
-export default SimpleDropDownList;
-
-declare namespace SimpleDropDownList {
-    interface IConfig extends BuildListConfig.IConfig {
-    }
-
-    interface ICreatorsConfig extends BuildListConfig.ICreators {
-    }
-}
-
-declare class SimpleDropDownList extends DropDownList {
-    constructor(
-        scene: Phaser.Scene,
-        config?: SimpleDropDownList.IConfig,
-        creators?: SimpleDropDownList.ICreatorsConfig
-    );
-}

spaces/AlexWang/lama/saicinpainting/training/modules/__init__.py

@@ -1,31 +0,0 @@
-import logging
-
-from saicinpainting.training.modules.ffc import FFCResNetGenerator
-from saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \
-    NLayerDiscriminator, MultidilatedNLayerDiscriminator
-
-def make_generator(config, kind, **kwargs):
-    logging.info(f'Make generator {kind}')
-
-    if kind == 'pix2pixhd_multidilated':
-        return MultiDilatedGlobalGenerator(**kwargs)
-    
-    if kind == 'pix2pixhd_global':
-        return GlobalGenerator(**kwargs)
-
-    if kind == 'ffc_resnet':
-        return FFCResNetGenerator(**kwargs)
-
-    raise ValueError(f'Unknown generator kind {kind}')
-
-
-def make_discriminator(kind, **kwargs):
-    logging.info(f'Make discriminator {kind}')
-
-    if kind == 'pix2pixhd_nlayer_multidilated':
-        return MultidilatedNLayerDiscriminator(**kwargs)
-
-    if kind == 'pix2pixhd_nlayer':
-        return NLayerDiscriminator(**kwargs)
-
-    raise ValueError(f'Unknown discriminator kind {kind}')

spaces/Alpaca233/SadTalker/src/utils/paste_pic.py

@@ -1,69 +0,0 @@
-import cv2, os
-import numpy as np
-from tqdm import tqdm
-import uuid
-
-from src.utils.videoio import save_video_with_watermark 
-
-def paste_pic(video_path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop=False):
-
-    if not os.path.isfile(pic_path):
-        raise ValueError('pic_path must be a valid path to video/image file')
-    elif pic_path.split('.')[-1] in ['jpg', 'png', 'jpeg']:
-        # loader for first frame
-        full_img = cv2.imread(pic_path)
-    else:
-        # loader for videos
-        video_stream = cv2.VideoCapture(pic_path)
-        fps = video_stream.get(cv2.CAP_PROP_FPS)
-        full_frames = [] 
-        while 1:
-            still_reading, frame = video_stream.read()
-            if not still_reading:
-                video_stream.release()
-                break 
-            break 
-        full_img = frame
-    frame_h = full_img.shape[0]
-    frame_w = full_img.shape[1]
-
-    video_stream = cv2.VideoCapture(video_path)
-    fps = video_stream.get(cv2.CAP_PROP_FPS)
-    crop_frames = []
-    while 1:
-        still_reading, frame = video_stream.read()
-        if not still_reading:
-            video_stream.release()
-            break
-        crop_frames.append(frame)
-    
-    if len(crop_info) != 3:
-        print("you didn't crop the image")
-        return
-    else:
-        r_w, r_h = crop_info[0]
-        clx, cly, crx, cry = crop_info[1]
-        lx, ly, rx, ry = crop_info[2]
-        lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
-        # oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-        # oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-
-        if extended_crop:
-            oy1, oy2, ox1, ox2 = cly, cry, clx, crx
-        else:
-            oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx
-
-    tmp_path = str(uuid.uuid4())+'.mp4'
-    out_tmp = cv2.VideoWriter(tmp_path, cv2.VideoWriter_fourcc(*'MP4V'), fps, (frame_w, frame_h))
-    for crop_frame in tqdm(crop_frames, 'seamlessClone:'):
-        p = cv2.resize(crop_frame.astype(np.uint8), (ox2-ox1, oy2 - oy1)) 
-
-        mask = 255*np.ones(p.shape, p.dtype)
-        location = ((ox1+ox2) // 2, (oy1+oy2) // 2)
-        gen_img = cv2.seamlessClone(p, full_img, mask, location, cv2.NORMAL_CLONE)
-        out_tmp.write(gen_img)
-
-    out_tmp.release()
-
-    save_video_with_watermark(tmp_path, new_audio_path, full_video_path, watermark=False)
-    os.remove(tmp_path)

spaces/Amrrs/DragGan-Inversion/stylegan_human/edit/edit_config.py

@@ -1,19 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-attr_dict = dict(
-    interface_gan={  # strength
-        # strength: negative for shorter, positive for longer
-        'upper_length': [-1],
-        'bottom_length': [1]
-    },
-    stylespace={  # layer, strength, threshold
-        # strength: negative for shorter, positive for longer
-        'upper_length': [5, -5, 0.0028],
-        'bottom_length': [3, 5, 0.003]
-    },
-    sefa={  # layer, strength
-        # -5 # strength: negative for longer, positive for shorter
-        'upper_length': [[4, 5, 6, 7], 5],
-        'bottom_length': [[4, 5, 6, 7], 5]
-    }
-)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/ko/in_translation.md

@@ -1,16 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# 번역중
-
-열심히 번역을 진행중입니다. 조금만 기다려주세요.
-감사합니다!

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/inference/inpainting.py

@@ -1,9 +0,0 @@
-import warnings
-
-from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401
-
-
-warnings.warn(
-    "The `inpainting.py` script is outdated. Please use directly `from diffusers import"
-    " StableDiffusionInpaintPipeline` instead."
-)

spaces/Andy1621/uniformer_image_detection/tools/model_converters/publish_model.py

@@ -1,39 +0,0 @@
-import argparse
-import subprocess
-
-import torch
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(
-        description='Process a checkpoint to be published')
-    parser.add_argument('in_file', help='input checkpoint filename')
-    parser.add_argument('out_file', help='output checkpoint filename')
-    args = parser.parse_args()
-    return args
-
-
-def process_checkpoint(in_file, out_file):
-    checkpoint = torch.load(in_file, map_location='cpu')
-    # remove optimizer for smaller file size
-    if 'optimizer' in checkpoint:
-        del checkpoint['optimizer']
-    # if it is necessary to remove some sensitive data in checkpoint['meta'],
-    # add the code here.
-    torch.save(checkpoint, out_file)
-    sha = subprocess.check_output(['sha256sum', out_file]).decode()
-    if out_file.endswith('.pth'):
-        out_file_name = out_file[:-4]
-    else:
-        out_file_name = out_file
-    final_file = out_file_name + f'-{sha[:8]}.pth'
-    subprocess.Popen(['mv', out_file, final_file])
-
-
-def main():
-    args = parse_args()
-    process_checkpoint(args.in_file, args.out_file)
-
-
-if __name__ == '__main__':
-    main()

spaces/AriaMei/TTSdemo/app.py

@@ -1,164 +0,0 @@
-import gradio as gr
-import torch
-import commons
-import utils
-from models import SynthesizerTrn
-from text.symbols import symbols
-from text import text_to_sequence
-import random
-import os
-import datetime
-import numpy as np
-
-
-def get_text(text, hps):
-    text_norm = text_to_sequence(text, hps.data.text_cleaners)
-    if hps.data.add_blank:
-        text_norm = commons.intersperse(text_norm, 0)
-    text_norm = torch.LongTensor(text_norm)
-    return text_norm
-
-
-def tts(txt, emotion, index, hps, net_g, random_emotion_root):
-    """emotion为参考情感音频路径 或random_sample（随机抽取）"""
-    stn_tst = get_text(txt, hps)
-    rand_wav = ""
-    with torch.no_grad():
-        x_tst = stn_tst.unsqueeze(0)
-        x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-        sid = torch.LongTensor([index])  ##appoint character
-        if os.path.exists(f"{emotion}"):
-            emo = torch.FloatTensor(np.load(f"{emotion}")).unsqueeze(0)
-            rand_wav = emotion
-        elif emotion == "random_sample":
-            while True:
-                rand_wav = random.sample(os.listdir(random_emotion_root), 1)[0]
-                if os.path.exists(f"{random_emotion_root}/{rand_wav}"):
-                    break
-            emo = torch.FloatTensor(np.load(f"{random_emotion_root}/{rand_wav}")).unsqueeze(0)
-            print(f"{random_emotion_root}/{rand_wav}")
-        else:
-            print("emotion参数不正确")
-
-        audio = \
-            net_g.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.8, length_scale=1, emo=emo)[
-                0][
-                0, 0].data.float().numpy()
-        path = random_emotion_root+"/"+rand_wav
-        return audio,path
-
-
-def random_generate(txt, index, hps, net_g, random_emotion_root):
-
-    audio ,rand_wav= tts(txt, emotion='random_sample', index=index, hps=hps, net_g=net_g,
-                random_emotion_root=random_emotion_root)
-    return audio,rand_wav
-
-
-def charaterRoot(name):
-    global random_emotion_root
-    if name == '九条都':
-        random_emotion_root = "9nineEmo/my"
-        index = 0
-    elif name == '新海天':
-        random_emotion_root = "9nineEmo/sr"
-        index = 1
-    elif name == '结城希亚':
-        random_emotion_root = "9nineEmo/na"
-        index = 2
-    elif name == '蕾娜':
-        random_emotion_root = "9nineEmo/gt"
-        index = 3
-    elif name == '索菲':
-        random_emotion_root = "9nineEmo/sf"
-        index = 4
-    return random_emotion_root, index
-
-
-def configSelect(config):
-    global checkPonit, config_file
-    if config == 'mul':
-        config_file = "./configs/9nine_multi.json"
-        checkPonit = "logs/9nineM/G_252000.pth"
-    elif config == "single":
-        config_file = "./configs/sora.json"
-        checkPonit = "logs/sora/G_341200.pth"
-    return config_file, checkPonit
-
-
-def runVits(name, config, txt,emotion):
-    config_file, checkPoint = configSelect(config)
-    random_emotion_root, index = charaterRoot(name=name)
-    checkPonit = checkPoint
-    hps = utils.get_hparams_from_file(config_file)
-    net_g = SynthesizerTrn(
-        len(symbols),
-        hps.data.filter_length // 2 + 1,
-        hps.train.segment_size // hps.data.hop_length,
-        n_speakers=hps.data.n_speakers,
-        **hps.model)
-    _ = net_g.eval()
-
-    _ = utils.load_checkpoint(checkPonit, net_g, None)
-    audio, rand_wav = tts(txt, emotion=emotion, index=index, hps=hps, net_g=net_g,
-                          random_emotion_root=random_emotion_root)
-    return (hps.data.sampling_rate, audio),rand_wav
-
-
-def nineMul(name, txt):
-    config = 'mul'
-    audio ,rand_wav= runVits(name, config, txt,'random_sample')
-    return "multiple model success", audio,rand_wav
-
-
-def nineSingle(name,txt):
-    config = 'single'
-    # name = "新海天"
-    audio ,rand_wav= runVits(name, config, txt,'random_sample')
-    return "single model success", audio,rand_wav
-
-def nineMul_select_emo(name, txt,emo):
-    config = 'mul'
-    # emo = "./9nine"emotion
-    print(emo)
-    audio, _ = runVits(name, config, txt, emo)
-    message = "情感依赖：" + emo + "sythesis success!"
-    return message,audio
-
-app = gr.Blocks()
-with app:
-    with gr.Tabs():
-        with gr.TabItem("9nine multiple model"):
-            character = gr.Radio(['九条都', '新海天', '结城希亚', '蕾娜', '索菲'], label='character',
-                                 info="select character you want")
-
-            text = gr.TextArea(label="input content，Japanese support only", value="祭りに行っただよね、知らない女の子と一緒にいて。")
-
-            submit = gr.Button("generate", variant='privite')
-            message = gr.Textbox(label="Message")
-            audio = gr.Audio(label="output")
-            emotion = gr.Textbox(label="参照情感:")
-            submit.click(nineMul, [character, text], [message, audio,emotion])
-        with gr.TabItem("9nine single model"):
-            character = gr.Radio(['新海天'], label='character',
-                                 info="single model for 新海天 only")
-
-            text = gr.TextArea(label="input content，Japanese support only", value="祭りに行っただよね、知らない女の子と一緒にいて。")
-
-            submit = gr.Button("generate", variant='privite')
-            message = gr.Textbox(label="Message")
-            audio = gr.Audio(label="output")
-            emotion = gr.Textbox(label="参照情感:")
-            submit.click(nineSingle, [character, text], [message, audio,emotion])
-        with gr.TabItem("Choose Emotion Embedding"):
-            character = gr.Radio(['九条都', '新海天', '结城希亚', '蕾娜', '索菲'], label='character',
-                                 info="select character you want")
-
-            text = gr.TextArea(label="input content, Japanese support only", value="祭りに行っただよね、知らない女の子と一緒にいて。")
-            emotion = gr.Textbox(label="从多人模型中获得的情感依照。例如”./9nineEmo/sf/sf0207.wav.emo.npy“,尽量使用本人的情感他人的情感会串味")
-            submit = gr.Button("generate", variant='privite')
-            message = gr.Textbox(label="Message")
-            audio = gr.Audio(label="output")
-
-            submit.click(nineMul_select_emo, [character, text,emotion], [message, audio])
-app.launch()

spaces/Artrajz/vits-simple-api/vits/hubert_model.py

@@ -1,221 +0,0 @@
-import copy
-from typing import Optional, Tuple
-import random
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-        self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = F.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = F.gelu(self.norm0(self.conv0(x)))
-        x = F.gelu(self.conv1(x))
-        x = F.gelu(self.conv2(x))
-        x = F.gelu(self.conv3(x))
-        x = F.gelu(self.conv4(x))
-        x = F.gelu(self.conv5(x))
-        x = F.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = F.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-        self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-        self,
-        src: torch.Tensor,
-        mask: torch.Tensor = None,
-        src_key_padding_mask: torch.Tensor = None,
-        output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-    shape: Tuple[int, int],
-    mask_prob: float,
-    mask_length: int,
-    device: torch.device,
-    min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-    path: str
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/dotenv/version.py

@@ -1 +0,0 @@
-__version__ = "1.0.0"

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

@@ -1,2 +0,0 @@
-"""Contains purely network-related utilities.
-"""

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pip/_vendor/colorama/tests/initialise_test.py

@@ -1,189 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-import sys
-from unittest import TestCase, main, skipUnless
-
-try:
-    from unittest.mock import patch, Mock
-except ImportError:
-    from mock import patch, Mock
-
-from ..ansitowin32 import StreamWrapper
-from ..initialise import init, just_fix_windows_console, _wipe_internal_state_for_tests
-from .utils import osname, replace_by
-
-orig_stdout = sys.stdout
-orig_stderr = sys.stderr
-
-
-class InitTest(TestCase):
-
-    @skipUnless(sys.stdout.isatty(), "sys.stdout is not a tty")
-    def setUp(self):
-        # sanity check
-        self.assertNotWrapped()
-
-    def tearDown(self):
-        _wipe_internal_state_for_tests()
-        sys.stdout = orig_stdout
-        sys.stderr = orig_stderr
-
-    def assertWrapped(self):
-        self.assertIsNot(sys.stdout, orig_stdout, 'stdout should be wrapped')
-        self.assertIsNot(sys.stderr, orig_stderr, 'stderr should be wrapped')
-        self.assertTrue(isinstance(sys.stdout, StreamWrapper),
-            'bad stdout wrapper')
-        self.assertTrue(isinstance(sys.stderr, StreamWrapper),
-            'bad stderr wrapper')
-
-    def assertNotWrapped(self):
-        self.assertIs(sys.stdout, orig_stdout, 'stdout should not be wrapped')
-        self.assertIs(sys.stderr, orig_stderr, 'stderr should not be wrapped')
-
-    @patch('colorama.initialise.reset_all')
-    @patch('colorama.ansitowin32.winapi_test', lambda *_: True)
-    @patch('colorama.ansitowin32.enable_vt_processing', lambda *_: False)
-    def testInitWrapsOnWindows(self, _):
-        with osname("nt"):
-            init()
-            self.assertWrapped()
-
-    @patch('colorama.initialise.reset_all')
-    @patch('colorama.ansitowin32.winapi_test', lambda *_: False)
-    def testInitDoesntWrapOnEmulatedWindows(self, _):
-        with osname("nt"):
-            init()
-            self.assertNotWrapped()
-
-    def testInitDoesntWrapOnNonWindows(self):
-        with osname("posix"):
-            init()
-            self.assertNotWrapped()
-
-    def testInitDoesntWrapIfNone(self):
-        with replace_by(None):
-            init()
-            # We can't use assertNotWrapped here because replace_by(None)
-            # changes stdout/stderr already.
-            self.assertIsNone(sys.stdout)
-            self.assertIsNone(sys.stderr)
-
-    def testInitAutoresetOnWrapsOnAllPlatforms(self):
-        with osname("posix"):
-            init(autoreset=True)
-            self.assertWrapped()
-
-    def testInitWrapOffDoesntWrapOnWindows(self):
-        with osname("nt"):
-            init(wrap=False)
-            self.assertNotWrapped()
-
-    def testInitWrapOffIncompatibleWithAutoresetOn(self):
-        self.assertRaises(ValueError, lambda: init(autoreset=True, wrap=False))
-
-    @patch('colorama.win32.SetConsoleTextAttribute')
-    @patch('colorama.initialise.AnsiToWin32')
-    def testAutoResetPassedOn(self, mockATW32, _):
-        with osname("nt"):
-            init(autoreset=True)
-            self.assertEqual(len(mockATW32.call_args_list), 2)
-            self.assertEqual(mockATW32.call_args_list[1][1]['autoreset'], True)
-            self.assertEqual(mockATW32.call_args_list[0][1]['autoreset'], True)
-
-    @patch('colorama.initialise.AnsiToWin32')
-    def testAutoResetChangeable(self, mockATW32):
-        with osname("nt"):
-            init()
-
-            init(autoreset=True)
-            self.assertEqual(len(mockATW32.call_args_list), 4)
-            self.assertEqual(mockATW32.call_args_list[2][1]['autoreset'], True)
-            self.assertEqual(mockATW32.call_args_list[3][1]['autoreset'], True)
-
-            init()
-            self.assertEqual(len(mockATW32.call_args_list), 6)
-            self.assertEqual(
-                mockATW32.call_args_list[4][1]['autoreset'], False)
-            self.assertEqual(
-                mockATW32.call_args_list[5][1]['autoreset'], False)
-
-
-    @patch('colorama.initialise.atexit.register')
-    def testAtexitRegisteredOnlyOnce(self, mockRegister):
-        init()
-        self.assertTrue(mockRegister.called)
-        mockRegister.reset_mock()
-        init()
-        self.assertFalse(mockRegister.called)
-
-
-class JustFixWindowsConsoleTest(TestCase):
-    def _reset(self):
-        _wipe_internal_state_for_tests()
-        sys.stdout = orig_stdout
-        sys.stderr = orig_stderr
-
-    def tearDown(self):
-        self._reset()
-
-    @patch("colorama.ansitowin32.winapi_test", lambda: True)
-    def testJustFixWindowsConsole(self):
-        if sys.platform != "win32":
-            # just_fix_windows_console should be a no-op
-            just_fix_windows_console()
-            self.assertIs(sys.stdout, orig_stdout)
-            self.assertIs(sys.stderr, orig_stderr)
-        else:
-            def fake_std():
-                # Emulate stdout=not a tty, stderr=tty
-                # to check that we handle both cases correctly
-                stdout = Mock()
-                stdout.closed = False
-                stdout.isatty.return_value = False
-                stdout.fileno.return_value = 1
-                sys.stdout = stdout
-
-                stderr = Mock()
-                stderr.closed = False
-                stderr.isatty.return_value = True
-                stderr.fileno.return_value = 2
-                sys.stderr = stderr
-
-            for native_ansi in [False, True]:
-                with patch(
-                    'colorama.ansitowin32.enable_vt_processing',
-                    lambda *_: native_ansi
-                ):
-                    self._reset()
-                    fake_std()
-
-                    # Regular single-call test
-                    prev_stdout = sys.stdout
-                    prev_stderr = sys.stderr
-                    just_fix_windows_console()
-                    self.assertIs(sys.stdout, prev_stdout)
-                    if native_ansi:
-                        self.assertIs(sys.stderr, prev_stderr)
-                    else:
-                        self.assertIsNot(sys.stderr, prev_stderr)
-
-                    # second call without resetting is always a no-op
-                    prev_stdout = sys.stdout
-                    prev_stderr = sys.stderr
-                    just_fix_windows_console()
-                    self.assertIs(sys.stdout, prev_stdout)
-                    self.assertIs(sys.stderr, prev_stderr)
-
-                    self._reset()
-                    fake_std()
-
-                    # If init() runs first, just_fix_windows_console should be a no-op
-                    init()
-                    prev_stdout = sys.stdout
-                    prev_stderr = sys.stderr
-                    just_fix_windows_console()
-                    self.assertIs(prev_stdout, sys.stdout)
-                    self.assertIs(prev_stderr, sys.stderr)
-
-
-if __name__ == '__main__':
-    main()

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/checkpoint/catalog.py

@@ -1,115 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import logging
-
-from detectron2.utils.file_io import PathHandler, PathManager
-
-
-class ModelCatalog(object):
-    """
-    Store mappings from names to third-party models.
-    """
-
-    S3_C2_DETECTRON_PREFIX = "https://dl.fbaipublicfiles.com/detectron"
-
-    # MSRA models have STRIDE_IN_1X1=True. False otherwise.
-    # NOTE: all BN models here have fused BN into an affine layer.
-    # As a result, you should only load them to a model with "FrozenBN".
-    # Loading them to a model with regular BN or SyncBN is wrong.
-    # Even when loaded to FrozenBN, it is still different from affine by an epsilon,
-    # which should be negligible for training.
-    # NOTE: all models here uses PIXEL_STD=[1,1,1]
-    # NOTE: Most of the BN models here are no longer used. We use the
-    # re-converted pre-trained models under detectron2 model zoo instead.
-    C2_IMAGENET_MODELS = {
-        "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
-        "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
-        "FAIR/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
-        "FAIR/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
-        "FAIR/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
-        "FAIR/X-101-64x4d": "ImageNetPretrained/FBResNeXt/X-101-64x4d.pkl",
-        "FAIR/X-152-32x8d-IN5k": "ImageNetPretrained/25093814/X-152-32x8d-IN5k.pkl",
-    }
-
-    C2_DETECTRON_PATH_FORMAT = (
-        "{prefix}/{url}/output/train/{dataset}/{type}/model_final.pkl"  # noqa B950
-    )
-
-    C2_DATASET_COCO = "coco_2014_train%3Acoco_2014_valminusminival"
-    C2_DATASET_COCO_KEYPOINTS = "keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival"
-
-    # format: {model_name} -> part of the url
-    C2_DETECTRON_MODELS = {
-        "35857197/e2e_faster_rcnn_R-50-C4_1x": "35857197/12_2017_baselines/e2e_faster_rcnn_R-50-C4_1x.yaml.01_33_49.iAX0mXvW",  # noqa B950
-        "35857345/e2e_faster_rcnn_R-50-FPN_1x": "35857345/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_1x.yaml.01_36_30.cUF7QR7I",  # noqa B950
-        "35857890/e2e_faster_rcnn_R-101-FPN_1x": "35857890/12_2017_baselines/e2e_faster_rcnn_R-101-FPN_1x.yaml.01_38_50.sNxI7sX7",  # noqa B950
-        "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "36761737/12_2017_baselines/e2e_faster_rcnn_X-101-32x8d-FPN_1x.yaml.06_31_39.5MIHi1fZ",  # noqa B950
-        "35858791/e2e_mask_rcnn_R-50-C4_1x": "35858791/12_2017_baselines/e2e_mask_rcnn_R-50-C4_1x.yaml.01_45_57.ZgkA7hPB",  # noqa B950
-        "35858933/e2e_mask_rcnn_R-50-FPN_1x": "35858933/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_1x.yaml.01_48_14.DzEQe4wC",  # noqa B950
-        "35861795/e2e_mask_rcnn_R-101-FPN_1x": "35861795/12_2017_baselines/e2e_mask_rcnn_R-101-FPN_1x.yaml.02_31_37.KqyEK4tT",  # noqa B950
-        "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "36761843/12_2017_baselines/e2e_mask_rcnn_X-101-32x8d-FPN_1x.yaml.06_35_59.RZotkLKI",  # noqa B950
-        "48616381/e2e_mask_rcnn_R-50-FPN_2x_gn": "GN/48616381/04_2018_gn_baselines/e2e_mask_rcnn_R-50-FPN_2x_gn_0416.13_23_38.bTlTI97Q",  # noqa B950
-        "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "37697547/12_2017_baselines/e2e_keypoint_rcnn_R-50-FPN_1x.yaml.08_42_54.kdzV35ao",  # noqa B950
-        "35998355/rpn_R-50-C4_1x": "35998355/12_2017_baselines/rpn_R-50-C4_1x.yaml.08_00_43.njH5oD9L",  # noqa B950
-        "35998814/rpn_R-50-FPN_1x": "35998814/12_2017_baselines/rpn_R-50-FPN_1x.yaml.08_06_03.Axg0r179",  # noqa B950
-        "36225147/fast_R-50-FPN_1x": "36225147/12_2017_baselines/fast_rcnn_R-50-FPN_1x.yaml.08_39_09.L3obSdQ2",  # noqa B950
-    }
-
-    @staticmethod
-    def get(name):
-        if name.startswith("Caffe2Detectron/COCO"):
-            return ModelCatalog._get_c2_detectron_baseline(name)
-        if name.startswith("ImageNetPretrained/"):
-            return ModelCatalog._get_c2_imagenet_pretrained(name)
-        raise RuntimeError("model not present in the catalog: {}".format(name))
-
-    @staticmethod
-    def _get_c2_imagenet_pretrained(name):
-        prefix = ModelCatalog.S3_C2_DETECTRON_PREFIX
-        name = name[len("ImageNetPretrained/") :]
-        name = ModelCatalog.C2_IMAGENET_MODELS[name]
-        url = "/".join([prefix, name])
-        return url
-
-    @staticmethod
-    def _get_c2_detectron_baseline(name):
-        name = name[len("Caffe2Detectron/COCO/") :]
-        url = ModelCatalog.C2_DETECTRON_MODELS[name]
-        if "keypoint_rcnn" in name:
-            dataset = ModelCatalog.C2_DATASET_COCO_KEYPOINTS
-        else:
-            dataset = ModelCatalog.C2_DATASET_COCO
-
-        if "35998355/rpn_R-50-C4_1x" in name:
-            # this one model is somehow different from others ..
-            type = "rpn"
-        else:
-            type = "generalized_rcnn"
-
-        # Detectron C2 models are stored in the structure defined in `C2_DETECTRON_PATH_FORMAT`.
-        url = ModelCatalog.C2_DETECTRON_PATH_FORMAT.format(
-            prefix=ModelCatalog.S3_C2_DETECTRON_PREFIX, url=url, type=type, dataset=dataset
-        )
-        return url
-
-
-class ModelCatalogHandler(PathHandler):
-    """
-    Resolve URL like catalog://.
-    """
-
-    PREFIX = "catalog://"
-
-    def _get_supported_prefixes(self):
-        return [self.PREFIX]
-
-    def _get_local_path(self, path, **kwargs):
-        logger = logging.getLogger(__name__)
-        catalog_path = ModelCatalog.get(path[len(self.PREFIX) :])
-        logger.info("Catalog entry {} points to {}".format(path, catalog_path))
-        return PathManager.get_local_path(catalog_path, **kwargs)
-
-    def _open(self, path, mode="r", **kwargs):
-        return PathManager.open(self._get_local_path(path), mode, **kwargs)
-
-
-PathManager.register_handler(ModelCatalogHandler())

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/detectron2/structures/masks.py

@@ -1,532 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import itertools
-import numpy as np
-from typing import Any, Iterator, List, Union
-import pycocotools.mask as mask_util
-import torch
-from torch import device
-
-from detectron2.layers.roi_align import ROIAlign
-from detectron2.utils.memory import retry_if_cuda_oom
-
-from .boxes import Boxes
-
-
-def polygon_area(x, y):
-    # Using the shoelace formula
-    # https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
-    return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
-
-
-def polygons_to_bitmask(polygons: List[np.ndarray], height: int, width: int) -> np.ndarray:
-    """
-    Args:
-        polygons (list[ndarray]): each array has shape (Nx2,)
-        height, width (int)
-
-    Returns:
-        ndarray: a bool mask of shape (height, width)
-    """
-    if len(polygons) == 0:
-        # COCOAPI does not support empty polygons
-        return np.zeros((height, width)).astype(np.bool)
-    rles = mask_util.frPyObjects(polygons, height, width)
-    rle = mask_util.merge(rles)
-    return mask_util.decode(rle).astype(np.bool)
-
-
-def rasterize_polygons_within_box(
-    polygons: List[np.ndarray], box: np.ndarray, mask_size: int
-) -> torch.Tensor:
-    """
-    Rasterize the polygons into a mask image and
-    crop the mask content in the given box.
-    The cropped mask is resized to (mask_size, mask_size).
-
-    This function is used when generating training targets for mask head in Mask R-CNN.
-    Given original ground-truth masks for an image, new ground-truth mask
-    training targets in the size of `mask_size x mask_size`
-    must be provided for each predicted box. This function will be called to
-    produce such targets.
-
-    Args:
-        polygons (list[ndarray[float]]): a list of polygons, which represents an instance.
-        box: 4-element numpy array
-        mask_size (int):
-
-    Returns:
-        Tensor: BoolTensor of shape (mask_size, mask_size)
-    """
-    # 1. Shift the polygons w.r.t the boxes
-    w, h = box[2] - box[0], box[3] - box[1]
-
-    polygons = copy.deepcopy(polygons)
-    for p in polygons:
-        p[0::2] = p[0::2] - box[0]
-        p[1::2] = p[1::2] - box[1]
-
-    # 2. Rescale the polygons to the new box size
-    # max() to avoid division by small number
-    ratio_h = mask_size / max(h, 0.1)
-    ratio_w = mask_size / max(w, 0.1)
-
-    if ratio_h == ratio_w:
-        for p in polygons:
-            p *= ratio_h
-    else:
-        for p in polygons:
-            p[0::2] *= ratio_w
-            p[1::2] *= ratio_h
-
-    # 3. Rasterize the polygons with coco api
-    mask = polygons_to_bitmask(polygons, mask_size, mask_size)
-    mask = torch.from_numpy(mask)
-    return mask
-
-
-class BitMasks:
-    """
-    This class stores the segmentation masks for all objects in one image, in
-    the form of bitmaps.
-
-    Attributes:
-        tensor: bool Tensor of N,H,W, representing N instances in the image.
-    """
-
-    def __init__(self, tensor: Union[torch.Tensor, np.ndarray]):
-        """
-        Args:
-            tensor: bool Tensor of N,H,W, representing N instances in the image.
-        """
-        device = tensor.device if isinstance(tensor, torch.Tensor) else torch.device("cpu")
-        tensor = torch.as_tensor(tensor, dtype=torch.bool, device=device)
-        assert tensor.dim() == 3, tensor.size()
-        self.image_size = tensor.shape[1:]
-        self.tensor = tensor
-
-    @torch.jit.unused
-    def to(self, *args: Any, **kwargs: Any) -> "BitMasks":
-        return BitMasks(self.tensor.to(*args, **kwargs))
-
-    @property
-    def device(self) -> torch.device:
-        return self.tensor.device
-
-    @torch.jit.unused
-    def __getitem__(self, item: Union[int, slice, torch.BoolTensor]) -> "BitMasks":
-        """
-        Returns:
-            BitMasks: Create a new :class:`BitMasks` by indexing.
-
-        The following usage are allowed:
-
-        1. `new_masks = masks[3]`: return a `BitMasks` which contains only one mask.
-        2. `new_masks = masks[2:10]`: return a slice of masks.
-        3. `new_masks = masks[vector]`, where vector is a torch.BoolTensor
-           with `length = len(masks)`. Nonzero elements in the vector will be selected.
-
-        Note that the returned object might share storage with this object,
-        subject to Pytorch's indexing semantics.
-        """
-        if isinstance(item, int):
-            return BitMasks(self.tensor[item].unsqueeze(0))
-        m = self.tensor[item]
-        assert m.dim() == 3, "Indexing on BitMasks with {} returns a tensor with shape {}!".format(
-            item, m.shape
-        )
-        return BitMasks(m)
-
-    @torch.jit.unused
-    def __iter__(self) -> torch.Tensor:
-        yield from self.tensor
-
-    @torch.jit.unused
-    def __repr__(self) -> str:
-        s = self.__class__.__name__ + "("
-        s += "num_instances={})".format(len(self.tensor))
-        return s
-
-    def __len__(self) -> int:
-        return self.tensor.shape[0]
-
-    def nonempty(self) -> torch.Tensor:
-        """
-        Find masks that are non-empty.
-
-        Returns:
-            Tensor: a BoolTensor which represents
-                whether each mask is empty (False) or non-empty (True).
-        """
-        return self.tensor.flatten(1).any(dim=1)
-
-    @staticmethod
-    def from_polygon_masks(
-        polygon_masks: Union["PolygonMasks", List[List[np.ndarray]]], height: int, width: int
-    ) -> "BitMasks":
-        """
-        Args:
-            polygon_masks (list[list[ndarray]] or PolygonMasks)
-            height, width (int)
-        """
-        if isinstance(polygon_masks, PolygonMasks):
-            polygon_masks = polygon_masks.polygons
-        masks = [polygons_to_bitmask(p, height, width) for p in polygon_masks]
-        if len(masks):
-            return BitMasks(torch.stack([torch.from_numpy(x) for x in masks]))
-        else:
-            return BitMasks(torch.empty(0, height, width, dtype=torch.bool))
-
-    @staticmethod
-    def from_roi_masks(roi_masks: "ROIMasks", height: int, width: int) -> "BitMasks":
-        """
-        Args:
-            roi_masks:
-            height, width (int):
-        """
-        return roi_masks.to_bitmasks(height, width)
-
-    def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:
-        """
-        Crop each bitmask by the given box, and resize results to (mask_size, mask_size).
-        This can be used to prepare training targets for Mask R-CNN.
-        It has less reconstruction error compared to rasterization with polygons.
-        However we observe no difference in accuracy,
-        but BitMasks requires more memory to store all the masks.
-
-        Args:
-            boxes (Tensor): Nx4 tensor storing the boxes for each mask
-            mask_size (int): the size of the rasterized mask.
-
-        Returns:
-            Tensor:
-                A bool tensor of shape (N, mask_size, mask_size), where
-                N is the number of predicted boxes for this image.
-        """
-        assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self))
-        device = self.tensor.device
-
-        batch_inds = torch.arange(len(boxes), device=device).to(dtype=boxes.dtype)[:, None]
-        rois = torch.cat([batch_inds, boxes], dim=1)  # Nx5
-
-        bit_masks = self.tensor.to(dtype=torch.float32)
-        rois = rois.to(device=device)
-        output = (
-            ROIAlign((mask_size, mask_size), 1.0, 0, aligned=True)
-            .forward(bit_masks[:, None, :, :], rois)
-            .squeeze(1)
-        )
-        output = output >= 0.5
-        return output
-
-    def get_bounding_boxes(self) -> Boxes:
-        """
-        Returns:
-            Boxes: tight bounding boxes around bitmasks.
-            If a mask is empty, it's bounding box will be all zero.
-        """
-        boxes = torch.zeros(self.tensor.shape[0], 4, dtype=torch.float32)
-        x_any = torch.any(self.tensor, dim=1)
-        y_any = torch.any(self.tensor, dim=2)
-        for idx in range(self.tensor.shape[0]):
-            x = torch.where(x_any[idx, :])[0]
-            y = torch.where(y_any[idx, :])[0]
-            if len(x) > 0 and len(y) > 0:
-                boxes[idx, :] = torch.as_tensor(
-                    [x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32
-                )
-        return Boxes(boxes)
-
-    @staticmethod
-    def cat(bitmasks_list: List["BitMasks"]) -> "BitMasks":
-        """
-        Concatenates a list of BitMasks into a single BitMasks
-
-        Arguments:
-            bitmasks_list (list[BitMasks])
-
-        Returns:
-            BitMasks: the concatenated BitMasks
-        """
-        assert isinstance(bitmasks_list, (list, tuple))
-        assert len(bitmasks_list) > 0
-        assert all(isinstance(bitmask, BitMasks) for bitmask in bitmasks_list)
-
-        cat_bitmasks = type(bitmasks_list[0])(torch.cat([bm.tensor for bm in bitmasks_list], dim=0))
-        return cat_bitmasks
-
-
-class PolygonMasks:
-    """
-    This class stores the segmentation masks for all objects in one image, in the form of polygons.
-
-    Attributes:
-        polygons: list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.
-    """
-
-    def __init__(self, polygons: List[List[Union[torch.Tensor, np.ndarray]]]):
-        """
-        Arguments:
-            polygons (list[list[np.ndarray]]): The first
-                level of the list correspond to individual instances,
-                the second level to all the polygons that compose the
-                instance, and the third level to the polygon coordinates.
-                The third level array should have the format of
-                [x0, y0, x1, y1, ..., xn, yn] (n >= 3).
-        """
-        if not isinstance(polygons, list):
-            raise ValueError(
-                "Cannot create PolygonMasks: Expect a list of list of polygons per image. "
-                "Got '{}' instead.".format(type(polygons))
-            )
-
-        def _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray:
-            # Use float64 for higher precision, because why not?
-            # Always put polygons on CPU (self.to is a no-op) since they
-            # are supposed to be small tensors.
-            # May need to change this assumption if GPU placement becomes useful
-            if isinstance(t, torch.Tensor):
-                t = t.cpu().numpy()
-            return np.asarray(t).astype("float64")
-
-        def process_polygons(
-            polygons_per_instance: List[Union[torch.Tensor, np.ndarray]]
-        ) -> List[np.ndarray]:
-            if not isinstance(polygons_per_instance, list):
-                raise ValueError(
-                    "Cannot create polygons: Expect a list of polygons per instance. "
-                    "Got '{}' instead.".format(type(polygons_per_instance))
-                )
-            # transform each polygon to a numpy array
-            polygons_per_instance = [_make_array(p) for p in polygons_per_instance]
-            for polygon in polygons_per_instance:
-                if len(polygon) % 2 != 0 or len(polygon) < 6:
-                    raise ValueError(f"Cannot create a polygon from {len(polygon)} coordinates.")
-            return polygons_per_instance
-
-        self.polygons: List[List[np.ndarray]] = [
-            process_polygons(polygons_per_instance) for polygons_per_instance in polygons
-        ]
-
-    def to(self, *args: Any, **kwargs: Any) -> "PolygonMasks":
-        return self
-
-    @property
-    def device(self) -> torch.device:
-        return torch.device("cpu")
-
-    def get_bounding_boxes(self) -> Boxes:
-        """
-        Returns:
-            Boxes: tight bounding boxes around polygon masks.
-        """
-        boxes = torch.zeros(len(self.polygons), 4, dtype=torch.float32)
-        for idx, polygons_per_instance in enumerate(self.polygons):
-            minxy = torch.as_tensor([float("inf"), float("inf")], dtype=torch.float32)
-            maxxy = torch.zeros(2, dtype=torch.float32)
-            for polygon in polygons_per_instance:
-                coords = torch.from_numpy(polygon).view(-1, 2).to(dtype=torch.float32)
-                minxy = torch.min(minxy, torch.min(coords, dim=0).values)
-                maxxy = torch.max(maxxy, torch.max(coords, dim=0).values)
-            boxes[idx, :2] = minxy
-            boxes[idx, 2:] = maxxy
-        return Boxes(boxes)
-
-    def nonempty(self) -> torch.Tensor:
-        """
-        Find masks that are non-empty.
-
-        Returns:
-            Tensor:
-                a BoolTensor which represents whether each mask is empty (False) or not (True).
-        """
-        keep = [1 if len(polygon) > 0 else 0 for polygon in self.polygons]
-        return torch.from_numpy(np.asarray(keep, dtype=np.bool))
-
-    def __getitem__(self, item: Union[int, slice, List[int], torch.BoolTensor]) -> "PolygonMasks":
-        """
-        Support indexing over the instances and return a `PolygonMasks` object.
-        `item` can be:
-
-        1. An integer. It will return an object with only one instance.
-        2. A slice. It will return an object with the selected instances.
-        3. A list[int]. It will return an object with the selected instances,
-           correpsonding to the indices in the list.
-        4. A vector mask of type BoolTensor, whose length is num_instances.
-           It will return an object with the instances whose mask is nonzero.
-        """
-        if isinstance(item, int):
-            selected_polygons = [self.polygons[item]]
-        elif isinstance(item, slice):
-            selected_polygons = self.polygons[item]
-        elif isinstance(item, list):
-            selected_polygons = [self.polygons[i] for i in item]
-        elif isinstance(item, torch.Tensor):
-            # Polygons is a list, so we have to move the indices back to CPU.
-            if item.dtype == torch.bool:
-                assert item.dim() == 1, item.shape
-                item = item.nonzero().squeeze(1).cpu().numpy().tolist()
-            elif item.dtype in [torch.int32, torch.int64]:
-                item = item.cpu().numpy().tolist()
-            else:
-                raise ValueError("Unsupported tensor dtype={} for indexing!".format(item.dtype))
-            selected_polygons = [self.polygons[i] for i in item]
-        return PolygonMasks(selected_polygons)
-
-    def __iter__(self) -> Iterator[List[np.ndarray]]:
-        """
-        Yields:
-            list[ndarray]: the polygons for one instance.
-            Each Tensor is a float64 vector representing a polygon.
-        """
-        return iter(self.polygons)
-
-    def __repr__(self) -> str:
-        s = self.__class__.__name__ + "("
-        s += "num_instances={})".format(len(self.polygons))
-        return s
-
-    def __len__(self) -> int:
-        return len(self.polygons)
-
-    def crop_and_resize(self, boxes: torch.Tensor, mask_size: int) -> torch.Tensor:
-        """
-        Crop each mask by the given box, and resize results to (mask_size, mask_size).
-        This can be used to prepare training targets for Mask R-CNN.
-
-        Args:
-            boxes (Tensor): Nx4 tensor storing the boxes for each mask
-            mask_size (int): the size of the rasterized mask.
-
-        Returns:
-            Tensor: A bool tensor of shape (N, mask_size, mask_size), where
-            N is the number of predicted boxes for this image.
-        """
-        assert len(boxes) == len(self), "{} != {}".format(len(boxes), len(self))
-
-        device = boxes.device
-        # Put boxes on the CPU, as the polygon representation is not efficient GPU-wise
-        # (several small tensors for representing a single instance mask)
-        boxes = boxes.to(torch.device("cpu"))
-
-        results = [
-            rasterize_polygons_within_box(poly, box.numpy(), mask_size)
-            for poly, box in zip(self.polygons, boxes)
-        ]
-        """
-        poly: list[list[float]], the polygons for one instance
-        box: a tensor of shape (4,)
-        """
-        if len(results) == 0:
-            return torch.empty(0, mask_size, mask_size, dtype=torch.bool, device=device)
-        return torch.stack(results, dim=0).to(device=device)
-
-    def area(self):
-        """
-        Computes area of the mask.
-        Only works with Polygons, using the shoelace formula:
-        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
-
-        Returns:
-            Tensor: a vector, area for each instance
-        """
-
-        area = []
-        for polygons_per_instance in self.polygons:
-            area_per_instance = 0
-            for p in polygons_per_instance:
-                area_per_instance += polygon_area(p[0::2], p[1::2])
-            area.append(area_per_instance)
-
-        return torch.tensor(area)
-
-    @staticmethod
-    def cat(polymasks_list: List["PolygonMasks"]) -> "PolygonMasks":
-        """
-        Concatenates a list of PolygonMasks into a single PolygonMasks
-
-        Arguments:
-            polymasks_list (list[PolygonMasks])
-
-        Returns:
-            PolygonMasks: the concatenated PolygonMasks
-        """
-        assert isinstance(polymasks_list, (list, tuple))
-        assert len(polymasks_list) > 0
-        assert all(isinstance(polymask, PolygonMasks) for polymask in polymasks_list)
-
-        cat_polymasks = type(polymasks_list[0])(
-            list(itertools.chain.from_iterable(pm.polygons for pm in polymasks_list))
-        )
-        return cat_polymasks
-
-
-class ROIMasks:
-    """
-    Represent masks by N smaller masks defined in some ROIs. Once ROI boxes are given,
-    full-image bitmask can be obtained by "pasting" the mask on the region defined
-    by the corresponding ROI box.
-    """
-
-    def __init__(self, tensor: torch.Tensor):
-        """
-        Args:
-            tensor: (N, M, M) mask tensor that defines the mask within each ROI.
-        """
-        if tensor.dim() != 3:
-            raise ValueError("ROIMasks must take a masks of 3 dimension.")
-        self.tensor = tensor
-
-    def to(self, device: torch.device) -> "ROIMasks":
-        return ROIMasks(self.tensor.to(device))
-
-    @property
-    def device(self) -> device:
-        return self.tensor.device
-
-    def __len__(self):
-        return self.tensor.shape[0]
-
-    def __getitem__(self, item) -> "ROIMasks":
-        """
-        Returns:
-            ROIMasks: Create a new :class:`ROIMasks` by indexing.
-
-        The following usage are allowed:
-
-        1. `new_masks = masks[2:10]`: return a slice of masks.
-        2. `new_masks = masks[vector]`, where vector is a torch.BoolTensor
-           with `length = len(masks)`. Nonzero elements in the vector will be selected.
-
-        Note that the returned object might share storage with this object,
-        subject to Pytorch's indexing semantics.
-        """
-        t = self.tensor[item]
-        if t.dim() != 3:
-            raise ValueError(
-                f"Indexing on ROIMasks with {item} returns a tensor with shape {t.shape}!"
-            )
-        return ROIMasks(t)
-
-    @torch.jit.unused
-    def __repr__(self) -> str:
-        s = self.__class__.__name__ + "("
-        s += "num_instances={})".format(len(self.tensor))
-        return s
-
-    @torch.jit.unused
-    def to_bitmasks(self, boxes: torch.Tensor, height, width, threshold=0.5):
-        """
-        Args: see documentation of :func:`paste_masks_in_image`.
-        """
-        from detectron2.layers.mask_ops import paste_masks_in_image, _paste_masks_tensor_shape
-
-        if torch.jit.is_tracing():
-            if isinstance(height, torch.Tensor):
-                paste_func = _paste_masks_tensor_shape
-            else:
-                paste_func = paste_masks_in_image
-        else:
-            paste_func = retry_if_cuda_oom(paste_masks_in_image)
-        bitmasks = paste_func(self.tensor, boxes.tensor, (height, width), threshold=threshold)
-        return BitMasks(bitmasks)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/tests/test_model_analysis.py

@@ -1,80 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-
-import unittest
-import torch
-from torch import nn
-
-from detectron2.utils.analysis import find_unused_parameters, flop_count_operators, parameter_count
-from detectron2.utils.testing import get_model_no_weights
-
-
-class RetinaNetTest(unittest.TestCase):
-    def setUp(self):
-        self.model = get_model_no_weights("COCO-Detection/retinanet_R_50_FPN_1x.yaml")
-
-    def test_flop(self):
-        # RetinaNet supports flop-counting with random inputs
-        inputs = [{"image": torch.rand(3, 800, 800), "test_unused": "abcd"}]
-        res = flop_count_operators(self.model, inputs)
-        self.assertEqual(int(res["conv"]), 146)  # 146B flops
-
-    def test_param_count(self):
-        res = parameter_count(self.model)
-        self.assertEqual(res[""], 37915572)
-        self.assertEqual(res["backbone"], 31452352)
-
-
-class FasterRCNNTest(unittest.TestCase):
-    def setUp(self):
-        self.model = get_model_no_weights("COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml")
-
-    def test_flop(self):
-        # Faster R-CNN supports flop-counting with random inputs
-        inputs = [{"image": torch.rand(3, 800, 800)}]
-        res = flop_count_operators(self.model, inputs)
-
-        # This only checks flops for backbone & proposal generator
-        # Flops for box head is not conv, and depends on #proposals, which is
-        # almost 0 for random inputs.
-        self.assertEqual(int(res["conv"]), 117)
-
-    def test_flop_with_output_shape(self):
-        inputs = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}]
-        res = flop_count_operators(self.model, inputs)
-        self.assertEqual(int(res["conv"]), 117)
-
-    def test_param_count(self):
-        res = parameter_count(self.model)
-        self.assertEqual(res[""], 41699936)
-        self.assertEqual(res["backbone"], 26799296)
-
-
-class MaskRCNNTest(unittest.TestCase):
-    def setUp(self):
-        self.model = get_model_no_weights("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml")
-
-    def test_flop(self):
-        inputs1 = [{"image": torch.rand(3, 800, 800)}]
-        inputs2 = [{"image": torch.rand(3, 800, 800), "height": 700, "width": 700}]
-
-        for inputs in [inputs1, inputs2]:
-            res = flop_count_operators(self.model, inputs)
-            # The mask head could have extra conv flops, so total >= 117
-            self.assertGreaterEqual(int(res["conv"]), 117)
-
-
-class UnusedParamTest(unittest.TestCase):
-    def test_unused(self):
-        class TestMod(nn.Module):
-            def __init__(self):
-                super().__init__()
-                self.fc1 = nn.Linear(10, 10)
-                self.t = nn.Linear(10, 10)
-
-            def forward(self, x):
-                return self.fc1(x).mean()
-
-        m = TestMod()
-        ret = find_unused_parameters(m, torch.randn(10, 10))
-        self.assertEqual(set(ret), {"t.weight", "t.bias"})

spaces/AzumaSeren100/XuanShen-Bert-VITS2/resample.py

@@ -1,42 +0,0 @@
-import os
-import argparse
-import librosa
-import numpy as np
-from multiprocessing import Pool, cpu_count
-
-import soundfile
-from scipy.io import wavfile
-from tqdm import tqdm
-
-
-def process(item):
-    spkdir, wav_name, args = item
-    speaker = spkdir.replace("\\", "/").split("/")[-1]
-    wav_path = os.path.join(args.in_dir, speaker, wav_name)
-    if os.path.exists(wav_path) and '.wav' in wav_path:
-        os.makedirs(os.path.join(args.out_dir, speaker), exist_ok=True)
-        wav, sr = librosa.load(wav_path, sr=args.sr)
-        soundfile.write(
-            os.path.join(args.out_dir, speaker, wav_name),
-            wav,
-            sr
-        )
-
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--sr", type=int, default=44100, help="sampling rate")
-    parser.add_argument("--in_dir", type=str, default="./raw", help="path to source dir")
-    parser.add_argument("--out_dir", type=str, default="./dataset", help="path to target dir")
-    args = parser.parse_args()
-    # processs = 8
-    processs = cpu_count()-2 if cpu_count() >4 else 1
-    pool = Pool(processes=processs)
-
-    for speaker in os.listdir(args.in_dir):
-        spk_dir = os.path.join(args.in_dir, speaker)
-        if os.path.isdir(spk_dir):
-            print(spk_dir)
-            for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
-                pass

spaces/Bart92/RVC_HF/infer/lib/infer_pack/attentions.py

@@ -1,417 +0,0 @@
-import copy
-import math
-
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer.lib.infer_pack import commons, modules
-from infer.lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        self.drop = nn.Dropout(p_dropout)
-        self.attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask):
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.attn_layers[i](x, x, attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-        **kwargs
-    ):
-        super().__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_attn_layers = nn.ModuleList()
-        self.norm_layers_1 = nn.ModuleList()
-        self.ffn_layers = nn.ModuleList()
-        self.norm_layers_2 = nn.ModuleList()
-        for i in range(self.n_layers):
-            self.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
-                )
-            )
-            self.norm_layers_1.append(LayerNorm(hidden_channels))
-            self.ffn_layers.append(
-                FFN(
-                    hidden_channels,
-                    hidden_channels,
-                    filter_channels,
-                    kernel_size,
-                    p_dropout=p_dropout,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        """
-        x: decoder input
-        h: encoder output
-        """
-        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_1[i](x + y)
-
-            y = self.ffn_layers[i](x, x_mask)
-            y = self.drop(y)
-            x = self.norm_layers_2[i](x + y)
-        x = x * x_mask
-        return x
-
-
-class MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        proximal_bias=False,
-        proximal_init=False,
-    ):
-        super().__init__()
-        assert channels % n_heads == 0
-
-        self.channels = channels
-        self.out_channels = out_channels
-        self.n_heads = n_heads
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        self.attn = None
-
-        self.k_channels = channels // n_heads
-        self.conv_q = nn.Conv1d(channels, channels, 1)
-        self.conv_k = nn.Conv1d(channels, channels, 1)
-        self.conv_v = nn.Conv1d(channels, channels, 1)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        if window_size is not None:
-            n_heads_rel = 1 if heads_share else n_heads
-            rel_stddev = self.k_channels**-0.5
-            self.emb_rel_k = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-            self.emb_rel_v = nn.Parameter(
-                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
-                * rel_stddev
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(self, x, c, attn_mask=None):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(self, query, key, value, mask=None):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s, t_t = (*key.size(), query.size(2))
-        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        if self.window_size is not None:
-            assert (
-                t_s == t_t
-            ), "Relative attention is only available for self-attention."
-            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-            rel_logits = self._matmul_with_relative_keys(
-                query / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            scores = scores + scores_local
-        if self.proximal_bias:
-            assert t_s == t_t, "Proximal bias is only available for self-attention."
-            scores = scores + self._attention_bias_proximal(t_s).to(
-                device=scores.device, dtype=scores.dtype
-            )
-        if mask is not None:
-            scores = scores.masked_fill(mask == 0, -1e4)
-            if self.block_length is not None:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
-        p_attn = self.drop(p_attn)
-        output = torch.matmul(p_attn, value)
-        if self.window_size is not None:
-            relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(
-                self.emb_rel_v, t_s
-            )
-            output = output + self._matmul_with_relative_values(
-                relative_weights, value_relative_embeddings
-            )
-        output = (
-            output.transpose(2, 3).contiguous().view(b, d, t_t)
-        )  # [b, n_h, t_t, d_k] -> [b, d, t_t]
-        return output, p_attn
-
-    def _matmul_with_relative_values(self, x, y):
-        """
-        x: [b, h, l, m]
-        y: [h or 1, m, d]
-        ret: [b, h, l, d]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0))
-        return ret
-
-    def _matmul_with_relative_keys(self, x, y):
-        """
-        x: [b, h, l, d]
-        y: [h or 1, m, d]
-        ret: [b, h, l, m]
-        """
-        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-        return ret
-
-    def _get_relative_embeddings(self, relative_embeddings, length):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length = max(length - (self.window_size + 1), 0)
-        slice_start_position = max((self.window_size + 1) - length, 0)
-        slice_end_position = slice_start_position + 2 * length - 1
-        if pad_length > 0:
-            padded_relative_embeddings = F.pad(
-                relative_embeddings,
-                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
-            )
-        else:
-            padded_relative_embeddings = relative_embeddings
-        used_relative_embeddings = padded_relative_embeddings[
-            :, slice_start_position:slice_end_position
-        ]
-        return used_relative_embeddings
-
-    def _relative_position_to_absolute_position(self, x):
-        """
-        x: [b, h, l, 2*l-1]
-        ret: [b, h, l, l]
-        """
-        batch, heads, length, _ = x.size()
-        # Concat columns of pad to shift from relative to absolute indexing.
-        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
-
-        # Concat extra elements so to add up to shape (len+1, 2*len-1).
-        x_flat = x.view([batch, heads, length * 2 * length])
-        x_flat = F.pad(
-            x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
-        )
-
-        # Reshape and slice out the padded elements.
-        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
-            :, :, :length, length - 1 :
-        ]
-        return x_final
-
-    def _absolute_position_to_relative_position(self, x):
-        """
-        x: [b, h, l, l]
-        ret: [b, h, l, 2*l-1]
-        """
-        batch, heads, length, _ = x.size()
-        # padd along column
-        x = F.pad(
-            x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
-        )
-        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length):
-        """Bias for self-attention to encourage attention to close positions.
-        Args:
-          length: an integer scalar.
-        Returns:
-          a Tensor with shape [1, 1, length, length]
-        """
-        r = torch.arange(length, dtype=torch.float32)
-        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        filter_channels,
-        kernel_size,
-        p_dropout=0.0,
-        activation=None,
-        causal=False,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.activation = activation
-        self.causal = causal
-
-        if causal:
-            self.padding = self._causal_padding
-        else:
-            self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def forward(self, x, x_mask):
-        x = self.conv_1(self.padding(x * x_mask))
-        if self.activation == "gelu":
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-        x = self.conv_2(self.padding(x * x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = self.kernel_size - 1
-        pad_r = 0
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l = (self.kernel_size - 1) // 2
-        pad_r = self.kernel_size // 2
-        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, commons.convert_pad_shape(padding))
-        return x

spaces/Bart92/RVC_HF/tools/infer/train-index-v2.py

@@ -1,79 +0,0 @@
-"""
-格式：直接cid为自带的index位；aid放不下了，通过字典来查，反正就5w个
-"""
-import os
-import traceback
-import logging
-
-logger = logging.getLogger(__name__)
-
-from multiprocessing import cpu_count
-
-import faiss
-import numpy as np
-from sklearn.cluster import MiniBatchKMeans
-
-# ###########如果是原始特征要先写save
-n_cpu = 0
-if n_cpu == 0:
-    n_cpu = cpu_count()
-inp_root = r"./logs/anz/3_feature768"
-npys = []
-listdir_res = list(os.listdir(inp_root))
-for name in sorted(listdir_res):
-    phone = np.load("%s/%s" % (inp_root, name))
-    npys.append(phone)
-big_npy = np.concatenate(npys, 0)
-big_npy_idx = np.arange(big_npy.shape[0])
-np.random.shuffle(big_npy_idx)
-big_npy = big_npy[big_npy_idx]
-logger.debug(big_npy.shape)  # (6196072, 192)#fp32#4.43G
-if big_npy.shape[0] > 2e5:
-    # if(1):
-    info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
-    logger.info(info)
-    try:
-        big_npy = (
-            MiniBatchKMeans(
-                n_clusters=10000,
-                verbose=True,
-                batch_size=256 * n_cpu,
-                compute_labels=False,
-                init="random",
-            )
-            .fit(big_npy)
-            .cluster_centers_
-        )
-    except:
-        info = traceback.format_exc()
-        logger.warn(info)
-
-np.save("tools/infer/big_src_feature_mi.npy", big_npy)
-
-##################train+add
-# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
-n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
-index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf)  # mi
-logger.info("Training...")
-index_ivf = faiss.extract_index_ivf(index)  #
-index_ivf.nprobe = 1
-index.train(big_npy)
-faiss.write_index(
-    index, "tools/infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
-)
-logger.info("Adding...")
-batch_size_add = 8192
-for i in range(0, big_npy.shape[0], batch_size_add):
-    index.add(big_npy[i : i + batch_size_add])
-faiss.write_index(
-    index, "tools/infer/added_IVF%s_Flat_mi_baseline_src_feat.index" % (n_ivf)
-)
-"""
-大小（都是FP32）
-big_src_feature 2.95G
-    (3098036, 256)
-big_emb         4.43G
-    (6196072, 192)
-big_emb双倍是因为求特征要repeat后再加pitch
-
-"""

spaces/BetterAPI/BetterChat/src/lib/utils/sha256.ts

@@ -1,7 +0,0 @@
-export async function sha256(input: string): Promise<string> {
-	const utf8 = new TextEncoder().encode(input);
-	const hashBuffer = await crypto.subtle.digest("SHA-256", utf8);
-	const hashArray = Array.from(new Uint8Array(hashBuffer));
-	const hashHex = hashArray.map((bytes) => bytes.toString(16).padStart(2, "0")).join("");
-	return hashHex;
-}

spaces/CVH-vn1210/make_hair/minigpt4/common/config.py

@@ -1,468 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
-"""
-
-import logging
-import json
-from typing import Dict
-
-from omegaconf import OmegaConf
-from minigpt4.common.registry import registry
-
-
-class Config:
-    def __init__(self, args):
-        self.config = {}
-
-        self.args = args
-
-        # Register the config and configuration for setup
-        registry.register("configuration", self)
-
-        user_config = self._build_opt_list(self.args.options)
-
-        config = OmegaConf.load(self.args.cfg_path)
-
-        runner_config = self.build_runner_config(config)
-        model_config = self.build_model_config(config, **user_config)
-        dataset_config = self.build_dataset_config(config)
-
-        # Validate the user-provided runner configuration
-        # model and dataset configuration are supposed to be validated by the respective classes
-        # [TODO] validate the model/dataset configuration
-        # self._validate_runner_config(runner_config)
-
-        # Override the default configuration with user options.
-        self.config = OmegaConf.merge(
-            runner_config, model_config, dataset_config, user_config
-        )
-
-    def _validate_runner_config(self, runner_config):
-        """
-        This method validates the configuration, such that
-            1) all the user specified options are valid;
-            2) no type mismatches between the user specified options and the config.
-        """
-        runner_config_validator = create_runner_config_validator()
-        runner_config_validator.validate(runner_config)
-
-    def _build_opt_list(self, opts):
-        opts_dot_list = self._convert_to_dot_list(opts)
-        return OmegaConf.from_dotlist(opts_dot_list)
-
-    @staticmethod
-    def build_model_config(config, **kwargs):
-        model = config.get("model", None)
-        assert model is not None, "Missing model configuration file."
-
-        model_cls = registry.get_model_class(model.arch)
-        assert model_cls is not None, f"Model '{model.arch}' has not been registered."
-
-        model_type = kwargs.get("model.model_type", None)
-        if not model_type:
-            model_type = model.get("model_type", None)
-        # else use the model type selected by user.
-
-        assert model_type is not None, "Missing model_type."
-
-        model_config_path = model_cls.default_config_path(model_type=model_type)
-
-        model_config = OmegaConf.create()
-        # hiararchy override, customized config > default config
-        model_config = OmegaConf.merge(
-            model_config,
-            OmegaConf.load(model_config_path),
-            {"model": config["model"]},
-        )
-
-        return model_config
-
-    @staticmethod
-    def build_runner_config(config):
-        return {"run": config.run}
-
-    @staticmethod
-    def build_dataset_config(config):
-        datasets = config.get("datasets", None)
-        if datasets is None:
-            raise KeyError(
-                "Expecting 'datasets' as the root key for dataset configuration."
-            )
-
-        dataset_config = OmegaConf.create()
-
-        for dataset_name in datasets:
-            builder_cls = registry.get_builder_class(dataset_name)
-
-            dataset_config_type = datasets[dataset_name].get("type", "default")
-            dataset_config_path = builder_cls.default_config_path(
-                type=dataset_config_type
-            )
-
-            # hiararchy override, customized config > default config
-            dataset_config = OmegaConf.merge(
-                dataset_config,
-                OmegaConf.load(dataset_config_path),
-                {"datasets": {dataset_name: config["datasets"][dataset_name]}},
-            )
-
-        return dataset_config
-
-    def _convert_to_dot_list(self, opts):
-        if opts is None:
-            opts = []
-
-        if len(opts) == 0:
-            return opts
-
-        has_equal = opts[0].find("=") != -1
-
-        if has_equal:
-            return opts
-
-        return [(opt + "=" + value) for opt, value in zip(opts[0::2], opts[1::2])]
-
-    def get_config(self):
-        return self.config
-
-    @property
-    def run_cfg(self):
-        return self.config.run
-
-    @property
-    def datasets_cfg(self):
-        return self.config.datasets
-
-    @property
-    def model_cfg(self):
-        return self.config.model
-
-    def pretty_print(self):
-        logging.info("\n=====  Running Parameters    =====")
-        logging.info(self._convert_node_to_json(self.config.run))
-
-        logging.info("\n======  Dataset Attributes  ======")
-        datasets = self.config.datasets
-
-        for dataset in datasets:
-            if dataset in self.config.datasets:
-                logging.info(f"\n======== {dataset} =======")
-                dataset_config = self.config.datasets[dataset]
-                logging.info(self._convert_node_to_json(dataset_config))
-            else:
-                logging.warning(f"No dataset named '{dataset}' in config. Skipping")
-
-        logging.info(f"\n======  Model Attributes  ======")
-        logging.info(self._convert_node_to_json(self.config.model))
-
-    def _convert_node_to_json(self, node):
-        container = OmegaConf.to_container(node, resolve=True)
-        return json.dumps(container, indent=4, sort_keys=True)
-
-    def to_dict(self):
-        return OmegaConf.to_container(self.config)
-
-
-def node_to_dict(node):
-    return OmegaConf.to_container(node)
-
-
-class ConfigValidator:
-    """
-    This is a preliminary implementation to centralize and validate the configuration.
-    May be altered in the future.
-
-    A helper class to validate configurations from yaml file.
-
-    This serves the following purposes:
-        1. Ensure all the options in the yaml are defined, raise error if not.
-        2. when type mismatches are found, the validator will raise an error.
-        3. a central place to store and display helpful messages for supported configurations.
-
-    """
-
-    class _Argument:
-        def __init__(self, name, choices=None, type=None, help=None):
-            self.name = name
-            self.val = None
-            self.choices = choices
-            self.type = type
-            self.help = help
-
-        def __str__(self):
-            s = f"{self.name}={self.val}"
-            if self.type is not None:
-                s += f", ({self.type})"
-            if self.choices is not None:
-                s += f", choices: {self.choices}"
-            if self.help is not None:
-                s += f", ({self.help})"
-            return s
-
-    def __init__(self, description):
-        self.description = description
-
-        self.arguments = dict()
-
-        self.parsed_args = None
-
-    def __getitem__(self, key):
-        assert self.parsed_args is not None, "No arguments parsed yet."
-
-        return self.parsed_args[key]
-
-    def __str__(self) -> str:
-        return self.format_help()
-
-    def add_argument(self, *args, **kwargs):
-        """
-        Assume the first argument is the name of the argument.
-        """
-        self.arguments[args[0]] = self._Argument(*args, **kwargs)
-
-    def validate(self, config=None):
-        """
-        Convert yaml config (dict-like) to list, required by argparse.
-        """
-        for k, v in config.items():
-            assert (
-                k in self.arguments
-            ), f"""{k} is not a valid argument. Support arguments are {self.format_arguments()}."""
-
-            if self.arguments[k].type is not None:
-                try:
-                    self.arguments[k].val = self.arguments[k].type(v)
-                except ValueError:
-                    raise ValueError(f"{k} is not a valid {self.arguments[k].type}.")
-
-            if self.arguments[k].choices is not None:
-                assert (
-                    v in self.arguments[k].choices
-                ), f"""{k} must be one of {self.arguments[k].choices}."""
-
-        return config
-
-    def format_arguments(self):
-        return str([f"{k}" for k in sorted(self.arguments.keys())])
-
-    def format_help(self):
-        # description + key-value pair string for each argument
-        help_msg = str(self.description)
-        return help_msg + ", available arguments: " + self.format_arguments()
-
-    def print_help(self):
-        # display help message
-        print(self.format_help())
-
-
-def create_runner_config_validator():
-    validator = ConfigValidator(description="Runner configurations")
-
-    validator.add_argument(
-        "runner",
-        type=str,
-        choices=["runner_base", "runner_iter"],
-        help="""Runner to use. The "runner_base" uses epoch-based training while iter-based
-            runner runs based on iters. Default: runner_base""",
-    )
-    # add argumetns for training dataset ratios
-    validator.add_argument(
-        "train_dataset_ratios",
-        type=Dict[str, float],
-        help="""Ratios of training dataset. This is used in iteration-based runner.
-        Do not support for epoch-based runner because how to define an epoch becomes tricky.
-        Default: None""",
-    )
-    validator.add_argument(
-        "max_iters",
-        type=float,
-        help="Maximum number of iterations to run.",
-    )
-    validator.add_argument(
-        "max_epoch",
-        type=int,
-        help="Maximum number of epochs to run.",
-    )
-    # add arguments for iters_per_inner_epoch
-    validator.add_argument(
-        "iters_per_inner_epoch",
-        type=float,
-        help="Number of iterations per inner epoch. This is required when runner is runner_iter.",
-    )
-    lr_scheds_choices = registry.list_lr_schedulers()
-    validator.add_argument(
-        "lr_sched",
-        type=str,
-        choices=lr_scheds_choices,
-        help="Learning rate scheduler to use, from {}".format(lr_scheds_choices),
-    )
-    task_choices = registry.list_tasks()
-    validator.add_argument(
-        "task",
-        type=str,
-        choices=task_choices,
-        help="Task to use, from {}".format(task_choices),
-    )
-    # add arguments for init_lr
-    validator.add_argument(
-        "init_lr",
-        type=float,
-        help="Initial learning rate. This will be the learning rate after warmup and before decay.",
-    )
-    # add arguments for min_lr
-    validator.add_argument(
-        "min_lr",
-        type=float,
-        help="Minimum learning rate (after decay).",
-    )
-    # add arguments for warmup_lr
-    validator.add_argument(
-        "warmup_lr",
-        type=float,
-        help="Starting learning rate for warmup.",
-    )
-    # add arguments for learning rate decay rate
-    validator.add_argument(
-        "lr_decay_rate",
-        type=float,
-        help="Learning rate decay rate. Required if using a decaying learning rate scheduler.",
-    )
-    # add arguments for weight decay
-    validator.add_argument(
-        "weight_decay",
-        type=float,
-        help="Weight decay rate.",
-    )
-    # add arguments for training batch size
-    validator.add_argument(
-        "batch_size_train",
-        type=int,
-        help="Training batch size.",
-    )
-    # add arguments for evaluation batch size
-    validator.add_argument(
-        "batch_size_eval",
-        type=int,
-        help="Evaluation batch size, including validation and testing.",
-    )
-    # add arguments for number of workers for data loading
-    validator.add_argument(
-        "num_workers",
-        help="Number of workers for data loading.",
-    )
-    # add arguments for warm up steps
-    validator.add_argument(
-        "warmup_steps",
-        type=int,
-        help="Number of warmup steps. Required if a warmup schedule is used.",
-    )
-    # add arguments for random seed
-    validator.add_argument(
-        "seed",
-        type=int,
-        help="Random seed.",
-    )
-    # add arguments for output directory
-    validator.add_argument(
-        "output_dir",
-        type=str,
-        help="Output directory to save checkpoints and logs.",
-    )
-    # add arguments for whether only use evaluation
-    validator.add_argument(
-        "evaluate",
-        help="Whether to only evaluate the model. If true, training will not be performed.",
-    )
-    # add arguments for splits used for training, e.g. ["train", "val"]
-    validator.add_argument(
-        "train_splits",
-        type=list,
-        help="Splits to use for training.",
-    )
-    # add arguments for splits used for validation, e.g. ["val"]
-    validator.add_argument(
-        "valid_splits",
-        type=list,
-        help="Splits to use for validation. If not provided, will skip the validation.",
-    )
-    # add arguments for splits used for testing, e.g. ["test"]
-    validator.add_argument(
-        "test_splits",
-        type=list,
-        help="Splits to use for testing. If not provided, will skip the testing.",
-    )
-    # add arguments for accumulating gradient for iterations
-    validator.add_argument(
-        "accum_grad_iters",
-        type=int,
-        help="Number of iterations to accumulate gradient for.",
-    )
-
-    # ====== distributed training ======
-    validator.add_argument(
-        "device",
-        type=str,
-        choices=["cpu", "cuda"],
-        help="Device to use. Support 'cuda' or 'cpu' as for now.",
-    )
-    validator.add_argument(
-        "world_size",
-        type=int,
-        help="Number of processes participating in the job.",
-    )
-    validator.add_argument("dist_url", type=str)
-    validator.add_argument("distributed", type=bool)
-    # add arguments to opt using distributed sampler during evaluation or not
-    validator.add_argument(
-        "use_dist_eval_sampler",
-        type=bool,
-        help="Whether to use distributed sampler during evaluation or not.",
-    )
-
-    # ====== task specific ======
-    # generation task specific arguments
-    # add arguments for maximal length of text output
-    validator.add_argument(
-        "max_len",
-        type=int,
-        help="Maximal length of text output.",
-    )
-    # add arguments for minimal length of text output
-    validator.add_argument(
-        "min_len",
-        type=int,
-        help="Minimal length of text output.",
-    )
-    # add arguments number of beams
-    validator.add_argument(
-        "num_beams",
-        type=int,
-        help="Number of beams used for beam search.",
-    )
-
-    # vqa task specific arguments
-    # add arguments for number of answer candidates
-    validator.add_argument(
-        "num_ans_candidates",
-        type=int,
-        help="""For ALBEF and BLIP, these models first rank answers according to likelihood to select answer candidates.""",
-    )
-    # add arguments for inference method
-    validator.add_argument(
-        "inference_method",
-        type=str,
-        choices=["genearte", "rank"],
-        help="""Inference method to use for question answering. If rank, requires a answer list.""",
-    )
-
-    # ====== model specific ======
-    validator.add_argument(
-        "k_test",
-        type=int,
-        help="Number of top k most similar samples from ITC/VTC selection to be tested.",
-    )
-
-    return validator

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/apply_net.py

@@ -1,318 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-import argparse
-import glob
-import logging
-import os
-import pickle
-import sys
-from typing import Any, ClassVar, Dict, List
-import torch
-
-from detectron2.config import get_cfg
-from detectron2.data.detection_utils import read_image
-from detectron2.engine.defaults import DefaultPredictor
-from detectron2.structures.boxes import BoxMode
-from detectron2.structures.instances import Instances
-from detectron2.utils.logger import setup_logger
-
-from densepose import add_densepose_config
-from densepose.utils.logger import verbosity_to_level
-from densepose.vis.base import CompoundVisualizer
-from densepose.vis.bounding_box import ScoredBoundingBoxVisualizer
-from densepose.vis.densepose import (
-    DensePoseResultsContourVisualizer,
-    DensePoseResultsFineSegmentationVisualizer,
-    DensePoseResultsUVisualizer,
-    DensePoseResultsVVisualizer,
-)
-from densepose.vis.extractor import CompoundExtractor, create_extractor
-
-DOC = """Apply Net - a tool to print / visualize DensePose results
-"""
-
-LOGGER_NAME = "apply_net"
-logger = logging.getLogger(LOGGER_NAME)
-
-_ACTION_REGISTRY: Dict[str, "Action"] = {}
-
-
-class Action(object):
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        parser.add_argument(
-            "-v",
-            "--verbosity",
-            action="count",
-            help="Verbose mode. Multiple -v options increase the verbosity.",
-        )
-
-
-def register_action(cls: type):
-    """
-    Decorator for action classes to automate action registration
-    """
-    global _ACTION_REGISTRY
-    _ACTION_REGISTRY[cls.COMMAND] = cls
-    return cls
-
-
-class InferenceAction(Action):
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(InferenceAction, cls).add_arguments(parser)
-        parser.add_argument("cfg", metavar="<config>", help="Config file")
-        parser.add_argument("model", metavar="<model>", help="Model file")
-        parser.add_argument("input", metavar="<input>", help="Input data")
-        parser.add_argument(
-            "--opts",
-            help="Modify config options using the command-line 'KEY VALUE' pairs",
-            default=[],
-            nargs=argparse.REMAINDER,
-        )
-
-    @classmethod
-    def execute(cls: type, args: argparse.Namespace):
-        logger.info(f"Loading config from {args.cfg}")
-        opts = []
-        cfg = cls.setup_config(args.cfg, args.model, args, opts)
-        logger.info(f"Loading model from {args.model}")
-        predictor = DefaultPredictor(cfg)
-        logger.info(f"Loading data from {args.input}")
-        file_list = cls._get_input_file_list(args.input)
-        if len(file_list) == 0:
-            logger.warning(f"No input images for {args.input}")
-            return
-        context = cls.create_context(args)
-        for file_name in file_list:
-            img = read_image(file_name, format="BGR")  # predictor expects BGR image.
-            with torch.no_grad():
-                outputs = predictor(img)["instances"]
-                cls.execute_on_outputs(context, {"file_name": file_name, "image": img}, outputs)
-        cls.postexecute(context)
-
-    @classmethod
-    def setup_config(
-        cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
-    ):
-        cfg = get_cfg()
-        add_densepose_config(cfg)
-        cfg.merge_from_file(config_fpath)
-        cfg.merge_from_list(args.opts)
-        if opts:
-            cfg.merge_from_list(opts)
-        cfg.MODEL.WEIGHTS = model_fpath
-        cfg.freeze()
-        return cfg
-
-    @classmethod
-    def _get_input_file_list(cls: type, input_spec: str):
-        if os.path.isdir(input_spec):
-            file_list = [
-                os.path.join(input_spec, fname)
-                for fname in os.listdir(input_spec)
-                if os.path.isfile(os.path.join(input_spec, fname))
-            ]
-        elif os.path.isfile(input_spec):
-            file_list = [input_spec]
-        else:
-            file_list = glob.glob(input_spec)
-        return file_list
-
-
-@register_action
-class DumpAction(InferenceAction):
-    """
-    Dump action that outputs results to a pickle file
-    """
-
-    COMMAND: ClassVar[str] = "dump"
-
-    @classmethod
-    def add_parser(cls: type, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Dump model outputs to a file.")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(DumpAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "--output",
-            metavar="<dump_file>",
-            default="results.pkl",
-            help="File name to save dump to",
-        )
-
-    @classmethod
-    def execute_on_outputs(
-        cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
-    ):
-        image_fpath = entry["file_name"]
-        logger.info(f"Processing {image_fpath}")
-        result = {"file_name": image_fpath}
-        if outputs.has("scores"):
-            result["scores"] = outputs.get("scores").cpu()
-        if outputs.has("pred_boxes"):
-            result["pred_boxes_XYXY"] = outputs.get("pred_boxes").tensor.cpu()
-            if outputs.has("pred_densepose"):
-                boxes_XYWH = BoxMode.convert(
-                    result["pred_boxes_XYXY"], BoxMode.XYXY_ABS, BoxMode.XYWH_ABS
-                )
-                result["pred_densepose"] = outputs.get("pred_densepose").to_result(boxes_XYWH)
-        context["results"].append(result)
-
-    @classmethod
-    def create_context(cls: type, args: argparse.Namespace):
-        context = {"results": [], "out_fname": args.output}
-        return context
-
-    @classmethod
-    def postexecute(cls: type, context: Dict[str, Any]):
-        out_fname = context["out_fname"]
-        out_dir = os.path.dirname(out_fname)
-        if len(out_dir) > 0 and not os.path.exists(out_dir):
-            os.makedirs(out_dir)
-        with open(out_fname, "wb") as hFile:
-            pickle.dump(context["results"], hFile)
-            logger.info(f"Output saved to {out_fname}")
-
-
-@register_action
-class ShowAction(InferenceAction):
-    """
-    Show action that visualizes selected entries on an image
-    """
-
-    COMMAND: ClassVar[str] = "show"
-    VISUALIZERS: ClassVar[Dict[str, object]] = {
-        "dp_contour": DensePoseResultsContourVisualizer,
-        "dp_segm": DensePoseResultsFineSegmentationVisualizer,
-        "dp_u": DensePoseResultsUVisualizer,
-        "dp_v": DensePoseResultsVVisualizer,
-        "bbox": ScoredBoundingBoxVisualizer,
-    }
-
-    @classmethod
-    def add_parser(cls: type, subparsers: argparse._SubParsersAction):
-        parser = subparsers.add_parser(cls.COMMAND, help="Visualize selected entries")
-        cls.add_arguments(parser)
-        parser.set_defaults(func=cls.execute)
-
-    @classmethod
-    def add_arguments(cls: type, parser: argparse.ArgumentParser):
-        super(ShowAction, cls).add_arguments(parser)
-        parser.add_argument(
-            "visualizations",
-            metavar="<visualizations>",
-            help="Comma separated list of visualizations, possible values: "
-            "[{}]".format(",".join(sorted(cls.VISUALIZERS.keys()))),
-        )
-        parser.add_argument(
-            "--min_score",
-            metavar="<score>",
-            default=0.8,
-            type=float,
-            help="Minimum detection score to visualize",
-        )
-        parser.add_argument(
-            "--nms_thresh", metavar="<threshold>", default=None, type=float, help="NMS threshold"
-        )
-        parser.add_argument(
-            "--output",
-            metavar="<image_file>",
-            default="outputres.png",
-            help="File name to save output to",
-        )
-
-    @classmethod
-    def setup_config(
-        cls: type, config_fpath: str, model_fpath: str, args: argparse.Namespace, opts: List[str]
-    ):
-        opts.append("MODEL.ROI_HEADS.SCORE_THRESH_TEST")
-        opts.append(str(args.min_score))
-        if args.nms_thresh is not None:
-            opts.append("MODEL.ROI_HEADS.NMS_THRESH_TEST")
-            opts.append(str(args.nms_thresh))
-        cfg = super(ShowAction, cls).setup_config(config_fpath, model_fpath, args, opts)
-        return cfg
-
-    @classmethod
-    def execute_on_outputs(
-        cls: type, context: Dict[str, Any], entry: Dict[str, Any], outputs: Instances
-    ):
-        import cv2
-        import numpy as np
-
-        visualizer = context["visualizer"]
-        extractor = context["extractor"]
-        image_fpath = entry["file_name"]
-        logger.info(f"Processing {image_fpath}")
-        image = cv2.cvtColor(entry["image"], cv2.COLOR_BGR2GRAY)
-        image = np.tile(image[:, :, np.newaxis], [1, 1, 3])
-        data = extractor(outputs)
-        image_vis = visualizer.visualize(image, data)
-        entry_idx = context["entry_idx"] + 1
-        out_fname = cls._get_out_fname(entry_idx, context["out_fname"])
-        out_dir = os.path.dirname(out_fname)
-        if len(out_dir) > 0 and not os.path.exists(out_dir):
-            os.makedirs(out_dir)
-        cv2.imwrite(out_fname, image_vis)
-        logger.info(f"Output saved to {out_fname}")
-        context["entry_idx"] += 1
-
-    @classmethod
-    def postexecute(cls: type, context: Dict[str, Any]):
-        pass
-
-    @classmethod
-    def _get_out_fname(cls: type, entry_idx: int, fname_base: str):
-        base, ext = os.path.splitext(fname_base)
-        return base + ".{0:04d}".format(entry_idx) + ext
-
-    @classmethod
-    def create_context(cls: type, args: argparse.Namespace) -> Dict[str, Any]:
-        vis_specs = args.visualizations.split(",")
-        visualizers = []
-        extractors = []
-        for vis_spec in vis_specs:
-            vis = cls.VISUALIZERS[vis_spec]()
-            visualizers.append(vis)
-            extractor = create_extractor(vis)
-            extractors.append(extractor)
-        visualizer = CompoundVisualizer(visualizers)
-        extractor = CompoundExtractor(extractors)
-        context = {
-            "extractor": extractor,
-            "visualizer": visualizer,
-            "out_fname": args.output,
-            "entry_idx": 0,
-        }
-        return context
-
-
-def create_argument_parser() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser(
-        description=DOC,
-        formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=120),
-    )
-    parser.set_defaults(func=lambda _: parser.print_help(sys.stdout))
-    subparsers = parser.add_subparsers(title="Actions")
-    for _, action in _ACTION_REGISTRY.items():
-        action.add_parser(subparsers)
-    return parser
-
-
-def main():
-    parser = create_argument_parser()
-    args = parser.parse_args()
-    verbosity = args.verbosity if hasattr(args, "verbosity") else None
-    global logger
-    logger = setup_logger(name=LOGGER_NAME)
-    logger.setLevel(verbosity_to_level(verbosity))
-    args.func(args)
-
-
-if __name__ == "__main__":
-    main()

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

@@ -1,91 +0,0 @@
-/*
-    tests/test_eval.cpp -- Usage of eval() and eval_file()
-
-    Copyright (c) 2016 Klemens D. Morgenstern
-
-    All rights reserved. Use of this source code is governed by a
-    BSD-style license that can be found in the LICENSE file.
-*/
-
-
-#include <pybind11/eval.h>
-#include "pybind11_tests.h"
-
-TEST_SUBMODULE(eval_, m) {
-    // test_evals
-
-    auto global = py::dict(py::module::import("__main__").attr("__dict__"));
-
-    m.def("test_eval_statements", [global]() {
-        auto local = py::dict();
-        local["call_test"] = py::cpp_function([&]() -> int {
-            return 42;
-        });
-
-        // Regular string literal
-        py::exec(
-            "message = 'Hello World!'\n"
-            "x = call_test()",
-            global, local
-        );
-
-        // Multi-line raw string literal
-        py::exec(R"(
-            if x == 42:
-                print(message)
-            else:
-                raise RuntimeError
-            )", global, local
-        );
-        auto x = local["x"].cast<int>();
-
-        return x == 42;
-    });
-
-    m.def("test_eval", [global]() {
-        auto local = py::dict();
-        local["x"] = py::int_(42);
-        auto x = py::eval("x", global, local);
-        return x.cast<int>() == 42;
-    });
-
-    m.def("test_eval_single_statement", []() {
-        auto local = py::dict();
-        local["call_test"] = py::cpp_function([&]() -> int {
-            return 42;
-        });
-
-        auto result = py::eval<py::eval_single_statement>("x = call_test()", py::dict(), local);
-        auto x = local["x"].cast<int>();
-        return result.is_none() && x == 42;
-    });
-
-    m.def("test_eval_file", [global](py::str filename) {
-        auto local = py::dict();
-        local["y"] = py::int_(43);
-
-        int val_out;
-        local["call_test2"] = py::cpp_function([&](int value) { val_out = value; });
-
-        auto result = py::eval_file(filename, global, local);
-        return val_out == 43 && result.is_none();
-    });
-
-    m.def("test_eval_failure", []() {
-        try {
-            py::eval("nonsense code ...");
-        } catch (py::error_already_set &) {
-            return true;
-        }
-        return false;
-    });
-
-    m.def("test_eval_file_failure", []() {
-        try {
-            py::eval_file("non-existing file");
-        } catch (std::exception &) {
-            return true;
-        }
-        return false;
-    });
-}

spaces/CVPR/LIVE/thrust/cmake/ThrustCudaConfig.cmake

@@ -1,140 +0,0 @@
-enable_language(CUDA)
-
-set(THRUST_KNOWN_COMPUTE_ARCHS 35 37 50 52 53 60 61 62 70 72 75 80)
-
-# Split CUDA_FLAGS into 3 parts:
-#
-# THRUST_CUDA_FLAGS_BASE: Common CUDA flags for all targets.
-# THRUST_CUDA_FLAGS_RDC: Additional CUDA flags for targets compiled with RDC.
-# THRUST_CUDA_FLAGS_NO_RDC: Additional CUDA flags for targets compiled without RDC.
-#
-# This is necessary because CUDA SMs 5.3, 6.2, and 7.2 do not support RDC, but
-# we want to always build some targets (e.g. testing/cuda/*) with RDC.
-# We work around this by building the "always RDC" targets without support for
-# those SMs. This requires two sets of CUDA_FLAGS.
-#
-# Enabling any of those SMs along with the ENABLE_RDC options will result in a
-# configuration error.
-#
-# Because of how CMake handles the CMAKE_CUDA_FLAGS variables, every target
-# generated in a given directory will use the same value for CMAKE_CUDA_FLAGS,
-# which is determined at the end of the directory's scope. This means caution
-# should be used when trying to build different targets with different flags,
-# since they might not behave as expected. This will improve with CMake 3.18,
-# which add the DEVICE_LINK genex, fixing the issue with using per-target
-# CUDA_FLAGS: https://gitlab.kitware.com/cmake/cmake/-/issues/18265
-set(THRUST_CUDA_FLAGS_BASE "${CMAKE_CUDA_FLAGS}")
-set(THRUST_CUDA_FLAGS_RDC)
-set(THRUST_CUDA_FLAGS_NO_RDC)
-
-# Archs that don't support RDC:
-set(no_rdc_archs 53 62 72)
-
-# Find the highest arch:
-list(SORT THRUST_KNOWN_COMPUTE_ARCHS)
-list(LENGTH THRUST_KNOWN_COMPUTE_ARCHS max_idx)
-math(EXPR max_idx "${max_idx} - 1")
-list(GET THRUST_KNOWN_COMPUTE_ARCHS ${max_idx} highest_arch)
-
-set(option_init OFF)
-if ("Feta" STREQUAL "${CMAKE_CUDA_COMPILER_ID}")
-  set(option_init ON)
-endif()
-option(THRUST_DISABLE_ARCH_BY_DEFAULT
-  "If ON, then all CUDA architectures are disabled on the initial CMake run."
-  ${option_init}
-)
-
-set(option_init ON)
-if (THRUST_DISABLE_ARCH_BY_DEFAULT)
-  set(option_init OFF)
-endif()
-
-set(num_archs_enabled 0)
-foreach (arch IN LISTS THRUST_KNOWN_COMPUTE_ARCHS)
-  option(THRUST_ENABLE_COMPUTE_${arch}
-    "Enable code generation for tests for sm_${arch}"
-    ${option_init}
-  )
-
-  if (NOT THRUST_ENABLE_COMPUTE_${arch})
-    continue()
-  endif()
-
-  math(EXPR num_archs_enabled "${num_archs_enabled} + 1")
-
-  if ("Feta" STREQUAL "${CMAKE_CUDA_COMPILER_ID}")
-    if (NOT ${num_archs_enabled} EQUAL 1)
-      message(FATAL_ERROR
-        "Feta does not support compilation for multiple device architectures "
-        "at once."
-      )
-    endif()
-    set(arch_flag "-gpu=cc${arch}")
-  else()
-    set(arch_flag "-gencode arch=compute_${arch},code=sm_${arch}")
-  endif()
-
-  string(APPEND COMPUTE_MESSAGE " sm_${arch}")
-  string(APPEND THRUST_CUDA_FLAGS_NO_RDC " ${arch_flag}")
-  if (NOT arch IN_LIST no_rdc_archs)
-    string(APPEND THRUST_CUDA_FLAGS_RDC " ${arch_flag}")
-  endif()
-endforeach()
-
-if (NOT "Feta" STREQUAL "${CMAKE_CUDA_COMPILER_ID}")
-  option(THRUST_ENABLE_COMPUTE_FUTURE
-    "Enable code generation for tests for compute_${highest_arch}"
-    ${option_init}
-  )
-  if (THRUST_ENABLE_COMPUTE_FUTURE)
-    string(APPEND THRUST_CUDA_FLAGS_BASE
-      " -gencode arch=compute_${highest_arch},code=compute_${highest_arch}"
-    )
-    string(APPEND COMPUTE_MESSAGE " compute_${highest_arch}")
-  endif()
-endif()
-
-message(STATUS "Thrust: Enabled CUDA architectures:${COMPUTE_MESSAGE}")
-
-# RDC is off by default in NVCC and on by default in Feta. Turning off RDC
-# isn't currently supported by Feta. So, we default to RDC off for NVCC and
-# RDC on for Feta.
-set(option_init OFF)
-if ("Feta" STREQUAL "${CMAKE_CUDA_COMPILER_ID}")
-  set(option_init ON)
-endif()
-
-option(THRUST_ENABLE_TESTS_WITH_RDC
-  "Build all Thrust tests with RDC; tests that require RDC are not affected by this option."
-  ${option_init}
-)
-
-option(THRUST_ENABLE_EXAMPLES_WITH_RDC
-  "Build all Thrust examples with RDC; examples which require RDC are not affected by this option."
-  ${option_init}
-)
-
-# Check for RDC/SM compatibility and error/warn if necessary
-foreach (sm IN LISTS no_rdc_archs)
-  set(sm_opt THRUST_ENABLE_COMPUTE_${sm})
-  if (${sm_opt})
-    foreach (opt IN ITEMS TESTS EXAMPLES)
-      set(rdc_opt THRUST_ENABLE_${opt}_WITH_RDC)
-      if (${rdc_opt})
-        message(FATAL_ERROR
-          "${rdc_opt} is incompatible with ${sm_opt}, since sm_${sm} does not "
-          "support RDC."
-        )
-      endif()
-    endforeach()
-
-    message(NOTICE
-      "sm_${sm} does not support RDC. Targets that require RDC will be built "
-      "without support for this architecture."
-    )
-  endif()
-endforeach()
-
-# By default RDC is not used:
-set(CMAKE_CUDA_FLAGS "${THRUST_CUDA_FLAGS_BASE} ${THRUST_CUDA_FLAGS_NO_RDC}")

spaces/ChallengeHub/Chinese-LangChain/README.md

@@ -1,116 +0,0 @@
----
-license: openrail
-title: 'Chinese-LangChain '
-sdk: gradio
-emoji: 🚀
-colorFrom: yellow
-colorTo: yellow
-pinned: true
-app_file: app.py
----
-
-# Chinese-LangChain
-
-> Chinese-LangChain：中文langchain项目，基于ChatGLM-6b+langchain实现本地化知识库检索与智能答案生成
-
-https://github.com/yanqiangmiffy/Chinese-LangChain
-
-俗称：小必应，Q.Talk，强聊，QiangTalk
-
-## 🔥 效果演示
-
-![](https://github.com/yanqiangmiffy/Chinese-LangChain/blob/master/images/web_demos/v1.png)
-![](https://github.com/yanqiangmiffy/Chinese-LangChain/blob/master/images/web_demos/v3.png)
-
-## 🚋 使用教程
-
-- 选择知识库询问相关领域的问题
-
-## 🏗️ 部署教程
-
-### 运行配置
-
-- 显存：12g，实际运行9g够了
-- 运行内存：32g
-
-### 运行环境
-
-```text
-langchain
-gradio
-transformers
-sentence_transformers
-faiss-cpu
-unstructured
-duckduckgo_search
-mdtex2html
-chardet
-cchardet
-```
-
-### 启动Gradio
-
-```shell
-python main.py
-```
-
-## 🚀 特性
-
-- 🔭 2023/04/20 支持模型问答与检索问答模式切换
-- 💻 2023/04/20 感谢HF官方提供免费算力，添加HuggingFace
-  Spaces在线体验[[🤗 DEMO](https://huggingface.co/spaces/ChallengeHub/Chinese-LangChain)
-- 🧫 2023/04/19 发布45万Wikipedia的文本预处理语料以及FAISS索引向量
-- 🐯 2023/04/19 引入ChuanhuChatGPT皮肤
-- 📱 2023/04/19 增加web search功能，需要确保网络畅通！(感谢[@wanghao07456](https://github.com/wanghao07456),提供的idea)
-- 📚 2023/04/18 webui增加知识库选择功能
-- 🚀 2023/04/18 修复推理预测超时5s报错问题
-- 🎉 2023/04/17 支持多种文档上传与内容解析：pdf、docx，ppt等
-- 🎉 2023/04/17 支持知识增量更新
-
-[//]: # (- 支持检索结果与LLM生成结果对比)
-
-## 🧰 知识库
-
-### 构建知识库
-
-- Wikipedia-zh
-
-> 详情见：corpus/zh_wikipedia/README.md
-
-### 知识库向量索引
-
-| 知识库数据                                                                         | FAISS向量                                                              |
-|-------------------------------------------------------------------------------|----------------------------------------------------------------------|
-| 中文维基百科截止4月份数据，45万                                                             | 链接：https://pan.baidu.com/s/1VQeA_dq92fxKOtLL3u3Zpg?pwd=l3pn 提取码：l3pn |
-| 截止去年九月的130w条中文维基百科处理结果和对应faiss向量文件 @[yubuyuabc](https://github.com/yubuyuabc) | 链接：https://pan.baidu.com/s/1Yls_Qtg15W1gneNuFP9O_w?pwd=exij 提取码：exij |
-| 💹 [大规模金融研报知识图谱](http://openkg.cn/dataset/fr2kg)                              | 链接：https://pan.baidu.com/s/1FcIH5Fi3EfpS346DnDu51Q?pwd=ujjv 提取码：ujjv |
-
-## 🔨 TODO
-
-* [x] 支持上下文
-* [x] 支持知识增量更新
-* [x] 支持加载不同知识库
-* [x] 支持检索结果与LLM生成结果对比
-* [ ] 支持检索生成结果与原始LLM生成结果对比
-* [ ] 支持模型问答与检索问答
-* [ ] 检索结果过滤与排序
-* [x] 互联网检索结果接入
-* [ ] 模型初始化有问题
-* [ ] 增加非LangChain策略
-* [ ] 显示当前对话策略
-* [ ] 构建一个垂直业务场景知识库，非通用性
-
-## 交流
-
-欢迎多提建议、Bad cases，目前尚不完善，欢迎进群及时交流，也欢迎大家多提PR</br>
-
-<figure class="third">
-    <img src="https://raw.githubusercontent.com/yanqiangmiffy/Chinese-LangChain/master/images/ch.jpg" width="180px"><img src="https://raw.githubusercontent.com/yanqiangmiffy/Chinese-LangChain/master/images/chatgroup.jpg" width="180px" height="270px"><img src="https://raw.githubusercontent.com/yanqiangmiffy/Chinese-LangChain/master/images/personal.jpg" width="180px">
-</figure>
-
-## ❤️引用
-
-- webui参考：https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui
-- knowledge问答参考：https://github.com/imClumsyPanda/langchain-ChatGLM
-- LLM模型：https://github.com/THUDM/ChatGLM-6B
-- CSS：https://huggingface.co/spaces/JohnSmith9982/ChuanhuChatGPT

spaces/ChandraMohanNayal/AutoGPT/autogpt/json_utils/utilities.py

@@ -1,54 +0,0 @@
-"""Utilities for the json_fixes package."""
-import json
-import re
-
-from jsonschema import Draft7Validator
-
-from autogpt.config import Config
-from autogpt.logs import logger
-
-CFG = Config()
-
-
-def extract_char_position(error_message: str) -> int:
-    """Extract the character position from the JSONDecodeError message.
-
-    Args:
-        error_message (str): The error message from the JSONDecodeError
-          exception.
-
-    Returns:
-        int: The character position.
-    """
-
-    char_pattern = re.compile(r"\(char (\d+)\)")
-    if match := char_pattern.search(error_message):
-        return int(match[1])
-    else:
-        raise ValueError("Character position not found in the error message.")
-
-
-def validate_json(json_object: object, schema_name: object) -> object:
-    """
-    :type schema_name: object
-    :param schema_name:
-    :type json_object: object
-    """
-    with open(f"autogpt/json_utils/{schema_name}.json", "r") as f:
-        schema = json.load(f)
-    validator = Draft7Validator(schema)
-
-    if errors := sorted(validator.iter_errors(json_object), key=lambda e: e.path):
-        logger.error("The JSON object is invalid.")
-        if CFG.debug_mode:
-            logger.error(
-                json.dumps(json_object, indent=4)
-            )  # Replace 'json_object' with the variable containing the JSON data
-            logger.error("The following issues were found:")
-
-            for error in errors:
-                logger.error(f"Error: {error.message}")
-    elif CFG.debug_mode:
-        print("The JSON object is valid.")
-
-    return json_object

spaces/CikeyQI/meme-api/meme_generator/memes/capoo_rub/__init__.py

@@ -1,30 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from PIL.Image import Image as IMG
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import save_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def capoo_rub(images: List[BuildImage], texts, args):
-    img = images[0].convert("RGBA").square().resize((180, 180))
-    frames: List[IMG] = []
-    locs = [
-        (178, 184, 78, 260),
-        (178, 174, 84, 269),
-        (178, 174, 84, 269),
-        (178, 178, 84, 264),
-    ]
-    for i in range(4):
-        frame = BuildImage.open(img_dir / f"{i}.png")
-        w, h, x, y = locs[i]
-        frame.paste(img.resize((w, h)), (x, y), below=True)
-        frames.append(frame.image)
-    return save_gif(frames, 0.1)
-
-
-add_meme("capoo_rub", capoo_rub, min_images=1, max_images=1, keywords=["咖波蹭", "咖波贴"])

spaces/CognitiveLabs/Research-Assistant/test/test.py

@@ -1,17 +0,0 @@
-import gradio as gr
-import test2 as test2
-import test3 as test3
-
-theme = gr.themes.Soft(
-    font_mono=[gr.themes.GoogleFont('Fira Code'), 'ui-monospace', 'Consolas', 'monospace'],
-).set(
-    embed_radius='*radius_md'
-)
-
-with gr.Blocks(theme=theme, title="AI Research Assistant") as demo:
-    output = gr.Textbox(label="Output")
-    button = gr.Button("Start")
-    button.click(fn=test2.generator_, outputs=output)
-    
-demo.queue()
-demo.launch()

spaces/CorvaeOboro/gen_ability_icon/dnnlib/util.py

@@ -1,477 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-
-
-# Cache directories
-# ------------------------------------------------------------------------------------------
-
-_dnnlib_cache_dir = None
-
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name) # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-
-    assert is_url(url)
-
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError("Google Drive download quota exceeded -- please try again later")
-
-                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file) # atomic
-        if return_filename:
-            return cache_file
-
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)

spaces/Cpp4App/Cpp4App/CDM/detect_compo/ip_region_proposal.py

@@ -1,200 +0,0 @@
-import cv2
-from os.path import join as pjoin
-import time
-
-import CDM.detect_compo.lib_ip.ip_preprocessing as pre
-import CDM.detect_compo.lib_ip.ip_draw as draw
-import CDM.detect_compo.lib_ip.ip_detection as det
-import CDM.detect_compo.lib_ip.file_utils as file
-import CDM.detect_compo.lib_ip.Component as Compo
-from CDM.config.CONFIG_UIED import Config
-C = Config()
-
-
-def nesting_inspection(org, grey, compos, ffl_block):
-    '''
-    Inspect all big compos through block division by flood-fill
-    :param ffl_block: gradient threshold for flood-fill
-    :return: nesting compos
-    '''
-    nesting_compos = []
-    for i, compo in enumerate(compos):
-        if compo.height > 50:
-            replace = False
-            clip_grey = compo.compo_clipping(grey)
-            n_compos = det.nested_components_detection(clip_grey, org, grad_thresh=ffl_block, show=False)
-            Compo.cvt_compos_relative_pos(n_compos, compo.bbox.col_min, compo.bbox.row_min)
-
-            for n_compo in n_compos:
-                if n_compo.redundant:
-                    compos[i] = n_compo
-                    replace = True
-                    break
-            if not replace:
-                nesting_compos += n_compos
-    return nesting_compos
-
-
-def compo_detection(input_img_path, output_root, uied_params,
-                    resize_by_height=800, classifier=None, show=False, wai_key=0):
-
-    start = time.process_time()
-    name = input_img_path.split('/')[-1][:-4] if '/' in input_img_path else input_img_path.split('\\')[-1][:-4]
-    ip_root = file.build_directory(pjoin(output_root, "ip"))
-
-    # *** Step 1 *** pre-processing: read img -> get binary map
-    org, grey = pre.read_img(input_img_path, resize_by_height)
-    binary = pre.binarization(org, grad_min=int(uied_params['min-grad']))
-
-    full_size_org, full_size_grey = pre.read_img(input_img_path)
-    ratio = full_size_org.shape[0] / org.shape[0]
-
-    # *** Step 2 *** element detection
-    det.rm_line(binary, show=show, wait_key=wai_key)
-    uicompos = det.component_detection(binary, min_obj_area=int(uied_params['min-ele-area']))
-
-    # *** Step 3 *** results refinement
-    uicompos = det.compo_filter(uicompos, min_area=int(uied_params['min-ele-area']), img_shape=binary.shape)
-    uicompos = det.merge_intersected_compos(uicompos)
-    det.compo_block_recognition(binary, uicompos)
-    if uied_params['merge-contained-ele']:
-        uicompos = det.rm_contained_compos_not_in_block(uicompos)
-    Compo.compos_update(uicompos, org.shape)
-    Compo.compos_containment(uicompos)
-
-    # *** Step 4 ** nesting inspection: check if big compos have nesting element
-    uicompos += nesting_inspection(org, grey, uicompos, ffl_block=uied_params['ffl-block'])
-    Compo.compos_update(uicompos, org.shape)
-    draw.draw_bounding_box(full_size_org, ratio, uicompos, show=show, name='merged compo', write_path=pjoin(ip_root, name + '.jpg'), wait_key=wai_key)
-
-    # # classify icons
-    # model = models.resnet18().to('cpu')
-    # in_feature_num = model.fc.in_features
-    # model.fc = nn.Linear(in_feature_num, 99)
-    # # model.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=(3,3), padding=(3,3), stride=(2,2), bias=False)
-    # model.conv1 = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=(5, 5), padding=(3, 3), stride=(2, 2),
-    #                         bias=False)
-    # # PATH = "C:/ANU/2022 s2/honours project/code/UIED-master/model/model-99-resnet18.pkl"
-    # PATH = "./model/model-99-resnet18.pkl"
-    # # trained_model = model()
-    # model.load_state_dict(torch.load(PATH, map_location=torch.device('cpu')))
-    #
-    # model.eval()
-    #
-    # # ----------------- try on semantics dataset---------------------
-    #
-    # # sample_data = np.load('C:/ANU/2022 s2/honours project/code/semantic-icon-classifier-master/data/training_x.npy')
-    # #
-    # # array = np.reshape(sample_data[0, :, :, :], [32, 32])
-    # #
-    # # print("array: ", array)
-    # #
-    # # cv2.imshow("array", array)
-    # # cv2.waitKey(0)
-    # #
-    # # array = array.astype('float32')
-    # # array = array / 255
-    # # array = (array - array.mean()) / array.std()
-    # #
-    # # print("array mean: ", array.mean())
-    # # print("array std: ", array.std())
-    # #
-    # # array = array.reshape(1, 1, 32, 32)
-    # #
-    # # array = torch.tensor(array)
-    # # print("array_tensor: ", array)
-    # # array_pred_label = model(array)
-    # # print("output: ", array_pred_label)
-    #
-    # # ----------------- end trying ---------------------
-    #
-    # grey = grey.astype('float32')
-    # grey = grey / 255
-    # # grey = grey / np.linalg.norm(grey)
-    #
-    # grey = (grey-grey.mean())/grey.std()
-    # print("grey mean: ", grey.mean())
-    # print("grey std: ", grey.std())
-    #
-    # # grey = grey.to(torch.float32)
-    #
-    # # plt.imshow(Image.fromarray(binary))
-    # # plt.show()
-    # # cv2.imshow("grey", grey)
-    #
-    # privacy_compos = []
-    # for comp in uicompos:
-    #
-    #     # cv2.imshow("comp", grey[comp.bbox.row_min:comp.bbox.row_max, comp.bbox.col_min:comp.bbox.col_max])
-    #     # cv2.waitKey(0)
-    #
-    #     # col_mid = int((comp.bbox.col_min+comp.bbox.col_max)/2)
-    #     # row_mid = int((comp.bbox.row_min+comp.bbox.row_max)/2)
-    #     # comp_crop = grey[max(0, row_mid-16):min(grey.shape[1], row_mid+16), max(0, col_mid-16):min(grey.shape[0], col_mid+16)]
-    #     #
-    #     # if comp_crop.shape[0] != 32 or comp_crop.shape[1] != 32:
-    #     #     print("A component is not classified, size: ", comp_crop.shape)
-    #     #     print("col_mid: ", col_mid)
-    #     #     print("row_mid: ", row_mid)
-    #     #     print("shape[0]: ", comp_crop.shape[0])
-    #     #     print("shape[1]: ", comp_crop.shape[1])
-    #     #     print("max(0, row_mid-16) and min(binary.shape[1], row_mid+16): ", max(0, row_mid-16), min(grey.shape[1], row_mid+16))
-    #
-    #     comp_grey = grey[comp.bbox.row_min:comp.bbox.row_max, comp.bbox.col_min:comp.bbox.col_max]
-    #
-    #     # cv2.imshow("comp_grey", comp_grey)
-    #     # cv2.waitKey(0)
-    #
-    #     # print("comp_crop: ", comp_crop)
-    #     # comp_crop = comp_grey.reshape(1, 1, 32, 32)
-    #     comp_crop = cv2.resize(comp_grey, (32, 32))
-    #     print("comp_crop: ", comp_crop)
-    #
-    #     # cv2.imshow("comp_crop", comp_crop)
-    #     # cv2.waitKey(0)
-    #
-    #     comp_crop = comp_crop.reshape(1, 1, 32, 32)
-    #
-    #     comp_tensor = torch.tensor(comp_crop)
-    #     comp_tensor = comp_tensor.permute(0, 1, 3, 2)
-    #     print("comp_tensor: ", comp_tensor)
-    #     # comp_float = comp_tensor.to(torch.float32)
-    #     # print("comp_float: ", comp_float)
-    #     # pred_label = model(comp_float)
-    #     pred_label = model(comp_tensor)
-    #     print("output: ", pred_label)
-    #     print("label: ", np.argmax(pred_label.cpu().data.numpy(), axis=1))
-    #     if np.argmax(pred_label.cpu().data.numpy(), axis=1) in [72.0, 42.0, 77.0, 91.0, 6.0, 89.0, 40.0, 43.0, 82.0, 3.0, 68.0,
-    #                                                             49.0, 56.0, 89.0]:
-    #         privacy_compos.append(comp)
-    #
-    # draw.draw_bounding_box(org, privacy_compos, show=show, name='merged compo', write_path=pjoin(ip_root, name + '.jpg'), wait_key=wai_key)
-
-    # *** Step 5 *** image inspection: recognize image -> remove noise in image -> binarize with larger threshold and reverse -> rectangular compo detection
-    # if classifier is not None:
-    #     classifier['Image'].predict(seg.clipping(org, uicompos), uicompos)
-    #     draw.draw_bounding_box_class(org, uicompos, show=show)
-    #     uicompos = det.rm_noise_in_large_img(uicompos, org)
-    #     draw.draw_bounding_box_class(org, uicompos, show=show)
-    #     det.detect_compos_in_img(uicompos, binary_org, org)
-    #     draw.draw_bounding_box(org, uicompos, show=show)
-    # if classifier is not None:
-    #     classifier['Noise'].predict(seg.clipping(org, uicompos), uicompos)
-    #     draw.draw_bounding_box_class(org, uicompos, show=show)
-    #     uicompos = det.rm_noise_compos(uicompos)
-
-    # *** Step 6 *** element classification: all category classification
-    # if classifier is not None:
-    #     classifier['Elements'].predict([compo.compo_clipping(org) for compo in uicompos], uicompos)
-    #     draw.draw_bounding_box_class(org, uicompos, show=show, name='cls', write_path=pjoin(ip_root, 'result.jpg'))
-    #     draw.draw_bounding_box_class(org, uicompos, write_path=pjoin(output_root, 'result.jpg'))
-
-    # *** Step 7 *** save detection result
-
-    Compo.compos_update(uicompos, org.shape)
-    file.save_corners_json(pjoin(ip_root, name + '.json'), uicompos)
-    # file.save_corners_json(pjoin(ip_root, name + '.json'), uicompos, full_size_org, ratio)
-
-    cd_time = time.process_time() - start
-    print("[Compo Detection Completed in %.3f s] Input: %s Output: %s" % (cd_time, input_img_path, pjoin(ip_root, name + '.json')))
-    return cd_time

spaces/DCandE/rvc-models/infer_pack/modules.py

@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x