Update parquet files (step 2 of 397)

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

@@ -1,19 +0,0 @@
-aiassist.site
-
-### Example: `aiassist` <a name="example-assist"></a>
-
-```python
-import aiassist
-
-question1 = "Who won the world series in 2020?"
-req = aiassist.Completion.create(prompt=question1)
-answer = req["text"]
-message_id = req["parentMessageId"]
-
-question2 = "Where was it played?"
-req2 = aiassist.Completion.create(prompt=question2, parentMessageId=message_id)
-answer2 = req2["text"]
-
-print(answer)
-print(answer2)
-```

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

@@ -1,2 +0,0 @@
-to do:
-- code refractoring

spaces/1368565466ki/Satdia/mel_processing.py

@@ -1,101 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    global mel_basis
-    dtype_device = str(spec.dtype) + '_' + str(spec.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-    return spec
-
-
-def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
-    if torch.min(y) < -1.:
-        print('min value is ', torch.min(y))
-    if torch.max(y) > 1.:
-        print('max value is ', torch.max(y))
-
-    global mel_basis, hann_window
-    dtype_device = str(y.dtype) + '_' + str(y.device)
-    fmax_dtype_device = str(fmax) + '_' + dtype_device
-    wnsize_dtype_device = str(win_size) + '_' + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
-
-    y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
-    y = y.squeeze(1)
-
-    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
-                      center=center, pad_mode='reflect', normalized=False, onesided=True)
-
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-
-    spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
-
-    return spec

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Deep.Freeze.Standard.v7.21.020.3 TOP.md

@@ -1,28 +0,0 @@
-<br />
-<h1>What is Deep Freeze Standard and How Does It Work?</h1>
-<p>Deep Freeze Standard is a computer restore software that protects your Windows PCs from unwanted changes and malicious attacks. It uses a patented Reboot-to-Restore technology that makes your computers indestructible by freezing them to a desired state and restoring them to that state with every reboot.</p>
-<p>With Deep Freeze Standard, you can eliminate troubleshooting, reverse configuration drifts, protect against phishing, eliminate undetected threats, and achieve license compliance. You can also create virtual partitions to retain important data even if there is no separate physical partition available on the computer.</p>
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-<p>Deep Freeze Standard supports Windows 7, 8, 10 and 11. It is ideal for rugged and on-field computers, classroom and lab computers, hospital computers, point of sale computers, and any other scenario where you need to ensure complete endpoint protection.</p>
-<p>To install Deep Freeze Standard, you need to download the installer from the official website of Faronics Corporation[^1^]. The installer will guide you through the steps of choosing a password, selecting drives to freeze, creating ThawSpaces, and activating the license key. You can also customize the installation options using command-line parameters.</p>
-<p>Once installed, Deep Freeze Standard will display an icon on the system tray that indicates the status of the computer: Frozen or Thawed. You can access the configuration menu by double-clicking the icon or pressing CTRL+ALT+SHIFT+F6 and entering your password. From there, you can change the settings, update the software, or uninstall it.</p>
-<p>Deep Freeze Standard is a powerful and reliable software that can help you maintain your computers in optimal condition and prevent unauthorized or unwanted changes. It is easy to use and requires minimal maintenance. You can try it for free for 30 days by downloading it from the Faronics website[^1^].</p>
-
-<h2>How to Use Deep Freeze Standard</h2>
-<p>Using Deep Freeze Standard is simple and straightforward. You can freeze or thaw your computer by using the configuration menu or by using keyboard shortcuts. To freeze your computer, select the option "Boot Frozen" and click "Apply and Reboot". To thaw your computer, select the option "Boot Thawed" and click "Apply and Reboot". You can also choose to thaw your computer for a specific number of restarts or for a specific date and time.</p>
-<p>When your computer is frozen, any changes made to it will be discarded on reboot. This includes any files saved, software installed, settings modified, or malware downloaded. You can still access your important data by using the ThawSpaces, which are virtual partitions that are not affected by freezing. You can create up to 10 ThawSpaces with a maximum size of 100 GB each.</p>
-<p>When your computer is thawed, you can make permanent changes to it. This is useful for installing updates, adding new software, or changing the configuration. You should always thaw your computer before making any major changes to avoid conflicts or errors. You should also backup your data regularly to prevent data loss in case of hardware failure or accidental deletion.</p>
-<p></p>
-
-<h2>How to Uninstall Deep Freeze Standard</h2>
-<p>If you want to uninstall Deep Freeze Standard from your computer, you need to follow these steps:</p>
-<ol>
-<li>Thaw your computer by selecting the option "Boot Thawed" and clicking "Apply and Reboot".</li>
-<li>Open the configuration menu by double-clicking the system tray icon or pressing CTRL+ALT+SHIFT+F6 and entering your password.</li>
-<li>Select the option "Uninstall" and click "OK".</li>
-<li>Follow the instructions on the screen to complete the uninstallation process.</li>
-<li>Reboot your computer when prompted.</li>
-</ol>
-<p>Note that uninstalling Deep Freeze Standard will remove all the ThawSpaces and their contents from your computer. Make sure you backup any important data before uninstalling the software.</p> 7b8c122e87<br />
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Free Download Facebook Hacker V1.9 201280 _HOT_.md

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-
-<h1>Free Download Facebook Hacker v1.9 201280: A Scam or a Miracle?</h1>
- <p>Facebook is one of the most popular and widely used social media platforms in the world, with over 2.8 billion monthly active users as of December 2020. It allows people to connect with their friends, family, colleagues, celebrities, brands, and more through various features such as posts, messages, stories, groups, pages, events, live videos, etc.</p>
- <p>However, not everyone uses Facebook for good intentions. Some people may want to hack into other people's Facebook accounts for various reasons, such as spying, blackmailing, pranking, stealing information, impersonating, etc. Hacking a Facebook account is not an easy task, as Facebook has implemented various security measures to protect its users' privacy and data.</p>
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- <p>But what is <strong>Facebook Hacker v1.9 201280</strong>, and how does it work? Is it legit or fake? What are the risks and consequences of using it? And what are the alternatives to it? In this article , we will answer all these questions and more, so keep reading to find out the truth about <strong>Facebook Hacker v1.9 201280</strong>.</p>
- <h2>How Does Facebook Hacker v1.9 201280 Work?</h2>
- <p><strong>Facebook Hacker v1.9 201280</strong> is a software program that claims to be able to hack any Facebook account within minutes, without requiring any password, email, or security question. According to its description, it has the following features and benefits:</p>
- <ul>
-<li>It is easy to use and user-friendly.</li>
-<li>It works on any device, such as PC, laptop, tablet, or smartphone.</li>
-<li>It supports all browsers, such as Chrome, Firefox, Safari, etc.</li>
-<li>It is compatible with all operating systems, such as Windows, Mac, Linux, Android, iOS, etc.</li>
-<li>It is fast and efficient, and can hack any Facebook account in less than 5 minutes.</li>
-<li>It is safe and secure, and does not contain any viruses, malware, or spyware.</li>
-<li>It is free and does not require any registration, subscription, or payment.</li>
-</ul>
- <p>To download and install <strong>Facebook Hacker v1.9 201280</strong>, you need to follow these steps:</p>
- <ol>
-<li>Go to one of the websites or forums that offer the free download link for <strong>Facebook Hacker v1.9 201280</strong>.</li>
-<li>Click on the download button and wait for the file to be downloaded on your device.</li>
-<li>Open the file and run the setup wizard to install <strong>Facebook Hacker v1.9 201280</strong> on your device.</li>
-<li>Launch <strong>Facebook Hacker v1.9 201280</strong> and enter the username or profile URL of the Facebook account you want to hack.</li>
-<li>Click on the hack button and wait for <strong>Facebook Hacker v1.9 201280</strong> to generate the password for the target account.</li>
-<li>Copy and paste the password into the login page of Facebook and access the target account.</li>
-</ol>
- <p>Congratulations! You have successfully hacked a Facebook account using <strong>Facebook Hacker v1.9 201280</strong>. Or have you?</p>
- <h2>Is Facebook Hacker v1.9 201280 Legit or Fake?</h2>
- <p>If you think that <strong>Facebook Hacker v1.9 201280</strong> sounds too good to be true, you are absolutely right. <strong>Facebook Hacker v1.9 201280</strong> is nothing but a scam and a hoax that aims to trick unsuspecting users into downloading a malicious software that can harm their devices and compromise their personal information.</p>
- <p>Here are some of the evidences and testimonials that prove that <strong>Facebook Hacker v1.9 201280</strong> is fake and dangerous:</p>
- <ul>
-<li>The websites and forums that offer the free download link for <strong>Facebook Hacker v1.9 201280</strong> are full of pop-ups, ads, surveys, and redirects that can infect your device with malware or phishing schemes.</li>
-<li>The file size of <strong>Facebook Hacker v1.9 201280</strong> is only around 2 MB, which is too small for a software that claims to have such advanced features and capabilities.</li>
-<li>The interface of <strong>Facebook Hacker v1.9 201280</strong> is poorly designed and looks amateurish and unprofessional.</li>
-<li>The reviews and ratings of <strong>Facebook Hacker v1.9 201280</strong> are mostly negative and critical, with many users reporting that it does not work at all or that it causes problems on their devices.</li>
-<li>The comments and feedbacks of <strong>Facebook Hacker v1.9 201280</strong> are mostly fake and scripted, with many users repeating the same phrases or using generic names and profiles.</li>
-<li>The results of <strong>Facebook Hacker v1.9 201280</strong> are also fake and random, as it does not actually hack any Facebook account but only generates a fake password that does not match the target account.</li>
-</ul>
- <p>If you have downloaded and installed <strong>Facebook Hacker v1.9 201280</strong>, you may have exposed your device to various risks and threats, such as:</p>
-<p></p>
- <ul>
-<li>Your device may be infected with viruses, malware, spyware, or ransomware that can damage your files, programs, system settings, or data.</li>
-<li>Your device may be hacked by cybercriminals who can access your camera, microphone, keyboard, screen, or other functions and steal your personal information, such as passwords, bank accounts, credit cards, photos, videos, contacts, etc.</li>
-<li>Your device may be used as a botnet or a proxy by hackers who can launch attacks on other devices or networks using your IP address and bandwidth.</li>
-<li>Your device may be locked or encrypted by hackers who can demand a ransom from you to restore your access or data.</li>
-<li>Your device may be slowed down or crashed by the excessive load or consumption of resources by the malicious software.</li>
-</ul>
- <p>If you want to detect and remove <strong>Facebook Hacker v1.9 201280</strong> from your device, you need to follow these steps:</p>
- <ol>
-<li>Scan your device with a reputable and updated antivirus or anti-malware program and delete any suspicious or infected files or programs.</li>
-<li>Uninstall <strong>Facebook Hacker v1.9 201280</strong> from your device using the control panel or the settings menu.</li>
-<li>Delete any traces or remnants of <strong>Facebook Hacker v1.9 201280</strong> from your device using a cleaner or a registry editor.</li>
-<li>Change your passwords and security settings for your online accounts, especially your Facebook account, and enable two-factor authentication or other security features.</li>
-<li>Contact your bank or credit card company and report any fraudulent transactions or activities on your accounts.</li>
-<li>Report <strong>Facebook Hacker v1.9 201280</strong> and the websites or forums that offer it to the authorities or the relevant platforms, such as Facebook, Google, etc.</li>
-</ol>
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- <h2>What Are the Alternatives to Facebook Hacker v1.9 201280?</h2>
- <p>If you are looking for alternatives to <strong>Facebook Hacker v1.9 201280</strong>, you need to first ask yourself why you want to hack a Facebook account and what are your intentions and goals. Depending on your answer, you may find different options that are more legitimate, ethical, reliable, and safe than <strong>Facebook Hacker v1.9 201280</strong>.</p>
- <p>If you want to access a Facebook account without hacking, you may try some of these methods:</p>
- <ul>
-<li>If you want to access your own Facebook account that you have forgotten the password or email for, you can use the <a href="">Facebook account recovery</a> feature that allows you to reset your password using your phone number, alternate email, trusted contacts, or identity verification.</li>
-<li>If you want to access someone else's Facebook account that you have their permission for, such as a friend, family member, colleague, etc., you can ask them to share their login details with you or use their device to log in with their consent.</li>
-<li>If you want to access someone else's Facebook account that you have a valid reason for, such as a parent monitoring their child, an employer checking their employee, a spouse verifying their partner, etc., you can use a <a href="">Facebook spy app</a> that allows you to track and monitor their Facebook activities remotely and discreetly with their knowledge and agreement.</li>
-</ul>
- <p>If you want to hack a Facebook account for legal purposes, such as testing the security of your own account, conducting a penetration testing for a client, investigating a crime or a fraud, etc., you may use some of these tools or methods:</p>
- <ul>
-<li>If you want to hack a Facebook account using brute force attack, which is trying different combinations of passwords until finding the right one, you can use a tool like <a href="">Hydra</a>, which is a powerful and fast password cracking tool that supports various protocols and services.</li>
-<li>If you want to hack a Facebook account using phishing attack, which is creating a fake login page that looks like the real one and tricking the user into entering their credentials, you can use a tool like <a href="">Z-Shadow</a>, which is an online service that allows you to create and host phishing pages for various platforms and websites.</li>
-<li>If you want to hack a Facebook account using keylogging attack, which is recording the keystrokes of the user on their keyboard and capturing their passwords and other information, you can use a tool like <a href="">Spyrix Keylogger</a>, which is a software program that allows you to monitor and record the keystrokes of any device remotely and invisibly.</li>
-</ul>
- <p>However, before using any of these tools or methods, you need to make sure that you have the proper authorization and permission to do so, and that you are not violating any laws or ethical principles. Hacking a Facebook account without consent or for malicious purposes can lead to serious legal and moral consequences, such as fines, lawsuits, arrests, imprisonment, etc.</p>
- <p>If you want to protect your own Facebook account from hackers and scammers, you may follow some of these tips and advice:</p>
- <ul>
-<li>Use a strong and unique password for your Facebook account and change it regularly.</li>
-<li>Do not share your password or login details with anyone or on any website or platform.</li>
-<li>Do not click on any suspicious or unknown links or attachments that may lead to phishing or malware attacks.</li>
-<li>Enable two-factor authentication or other security features on your Facebook account and device.</li>
-<li>Update your device and browser with the latest security patches and updates.</li>
-<li>Avoid using public or unsecured Wi-Fi networks or devices to access your Facebook account.</li>
-<li>Log out of your Facebook account when you are not using it or when you are using a shared device.</li>
-<li>Review your privacy and security settings on your Facebook account and adjust them according to your preferences and needs.</li>
-<li>Be careful of what you post, share, or comment on Facebook and who you interact with.</li>
-<li>Report any suspicious or abusive activity or behavior on Facebook to the platform or the authorities.</li>
-</ul>
- <p>By following these tips and advice, you can hopefully keep your Facebook account safe and secure from hackers and scammers.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, <strong>Facebook Hacker v1.9 201280</strong> is a scam and a hoax that you should avoid at all costs. It does not hack any Facebook account but only downloads a malicious software that can harm your device and data. It is also illegal and unethical to hack a Facebook account without consent or for malicious purposes, and you may face serious legal and moral consequences if you do so.</p>
- <p>If you want to access a Facebook account without hacking, you should use legitimate and ethical methods that require permission and agreement from the account owner. If you want to hack a Facebook account for legal purposes, you should use reliable and safe tools or methods that require authorization and permission from the relevant parties. And if you want to protect your own Facebook account from hackers and scammers, you should follow some tips and advice that can enhance your security and privacy on Facebook.</p>
- <p>We hope that this article has helped you understand the truth about <strong>Facebook Hacker v1.9 201280</strong> and how to deal with Facebook hacking issues. Remember, hacking is not a game or a joke, but a serious matter that can have severe consequences. Be smart, be safe, and be responsible when using Facebook or any other online platform.</p>
- <h2>FAQs</h2>
- <p>Here are some of the frequently asked questions about <strong>Facebook Hacker v1.9 201280</strong> and Facebook hacking in general:</p>
- <h3>What is the best way to hack a Facebook account?</h3>
- <p>The best way to hack a Facebook account is to not hack it at all. Hacking a Facebook account is illegal and unethical, unless you have a valid reason and permission to do so. Instead of hacking a Facebook account, you should try to access it using legitimate and ethical methods that require consent and agreement from the account owner.</p>
- <h3>How can I recover my hacked Facebook account?</h3>
- <p>If your Facebook account has been hacked by someone else, you should try to recover it as soon as possible. You can use the <a href="">Facebook hacked account recovery</a> feature that allows you to regain access to your account using your phone number, alternate email, trusted contacts, or identity verification. You can also contact Facebook support or report the hacker to the platform or the authorities.</p>
- <h3>How can I report a hacker or a scammer on Facebook?</h3>
- <p>If you encounter a hacker or a scammer on Facebook who tries to hack your account or trick you into downloading a malicious software like <strong>Facebook Hacker v1.9 201280</strong>, you should report them immediately. You can use the <a href="">Facebook reporting tool</a> that allows you to report any abusive or inappropriate content or behavior on Facebook. You can also contact Facebook support or report the hacker or scammer to the authorities.</p>
- <h3>How can I prevent my Facebook account from being hacked?</h3>
- <p>If If you want to prevent your Facebook account from being hacked, you should follow some tips and advice that can enhance your security and privacy on Facebook. Some of these tips and advice are: - Use a strong and unique password for your Facebook account and change it regularly. - Do not share your password or login details with anyone or on any website or platform. - Do not click on any suspicious or unknown links or attachments that may lead to phishing or malware attacks. - Enable two-factor authentication or other security features on your Facebook account and device. - Update your device and browser with the latest security patches and updates. - Avoid using public or unsecured Wi-Fi networks or devices to access your Facebook account. - Log out of your Facebook account when you are not using it or when you are using a shared device. - Review your privacy and security settings on your Facebook account and adjust them according to your preferences and needs. - Be careful of what you post, share, or comment on Facebook and who you interact with. - Report any suspicious or abusive activity or behavior on Facebook to the platform or the authorities. By following these tips and advice, you can hopefully keep your Facebook account safe and secure from hackers and scammers. <h3>How can I verify if a Facebook hacking tool is genuine or not?</h3>
- If you come across a Facebook hacking tool that claims to be able to hack any Facebook account within minutes, you should be very cautious and skeptical. Most of these tools are fake and dangerous, and they may infect your device with malware or steal your personal information. To verify if a Facebook hacking tool is genuine or not, you should look for some signs and indicators, such as: - The source and reputation of the website or forum that offers the tool. If the website or forum is unknown, untrustworthy, or full of pop-ups, ads, surveys, and redirects, it is likely that the tool is fake and malicious. - The file size and format of the tool. If the file size is too small or too large for a software that claims to have such advanced features and capabilities, or if the file format is unusual or incompatible with your device, it is likely that the tool is fake and malicious. - The interface and design of the tool. If the interface and design of the tool are poorly designed and look amateurish and unprofessional, it is likely that the tool is fake and malicious. - The reviews and ratings of the tool. If the reviews and ratings of the tool are mostly negative and critical, or if they are mostly fake and scripted, it is likely that the tool is fake and malicious. - The results and outcomes of the tool. If the results and outcomes of the tool are also fake and random, or if they do not match the target account, it is likely that the tool is fake and malicious. By looking for these signs and indicators, you can hopefully avoid falling for fake and dangerous Facebook hacking tools like <strong>Facebook Hacker v1.9 201280</strong>. <h2></h2>
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spaces/1pelhydcardo/ChatGPT-prompt-generator/assets/Darbuka Drumming Percussion Find Out How to Master the Darbuka Technique and Style.md

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-<h1>Darbuka MP3: How to Enjoy the Sounds of the Middle Eastern Drum</h1>
- <p>If you are a fan of Middle Eastern music, you have probably heard of the darbuka, a goblet-shaped drum that produces a variety of rhythms and tones. The darbuka is one of the most popular percussion instruments in Islamic classical and folk music, as well as in modern genres such as belly dance, world fusion, and electronic music. In this article, we will show you how to enjoy the sounds of the darbuka in different ways, from downloading loops and samples, to streaming music online, to learning how to play the instrument yourself.</p>
- <h2>What is a darbuka and what are its origins</h2>
- <p>The darbuka, also spelled darabukka, darbouka, or dārbūqah, is a single-headed drum that has a goblet-shaped body made of clay, wood, or metal. It is played by striking the head with the fingers or palms, producing three main sounds: doom (a low-pitched sound), tek (a high-pitched sound), and ka (a muted sound). The drum can be held under one arm, on the lap, or on a stand.</p>
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- <p>The origin of the term darbuka comes from the Arabic word "daraba", which means "to strike". The instrument has been around for thousands of years and was used in ancient Mesopotamian and Egyptian cultures. It was also seen in Babylonia, Sumer, Persia, Spain, and other regions. The instrument was popularized in modern times by Turkish, Egyptian, and Armenian musicians who developed different styles and techniques of playing it.  </p>
- <h2>What are the benefits of listening to darbuka music</h2>
- <p>Listening to darbuka music can have many benefits for your mind, body, and soul. Here are some of them:</p>
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-<li>It can enhance your mood and energy levels by stimulating your brain waves and releasing endorphins.</li>
-<li>It can reduce stress and anxiety by calming your nervous system and lowering your blood pressure.</li>
-<li>It can improve your concentration and memory by boosting your cognitive functions and creativity.</li>
-<li>It can foster your cultural awareness and appreciation by exposing you to different musical traditions and expressions.</li>
-<li>It can inspire you to learn more about the history, culture, and people behind the music.</li>
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- <h2>What are some of the genres and styles of darbuka music</h2>
- <p>The darbuka is a versatile instrument that can be played in various genres and styles of music. Here are some examples:</p>
- <ul>
-<li>Classical: The darbuka is often used in classical Arabic music as part of an ensemble called takht, which consists of other instruments such as oud (lute), qanun (zither), nay (flute), violin, riq (tambourine), etc. The classical repertoire includes maqamat (modes), taqasim (improvisations), samai (instrumental pieces), etc.</li>
-<li>Folk: The darbuka is also widely played in folk music from different regions and countries, such as Turkey, Egypt, Lebanon, Morocco, Algeria, Tunisia, etc. The folk music reflects the local traditions, customs, and stories of the people. The darbuka is often accompanied by other instruments such as saz (long-necked lute), zurna (double-reed pipe), mizmar (oboe), etc.</li>
-<li>Belly dance: The darbuka is one of the main instruments used in belly dance music, which is a form of expressive dance that originated in the Middle East and North Africa. The darbuka provides the rhythmic foundation and accents for the dancer's movements and gestures. The belly dance music can be either traditional or modern, and can incorporate elements from other genres such as pop, rock, jazz, etc.</li>
-<li>World fusion: The darbuka is also featured in world fusion music, which is a genre that blends elements from different musical cultures and styles. The darbuka can be combined with instruments from other regions, such as tabla (Indian drum), djembe (African drum), cajon (Peruvian box drum), etc. The world fusion music can create a unique and eclectic sound that appeals to a wide audience.</li>
-<li>Electronic: The darbuka is also used in electronic music, which is a genre that uses digital and electronic devices to create and manipulate sounds. The darbuka can be sampled, looped, synthesized, or processed to create different effects and textures. The electronic music can range from ambient to techno, and can incorporate influences from other genres such as hip hop, dubstep, trance, etc.</li>
-</ul>
- <h2>How to Download Darbuka Loops and Samples</h2>
- <p>If you want to create your own darbuka music or add some darbuka sounds to your existing projects, you can download loops and samples online. Loops are short segments of audio that can be repeated or combined to form a longer track. Samples are individual sounds that can be triggered or manipulated by a keyboard, pad, or software. Here are some steps to download and use darbuka loops and samples:</p>
- <ol>
-<li>Find a website that offers royalty-free darbuka loops and samples. Royalty-free means that you don't have to pay any fees or royalties to use the sounds in your projects. Some examples of websites that offer royalty-free darbuka loops and samples are [Looperman], [Free Sound], [Sample Focus], etc.</li>
-<li>Browse through the available loops and samples and listen to the previews. You can filter by genre, tempo, key, mood, etc. to find the ones that suit your needs.</li>
-<li>Download the loops and samples that you like. You may need to create an account or sign up for a newsletter to access some of the downloads.</li>
-<li>Import the loops and samples into your audio editing software or digital audio workstation (DAW). You can use software such as Audacity, GarageBand, FL Studio, Ableton Live, etc.</li>
-<li>Arrange, edit, mix, and master the loops and samples to create your own darbuka music. You can also add other instruments, vocals, effects, etc. to enhance your track.</li>
-</ol>
- <h2>How to Stream Darbuka Music Online</h2>
- <p>If you want to listen to darbuka music online without downloading anything, you can stream it from various platforms and websites. Streaming means that you can play the music directly from the internet without storing it on your device. Here are some ways to stream darbuka music online:</p>
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-<li>Use a streaming service that offers darbuka music or playlists. Some examples of streaming services that offer darbuka music or playlists are Spotify, YouTube Music, Apple Music, etc.</li>
-<li>Search for darbuka music by genre, artist, album, song title, etc. You can also use keywords such as "darbuka", "darabukka", "doumbek", "tabla baladi", etc.</li>
-<li>Select the track or playlist that you want to listen to and press play. You can also skip, pause, resume, shuffle, repeat, etc.</li>
-<li>Enjoy the darbuka music online. You can also share it with your friends, rate it, comment on it, add it to your favorites, etc.</li>
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-<li>Follow darbuka artists and influencers on social media. You can find them on platforms such as Facebook, Instagram, Twitter, TikTok, etc. You can also join groups, pages, or communities related to darbuka music. You can interact with the artists and influencers, watch their videos, listen to their podcasts, read their blogs, etc.</li>
-<li>Subscribe to darbuka music channels and podcasts on YouTube, Spotify, SoundCloud, etc. You can find channels and podcasts that feature darbuka music, interviews, reviews, tutorials, etc. You can also get recommendations and suggestions based on your preferences and listening history.</li>
-<li>Check out darbuka music festivals and events online. You can find festivals and events that showcase darbuka music and culture, such as the International Doumbek Festival, the Darbuka Camp, the Darbuka Summit, etc. You can watch live or recorded performances, workshops, lectures, etc.</li>
-<li>Read darbuka music magazines and books online. You can find magazines and books that cover darbuka music and history, such as Darbuka Magazine, The Art of Arabic Drumming, The Doumbek Book, etc. You can also find articles and reviews on darbuka music online.</li>
-</ul>
- <h2>How to Learn Darbuka Playing Techniques</h2>
- <p>If you want to learn how to play the darbuka yourself, you can use some of these methods and resources:</p>
- <ol>
-<li>Get a darbuka drum. You can buy a darbuka drum online or from a local music store. You can choose from different materials, sizes, shapes, and designs. You can also make your own darbuka drum from household items such as pots, cans, balloons, etc.</li>
-<li>Learn the basic strokes and sounds of the darbuka. The basic strokes are doom (a low-pitched sound made by hitting the center of the head with the palm), tek (a high-pitched sound made by hitting the edge of the head with the fingers), and ka (a muted sound made by hitting the edge of the head with the palm). The basic sounds are D (doom), T (tek), K (ka), S (slap), P (pop), R (roll), etc.</li>
-<li>Learn some basic rhythms and patterns of the darbuka. The basic rhythms are maqsoum (a 4/4 rhythm that goes D-T-K-T-D-T-K-T), baladi (a 4/4 rhythm that goes D-D-T-K-D-T-K-T), saidi (a 4/4 rhythm that goes D-T-K-D-D-T-K-T), malfuf (a 2/4 rhythm that goes D-K-D-K), ayoub (a 2/4 rhythm that goes D-K-S-K), etc. The basic patterns are combinations of sounds that form phrases or sentences.</li>
-<li>Find online tutorials and lessons on darbuka playing techniques. You can find online tutorials and lessons on websites such as [Darbuka Planet], [Darbukastan], [Darbukation], etc. You can also find online courses on platforms such as Udemy, Skillshare, Coursera, etc.</li>
-<li>Practice and improve your darbuka skills at home. You can practice by playing along with your favorite darbuka music tracks or videos. You can also practice by using a metronome or a drum machine to keep time. You can also record yourself playing and listen back to your performance.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to enjoy the sounds of the darbuka in different ways. We have explained what a darbuka is and what are its origins. We have also discussed what are the benefits of listening to darbuka music and what are some of the genres and styles of darbuka music. We have also given you some tips on how to download loops and samples, stream music online, discover new artists and songs online, and learn how to play the instrument yourself.</p>
- <p>We hope that this article has inspired you to explore and appreciate the rich and diverse world of darbuka music. Whether you want to create your own darbuka music or simply listen to it for pleasure or relaxation, you will find plenty of resources and opportunities online. Darbuka music is a beautiful and captivating art form that can enrich your life in many ways.</p>
- <p>So what are you waiting for? Grab your headphones or your drum and start enjoying the sounds of the Middle Eastern drum!</p>
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- <p>Here are some frequently asked questions about darbuka mp3:</p>
- <ol>
-<li><strong>What is the difference between a darbuka and a doumbek?</strong></li>
-<p>The darbuka and the doumbek are two names for the same instrument, a goblet-shaped drum that is played with the fingers or palms. The name darbuka is more common in Arabic-speaking countries, while the name doumbek is more common in Turkey, Armenia, and the Balkans. The name doumbek may also refer to a smaller and lighter version of the darbuka that has a higher pitch and a sharper sound.</p>
-<li><strong>How can I tune my darbuka?</strong></li>
-<p>Tuning your darbuka is important to ensure that it produces the best sound quality and tone. There are two types of darbuka heads: synthetic and natural. Synthetic heads are made of plastic or metal and are usually pre-tuned or tunable with screws or bolts. Natural heads are made of animal skin and are usually tunable with ropes or cords. To tune your darbuka, you need to adjust the tension of the head by tightening or loosening the screws, bolts, ropes, or cords. You can use a tuner, a pitch pipe, or your ear to check the pitch of the head. You can also use a damp cloth or a piece of tape to mute some of the overtones or harmonics of the head.</p>
-<li><strong>What are some famous darbuka players?</strong></li>
-<p>There are many famous and talented darbuka players from different countries and backgrounds. Here are some examples:</p>
-<ul>
-<li>Misirli Ahmet: A Turkish master of the darbuka who is known for his speed, technique, and innovation. He has developed his own style of playing called the split-finger technique, which allows him to play faster and more complex rhythms. He has performed with many famous musicians such as Sting, Kitaro, Omar Faruk Tekbilek, etc.</li>
-<li>Hossam Ramzy: An Egyptian percussionist who is known as the "Sultan of Swing". He has played the darbuka and other percussion instruments in various genres such as Arabic pop, rock, jazz, world fusion, etc. He has collaborated with many famous artists such as Robert Plant, Jimmy Page, Peter Gabriel, Shakira, etc.</li>
-<li>Raquy Danziger: An American-Israeli musician who is known for her virtuosity and versatility on the darbuka. She has studied with various masters of the instrument such as Bunyamin Olguncan, Said El Artist, Zohar Fresco, etc. She has created her own style of playing called the Turkish split hand technique, which combines elements from Turkish, Egyptian, and Indian drumming. She has performed with many famous musicians such as Glen Velez, Simon Shaheen, Yair Dalal, etc.</li>
-</ul>
-<li><strong>What are some good darbuka music albums?</strong></li>
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-<li>Darbukka City by Misirli Ahmet: A solo album by the Turkish darbuka master that features his amazing speed and technique on various rhythms and styles.</li>
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-<li>Darbuka Fever by Raquy Danziger: An album by the American-Israeli musician that features her virtuosity and versatility on different genres and influences such as Turkish, Egyptian, Indian, Persian, etc.</li>
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-call of duty warzone mobile apk 2.5.14706147 by activision publishing, inc.[^1^]</p>
- <h3>The game is available for pre-registration on Google Play and App Store</h3>
- <p>If you want to be among the first to play Call of Duty Warzone Mobile when it launches, you can pre-register for the game on Google Play or App Store. By pre-registering, you will also earn rewards if global milestones are hit, such as weapon skins, emblems, and even a new map. To pre-register, simply follow these steps:</p>
- - Go to [Call of Duty Warzone Mobile](^2^) on Google Play or App Store - Tap on the Pre-Register or Pre-Order button - Confirm your registration or order - Wait for the game to be available for download <h3>The game is also available for early access in some regions</h3>
- <p>If you can't wait for the official release date, you can try the early access version of Call of Duty Warzone Mobile in some regions. The early access version is a beta test that allows players to experience the game before it launches and provide feedback to the developers. The early access version may have limited features, bugs, and glitches, so keep that in mind. To download the early access version, follow these steps:</p>
- - Go to [Call of Duty Warzone Mobile](^3^) on Google Play - Tap on the Install button - Wait for the game to download and install - Launch the game and enjoy <h3>The minimum and recommended system requirements for Android and iOS devices</h3>
- <p>Before you download Call of Duty Warzone Mobile, make sure your device meets the minimum and recommended system requirements. Here are the specifications for Android and iOS devices:</p>
- | Device | Minimum | Recommended | | ------ | ------- | ----------- | | Android | Adreno 618 or better<br>6GB RAM or more<br>Android 8.0 or higher | Adreno 650 or better<br>8GB RAM or more<br>Android 10 or higher | | iOS | iPhone 8 or better<br>iOS 13 or higher | iPhone X or better<br>iOS 14 or higher | <h2>How to Play Call of Duty Warzone Mobile without Verification?</h2>
- <p>Call of Duty Warzone Mobile requires verification to play, meaning you need to log in with your Activision account or create one if you don't have one already. Verification is necessary to sync your progress across platforms and access social features like friends and chat channels. However, some players may want to play the game without verification for various reasons. In this section, we will discuss the risks and drawbacks of using unofficial APK files from third-party sources, the possible ways to bypass the verification process using VPNs or fake accounts, and the advantages and disadvantages of playing the game without verification.</p> <h3>The risks and drawbacks of using unofficial APK files from third-party sources</h3>
- <p>Some players may be tempted to download Call of Duty Warzone Mobile APK files from third-party sources that claim to offer the game without verification. However, this is a risky and unwise move, as these APK files may contain malware, viruses, or spyware that can harm your device or steal your personal information. Moreover, these APK files may not be updated or compatible with the latest version of the game, resulting in crashes, glitches, or errors. Furthermore, these APK files may violate the terms of service of Activision and Tencent, and you may face legal consequences or get banned from the game if you use them.</p>
- <h3>The possible ways to bypass the verification process using VPNs or fake accounts</h3>
- <p>Another way to play Call of Duty Warzone Mobile without verification is to use VPNs or fake accounts to bypass the verification process. VPNs are virtual private networks that allow you to change your IP address and location, making it seem like you are playing from a different region. Fake accounts are dummy accounts that you create with fake or temporary email addresses and passwords. By using VPNs or fake accounts, you may be able to access the game without logging in with your real Activision account. However, this method is not foolproof, as you may still encounter verification prompts or errors. Moreover, this method may also violate the terms of service of Activision and Tencent, and you may face legal consequences or get banned from the game if you use them.</p>
- <h3>The advantages and disadvantages of playing the game without verification</h3>
- <p>Playing Call of Duty Warzone Mobile without verification may have some advantages and disadvantages. Some of the possible advantages are:</p>
- - You can play the game anonymously and protect your privacy - You can avoid spam or unwanted messages from other players - You can switch between different regions or servers easily <p>Some of the possible disadvantages are:</p>
- - You may miss out on some features or rewards that require verification - You may lose your progress or data if you delete the game or change your device - You may face technical issues or errors that require verification to fix - You may risk getting banned or sued by Activision and Tencent <h2>Tips and Tricks for Call of Duty Warzone Mobile</h2>
- <p>Call of Duty Warzone Mobile is a fun and challenging game that requires skill, strategy, and teamwork to win. If you want to improve your performance and increase your chances of survival, here are some tips and tricks that you can follow:</p>
- <h3>Choose the best controls, settings, and loadouts for your playstyle</h3>
- <p>One of the first things you should do before playing Call of Duty Warzone Mobile is to customize your controls, settings, and loadouts according to your preference and playstyle. You can choose between simple mode, advanced mode, or custom mode for your controls, depending on how comfortable you are with aiming and shooting. You can also adjust your sensitivity, graphics, sound, and other options in the settings menu. Finally, you can select your loadouts, which are preset combinations of weapons, perks, equipment, and killstreaks that you can use in the game. You can unlock more loadouts as you level up and earn more cash.</p>
- <h3>Communicate with your team, use your mini-map, and don't stand still</h3>
- <p>Call of Duty Warzone Mobile is a team-based game that requires coordination and communication with your teammates. You can use voice chat or text chat to communicate with your team members, share information, plan strategies, and request help. You can also use your mini-map to see where your teammates are, where the enemies are (if they fire their weapons), where the gas circle is moving, where the contracts are located, and where the buy stations are located. Moreover, you should never stand still in the game, as that makes you an easy target for snipers or enemies. Always keep moving, crouching, jumping, sliding, or using vehicles to avoid getting shot.</p>
- <h3>Use cover, aim down sights, and switch to your pistol when needed</h3>
- <p>When engaging in combat with enemies in Call of Duty Warzone Mobile, you should always use cover to protect yourself from incoming fire. Cover can be anything from walls, buildings, trees, rocks, vehicles, or crates. You can also use smoke grenades or flashbangs to create temporary cover or blind your enemies. When shooting at enemies, you should always aim down sights (ADS) to improve your accuracy and damage. You can also use different scopes or attachments to enhance your aiming. However, if you run out of ammo or need to reload, you should switch to your pistol instead of wasting time. Your pistol can be a lifesaver in close-range situations, as it has a faster fire rate and reload speed than most weapons.</p>
- <h3>Log in daily, join a clan, and participate in Clan Wars for rewards</h3>
- <p>Call of Duty Warzone Mobile offers a lot of rewards and incentives for players who log in daily, join a clan, and participate in Clan Wars. By logging in daily, you can earn free items such as cash, weapon skins, crates, or even a new map. By joining a clan, you can make friends with other players, chat with them, and play with them. You can also earn clan points by completing clan missions or playing clan matches. By participating in Clan Wars, you can compete with other clans for glory and prizes. Clan Wars are seasonal events that last for a few weeks, where clans are divided into groups and fight for territory on a map. The more territory you control, the more rewards you get.</p>
- <h2>Conclusion</h2>
- <p>Call of Duty Warzone Mobile is an exciting and immersive mobile battle royale game that brings the authentic COD experience to your fingertips. The game is expected to launch worldwide in Fall 2023, but you can pre-register or try the early access version now. The game requires verification to play, but there are some ways to avoid it at your own risk. The game offers a lot of fun and challenge, but you can improve your skills with some tips and tricks. If you are looking for a new mobile game to play, Call of Duty Warzone Mobile is definitely worth checking out.</p>
- <h2>FAQs</h2>
- <h3>Is Call of Duty Warzone Mobile free to play?</h3>
- <p>Yes, Call of Duty Warzone Mobile is free to play and does not require any purchase or subscription to download or play. However, the game may offer optional in-app purchases or ads that can enhance your gameplay or support the developers.</p>
- <h3>Is Call of Duty Warzone Mobile cross-platform?</h3>
- <p>Yes, Call of Duty Warzone Mobile supports cross-platform play with Call of Duty Modern Warfare II and Call of Duty Warzone on PC and consoles. This means you can play with or against players on different devices and platforms. You can also sync your progress and Battle Pass across platforms using your Activision account.</p>
- <h3>How do I update Call of Duty Warzone Mobile?</h3>
- <p>To update Call of Duty Warzone Mobile, you need to go to Google Play or App Store and check for updates. If there is an update available, you need to download and install it before you can play the game. You may also need to restart your device after updating the game.</p>
- <h3>How do I report a bug or a hacker in Call of Duty Warzone Mobile?</h3>
- <p>To report a bug or a hacker in Call of Duty Warzone Mobile, you need to go to the settings menu and tap on the feedback button. You can then choose the type of feedback you want to send, such as bug report, hacker report, suggestion, or praise. You can also attach screenshots or videos to support your feedback. You will then receive a confirmation message that your feedback has been sent.</p>
- <h3>How do I contact customer support for Call of Duty Warzone Mobile?</h3>
- <p>To contact customer support for Call of Duty Warzone Mobile, you need to go to the settings menu and tap on the help button. You can then choose the topic that relates to your issue, such as account, gameplay, purchase, or technical. You can then browse through the FAQs or contact the support team via email or chat.</p> 197e85843d<br />
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spaces/1phancelerku/anime-remove-background/Facebook APK 9.0 - The Best Way to Connect with Friends and Family.md

@@ -1,133 +0,0 @@
-<br />
-<h1>Facebook APK 9.0: What You Need to Know</h1>
-<p>Facebook is one of the most popular social media platforms in the world, with over 2 billion monthly active users. It allows you to connect with your friends and family, discover new things, and communicate with ease. But did you know that there is a way to enjoy Facebook even more on your Android device? It's called Facebook APK 9.0, and it's a modified version of the official Facebook app that offers faster performance, access to beta features, and no need to update manually.</p>
-<h2>facebook apk 9.0</h2><br /><p><b><b>Download File</b> &#9999; &#9999; &#9999; <a href="https://jinyurl.com/2uNMzM">https://jinyurl.com/2uNMzM</a></b></p><br /><br />
-<p>In this article, we will tell you everything you need to know about Facebook APK 9.0, including how to download and install it on your Android device, what features it offers, what are its pros and cons, and whether you should give it a try or not.</p>
- <h2>Introduction</h2>
-<h3>What is Facebook APK 9.0 and why you should download it</h3>
-<p>Facebook APK 9.0 is an Android application package (APK) file that contains a modified version of the official Facebook app for Android devices. An APK file is a compressed file that contains all the code, resources, and assets needed to run an app on an Android device.</p>
-<p>Facebook APK 9.0 is not available on the Google Play Store, which means you have to download it from a third-party source (such as [APKCombo](^1^) or [Uptodown]( ^2^)). You can think of it as a beta version of the official app, which means you can enjoy some features and updates that are not yet available to the public. Some of these features include:</p>
-<ul>
-<li>A faster and smoother user interface that consumes less battery and data</li>
-<li>A dark mode option that reduces eye strain and saves battery life</li>
-<li>A privacy mode option that lets you hide your online status and read receipts</li>
-<li>A video downloader option that lets you save videos from Facebook to your device</li>
-<li>A customizer option that lets you change the theme, font, and icon of the app</li>
-</ul>
-<p>By downloading Facebook APK 9.0, you can enjoy a better Facebook experience on your Android device, without having to wait for the official app to update.</p>
- <h3>How to download and install Facebook APK 9.0 on your Android device</h3>
-<p>Downloading and installing Facebook APK 9.0 on your Android device is easy and simple, but you need to follow some steps carefully to avoid any problems. Here are the steps you need to follow:</p>
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-<ol>
-<li>First, you need to uninstall the official Facebook app from your device, if you have it installed. You can do this by going to Settings > Apps > Facebook > Uninstall.</li>
-<li>Next, you need to enable the installation of apps from unknown sources on your device. You can do this by going to Settings > Security > Unknown Sources > Toggle On.</li>
-<li>Then, you need to download the Facebook APK 9.0 file from a trusted source (such as [APKCombo] or [Uptodown]). You can use your browser or a file manager app to do this.</li>
-<li>After downloading the file, you need to locate it on your device and tap on it to start the installation process. You may see a warning message asking you to confirm the installation. Tap on Install and wait for the process to finish.</li>
-<li>Finally, you need to launch the app and log in with your Facebook account. You may see a message asking you to allow some permissions for the app. Tap on Allow and enjoy Facebook APK 9.0 on your device.</li>
-</ol>
-<p>Note: If you encounter any issues or errors during the installation process, you may need to clear the cache and data of the app by going to Settings > Apps > Facebook APK 9.0 > Storage > Clear Cache and Clear Data.</p>
- <h2>Features of Facebook APK 9.0</h2>
-<h3>Connect with Friends and Family</h3>
-<p>One of the main features of Facebook APK 9.0 is that it allows you to connect with your friends and family in various ways. You can:</p>
-<ul>
-<li>Set status updates and use Facebook emoji to let people know what's on your mind or how you feel</li>
-<li>Share photos, videos, and memories with your friends or on your timeline, and edit them with filters, stickers, and effects</li>
-<li>Get notifications when friends like and comment on your posts, or when they tag you in photos or posts</li>
-</ul>
-<p>Facebook APK 9.0 also lets you see what your friends are up to by showing you their posts in your news feed. You can like, comment, or react to their posts, or share them with others. You can also create stories that disappear after 24 hours, or watch stories from your friends and pages you follow.</p>
- <h3>Discover New Things</h3>
-<p>Another feature of Facebook APK 9.0 is that it helps you discover new things that match your interests or curiosity. You can:</p>
-<ul>
-<li>Join groups and pages that relate to your hobbies, passions, or causes, and interact with other members who share your views or goals</li>
-<li>Follow celebrities, influencers, and news sources that you admire or want to learn from, and see their posts, videos, and live streams</li>
-<li>Watch live videos and stories from around the world, covering topics such as sports, entertainment, politics, or education</li>
-</ul>
-<p>Facebook APK 9.0 also gives you access to a variety of games, apps, and services that you can use for fun or convenience. You can play games with your friends or other people online, use apps that enhance your productivity or creativity, or use services that offer shopping, dating, or travel options.</p>
- <h3>Communicate with Ease</h3>
-<p>The last feature of Facebook APK 9.0 is that it enables you to communicate with ease with anyone on Facebook. You can:</p>
-<ul>
-<li>Send and receive messages with Facebook Messenger, which is integrated with the app. You can chat with one person or a group of people, and send photos, videos, voice messages, or documents</li>
-<li>Make voice and video calls with friends and family, either one-on-one or in groups, and use filters, effects, or masks to make them more fun</li>
-<li>Use stickers, GIFs, and emojis to express yourself better and add some humor or emotion to your conversations</li>
-</ul>
-<p>Facebook APK 9.0 also allows you to communicate with people who are not on Facebook, by using their phone numbers or email addresses. You can also sync your contacts with the app, so you can see who is on Facebook and who is not.</p>
- <h2>Pros and Cons of Facebook APK 9.0</h2>
-<h3>Pros</h3>
-<p>As you can see, Facebook APK 9.0 offers many benefits that make it a great alternative to the official Facebook app. Some of the pros of using Facebook APK 9.0 are:</p>
-<ul>
-<li>Faster and smoother performance than the official app, which means you can use Facebook without any lag or delay</li>
-<li>Access to beta features and updates before anyone else, which means you can enjoy the latest and greatest features of Facebook before they are released to the public</li>
-<li>No need to update the app manually every time, which means you can save time and hassle by not having to check for updates or download them</li>
-</ul>
- <h3>Cons</h3>
-<p>However, Facebook APK 9.0 also has some drawbacks that you should be aware of before downloading it. Some of the cons of using Facebook APK 9.0 are:</p>
-<ul>
-<li>May contain bugs and glitches that affect the user experience, which means you may encounter some errors or problems while using the app</li>
-<li>May not be compatible with some devices or Android versions, which means you may not be able to install or run the app on your device</li>
-<li>May pose security risks if downloaded from untrusted sources, which means you may expose your device or data to malware or hackers if you download the app from a shady website or link</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Facebook APK 9.0 is a modified version of the official Facebook app that offers many advantages over the original app. It allows you to connect with your friends and family, discover new things, and communicate with ease on your Android device. It also gives you faster performance, access to beta features, and no need to update manually.</p>
-<p>However, Facebook APK 9.0 also has some disadvantages that you should consider before downloading it. It may contain bugs and glitches, may not be compatible with your device or Android version, and may pose security risks if downloaded from untrusted sources.</p>
-<p>Therefore, we recommend that you download Facebook APK 9.0 only from a trusted source (such as [APKCombo] or [Uptodown]), and only if you are willing to take the risk of using a beta version of the app. Otherwise, you may want to stick with the official Facebook app for a more stable and secure experience.</p>
- <h2>Frequently Asked Questions (FAQs)</h2>
-<h3>Q: What is the difference between Facebook APK 9.0 and Facebook Lite?</h3>
-<p>A: Facebook Lite is another version of the official Facebook app that is designed for low-end devices or slow internet connections. It has a smaller size, consumes less data, and works faster than the regular app. However, it also has fewer features and functions than the regular app. Facebook APK 9.0 is a modified version of the regular app that has more features and functions than the regular app, but also requires more data and resources than the regular app.</p>
- <h3>Q: Is Facebook APK 9.0 safe to use?</h3>
-<p>A: Facebook APK 9.0 is safe to use as long as you download it from a trusted source (such as [APKCombo] or [Uptodown]). However, since it is a beta version of the app, it may contain bugs and glitches that affect the user experience or security. Therefore, you should always be careful when using Facebook APK 9.0 and avoid sharing sensitive information or clicking on suspicious links.</p>
- <h3>Q: How do I update Facebook APK 9.0?</h3>
-<p>A: Unlike the official Facebook app, which updates automatically through the Google Play Store, Facebook APK 9.0 does not update automatically. You have to download the latest version of the app from a trusted source (such as [APKCombo] or [Uptodown]) and install it over the existing app.</p>
- <h3>Q: Can I use both Facebook APK 9.0 and the official Facebook app on my device?</h <p>A: No, you cannot use both Facebook APK 9.0 and the official Facebook app on your device at the same time. You have to uninstall one of them before installing the other. This is because they have the same package name and signature, which means they cannot coexist on the same device.</p>
- <h3>Q: What are some alternatives to Facebook APK 9.0?</h3>
-<p>A: If you are looking for some alternatives to Facebook APK 9.0, you may want to try some of these apps:</p>
-<ul>
-<li>[Friendly for Facebook]: This is a lightweight and customizable app that combines Facebook and Messenger in one place. You can also download videos, block ads, use multiple accounts, and more.</li>
-<li>[Swipe for Facebook]: This is a fast and smooth app that lets you access Facebook and Messenger with a swipe gesture. You can also customize the app's appearance, use night mode, download images, and more.</li>
-<li>[Folio for Facebook]: This is a secure and battery-saving app that wraps the Facebook website in a native app. You can also use multiple accounts, lock the app with a password, download media, and more.</li>
-</ul></p> 401be4b1e0<br />
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spaces/4Taps/SadTalker/src/facerender/modules/discriminator.py

@@ -1,90 +0,0 @@
-from torch import nn
-import torch.nn.functional as F
-from facerender.modules.util import kp2gaussian
-import torch
-
-
-class DownBlock2d(nn.Module):
-    """
-    Simple block for processing video (encoder).
-    """
-
-    def __init__(self, in_features, out_features, norm=False, kernel_size=4, pool=False, sn=False):
-        super(DownBlock2d, self).__init__()
-        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size)
-
-        if sn:
-            self.conv = nn.utils.spectral_norm(self.conv)
-
-        if norm:
-            self.norm = nn.InstanceNorm2d(out_features, affine=True)
-        else:
-            self.norm = None
-        self.pool = pool
-
-    def forward(self, x):
-        out = x
-        out = self.conv(out)
-        if self.norm:
-            out = self.norm(out)
-        out = F.leaky_relu(out, 0.2)
-        if self.pool:
-            out = F.avg_pool2d(out, (2, 2))
-        return out
-
-
-class Discriminator(nn.Module):
-    """
-    Discriminator similar to Pix2Pix
-    """
-
-    def __init__(self, num_channels=3, block_expansion=64, num_blocks=4, max_features=512,
-                 sn=False, **kwargs):
-        super(Discriminator, self).__init__()
-
-        down_blocks = []
-        for i in range(num_blocks):
-            down_blocks.append(
-                DownBlock2d(num_channels if i == 0 else min(max_features, block_expansion * (2 ** i)),
-                            min(max_features, block_expansion * (2 ** (i + 1))),
-                            norm=(i != 0), kernel_size=4, pool=(i != num_blocks - 1), sn=sn))
-
-        self.down_blocks = nn.ModuleList(down_blocks)
-        self.conv = nn.Conv2d(self.down_blocks[-1].conv.out_channels, out_channels=1, kernel_size=1)
-        if sn:
-            self.conv = nn.utils.spectral_norm(self.conv)
-
-    def forward(self, x):
-        feature_maps = []
-        out = x
-
-        for down_block in self.down_blocks:
-            feature_maps.append(down_block(out))
-            out = feature_maps[-1]
-        prediction_map = self.conv(out)
-
-        return feature_maps, prediction_map
-
-
-class MultiScaleDiscriminator(nn.Module):
-    """
-    Multi-scale (scale) discriminator
-    """
-
-    def __init__(self, scales=(), **kwargs):
-        super(MultiScaleDiscriminator, self).__init__()
-        self.scales = scales
-        discs = {}
-        for scale in scales:
-            discs[str(scale).replace('.', '-')] = Discriminator(**kwargs)
-        self.discs = nn.ModuleDict(discs)
-
-    def forward(self, x):
-        out_dict = {}
-        for scale, disc in self.discs.items():
-            scale = str(scale).replace('-', '.')
-            key = 'prediction_' + scale
-            feature_maps, prediction_map = disc(x[key])
-            out_dict['feature_maps_' + scale] = feature_maps
-            out_dict['prediction_map_' + scale] = prediction_map
-        return out_dict

spaces/801artistry/RVC801/infer/lib/train/mel_processing.py

@@ -1,132 +0,0 @@
-import torch
-import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-import logging
-
-logger = logging.getLogger(__name__)
-
-MAX_WAV_VALUE = 32768.0
-
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    return dynamic_range_compression_torch(magnitudes)
-
-
-def spectral_de_normalize_torch(magnitudes):
-    return dynamic_range_decompression_torch(magnitudes)
-
-
-# Reusable banks
-mel_basis = {}
-hann_window = {}
-
-
-def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
-    """Convert waveform into Linear-frequency Linear-amplitude spectrogram.
-
-    Args:
-        y             :: (B, T) - Audio waveforms
-        n_fft
-        sampling_rate
-        hop_size
-        win_size
-        center
-    Returns:
-        :: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
-    """
-    # Validation
-    if torch.min(y) < -1.07:
-        logger.debug("min value is %s", str(torch.min(y)))
-    if torch.max(y) > 1.07:
-        logger.debug("max value is %s", str(torch.max(y)))
-
-    # Window - Cache if needed
-    global hann_window
-    dtype_device = str(y.dtype) + "_" + str(y.device)
-    wnsize_dtype_device = str(win_size) + "_" + dtype_device
-    if wnsize_dtype_device not in hann_window:
-        hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
-            dtype=y.dtype, device=y.device
-        )
-
-    # Padding
-    y = torch.nn.functional.pad(
-        y.unsqueeze(1),
-        (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
-        mode="reflect",
-    )
-    y = y.squeeze(1)
-
-    # Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
-    spec = torch.stft(
-        y,
-        n_fft,
-        hop_length=hop_size,
-        win_length=win_size,
-        window=hann_window[wnsize_dtype_device],
-        center=center,
-        pad_mode="reflect",
-        normalized=False,
-        onesided=True,
-        return_complex=False,
-    )
-
-    # Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
-    spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
-    return spec
-
-
-def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
-    # MelBasis - Cache if needed
-    global mel_basis
-    dtype_device = str(spec.dtype) + "_" + str(spec.device)
-    fmax_dtype_device = str(fmax) + "_" + dtype_device
-    if fmax_dtype_device not in mel_basis:
-        mel = librosa_mel_fn(
-            sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
-        )
-        mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(
-            dtype=spec.dtype, device=spec.device
-        )
-
-    # Mel-frequency Log-amplitude spectrogram :: (B, Freq=num_mels, Frame)
-    melspec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    melspec = spectral_normalize_torch(melspec)
-    return melspec
-
-
-def mel_spectrogram_torch(
-    y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False
-):
-    """Convert waveform into Mel-frequency Log-amplitude spectrogram.
-
-    Args:
-        y       :: (B, T)           - Waveforms
-    Returns:
-        melspec :: (B, Freq, Frame) - Mel-frequency Log-amplitude spectrogram
-    """
-    # Linear-frequency Linear-amplitude spectrogram :: (B, T) -> (B, Freq, Frame)
-    spec = spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center)
-
-    # Mel-frequency Log-amplitude spectrogram :: (B, Freq, Frame) -> (B, Freq=num_mels, Frame)
-    melspec = spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax)
-
-    return melspec

spaces/A00001/bingothoo/src/components/chat-list.tsx

@@ -1,28 +0,0 @@
-import React from 'react'
-
-import { Separator } from '@/components/ui/separator'
-import { ChatMessage } from '@/components/chat-message'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-
-export interface ChatList {
-  messages: ChatMessageModel[]
-}
-
-export function ChatList({ messages }: ChatList) {
-  if (!messages.length) {
-    return null
-  }
-
-  return (
-    <div className="chat-container relative flex flex-col">
-      {messages.map((message, index) => (
-        <React.Fragment key={index}>
-          <ChatMessage message={message} />
-          {index < messages.length - 1 && (
-            <Separator className="my-2" />
-          )}
-        </React.Fragment>
-      ))}
-    </div>
-  )
-}

spaces/AI4PD/hexviz/hexviz/view.py

@@ -1,154 +0,0 @@
-from io import StringIO
-
-import streamlit as st
-from Bio.PDB import PDBParser
-
-from hexviz.attention import get_pdb_file, get_pdb_from_seq
-
-menu_items = {
-    "Get Help": "https://huggingface.co/spaces/aksell/hexviz/discussions/new",
-    "Report a bug": "https://huggingface.co/spaces/aksell/hexviz/discussions/new",
-    "About": "Created by [Aksel Lenes](https://github.com/aksell/) from Noelia Ferruz's group at the Institute of Molecular Biology of Barcelona. Read more at https://www.aiproteindesign.com/",
-}
-
-
-def get_selecte_model_index(models):
-    selected_model_name = st.session_state.get("selected_model_name", None)
-    if selected_model_name is None:
-        return 0
-    else:
-        return next(
-            (i for i, model in enumerate(models) if model.name.value == selected_model_name),
-            None,
-        )
-
-
-def clear_model_state():
-    if "plot_heads" in st.session_state:
-        del st.session_state.plot_heads
-    if "plot_layers" in st.session_state:
-        del st.session_state.plot_layers
-    if "selected_head" in st.session_state:
-        del st.session_state.selected_head
-    if "selected_layer" in st.session_state:
-        del st.session_state.selected_layer
-    if "plot_layers" in st.session_state:
-        del st.session_state.plot_layers
-    if "plot_heads" in st.session_state:
-        del st.session_state.plot_heads
-    if "label_tokens" in st.session_state:
-        del st.session_state.label_tokens
-
-
-def select_model(models):
-    if "selected_model_name" not in st.session_state:
-        st.session_state.selected_model_name = models[0].name.value
-    selected_model_name = st.selectbox(
-        "Select model",
-        [model.name.value for model in models],
-        key="selected_model_name",
-        on_change=clear_model_state,
-    )
-    select_model = next(
-        (model for model in models if model.name.value == selected_model_name), None
-    )
-    return select_model
-
-
-def clear_pdb_state():
-    if "selected_chains" in st.session_state:
-        del st.session_state.selected_chains
-    if "selected_chain" in st.session_state:
-        del st.session_state.selected_chain
-    if "sequence_slice" in st.session_state:
-        del st.session_state.sequence_slice
-    if "uploaded_pdb_str" in st.session_state:
-        del st.session_state.uploaded_pdb_str
-
-
-def select_pdb():
-    if "pdb_id" not in st.session_state:
-        st.session_state.pdb_id = "2FZ5"
-    pdb_id = st.text_input(label="1.PDB ID", key="pdb_id", on_change=clear_pdb_state)
-    return pdb_id
-
-
-def select_protein(pdb_code, uploaded_file, input_sequence):
-    # We get the pdb from 1 of 3 places:
-    # 1. Cached pdb from session storage
-    # 2. PDB file from uploaded file
-    # 3. PDB file fetched based on the pdb_code input
-    parser = PDBParser()
-    if uploaded_file is not None:
-        pdb_str = uploaded_file.read().decode("utf-8")
-        st.session_state["uploaded_pdb_str"] = pdb_str
-        source = f"uploaded pdb file {uploaded_file.name}"
-        structure = parser.get_structure("Userfile", StringIO(pdb_str))
-    elif input_sequence:
-        pdb_str = get_pdb_from_seq(str(input_sequence))
-        if not pdb_str:
-            st.error("ESMfold error, unable to fold sequence")
-            return None, None, None
-        else:
-            structure = parser.get_structure("ESMFold", StringIO(pdb_str))
-            if "selected_chains" in st.session_state:
-                del st.session_state.selected_chains
-            source = "Input sequence + ESM-fold"
-    elif "uploaded_pdb_str" in st.session_state:
-        pdb_str = st.session_state.uploaded_pdb_str
-        source = "Uploaded file stored in cache"
-        structure = parser.get_structure("userfile", StringIO(pdb_str))
-    else:
-        file = get_pdb_file(pdb_code)
-        pdb_str = file.read()
-        source = f"PDB ID: {pdb_code}"
-        structure = parser.get_structure(pdb_code, StringIO(pdb_str))
-
-    return pdb_str, structure, source
-
-
-def select_heads_and_layers(sidebar, model):
-    sidebar.markdown(
-        """
-        Select Heads and Layers
-        ---
-        """
-    )
-    if "plot_heads" not in st.session_state:
-        st.session_state.plot_heads = (1, model.heads // 2)
-    head_range = sidebar.slider(
-        "Heads to plot", min_value=1, max_value=model.heads, key="plot_heads", step=1
-    )
-    if "plot_layers" not in st.session_state:
-        st.session_state.plot_layers = (1, model.layers // 2)
-    layer_range = sidebar.slider(
-        "Layers to plot", min_value=1, max_value=model.layers, key="plot_layers", step=1
-    )
-
-    if "plot_step_size" not in st.session_state:
-        st.session_state.plot_step_size = 1
-    step_size = sidebar.number_input(
-        "Optional step size to skip heads and layers",
-        key="plot_step_size",
-        min_value=1,
-        max_value=model.layers,
-    )
-    layer_sequence = list(range(layer_range[0] - 1, layer_range[1], step_size))
-    head_sequence = list(range(head_range[0] - 1, head_range[1], step_size))
-
-    return layer_sequence, head_sequence
-
-
-def select_sequence_slice(sequence_length):
-    st.sidebar.markdown(
-        """
-        Sequence segment to plot
-        ---
-        """
-    )
-    if "sequence_slice" not in st.session_state:
-        st.session_state.sequence_slice = (1, min(50, sequence_length))
-    slice = st.sidebar.slider(
-        "Sequence", key="sequence_slice", min_value=1, max_value=sequence_length, step=1
-    )
-    return slice

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/portaspeech/fvae.py

@@ -1,202 +0,0 @@
-import numpy as np
-import torch
-import torch.distributions as dist
-from torch import nn
-
-from modules.commons.conv import ConditionalConvBlocks
-from modules.commons.normalizing_flow.res_flow import ResFlow
-from modules.commons.wavenet import WN
-
-
-class FVAEEncoder(nn.Module):
-    def __init__(self, c_in, hidden_size, c_latent, kernel_size,
-                 n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'):
-        super().__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        if np.prod(strides) == 1:
-            self.pre_net = nn.Conv1d(c_in, hidden_size, kernel_size=1)
-        else:
-            self.pre_net = nn.Sequential(*[
-                nn.Conv1d(c_in, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
-                if i == 0 else
-                nn.Conv1d(hidden_size, hidden_size, kernel_size=s * 2, stride=s, padding=s // 2)
-                for i, s in enumerate(strides)
-            ])
-        if nn_type == 'wn':
-            self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout)
-        elif nn_type == 'conv':
-            self.nn = ConditionalConvBlocks(
-                hidden_size, c_cond, hidden_size, None, kernel_size,
-                layers_in_block=2, is_BTC=False, num_layers=n_layers)
-
-        self.out_proj = nn.Conv1d(hidden_size, c_latent * 2, 1)
-        self.latent_channels = c_latent
-
-    def forward(self, x, nonpadding, cond):
-        x = self.pre_net(x)
-        nonpadding = nonpadding[:, :, ::np.prod(self.strides)][:, :, :x.shape[-1]]
-        x = x * nonpadding
-        x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding
-        x = self.out_proj(x)
-        m, logs = torch.split(x, self.latent_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs))
-        return z, m, logs, nonpadding
-
-
-class FVAEDecoder(nn.Module):
-    def __init__(self, c_latent, hidden_size, out_channels, kernel_size,
-                 n_layers, c_cond=0, p_dropout=0, strides=[4], nn_type='wn'):
-        super().__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        self.pre_net = nn.Sequential(*[
-            nn.ConvTranspose1d(c_latent, hidden_size, kernel_size=s, stride=s)
-            if i == 0 else
-            nn.ConvTranspose1d(hidden_size, hidden_size, kernel_size=s, stride=s)
-            for i, s in enumerate(strides)
-        ])
-        if nn_type == 'wn':
-            self.nn = WN(hidden_size, kernel_size, 1, n_layers, c_cond, p_dropout)
-        elif nn_type == 'conv':
-            self.nn = ConditionalConvBlocks(
-                hidden_size, c_cond, hidden_size, [1] * n_layers, kernel_size,
-                layers_in_block=2, is_BTC=False)
-        self.out_proj = nn.Conv1d(hidden_size, out_channels, 1)
-
-    def forward(self, x, nonpadding, cond):
-        x = self.pre_net(x)
-        x = x * nonpadding
-        x = self.nn(x, nonpadding=nonpadding, cond=cond) * nonpadding
-        x = self.out_proj(x)
-        return x
-
-
-class FVAE(nn.Module):
-    def __init__(self,
-                 c_in_out, hidden_size, c_latent,
-                 kernel_size, enc_n_layers, dec_n_layers, c_cond, strides,
-                 use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None,
-                 encoder_type='wn', decoder_type='wn'):
-        super(FVAE, self).__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        self.latent_size = c_latent
-        self.use_prior_flow = use_prior_flow
-        if np.prod(strides) == 1:
-            self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1)
-        else:
-            self.g_pre_net = nn.Sequential(*[
-                nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2)
-                for i, s in enumerate(strides)
-            ])
-        self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size,
-                                   enc_n_layers, c_cond, strides=strides, nn_type=encoder_type)
-        if use_prior_flow:
-            self.prior_flow = ResFlow(
-                c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond)
-        self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size,
-                                   dec_n_layers, c_cond, strides=strides, nn_type=decoder_type)
-        self.prior_dist = dist.Normal(0, 1)
-
-    def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0, **kwargs):
-        """
-
-        :param x: [B, C_in_out, T]
-        :param nonpadding: [B, 1, T]
-        :param cond: [B, C_g, T]
-        :return:
-        """
-        if nonpadding is None:
-            nonpadding = 1
-        cond_sqz = self.g_pre_net(cond)
-        if not infer:
-            z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz)
-            q_dist = dist.Normal(m_q, logs_q.exp())
-            if self.use_prior_flow:
-                logqx = q_dist.log_prob(z_q)
-                z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz)
-                logpx = self.prior_dist.log_prob(z_p)
-                loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1]
-            else:
-                loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist)
-                loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1]
-                z_p = None
-            return z_q, loss_kl, z_p, m_q, logs_q
-        else:
-            latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]]
-            z_p = torch.randn(latent_shape).to(cond.device) * noise_scale
-            if self.use_prior_flow:
-                z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True)
-            return z_p
-
-
-class SyntaFVAE(nn.Module):
-    def __init__(self,
-                 c_in_out, hidden_size, c_latent,
-                 kernel_size, enc_n_layers, dec_n_layers, c_cond, strides,
-                 use_prior_flow, flow_hidden=None, flow_kernel_size=None, flow_n_steps=None,
-                 encoder_type='wn', decoder_type='wn'):
-        super(SyntaFVAE, self).__init__()
-        self.strides = strides
-        self.hidden_size = hidden_size
-        self.latent_size = c_latent
-        self.use_prior_flow = use_prior_flow
-        if np.prod(strides) == 1:
-            self.g_pre_net = nn.Conv1d(c_cond, c_cond, kernel_size=1)
-        else:
-            self.g_pre_net = nn.Sequential(*[
-                nn.Conv1d(c_cond, c_cond, kernel_size=s * 2, stride=s, padding=s // 2)
-                for i, s in enumerate(strides)
-            ])
-        self.encoder = FVAEEncoder(c_in_out, hidden_size, c_latent, kernel_size,
-                                   enc_n_layers, c_cond, strides=strides, nn_type=encoder_type)
-        if use_prior_flow:
-            self.prior_flow = ResFlow(
-                c_latent, flow_hidden, flow_kernel_size, flow_n_steps, 4, c_cond=c_cond)
-        self.decoder = FVAEDecoder(c_latent, hidden_size, c_in_out, kernel_size,
-                                   dec_n_layers, c_cond, strides=strides, nn_type=decoder_type)
-        self.prior_dist = dist.Normal(0, 1)
-        self.graph_encoder = GraphAuxEnc(in_dim=hidden_size, hid_dim=hidden_size,out_dim=hidden_size)
-
-    def forward(self, x=None, nonpadding=None, cond=None, infer=False, noise_scale=1.0, 
-                mel2word=None, ph2word=None, graph_lst=None, etypes_lst=None):
-        """
-
-        :param x: target mel, [B, C_in_out, T] 
-        :param nonpadding: [B, 1, T]
-        :param cond: phoneme encoding, [B, C_g, T]
-        :return:
-        """
-        word_len = ph2word.max(dim=1)[0]
-        ph_encoding_for_graph = cond.detach() + 0.1 * (cond - cond.detach()) # only 0.1x grad can pass through
-        _, ph_out_word_encoding_for_graph = GraphAuxEnc.ph_encoding_to_word_encoding(ph_encoding_for_graph.transpose(1,2), mel2word, word_len)
-        t_m = mel2word.shape[-1]
-        g_graph = self.graph_encoder.word_forward(graph_lst=graph_lst, word_encoding=ph_out_word_encoding_for_graph, etypes_lst=etypes_lst)
-        g_graph = g_graph.transpose(1,2)
-        g_graph = GraphAuxEnc._postprocess_word2ph(g_graph,mel2word,t_m)
-        g_graph = g_graph.transpose(1,2)
-        cond = cond + g_graph * 1.
-
-        if nonpadding is None:
-            nonpadding = 1
-        cond_sqz = self.g_pre_net(cond)
-        if not infer:
-            z_q, m_q, logs_q, nonpadding_sqz = self.encoder(x, nonpadding, cond_sqz)
-            q_dist = dist.Normal(m_q, logs_q.exp())
-            if self.use_prior_flow:
-                logqx = q_dist.log_prob(z_q)
-                z_p = self.prior_flow(z_q, nonpadding_sqz, cond_sqz)
-                logpx = self.prior_dist.log_prob(z_p)
-                loss_kl = ((logqx - logpx) * nonpadding_sqz).sum() / nonpadding_sqz.sum() / logqx.shape[1]
-            else:
-                loss_kl = torch.distributions.kl_divergence(q_dist, self.prior_dist)
-                loss_kl = (loss_kl * nonpadding_sqz).sum() / nonpadding_sqz.sum() / z_q.shape[1]
-                z_p = None
-            return z_q, loss_kl, z_p, m_q, logs_q
-        else:
-            latent_shape = [cond_sqz.shape[0], self.latent_size, cond_sqz.shape[2]]
-            z_p = torch.randn(latent_shape).to(cond.device) * noise_scale
-            if self.use_prior_flow:
-                z_p = self.prior_flow(z_p, 1, cond_sqz, reverse=True)
-            return z_p

spaces/AIGC-Audio/AudioGPT/NeuralSeq/modules/syntaspeech/syntaspeech.py

@@ -1,274 +0,0 @@
-import math
-import torch
-from torch import nn
-from torch.nn import Linear
-from utils.hparams import hparams
-from modules.commons.conv import ConvBlocks, ConditionalConvBlocks
-from modules.commons.common_layers import Embedding
-from modules.commons.rel_transformer import RelTransformerEncoder
-from modules.commons.transformer import MultiheadAttention, FFTBlocks
-from modules.commons.align_ops import clip_mel2token_to_multiple, build_word_mask, expand_states, mel2ph_to_mel2word
-from modules.tts.fastspeech import FS_DECODERS, FastSpeech
-from modules.portaspeech.fvae import SyntaFVAE, FVAE
-from utils.nn.seq_utils import group_hidden_by_segs
-from modules.fastspeech.tts_modules import SyntaDurationPredictor
-
-
-class SinusoidalPosEmb(nn.Module):
-    def __init__(self, dim):
-        super().__init__()
-        self.dim = dim
-    def forward(self, x):
-        """
-
-        :param x: [B, T]
-        :return: [B, T, H]
-        """
-        device = x.device
-        half_dim = self.dim // 2
-        emb = math.log(10000) / (half_dim - 1)
-        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
-        emb = x[:, :, None] * emb[None, :]
-        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
-        return emb
-
-
-class SyntaSpeech(FastSpeech):
-    def __init__(self, ph_dict_size, word_dict_size, out_dims=None):
-        super().__init__(ph_dict_size, out_dims)
-        # build linguistic encoder
-        if hparams['num_spk'] > 1:
-            self.spk_embed_proj = Embedding(hparams['num_spk'], self.hidden_size)
-        if hparams['use_word_encoder']:
-            self.word_encoder = RelTransformerEncoder(
-                word_dict_size, self.hidden_size, self.hidden_size, self.hidden_size, 2,
-                hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
-        if hparams['dur_level'] == 'word':
-            if hparams['word_encoder_type'] == 'rel_fft':
-                self.ph2word_encoder = RelTransformerEncoder(
-                    0, self.hidden_size, self.hidden_size, self.hidden_size, 2,
-                    hparams['word_enc_layers'], hparams['enc_ffn_kernel_size'])
-            if hparams['word_encoder_type'] == 'fft':
-                self.ph2word_encoder = FFTBlocks(
-                    self.hidden_size, hparams['word_enc_layers'], 1, num_heads=hparams['num_heads'])
-            self.sin_pos = SinusoidalPosEmb(self.hidden_size)
-            self.enc_pos_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.dec_query_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.dec_res_proj = nn.Linear(2 * self.hidden_size, self.hidden_size)
-            self.attn = MultiheadAttention(self.hidden_size, 1, encoder_decoder_attention=True, bias=False)
-            self.attn.enable_torch_version = False
-            if hparams['text_encoder_postnet']:
-                self.text_encoder_postnet = ConvBlocks(
-                    self.hidden_size, self.hidden_size, [1] * 3, 5, layers_in_block=2)
-        else:
-            self.sin_pos = SinusoidalPosEmb(self.hidden_size)
-        
-        predictor_hidden = hparams['predictor_hidden'] if hparams['predictor_hidden'] > 0 else self.hidden_size
-        self.dur_predictor = SyntaDurationPredictor(
-            self.hidden_size,
-            n_chans=predictor_hidden,
-            n_layers=hparams['dur_predictor_layers'],
-            dropout_rate=hparams['predictor_dropout'],
-            kernel_size=hparams['dur_predictor_kernel'])
-        # build VAE decoder
-        if hparams['use_fvae']:
-            del self.decoder
-            del self.mel_out
-            if hparams.get("use_gae_in_prior", True):
-                self.fvae = SyntaFVAE(
-                    c_in_out=self.out_dims,
-                    hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'],
-                    kernel_size=hparams['fvae_kernel_size'],
-                    enc_n_layers=hparams['fvae_enc_n_layers'],
-                    dec_n_layers=hparams['fvae_dec_n_layers'],
-                    c_cond=self.hidden_size,
-                    use_prior_flow=hparams['use_prior_flow'],
-                    flow_hidden=hparams['prior_flow_hidden'],
-                    flow_kernel_size=hparams['prior_flow_kernel_size'],
-                    flow_n_steps=hparams['prior_flow_n_blocks'],
-                    strides=[hparams['fvae_strides']],
-                    encoder_type=hparams['fvae_encoder_type'],
-                    decoder_type=hparams['fvae_decoder_type'],
-                )
-            else:
-                self.fvae = FVAE(
-                    c_in_out=self.out_dims,
-                    hidden_size=hparams['fvae_enc_dec_hidden'], c_latent=hparams['latent_size'],
-                    kernel_size=hparams['fvae_kernel_size'],
-                    enc_n_layers=hparams['fvae_enc_n_layers'],
-                    dec_n_layers=hparams['fvae_dec_n_layers'],
-                    c_cond=self.hidden_size,
-                    use_prior_flow=hparams['use_prior_flow'],
-                    flow_hidden=hparams['prior_flow_hidden'],
-                    flow_kernel_size=hparams['prior_flow_kernel_size'],
-                    flow_n_steps=hparams['prior_flow_n_blocks'],
-                    strides=[hparams['fvae_strides']],
-                    encoder_type=hparams['fvae_encoder_type'],
-                    decoder_type=hparams['fvae_decoder_type'],
-                )
-        else:
-            self.decoder = FS_DECODERS[hparams['decoder_type']](hparams)
-            self.mel_out = Linear(self.hidden_size, self.out_dims, bias=True)
-        if hparams['use_pitch_embed']:
-            self.pitch_embed = Embedding(300, self.hidden_size, 0)
-        if hparams['add_word_pos']:
-            self.word_pos_proj = Linear(self.hidden_size, self.hidden_size)
-
-    def build_embedding(self, dictionary, embed_dim):
-        num_embeddings = len(dictionary)
-        emb = Embedding(num_embeddings, embed_dim, self.padding_idx)
-        return emb
-
-    def forward(self, txt_tokens, word_tokens, ph2word, word_len, mel2word=None, mel2ph=None,
-                spk_embed=None, spk_id=None, pitch=None, infer=False, tgt_mels=None,
-                global_step=None, graph_lst=None, etypes_lst=None, *args, **kwargs):     
-
-        if hparams['use_spk_embed']:
-            spk_embed = spk_embed
-        elif hparams['use_spk_id']:
-            spk_embed = self.spk_embed_proj(spk_id)[:, None, :]  
-        else:
-            spk_embed = 0
-
-        ret = {}
-        style_embed = self.forward_style_embed(spk_embed, spk_id) # speaker embedding, [B, 1, C]
-        x, tgt_nonpadding = self.run_text_encoder(
-            txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, graph_lst=graph_lst, etypes_lst=etypes_lst, **kwargs)
-        x = x + style_embed # it maybe necessary to achieve multi-speaker
-        x = x * tgt_nonpadding
-        ret['nonpadding'] = tgt_nonpadding
-        if hparams['use_pitch_embed']:
-            x = x + self.pitch_embed(pitch)
-        ret['decoder_inp'] = x
-        if infer and (mel2ph is None or mel2word is None):
-            mel2word = ret['mel2word']
-        ret['mel_out_fvae'] = ret['mel_out'] = self.run_decoder(x, tgt_nonpadding, ret, infer, tgt_mels, global_step,
-                mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst)
-        return ret
-
-    def run_text_encoder(self, txt_tokens, word_tokens, ph2word, word_len, mel2word, mel2ph, style_embed, ret, graph_lst, etypes_lst, **kwargs):
-        word2word = torch.arange(word_len)[None, :].to(ph2word.device) + 1  # [B, T_mel, T_word]
-        src_nonpadding = (txt_tokens > 0).float()[:, :, None]
-        use_bert = hparams.get("use_bert") is True
-        if use_bert:
-            ph_encoder_out = self.encoder(txt_tokens, bert_feats=kwargs['bert_feats'], ph2word=ph2word,
-                graph_lst=graph_lst, etypes_lst=etypes_lst,
-                cl_feats=kwargs['cl_feats'], ret=ret) * src_nonpadding + style_embed
-        else:
-            ph_encoder_out = self.encoder(txt_tokens) * src_nonpadding + style_embed
-        if hparams['use_word_encoder']:
-            word_encoder_out = self.word_encoder(word_tokens) + style_embed
-            ph_encoder_out = ph_encoder_out + expand_states(word_encoder_out, ph2word)
-        
-        dur_input = ph_encoder_out * src_nonpadding
-        if hparams['dur_level'] == 'word':
-            word_encoder_out = 0
-            h_ph_gb_word = group_hidden_by_segs(ph_encoder_out, ph2word, word_len)[0]
-            word_encoder_out = word_encoder_out + self.ph2word_encoder(h_ph_gb_word)
-            if hparams['use_word_encoder']:
-                word_encoder_out = word_encoder_out + self.word_encoder(word_tokens)
-            mel2word = self.forward_dur(dur_input, mel2word, ret, ph2word=ph2word, word_len=word_len, graph_lst=graph_lst, etypes_lst=etypes_lst)
-            mel2word = clip_mel2token_to_multiple(mel2word, hparams['frames_multiple'])
-            ret['mel2word'] = mel2word
-            tgt_nonpadding = (mel2word > 0).float()[:, :, None]
-            enc_pos = self.get_pos_embed(word2word, ph2word)  # [B, T_ph, H]
-            dec_pos = self.get_pos_embed(word2word, mel2word)  # [B, T_mel, H]
-            dec_word_mask = build_word_mask(mel2word, ph2word)  # [B, T_mel, T_ph]
-            x, weight = self.attention(ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask)
-            if hparams['add_word_pos']:
-                x = x + self.word_pos_proj(dec_pos)
-            ret['attn'] = weight
-        else:
-            mel2ph = self.forward_dur(dur_input, mel2ph, ret)
-            mel2ph = clip_mel2token_to_multiple(mel2ph, hparams['frames_multiple'])
-            mel2word = mel2ph_to_mel2word(mel2ph, ph2word)
-            x = expand_states(ph_encoder_out, mel2ph)
-            if hparams['add_word_pos']:
-                dec_pos = self.get_pos_embed(word2word, mel2word)  # [B, T_mel, H]
-                x = x + self.word_pos_proj(dec_pos)
-            tgt_nonpadding = (mel2ph > 0).float()[:, :, None]
-        if hparams['use_word_encoder']:
-            x = x + expand_states(word_encoder_out, mel2word)
-        return x, tgt_nonpadding
-
-    def attention(self, ph_encoder_out, enc_pos, word_encoder_out, dec_pos, mel2word, dec_word_mask):
-        ph_kv = self.enc_pos_proj(torch.cat([ph_encoder_out, enc_pos], -1))
-        word_enc_out_expend = expand_states(word_encoder_out, mel2word)
-        word_enc_out_expend = torch.cat([word_enc_out_expend, dec_pos], -1)
-        if hparams['text_encoder_postnet']:
-            word_enc_out_expend = self.dec_res_proj(word_enc_out_expend)
-            word_enc_out_expend = self.text_encoder_postnet(word_enc_out_expend)
-            dec_q = x_res = word_enc_out_expend
-        else:
-            dec_q = self.dec_query_proj(word_enc_out_expend)
-            x_res = self.dec_res_proj(word_enc_out_expend)
-        ph_kv, dec_q = ph_kv.transpose(0, 1), dec_q.transpose(0, 1)
-        x, (weight, _) = self.attn(dec_q, ph_kv, ph_kv, attn_mask=(1 - dec_word_mask) * -1e9)
-        x = x.transpose(0, 1)
-        x = x + x_res
-        return x, weight
-
-    def run_decoder(self, x, tgt_nonpadding, ret, infer, tgt_mels=None, global_step=0,
-                    mel2word=None, ph2word=None, graph_lst=None, etypes_lst=None):
-        if not hparams['use_fvae']:
-            x = self.decoder(x)
-            x = self.mel_out(x)
-            ret['kl'] = 0
-            return x * tgt_nonpadding
-        else:
-            # x is the phoneme encoding
-            x = x.transpose(1, 2)  # [B, H, T]
-            tgt_nonpadding_BHT = tgt_nonpadding.transpose(1, 2)  # [B, H, T]
-            if infer:
-                z = self.fvae(cond=x, infer=True, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst)
-            else:
-                tgt_mels = tgt_mels.transpose(1, 2)  # [B, 80, T]
-                z, ret['kl'], ret['z_p'], ret['m_q'], ret['logs_q'] = self.fvae(
-                    tgt_mels, tgt_nonpadding_BHT, cond=x, mel2word=mel2word, ph2word=ph2word, graph_lst=graph_lst, etypes_lst=etypes_lst)
-                if global_step < hparams['posterior_start_steps']:
-                    z = torch.randn_like(z)
-            x_recon = self.fvae.decoder(z, nonpadding=tgt_nonpadding_BHT, cond=x).transpose(1, 2)
-            ret['pre_mel_out'] = x_recon
-            return x_recon
-
-    def forward_dur(self, dur_input, mel2word, ret, **kwargs):
-        """
-
-        :param dur_input: [B, T_txt, H]
-        :param mel2ph: [B, T_mel]
-        :param txt_tokens: [B, T_txt]
-        :param ret:
-        :return:
-        """
-        word_len = kwargs['word_len']
-        ph2word = kwargs['ph2word']
-        graph_lst = kwargs['graph_lst']
-        etypes_lst = kwargs['etypes_lst']
-        src_padding = dur_input.data.abs().sum(-1) == 0
-        dur_input = dur_input.detach() + hparams['predictor_grad'] * (dur_input - dur_input.detach())
-        dur = self.dur_predictor(dur_input, src_padding, ph2word, graph_lst, etypes_lst)
-
-        B, T_ph = ph2word.shape
-        dur = torch.zeros([B, word_len.max() + 1]).to(ph2word.device).scatter_add(1, ph2word, dur)
-        dur = dur[:, 1:]
-        ret['dur'] = dur
-        if mel2word is None:
-            mel2word = self.length_regulator(dur).detach()
-        return mel2word
-
-    def get_pos_embed(self, word2word, x2word):
-        x_pos = build_word_mask(word2word, x2word).float()  # [B, T_word, T_ph]
-        x_pos = (x_pos.cumsum(-1) / x_pos.sum(-1).clamp(min=1)[..., None] * x_pos).sum(1)
-        x_pos = self.sin_pos(x_pos.float())  # [B, T_ph, H]
-        return x_pos
-
-    def store_inverse_all(self):
-        def remove_weight_norm(m):
-            try:
-                if hasattr(m, 'store_inverse'):
-                    m.store_inverse()
-                nn.utils.remove_weight_norm(m)
-            except ValueError:  # this module didn't have weight norm
-                return
-
-        self.apply(remove_weight_norm)

spaces/AIWaves/Software_Company/src/agents/Component/ExtraComponent.py

@@ -1,128 +0,0 @@
-from .ToolComponent import ToolComponent
-import json
-from utils import flatten_dict,get_embedding,matching_category,search_with_api,limit_keys,limit_values
-import os
-
-
-class CategoryRequirementsComponent(ToolComponent):
-    def __init__(self, information_path):
-        super().__init__()
-        self.information_dataset = []
-        self.leaf_name = []
-        for toy_path in information_path:
-            with open(toy_path, encoding="utf-8") as json_file:
-                data = json.load(json_file)
-            for d in data:
-                if "/" in d["cat_leaf_name"]:
-                    leaf_names = d["cat_leaf_name"].split("/") + [d["cat_leaf_name"]]
-                else:
-                    leaf_names = [d["cat_leaf_name"]]
-                for name in leaf_names:
-                    self.leaf_name.append(name)
-                    new_d = d.copy()
-                    new_d["cat_leaf_name"] = name
-                    new_d["information"] = flatten_dict(new_d["information"])
-                    self.information_dataset.append(new_d)
-
-        self.target_embbeding = get_embedding(
-            self.leaf_name
-        )
-
-    def search_information(self, category, information_dataset):
-        knowledge = {}
-        for d in information_dataset:
-            if category == d["cat_leaf_name"]:
-                knowledge = d["information"]
-                knowledge = {
-                    key: value
-                    for key, value in knowledge.items()
-                    if (value and key != "相关分类")
-                }
-                break
-        return knowledge
-
-    def func(self, agent):
-        prompt = ""
-        messages = agent.long_term_memory
-        outputdict = {}
-        functions = [
-            {
-                "name": "search_information",
-                "description": "根据用户所需要购买商品的种类跟用户的需求去寻找用户所需要的商品",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "category": {
-                            "type": "string",
-                            "description": "用户现在所需要的商品类别，比如纸尿布，笔记本电脑等，注意，只能有一个",
-                        },
-                        "requirements": {
-                            "type": "string",
-                            "description": "用户现在的需求，比如说便宜，安踏品牌等等，可以有多个需求，中间以“ ”分隔",
-                        },
-                    },
-                    "required": ["category", "requirements"],
-                },
-            }
-        ]
-        
-        response = agent.LLM.get_response(
-            messages,
-            None,
-            None,
-            functions=functions,
-            stream=False,
-            function_call={"name": "search_information"},
-        )
-        response_message = json.loads(response["function_call"]["arguments"])
-        category = (
-            response_message["category"] if response_message["category"] else None
-        )
-        requirements = (
-            response_message["requirements"]
-            if response_message["requirements"]
-            else category
-        )
-        if not (category or requirements):
-            return {}
-
-        topk_result = matching_category(
-            category, self.leaf_name, None, self.target_embbeding, top_k=3
-        )
-
-        top1_score = topk_result[1][0]
-        request_items, top_category = search_with_api(requirements, category)
-        
-
-        MIN_CATEGORY_SIM = eval(os.environ["MIN_CATEGORY_SIM"]
-                                ) if "MIN_CATEGORY_SIM" in os.environ else 0.7
-
-        if top1_score > MIN_CATEGORY_SIM:
-            agent.environment.shared_memory["category"] = topk_result[0][0]
-            category = topk_result[0][0]
-            information = self.search_information(
-                topk_result[0][0], self.information_dataset
-            )
-            information = limit_keys(information, 3)
-            information = limit_values(information, 2)
-            prompt += f"""你需要知道的是：用户目前选择的商品是{category}，该商品信息为{information}。你需要根据这些商品信息来详细介绍商品，比如详细介绍商品有哪些品牌，有哪些分类等等，并且询问用户是否有更多的需求。"""
-            if category in top_category:
-                top_category.remove(category)
-
-            recommend = "\n经过搜索后，推荐商品如下：\n"
-            prompt += "筛选出的商品如下：\n"
-            
-            for i, request_item in enumerate(request_items):
-                
-                itemTitle = request_item["itemTitle"]
-                itemPrice = request_item["itemPrice"]
-                itemPicUrl = request_item["itemPicUrl"]
-                recommend += f"[{i}.商品名称：{itemTitle},商品价格:{float(itemPrice)/100}]({itemPicUrl})\n"
-                prompt += f"[{i}.商品名称：{itemTitle},商品价格:{float(itemPrice)/100}]\n"
-            outputdict["recommend"] = recommend
-            print(recommend)
-        else:
-            prompt += f"""你需要知道的是：用户目前选择的商品是{category}，而我们店里没有这类商品，但是我们店里有一些近似商品，如{top_category},{topk_result[0][0]}，你需要对这些近似商品进行介绍，并引导用户购买"""
-        outputdict["prompt"] = prompt
-        return outputdict
-

spaces/ASJMO/freegpt/g4f/Provider/Providers/ChatgptLogin.py

@@ -1,96 +0,0 @@
-import os
-from ...typing import sha256, Dict, get_type_hints
-import requests
-import re
-import base64
-
-url = 'https://chatgptlogin.ac'
-model = ['gpt-3.5-turbo']
-supports_stream = False
-needs_auth = False
-
-
-def _create_completion(model: str, messages: list, stream: bool, **kwargs):
-    def get_nonce():
-        res = requests.get('https://chatgptlogin.ac/use-chatgpt-free/', headers={
-            "Referer": "https://chatgptlogin.ac/use-chatgpt-free/",
-            "User-Agent": 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36'
-        })
-
-        src = re.search(r'class="mwai-chat mwai-chatgpt">.*<span>Send</span></button></div></div></div> <script defer src="(.*?)">', res.text).group(1)
-        decoded_string = base64.b64decode(src.split(",")[-1]).decode('utf-8')
-        return re.search(r"let restNonce = '(.*?)';", decoded_string).group(1)
-    
-    def transform(messages: list) -> list:
-        def html_encode(string: str) -> str:
-            table = {
-                '"': '&quot;',
-                "'": '&#39;',
-                '&': '&amp;',
-                '>': '&gt;',
-                '<': '&lt;',
-                '\n': '<br>',
-                '\t': '&nbsp;&nbsp;&nbsp;&nbsp;',
-                ' ': '&nbsp;'
-            }
-            
-            for key in table:
-                string = string.replace(key, table[key])
-                
-            return string
-        
-        return [{
-            'id': os.urandom(6).hex(),
-            'role': message['role'],
-            'content': message['content'],
-            'who': 'AI: ' if message['role'] == 'assistant' else 'User: ',
-            'html': html_encode(message['content'])} for message in messages]
-    
-    headers = {
-        'authority': 'chatgptlogin.ac',
-        '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',
-        'content-type': 'application/json',
-        'origin': 'https://chatgptlogin.ac',
-        'referer': 'https://chatgptlogin.ac/use-chatgpt-free/',
-        'sec-ch-ua': '"Not.A/Brand";v="8", "Chromium";v="114", "Google Chrome";v="114"',
-        'sec-ch-ua-mobile': '?0',
-        'sec-ch-ua-platform': '"Windows"',
-        'sec-fetch-dest': 'empty',
-        'sec-fetch-mode': 'cors',
-        'sec-fetch-site': 'same-origin',
-        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
-        'x-wp-nonce': get_nonce()
-    }
-    
-    conversation = transform(messages)
-
-    json_data = {
-        'env': 'chatbot',
-        'session': 'N/A',
-        'prompt': 'Converse as if you were an AI assistant. Be friendly, creative.',
-        'context': 'Converse as if you were an AI assistant. Be friendly, creative.',
-        'messages': conversation,
-        'newMessage': messages[-1]['content'],
-        'userName': '<div class="mwai-name-text">User:</div>',
-        'aiName': '<div class="mwai-name-text">AI:</div>',
-        'model': 'gpt-3.5-turbo',
-        'temperature': 0.8,
-        'maxTokens': 1024,
-        'maxResults': 1,
-        'apiKey': '',
-        'service': 'openai',
-        'embeddingsIndex': '',
-        'stop': '',
-        'clientId': os.urandom(6).hex()
-    }
-
-    response = requests.post('https://chatgptlogin.ac/wp-json/ai-chatbot/v1/chat', 
-                             headers=headers, json=json_data)
-    
-    return response.json()['reply']
-
-
-params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
-    '(%s)' % ', '.join(
-        [f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_1_ClothesKeyPoint/mmpose_1_x/configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/td_hm_res50_4xb64-120e_deepfashion2_long_sleeved_shirt_256x192.py

@@ -1,172 +0,0 @@
-_base_ = [
-    '../../../_base_/default_runtime.py',
-    '../../../_base_/datasets/deepfashion2.py'
-]
-
-default_hooks = dict(checkpoint=dict(save_best='PCK', rule='greater'))
-
-resume = False  # 断点恢复
-load_from = None  # 模型权重加载
-train_cfg = dict(by_epoch=True, max_epochs=120, val_interval=10)  # 训练轮数，测试间隔
-param_scheduler = [
-    dict(  # warmup策略
-        type='LinearLR',
-        begin=0,
-        end=500,
-        start_factor=0.001,
-        by_epoch=False),
-    dict(  # scheduler
-        type='MultiStepLR',
-        begin=0,
-        end=60,
-        milestones=[20, 40],
-        gamma=0.1,
-        by_epoch=True)
-]
-optim_wrapper = dict(optimizer=dict(type='Adam', lr=0.0005))  # 优化器和学习率
-auto_scale_lr = dict(base_batch_size=512)  # 根据batch_size自动缩放学习率
-
-backend_args = dict(backend='local')  # 数据加载后端设置，默认从本地硬盘加载
-dataset_type = 'DeepFashion2Dataset'  # 数据集类名  DeepFashionDataset
-data_mode = 'topdown'  # 算法结构类型，用于指定标注信息加载策略
-data_root = 'data/deepfashion2/'  # 数据存放路径
-# 定义数据编解码器，用于生成target和对pred进行解码，同时包含了输入图片和输出heatmap尺寸等信息
-codec = dict(
-    type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
-
-train_pipeline = [
-    dict(type='LoadImage'),
-    dict(type='GetBBoxCenterScale'),
-    dict(type='RandomFlip', direction='horizontal'),
-    dict(
-        type='RandomBBoxTransform',
-        shift_prob=0,
-        rotate_factor=60,
-        scale_factor=(0.75, 1.25)),
-    dict(type='TopdownAffine', input_size=codec['input_size']),
-    dict(type='GenerateTarget', encoder=codec),
-    dict(type='PackPoseInputs')
-]
-val_pipeline = [  # 测试时数据增强
-    dict(type='LoadImage', backend_args=backend_args),  # 加载图片
-    dict(type='GetBBoxCenterScale'),  # 根据bbox获取center和scale
-    dict(type='TopdownAffine', input_size=codec['input_size']),  # 根据变换矩阵更新目标数据
-    dict(type='PackPoseInputs')  # 对target进行打包用于训练
-]
-train_dataloader = dict(  # 训练数据加载
-    batch_size=64,  # 批次大小
-    num_workers=6,  # 数据加载进程数
-    persistent_workers=True,  # 在不活跃时维持进程不终止，避免反复启动进程的开销
-    sampler=dict(type='DefaultSampler', shuffle=True),  # 采样策略，打乱数据
-    dataset=dict(
-        type=dataset_type,  # 数据集类名
-        data_root=data_root,  # 数据集路径
-        data_mode=data_mode,  # 算法类型
-        ann_file='train/deepfashion2_long_sleeved_shirt.json',  # 标注文件路径
-        data_prefix=dict(img='train/image/'),  # 图像路径
-        pipeline=train_pipeline  # 数据流水线
-    ))
-val_dataloader = dict(
-    batch_size=32,
-    num_workers=6,
-    persistent_workers=True,  # 在不活跃时维持进程不终止，避免反复启动进程的开销
-    drop_last=False,
-    sampler=dict(type='DefaultSampler', shuffle=False),  # 采样策略，不进行打乱
-    dataset=dict(
-        type=dataset_type,  # 数据集类名
-        data_root=data_root,  # 数据集路径
-        data_mode=data_mode,  # 算法类型
-        ann_file='validation/deepfashion2_long_sleeved_shirt.json',  # 标注文件路径
-        data_prefix=dict(img='validation/image/'),  # 图像路径
-        test_mode=True,  # 测试模式开关
-        pipeline=val_pipeline  # 数据流水线
-    ))
-test_dataloader = val_dataloader  # 默认情况下不区分验证集和测试集，用户根据需要来自行定义
-
-channel_cfg = dict(
-    num_output_channels=294,
-    dataset_joints=294,
-    dataset_channel=[
-        [
-            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
-            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
-            36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
-            53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69,
-            70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86,
-            87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102,
-            103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115,
-            116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128,
-            129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141,
-            142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154,
-            155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
-            168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180,
-            181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193,
-            194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206,
-            207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-            220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232,
-            233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245,
-            246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258,
-            259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271,
-            272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284,
-            285, 286, 287, 288, 289, 290, 291, 292, 293
-        ],
-    ],
-    inference_channel=[
-        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
-        20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37,
-        38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55,
-        56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73,
-        74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
-        92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107,
-        108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121,
-        122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135,
-        136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149,
-        150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163,
-        164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177,
-        178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191,
-        192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205,
-        206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219,
-        220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233,
-        234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247,
-        248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261,
-        262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275,
-        276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289,
-        290, 291, 292, 293
-    ])
-
-model = dict(
-    type='TopdownPoseEstimator',  # 模型结构决定了算法流程
-    data_preprocessor=dict(  # 数据归一化和通道顺序调整，作为模型的一部分
-        type='PoseDataPreprocessor',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        bgr_to_rgb=True),
-    backbone=dict(
-        type='ResNet',
-        depth=50,
-        init_cfg=dict(
-            type='Pretrained',  # 预训练参数，只加载backbone权重用于迁移学习
-            checkpoint='torchvision://resnet50')),
-    head=dict(  # 模型头部
-        type='HeatmapHead',
-        in_channels=2048,
-        out_channels=channel_cfg['num_output_channels'],
-        # deconv_out_channels=None,
-        loss=dict(type='KeypointMSELoss', use_target_weight=True),  # 损失函数
-        decoder=codec),  # 解码器，将heatmap解码成坐标值
-    test_cfg=dict(
-        flip_test=True,  # 开启测试时水平翻转集成
-        flip_mode='heatmap',  # 对heatmap进行翻转
-        shift_heatmap=True,  # 对翻转后的结果进行平移提高精度
-    ))
-
-val_evaluator = [
-    dict(type='PCKAccuracy', thr=0.2),
-    dict(type='AUC'),
-    dict(type='EPE'),
-]
-test_evaluator = val_evaluator  # 默认情况下不区分验证集和测试集，用户根据需要来自行定义
-
-visualizer = dict(
-    vis_backends=[dict(type='LocalVisBackend'),
-                  dict(type='WandbVisBackend')])

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/resnet/resnet18_8xb16_cifar10.py

@@ -1,4 +0,0 @@
-_base_ = [
-    '../_base_/models/resnet18_cifar.py', '../_base_/datasets/cifar10_bs16.py',
-    '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py'
-]

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/colorinput/colorcomponents/ColorComponents.js

@@ -1,187 +0,0 @@
-import Sizer from '../../sizer/Sizer.js';
-import IsGameObject from '../../../../plugins/utils/system/IsGameObject.js';
-import CreateLabel from '../../utils/build/CreateLabel.js';
-import CreateInputText from '../../utils/build/CreateInputText.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-const Color = Phaser.Display.Color;
-const ColorToRGBA = Phaser.Display.Color.ColorToRGBA;
-const HSVToRGB = Phaser.Display.Color.HSVToRGB;
-const Clamp = Phaser.Math.Clamp;
-
-class ColorComponents extends Sizer {
-    constructor(scene, config) {
-        if (config === undefined) {
-            config = {};
-        }
-        config.orientation = 0;
-        super(scene, config);
-        this.type = 'rexColorComponents';
-
-        this.colorObject = new Color();
-
-        // Add elements
-        var background = GetValue(config, 'background', undefined);
-
-        var formatLabel = GetValue(config, 'formatLabel', undefined);
-        if (!IsGameObject(formatLabel)) {
-            formatLabel = CreateLabel(scene, formatLabel)
-                .resetDisplayContent();
-        }
-
-        var components = [];
-        if (config.inputText0 && config.inputText1 && config.inputText2) {
-            components.push(config.inputText0);
-            components.push(config.inputText1);
-            components.push(config.inputText2);
-        } else {
-            var inputTextConfig = GetValue(config, 'inputText');
-            for (var i = 0; i < 3; i++) {
-                var inputText = CreateInputText(scene, inputTextConfig)
-                    .setMaxLength(3)
-                    .setNumberInput()
-
-                components.push(inputText);
-            }
-        }
-
-        if (background) {
-            this.addBackground(background);
-        }
-
-        var proportion = GetValue(config, 'proportion.formatLabel', 0);
-        var defaultExpand = (formatLabel.isRexContainerLite) ? true : false;
-        var expand = GetValue(config, 'expand.formatLabel', defaultExpand);
-        this.add(
-            formatLabel,
-            { proportion: proportion, expand: expand }
-        );
-
-        var proportion = (GetValue(inputTextConfig, 'width') === undefined) ? 1 : 0;
-        var expand = (GetValue(inputTextConfig, 'height') === undefined) ? true : false;
-        for (var i = 0, cnt = components.length; i < cnt; i++) {
-            this.add(
-                components[i],
-                { proportion: proportion, expand: expand }
-            )
-        }
-
-        this.addChildrenMap('background', background);
-        this.addChildrenMap('formatLabel', formatLabel);
-        this.addChildrenMap('components', components);
-
-        this.onClick(formatLabel, this.toggleColorFormat, this);
-
-        for (var i = 0, cnt = components.length; i < cnt; i++) {
-            components[i].on('close', function () {
-                this.updateColorObject();
-                this.setValue(this.colorObject.color);
-            }, this);
-        }
-
-        var callback = GetValue(config, 'valuechangeCallback', null);
-        if (callback !== null) {
-            var scope = GetValue(config, 'valuechangeCallbackScope', undefined);
-            this.on('valuechange', callback, scope);
-        }
-
-        formatLabel.setText('RGB');
-        this.setValue(GetValue(config, 'value', 0xffffff));
-    }
-
-    get value() {
-        return this._value;
-    }
-
-    set value(value) {
-        value = Clamp(Math.floor(value), 0, 0xffffff);
-
-        if (this._value === value) {
-            return;
-        }
-
-        this._value = value;
-
-        this.colorObject.setFromRGB(ColorToRGBA(value));
-        this.updateComponents();
-
-        this.emit('valuechange', this._value);
-    }
-
-    setValue(value) {
-        this.value = value;
-        return this;
-    }
-
-    get color() {
-        return this._value;
-    }
-
-    set color(color) {
-        this.value = color;
-    }
-
-    setColor(color) {
-        this.color = color;
-        return this;
-    }
-
-    get colorFormat() {
-        return this.childrenMap.formatLabel.text;
-    }
-
-    set colorFormat(value) {
-        if (this.colorFormat === value) {
-            return;
-        }
-        this.childrenMap.formatLabel.setText(value);
-        this.updateComponents();
-    }
-
-    setColorFormat(colrType) {
-        this.colorFormat = colrType;
-        return this;
-    }
-
-    toggleColorFormat() {
-        this.colorFormat = (this.colorFormat === 'RGB') ? 'HSV' : 'RGB';
-        return this;
-    }
-
-    updateComponents() {
-        var components = this.childrenMap.components;
-        var value0, value1, value2
-        if (this.colorFormat === 'RGB') {
-            value0 = this.colorObject.red;
-            value1 = this.colorObject.green;
-            value2 = this.colorObject.blue;
-        } else { // colorFormat === 'HSV'
-            value0 = Math.floor(this.colorObject.h * 360);
-            value1 = Math.floor(this.colorObject.s * 100);
-            value2 = Math.floor(this.colorObject.v * 100);
-        }
-
-        components[0].setValue(value0);
-        components[1].setValue(value1);
-        components[2].setValue(value2);
-        return this;
-    }
-
-    updateColorObject() {
-        var components = this.childrenMap.components;
-        if (this.colorFormat === 'RGB') {
-            var red = Clamp(components[0].value, 0, 255);
-            var green = Clamp(components[1].value, 0, 255);
-            var blue = Clamp(components[2].value, 0, 255);
-            this.colorObject.setTo(red, green, blue);
-        } else {
-            var h = Clamp(components[0].value, 0, 359) / 360;
-            var s = Clamp(components[1].value, 0, 100) / 100;
-            var v = Clamp(components[2].value, 0, 100) / 100;
-            this.colorObject.setFromRGB(HSVToRGB(h, s, v));
-        }
-        return this;
-    }
-}
-
-export default ColorComponents;

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/scrollablepanel/ScrollablePanel.js

@@ -1,71 +0,0 @@
-import Scrollable from '../utils/scrollable/Scrollable.js';
-import GetScrollMode from '../utils/GetScrollMode.js';
-import ScrollableBlock from './scrollableblock/ScrollableBlock.js';
-import SetChildrenInteractive from '../utils/setchildreninteractive/SetChildrenInteractive.js';
-
-const GetValue = Phaser.Utils.Objects.GetValue;
-
-class ScrollablePanel extends Scrollable {
-    constructor(scene, config) {
-        if (config === undefined) {
-            config = {};
-        }
-
-        // Create scrollable-block
-        var scrollMode = GetScrollMode(config);
-        var panelConfig = GetValue(config, 'panel', undefined);
-        if (panelConfig === undefined) {
-            panelConfig = {};
-        }
-        panelConfig.scrollMode = scrollMode;
-        panelConfig.clamplChildOY = GetValue(config, 'clamplChildOY', false);
-        var scrollableBlock = new ScrollableBlock(scene, panelConfig);
-        scene.add.existing(scrollableBlock); // Important: Add to display list for touch detecting
-        var panelWidth = GetValue(panelConfig, 'width', undefined);
-        var panelHeight = GetValue(panelConfig, 'height', undefined);
-        var proportion, expand;
-        if (scrollMode === 0) {
-            proportion = (panelWidth === undefined) ? 1 : 0;
-            expand = (panelHeight === undefined);
-        } else {
-            proportion = (panelHeight === undefined) ? 1 : 0;
-            expand = (panelWidth === undefined);
-        }
-
-        // Fill config of scrollable
-        config.type = 'rexScrollablePanel';
-        config.child = {
-            gameObject: scrollableBlock,
-            proportion: proportion,
-            expand: expand,
-        };
-        var spaceConfig = GetValue(config, 'space', undefined);
-        if (spaceConfig) {
-            spaceConfig.child = spaceConfig.panel;
-        }
-        super(scene, config);
-
-        this.addChildrenMap('panel', scrollableBlock.child);
-        this.addChildrenMap('panelLayer', scrollableBlock.maskLayer);
-        this.addChildrenMap('mask', scrollableBlock.maskGameObject);
-    }
-
-    setChildrenInteractive(config) {
-        if (config === undefined) {
-            config = {};
-        }
-
-        if (!config.hasOwnProperty('eventEmitter')) {
-            config.eventEmitter = this;
-        }
-
-        if (!config.hasOwnProperty('targets')) {
-            config.targets = [this.childrenMap.panel];
-        }
-
-        SetChildrenInteractive(this.childrenMap.child, config);
-        return this;
-    }
-}
-
-export default ScrollablePanel;

spaces/Akmyradov/TurkmenTTSweSTT/vits/text/symbols.py

@@ -1,16 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Defines the set of symbols used in text input to the model.
-'''
-_pad        = '_'
-_punctuation = ';:,.!?¡¿—…"«»“” '
-_letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
-_letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ"
-
-
-# Export all symbols:
-symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa)
-
-# Special symbol ids
-SPACE_ID = symbols.index(" ")

spaces/AlexWang/lama/models/ade20k/segm_lib/nn/modules/tests/test_sync_batchnorm.py

@@ -1,111 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : test_sync_batchnorm.py
-# Author : Jiayuan Mao
-# Email  : [email protected]
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-
-import unittest
-
-import torch
-import torch.nn as nn
-from torch.autograd import Variable
-
-from sync_batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, DataParallelWithCallback
-from sync_batchnorm.unittest import TorchTestCase
-
-
-def handy_var(a, unbias=True):
-    n = a.size(0)
-    asum = a.sum(dim=0)
-    as_sum = (a ** 2).sum(dim=0)  # a square sum
-    sumvar = as_sum - asum * asum / n
-    if unbias:
-        return sumvar / (n - 1)
-    else:
-        return sumvar / n
-
-
-def _find_bn(module):
-    for m in module.modules():
-        if isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d, SynchronizedBatchNorm1d, SynchronizedBatchNorm2d)):
-            return m
-
-
-class SyncTestCase(TorchTestCase):
-    def _syncParameters(self, bn1, bn2):
-        bn1.reset_parameters()
-        bn2.reset_parameters()
-        if bn1.affine and bn2.affine:
-            bn2.weight.data.copy_(bn1.weight.data)
-            bn2.bias.data.copy_(bn1.bias.data)
-
-    def _checkBatchNormResult(self, bn1, bn2, input, is_train, cuda=False):
-        """Check the forward and backward for the customized batch normalization."""
-        bn1.train(mode=is_train)
-        bn2.train(mode=is_train)
-
-        if cuda:
-            input = input.cuda()
-
-        self._syncParameters(_find_bn(bn1), _find_bn(bn2))
-
-        input1 = Variable(input, requires_grad=True)
-        output1 = bn1(input1)
-        output1.sum().backward()
-        input2 = Variable(input, requires_grad=True)
-        output2 = bn2(input2)
-        output2.sum().backward()
-
-        self.assertTensorClose(input1.data, input2.data)
-        self.assertTensorClose(output1.data, output2.data)
-        self.assertTensorClose(input1.grad, input2.grad)
-        self.assertTensorClose(_find_bn(bn1).running_mean, _find_bn(bn2).running_mean)
-        self.assertTensorClose(_find_bn(bn1).running_var, _find_bn(bn2).running_var)
-
-    def testSyncBatchNormNormalTrain(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True)
-
-    def testSyncBatchNormNormalEval(self):
-        bn = nn.BatchNorm1d(10)
-        sync_bn = SynchronizedBatchNorm1d(10)
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False)
-
-    def testSyncBatchNormSyncTrain(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), True, cuda=True)
-
-    def testSyncBatchNormSyncEval(self):
-        bn = nn.BatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10), False, cuda=True)
-
-    def testSyncBatchNorm2DSyncTrain(self):
-        bn = nn.BatchNorm2d(10)
-        sync_bn = SynchronizedBatchNorm2d(10)
-        sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1])
-
-        bn.cuda()
-        sync_bn.cuda()
-
-        self._checkBatchNormResult(bn, sync_bn, torch.rand(16, 10, 16, 16), True, cuda=True)
-
-
-if __name__ == '__main__':
-    unittest.main()

spaces/Alichuan/VITS-Umamusume-voice-synthesizer/ONNXVITS_utils.py

@@ -1,19 +0,0 @@
-import torch
-import numpy as np
-import random
-import onnxruntime as ort
-def set_random_seed(seed=0):
-    ort.set_seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.backends.cudnn.deterministic = True
-    random.seed(seed)
-    np.random.seed(seed)
-
-def runonnx(model_path, **kwargs):
-    ort_session = ort.InferenceSession(model_path)
-    outputs = ort_session.run(
-        None,
-        kwargs
-    )
-    return outputs

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/examples/dreambooth/train_dreambooth.py

@@ -1,1377 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2023 The HuggingFace Inc. 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
-
-import argparse
-import gc
-import hashlib
-import itertools
-import logging
-import math
-import os
-import shutil
-import warnings
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import ProjectConfiguration, set_seed
-from huggingface_hub import create_repo, model_info, upload_folder
-from packaging import version
-from PIL import Image
-from PIL.ImageOps import exif_transpose
-from torch.utils.data import Dataset
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import AutoTokenizer, PretrainedConfig
-
-import diffusers
-from diffusers import (
-    AutoencoderKL,
-    DDPMScheduler,
-    DiffusionPipeline,
-    DPMSolverMultistepScheduler,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-)
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version, is_wandb_available
-from diffusers.utils.import_utils import is_xformers_available
-
-
-if is_wandb_available():
-    import wandb
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.19.0")
-
-logger = get_logger(__name__)
-
-
-def save_model_card(
-    repo_id: str,
-    images=None,
-    base_model=str,
-    train_text_encoder=False,
-    prompt=str,
-    repo_folder=None,
-    pipeline: DiffusionPipeline = None,
-):
-    img_str = ""
-    for i, image in enumerate(images):
-        image.save(os.path.join(repo_folder, f"image_{i}.png"))
-        img_str += f"![img_{i}](./image_{i}.png)\n"
-
-    yaml = f"""
----
-license: creativeml-openrail-m
-base_model: {base_model}
-instance_prompt: {prompt}
-tags:
-- {'stable-diffusion' if isinstance(pipeline, StableDiffusionPipeline) else 'if'}
-- {'stable-diffusion-diffusers' if isinstance(pipeline, StableDiffusionPipeline) else 'if-diffusers'}
-- text-to-image
-- diffusers
-- dreambooth
-inference: true
----
-    """
-    model_card = f"""
-# DreamBooth - {repo_id}
-
-This is a dreambooth model derived from {base_model}. The weights were trained on {prompt} using [DreamBooth](https://dreambooth.github.io/).
-You can find some example images in the following. \n
-{img_str}
-
-DreamBooth for the text encoder was enabled: {train_text_encoder}.
-"""
-    with open(os.path.join(repo_folder, "README.md"), "w") as f:
-        f.write(yaml + model_card)
-
-
-def log_validation(
-    text_encoder, tokenizer, unet, vae, args, accelerator, weight_dtype, epoch, prompt_embeds, negative_prompt_embeds
-):
-    logger.info(
-        f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
-        f" {args.validation_prompt}."
-    )
-
-    pipeline_args = {}
-
-    if vae is not None:
-        pipeline_args["vae"] = vae
-
-    if text_encoder is not None:
-        text_encoder = accelerator.unwrap_model(text_encoder)
-
-    # create pipeline (note: unet and vae are loaded again in float32)
-    pipeline = DiffusionPipeline.from_pretrained(
-        args.pretrained_model_name_or_path,
-        tokenizer=tokenizer,
-        text_encoder=text_encoder,
-        unet=accelerator.unwrap_model(unet),
-        revision=args.revision,
-        torch_dtype=weight_dtype,
-        **pipeline_args,
-    )
-
-    # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
-    scheduler_args = {}
-
-    if "variance_type" in pipeline.scheduler.config:
-        variance_type = pipeline.scheduler.config.variance_type
-
-        if variance_type in ["learned", "learned_range"]:
-            variance_type = "fixed_small"
-
-        scheduler_args["variance_type"] = variance_type
-
-    pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-    pipeline = pipeline.to(accelerator.device)
-    pipeline.set_progress_bar_config(disable=True)
-
-    if args.pre_compute_text_embeddings:
-        pipeline_args = {
-            "prompt_embeds": prompt_embeds,
-            "negative_prompt_embeds": negative_prompt_embeds,
-        }
-    else:
-        pipeline_args = {"prompt": args.validation_prompt}
-
-    # run inference
-    generator = None if args.seed is None else torch.Generator(device=accelerator.device).manual_seed(args.seed)
-    images = []
-    if args.validation_images is None:
-        for _ in range(args.num_validation_images):
-            with torch.autocast("cuda"):
-                image = pipeline(**pipeline_args, num_inference_steps=25, generator=generator).images[0]
-            images.append(image)
-    else:
-        for image in args.validation_images:
-            image = Image.open(image)
-            image = pipeline(**pipeline_args, image=image, generator=generator).images[0]
-            images.append(image)
-
-    for tracker in accelerator.trackers:
-        if tracker.name == "tensorboard":
-            np_images = np.stack([np.asarray(img) for img in images])
-            tracker.writer.add_images("validation", np_images, epoch, dataformats="NHWC")
-        if tracker.name == "wandb":
-            tracker.log(
-                {
-                    "validation": [
-                        wandb.Image(image, caption=f"{i}: {args.validation_prompt}") for i, image in enumerate(images)
-                    ]
-                }
-            )
-
-    del pipeline
-    torch.cuda.empty_cache()
-
-    return images
-
-
-def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str):
-    text_encoder_config = PretrainedConfig.from_pretrained(
-        pretrained_model_name_or_path,
-        subfolder="text_encoder",
-        revision=revision,
-    )
-    model_class = text_encoder_config.architectures[0]
-
-    if model_class == "CLIPTextModel":
-        from transformers import CLIPTextModel
-
-        return CLIPTextModel
-    elif model_class == "RobertaSeriesModelWithTransformation":
-        from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
-
-        return RobertaSeriesModelWithTransformation
-    elif model_class == "T5EncoderModel":
-        from transformers import T5EncoderModel
-
-        return T5EncoderModel
-    else:
-        raise ValueError(f"{model_class} is not supported.")
-
-
-def parse_args(input_args=None):
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--pretrained_model_name_or_path",
-        type=str,
-        default=None,
-        required=True,
-        help="Path to pretrained model or model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help=(
-            "Revision of pretrained model identifier from huggingface.co/models. Trainable model components should be"
-            " float32 precision."
-        ),
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--instance_data_dir",
-        type=str,
-        default=None,
-        required=True,
-        help="A folder containing the training data of instance images.",
-    )
-    parser.add_argument(
-        "--class_data_dir",
-        type=str,
-        default=None,
-        required=False,
-        help="A folder containing the training data of class images.",
-    )
-    parser.add_argument(
-        "--instance_prompt",
-        type=str,
-        default=None,
-        required=True,
-        help="The prompt with identifier specifying the instance",
-    )
-    parser.add_argument(
-        "--class_prompt",
-        type=str,
-        default=None,
-        help="The prompt to specify images in the same class as provided instance images.",
-    )
-    parser.add_argument(
-        "--with_prior_preservation",
-        default=False,
-        action="store_true",
-        help="Flag to add prior preservation loss.",
-    )
-    parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
-    parser.add_argument(
-        "--num_class_images",
-        type=int,
-        default=100,
-        help=(
-            "Minimal class images for prior preservation loss. If there are not enough images already present in"
-            " class_data_dir, additional images will be sampled with class_prompt."
-        ),
-    )
-    parser.add_argument(
-        "--output_dir",
-        type=str,
-        default="text-inversion-model",
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop",
-        default=False,
-        action="store_true",
-        help=(
-            "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
-            " cropped. The images will be resized to the resolution first before cropping."
-        ),
-    )
-    parser.add_argument(
-        "--train_text_encoder",
-        action="store_true",
-        help="Whether to train the text encoder. If set, the text encoder should be float32 precision.",
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument(
-        "--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=1)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=None,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. Checkpoints can be used for resuming training via `--resume_from_checkpoint`. "
-            "In the case that the checkpoint is better than the final trained model, the checkpoint can also be used for inference."
-            "Using a checkpoint for inference requires separate loading of the original pipeline and the individual checkpointed model components."
-            "See https://huggingface.co/docs/diffusers/main/en/training/dreambooth#performing-inference-using-a-saved-checkpoint for step by step"
-            "instructions."
-        ),
-    )
-    parser.add_argument(
-        "--checkpoints_total_limit",
-        type=int,
-        default=None,
-        help=(
-            "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
-            " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
-            " for more details"
-        ),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=5e-6,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=False,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument(
-        "--lr_num_cycles",
-        type=int,
-        default=1,
-        help="Number of hard resets of the lr in cosine_with_restarts scheduler.",
-    )
-    parser.add_argument("--lr_power", type=float, default=1.0, help="Power factor of the polynomial scheduler.")
-    parser.add_argument(
-        "--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
-    )
-    parser.add_argument(
-        "--dataloader_num_workers",
-        type=int,
-        default=0,
-        help=(
-            "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process."
-        ),
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--hub_model_id",
-        type=str,
-        default=None,
-        help="The name of the repository to keep in sync with the local `output_dir`.",
-    )
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument(
-        "--validation_prompt",
-        type=str,
-        default=None,
-        help="A prompt that is used during validation to verify that the model is learning.",
-    )
-    parser.add_argument(
-        "--num_validation_images",
-        type=int,
-        default=4,
-        help="Number of images that should be generated during validation with `validation_prompt`.",
-    )
-    parser.add_argument(
-        "--validation_steps",
-        type=int,
-        default=100,
-        help=(
-            "Run validation every X steps. Validation consists of running the prompt"
-            " `args.validation_prompt` multiple times: `args.num_validation_images`"
-            " and logging the images."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to the value of accelerate config of the current system or the"
-            " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
-        ),
-    )
-    parser.add_argument(
-        "--prior_generation_precision",
-        type=str,
-        default=None,
-        choices=["no", "fp32", "fp16", "bf16"],
-        help=(
-            "Choose prior generation precision between fp32, fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
-            " 1.10.and an Nvidia Ampere GPU.  Default to  fp16 if a GPU is available else fp32."
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-    parser.add_argument(
-        "--set_grads_to_none",
-        action="store_true",
-        help=(
-            "Save more memory by using setting grads to None instead of zero. Be aware, that this changes certain"
-            " behaviors, so disable this argument if it causes any problems. More info:"
-            " https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html"
-        ),
-    )
-
-    parser.add_argument(
-        "--offset_noise",
-        action="store_true",
-        default=False,
-        help=(
-            "Fine-tuning against a modified noise"
-            " See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information."
-        ),
-    )
-    parser.add_argument(
-        "--pre_compute_text_embeddings",
-        action="store_true",
-        help="Whether or not to pre-compute text embeddings. If text embeddings are pre-computed, the text encoder will not be kept in memory during training and will leave more GPU memory available for training the rest of the model. This is not compatible with `--train_text_encoder`.",
-    )
-    parser.add_argument(
-        "--tokenizer_max_length",
-        type=int,
-        default=None,
-        required=False,
-        help="The maximum length of the tokenizer. If not set, will default to the tokenizer's max length.",
-    )
-    parser.add_argument(
-        "--text_encoder_use_attention_mask",
-        action="store_true",
-        required=False,
-        help="Whether to use attention mask for the text encoder",
-    )
-    parser.add_argument(
-        "--skip_save_text_encoder", action="store_true", required=False, help="Set to not save text encoder"
-    )
-    parser.add_argument(
-        "--validation_images",
-        required=False,
-        default=None,
-        nargs="+",
-        help="Optional set of images to use for validation. Used when the target pipeline takes an initial image as input such as when training image variation or superresolution.",
-    )
-    parser.add_argument(
-        "--class_labels_conditioning",
-        required=False,
-        default=None,
-        help="The optional `class_label` conditioning to pass to the unet, available values are `timesteps`.",
-    )
-
-    if input_args is not None:
-        args = parser.parse_args(input_args)
-    else:
-        args = parser.parse_args()
-
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.with_prior_preservation:
-        if args.class_data_dir is None:
-            raise ValueError("You must specify a data directory for class images.")
-        if args.class_prompt is None:
-            raise ValueError("You must specify prompt for class images.")
-    else:
-        # logger is not available yet
-        if args.class_data_dir is not None:
-            warnings.warn("You need not use --class_data_dir without --with_prior_preservation.")
-        if args.class_prompt is not None:
-            warnings.warn("You need not use --class_prompt without --with_prior_preservation.")
-
-    if args.train_text_encoder and args.pre_compute_text_embeddings:
-        raise ValueError("`--train_text_encoder` cannot be used with `--pre_compute_text_embeddings`")
-
-    return args
-
-
-class DreamBoothDataset(Dataset):
-    """
-    A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
-    It pre-processes the images and the tokenizes prompts.
-    """
-
-    def __init__(
-        self,
-        instance_data_root,
-        instance_prompt,
-        tokenizer,
-        class_data_root=None,
-        class_prompt=None,
-        class_num=None,
-        size=512,
-        center_crop=False,
-        encoder_hidden_states=None,
-        instance_prompt_encoder_hidden_states=None,
-        tokenizer_max_length=None,
-    ):
-        self.size = size
-        self.center_crop = center_crop
-        self.tokenizer = tokenizer
-        self.encoder_hidden_states = encoder_hidden_states
-        self.instance_prompt_encoder_hidden_states = instance_prompt_encoder_hidden_states
-        self.tokenizer_max_length = tokenizer_max_length
-
-        self.instance_data_root = Path(instance_data_root)
-        if not self.instance_data_root.exists():
-            raise ValueError(f"Instance {self.instance_data_root} images root doesn't exists.")
-
-        self.instance_images_path = list(Path(instance_data_root).iterdir())
-        self.num_instance_images = len(self.instance_images_path)
-        self.instance_prompt = instance_prompt
-        self._length = self.num_instance_images
-
-        if class_data_root is not None:
-            self.class_data_root = Path(class_data_root)
-            self.class_data_root.mkdir(parents=True, exist_ok=True)
-            self.class_images_path = list(self.class_data_root.iterdir())
-            if class_num is not None:
-                self.num_class_images = min(len(self.class_images_path), class_num)
-            else:
-                self.num_class_images = len(self.class_images_path)
-            self._length = max(self.num_class_images, self.num_instance_images)
-            self.class_prompt = class_prompt
-        else:
-            self.class_data_root = None
-
-        self.image_transforms = transforms.Compose(
-            [
-                transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
-                transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ]
-        )
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, index):
-        example = {}
-        instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
-        instance_image = exif_transpose(instance_image)
-
-        if not instance_image.mode == "RGB":
-            instance_image = instance_image.convert("RGB")
-        example["instance_images"] = self.image_transforms(instance_image)
-
-        if self.encoder_hidden_states is not None:
-            example["instance_prompt_ids"] = self.encoder_hidden_states
-        else:
-            text_inputs = tokenize_prompt(
-                self.tokenizer, self.instance_prompt, tokenizer_max_length=self.tokenizer_max_length
-            )
-            example["instance_prompt_ids"] = text_inputs.input_ids
-            example["instance_attention_mask"] = text_inputs.attention_mask
-
-        if self.class_data_root:
-            class_image = Image.open(self.class_images_path[index % self.num_class_images])
-            class_image = exif_transpose(class_image)
-
-            if not class_image.mode == "RGB":
-                class_image = class_image.convert("RGB")
-            example["class_images"] = self.image_transforms(class_image)
-
-            if self.instance_prompt_encoder_hidden_states is not None:
-                example["class_prompt_ids"] = self.instance_prompt_encoder_hidden_states
-            else:
-                class_text_inputs = tokenize_prompt(
-                    self.tokenizer, self.class_prompt, tokenizer_max_length=self.tokenizer_max_length
-                )
-                example["class_prompt_ids"] = class_text_inputs.input_ids
-                example["class_attention_mask"] = class_text_inputs.attention_mask
-
-        return example
-
-
-def collate_fn(examples, with_prior_preservation=False):
-    has_attention_mask = "instance_attention_mask" in examples[0]
-
-    input_ids = [example["instance_prompt_ids"] for example in examples]
-    pixel_values = [example["instance_images"] for example in examples]
-
-    if has_attention_mask:
-        attention_mask = [example["instance_attention_mask"] for example in examples]
-
-    # Concat class and instance examples for prior preservation.
-    # We do this to avoid doing two forward passes.
-    if with_prior_preservation:
-        input_ids += [example["class_prompt_ids"] for example in examples]
-        pixel_values += [example["class_images"] for example in examples]
-
-        if has_attention_mask:
-            attention_mask += [example["class_attention_mask"] for example in examples]
-
-    pixel_values = torch.stack(pixel_values)
-    pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
-
-    input_ids = torch.cat(input_ids, dim=0)
-
-    batch = {
-        "input_ids": input_ids,
-        "pixel_values": pixel_values,
-    }
-
-    if has_attention_mask:
-        attention_mask = torch.cat(attention_mask, dim=0)
-        batch["attention_mask"] = attention_mask
-
-    return batch
-
-
-class PromptDataset(Dataset):
-    "A simple dataset to prepare the prompts to generate class images on multiple GPUs."
-
-    def __init__(self, prompt, num_samples):
-        self.prompt = prompt
-        self.num_samples = num_samples
-
-    def __len__(self):
-        return self.num_samples
-
-    def __getitem__(self, index):
-        example = {}
-        example["prompt"] = self.prompt
-        example["index"] = index
-        return example
-
-
-def model_has_vae(args):
-    config_file_name = os.path.join("vae", AutoencoderKL.config_name)
-    if os.path.isdir(args.pretrained_model_name_or_path):
-        config_file_name = os.path.join(args.pretrained_model_name_or_path, config_file_name)
-        return os.path.isfile(config_file_name)
-    else:
-        files_in_repo = model_info(args.pretrained_model_name_or_path, revision=args.revision).siblings
-        return any(file.rfilename == config_file_name for file in files_in_repo)
-
-
-def tokenize_prompt(tokenizer, prompt, tokenizer_max_length=None):
-    if tokenizer_max_length is not None:
-        max_length = tokenizer_max_length
-    else:
-        max_length = tokenizer.model_max_length
-
-    text_inputs = tokenizer(
-        prompt,
-        truncation=True,
-        padding="max_length",
-        max_length=max_length,
-        return_tensors="pt",
-    )
-
-    return text_inputs
-
-
-def encode_prompt(text_encoder, input_ids, attention_mask, text_encoder_use_attention_mask=None):
-    text_input_ids = input_ids.to(text_encoder.device)
-
-    if text_encoder_use_attention_mask:
-        attention_mask = attention_mask.to(text_encoder.device)
-    else:
-        attention_mask = None
-
-    prompt_embeds = text_encoder(
-        text_input_ids,
-        attention_mask=attention_mask,
-    )
-    prompt_embeds = prompt_embeds[0]
-
-    return prompt_embeds
-
-
-def main(args):
-    logging_dir = Path(args.output_dir, args.logging_dir)
-
-    accelerator_project_config = ProjectConfiguration(project_dir=args.output_dir, logging_dir=logging_dir)
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        project_config=accelerator_project_config,
-    )
-
-    if args.report_to == "wandb":
-        if not is_wandb_available():
-            raise ImportError("Make sure to install wandb if you want to use it for logging during training.")
-
-    # Currently, it's not possible to do gradient accumulation when training two models with accelerate.accumulate
-    # This will be enabled soon in accelerate. For now, we don't allow gradient accumulation when training two models.
-    # TODO (patil-suraj): Remove this check when gradient accumulation with two models is enabled in accelerate.
-    if args.train_text_encoder and args.gradient_accumulation_steps > 1 and accelerator.num_processes > 1:
-        raise ValueError(
-            "Gradient accumulation is not supported when training the text encoder in distributed training. "
-            "Please set gradient_accumulation_steps to 1. This feature will be supported in the future."
-        )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Generate class images if prior preservation is enabled.
-    if args.with_prior_preservation:
-        class_images_dir = Path(args.class_data_dir)
-        if not class_images_dir.exists():
-            class_images_dir.mkdir(parents=True)
-        cur_class_images = len(list(class_images_dir.iterdir()))
-
-        if cur_class_images < args.num_class_images:
-            torch_dtype = torch.float16 if accelerator.device.type == "cuda" else torch.float32
-            if args.prior_generation_precision == "fp32":
-                torch_dtype = torch.float32
-            elif args.prior_generation_precision == "fp16":
-                torch_dtype = torch.float16
-            elif args.prior_generation_precision == "bf16":
-                torch_dtype = torch.bfloat16
-            pipeline = DiffusionPipeline.from_pretrained(
-                args.pretrained_model_name_or_path,
-                torch_dtype=torch_dtype,
-                safety_checker=None,
-                revision=args.revision,
-            )
-            pipeline.set_progress_bar_config(disable=True)
-
-            num_new_images = args.num_class_images - cur_class_images
-            logger.info(f"Number of class images to sample: {num_new_images}.")
-
-            sample_dataset = PromptDataset(args.class_prompt, num_new_images)
-            sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
-
-            sample_dataloader = accelerator.prepare(sample_dataloader)
-            pipeline.to(accelerator.device)
-
-            for example in tqdm(
-                sample_dataloader, desc="Generating class images", disable=not accelerator.is_local_main_process
-            ):
-                images = pipeline(example["prompt"]).images
-
-                for i, image in enumerate(images):
-                    hash_image = hashlib.sha1(image.tobytes()).hexdigest()
-                    image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
-                    image.save(image_filename)
-
-            del pipeline
-            if torch.cuda.is_available():
-                torch.cuda.empty_cache()
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-        if args.push_to_hub:
-            repo_id = create_repo(
-                repo_id=args.hub_model_id or Path(args.output_dir).name, exist_ok=True, token=args.hub_token
-            ).repo_id
-
-    # Load the tokenizer
-    if args.tokenizer_name:
-        tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=False)
-    elif args.pretrained_model_name_or_path:
-        tokenizer = AutoTokenizer.from_pretrained(
-            args.pretrained_model_name_or_path,
-            subfolder="tokenizer",
-            revision=args.revision,
-            use_fast=False,
-        )
-
-    # import correct text encoder class
-    text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path, args.revision)
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    text_encoder = text_encoder_cls.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision
-    )
-
-    if model_has_vae(args):
-        vae = AutoencoderKL.from_pretrained(
-            args.pretrained_model_name_or_path, subfolder="vae", revision=args.revision
-        )
-    else:
-        vae = None
-
-    unet = UNet2DConditionModel.from_pretrained(
-        args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision
-    )
-
-    # create custom saving & loading hooks so that `accelerator.save_state(...)` serializes in a nice format
-    def save_model_hook(models, weights, output_dir):
-        for model in models:
-            sub_dir = "unet" if isinstance(model, type(accelerator.unwrap_model(unet))) else "text_encoder"
-            model.save_pretrained(os.path.join(output_dir, sub_dir))
-
-            # make sure to pop weight so that corresponding model is not saved again
-            weights.pop()
-
-    def load_model_hook(models, input_dir):
-        while len(models) > 0:
-            # pop models so that they are not loaded again
-            model = models.pop()
-
-            if isinstance(model, type(accelerator.unwrap_model(text_encoder))):
-                # load transformers style into model
-                load_model = text_encoder_cls.from_pretrained(input_dir, subfolder="text_encoder")
-                model.config = load_model.config
-            else:
-                # load diffusers style into model
-                load_model = UNet2DConditionModel.from_pretrained(input_dir, subfolder="unet")
-                model.register_to_config(**load_model.config)
-
-            model.load_state_dict(load_model.state_dict())
-            del load_model
-
-    accelerator.register_save_state_pre_hook(save_model_hook)
-    accelerator.register_load_state_pre_hook(load_model_hook)
-
-    if vae is not None:
-        vae.requires_grad_(False)
-
-    if not args.train_text_encoder:
-        text_encoder.requires_grad_(False)
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            import xformers
-
-            xformers_version = version.parse(xformers.__version__)
-            if xformers_version == version.parse("0.0.16"):
-                logger.warn(
-                    "xFormers 0.0.16 cannot be used for training in some GPUs. If you observe problems during training, please update xFormers to at least 0.0.17. See https://huggingface.co/docs/diffusers/main/en/optimization/xformers for more details."
-                )
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    if args.gradient_checkpointing:
-        unet.enable_gradient_checkpointing()
-        if args.train_text_encoder:
-            text_encoder.gradient_checkpointing_enable()
-
-    # Check that all trainable models are in full precision
-    low_precision_error_string = (
-        "Please make sure to always have all model weights in full float32 precision when starting training - even if"
-        " doing mixed precision training. copy of the weights should still be float32."
-    )
-
-    if accelerator.unwrap_model(unet).dtype != torch.float32:
-        raise ValueError(
-            f"Unet loaded as datatype {accelerator.unwrap_model(unet).dtype}. {low_precision_error_string}"
-        )
-
-    if args.train_text_encoder and accelerator.unwrap_model(text_encoder).dtype != torch.float32:
-        raise ValueError(
-            f"Text encoder loaded as datatype {accelerator.unwrap_model(text_encoder).dtype}."
-            f" {low_precision_error_string}"
-        )
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Use 8-bit Adam for lower memory usage or to fine-tune the model in 16GB GPUs
-    if args.use_8bit_adam:
-        try:
-            import bitsandbytes as bnb
-        except ImportError:
-            raise ImportError(
-                "To use 8-bit Adam, please install the bitsandbytes library: `pip install bitsandbytes`."
-            )
-
-        optimizer_class = bnb.optim.AdamW8bit
-    else:
-        optimizer_class = torch.optim.AdamW
-
-    # Optimizer creation
-    params_to_optimize = (
-        itertools.chain(unet.parameters(), text_encoder.parameters()) if args.train_text_encoder else unet.parameters()
-    )
-    optimizer = optimizer_class(
-        params_to_optimize,
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    if args.pre_compute_text_embeddings:
-
-        def compute_text_embeddings(prompt):
-            with torch.no_grad():
-                text_inputs = tokenize_prompt(tokenizer, prompt, tokenizer_max_length=args.tokenizer_max_length)
-                prompt_embeds = encode_prompt(
-                    text_encoder,
-                    text_inputs.input_ids,
-                    text_inputs.attention_mask,
-                    text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
-                )
-
-            return prompt_embeds
-
-        pre_computed_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)
-        validation_prompt_negative_prompt_embeds = compute_text_embeddings("")
-
-        if args.validation_prompt is not None:
-            validation_prompt_encoder_hidden_states = compute_text_embeddings(args.validation_prompt)
-        else:
-            validation_prompt_encoder_hidden_states = None
-
-        if args.instance_prompt is not None:
-            pre_computed_instance_prompt_encoder_hidden_states = compute_text_embeddings(args.instance_prompt)
-        else:
-            pre_computed_instance_prompt_encoder_hidden_states = None
-
-        text_encoder = None
-        tokenizer = None
-
-        gc.collect()
-        torch.cuda.empty_cache()
-    else:
-        pre_computed_encoder_hidden_states = None
-        validation_prompt_encoder_hidden_states = None
-        validation_prompt_negative_prompt_embeds = None
-        pre_computed_instance_prompt_encoder_hidden_states = None
-
-    # Dataset and DataLoaders creation:
-    train_dataset = DreamBoothDataset(
-        instance_data_root=args.instance_data_dir,
-        instance_prompt=args.instance_prompt,
-        class_data_root=args.class_data_dir if args.with_prior_preservation else None,
-        class_prompt=args.class_prompt,
-        class_num=args.num_class_images,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        center_crop=args.center_crop,
-        encoder_hidden_states=pre_computed_encoder_hidden_states,
-        instance_prompt_encoder_hidden_states=pre_computed_instance_prompt_encoder_hidden_states,
-        tokenizer_max_length=args.tokenizer_max_length,
-    )
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        batch_size=args.train_batch_size,
-        shuffle=True,
-        collate_fn=lambda examples: collate_fn(examples, args.with_prior_preservation),
-        num_workers=args.dataloader_num_workers,
-    )
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,
-        num_training_steps=args.max_train_steps * accelerator.num_processes,
-        num_cycles=args.lr_num_cycles,
-        power=args.lr_power,
-    )
-
-    # Prepare everything with our `accelerator`.
-    if args.train_text_encoder:
-        unet, text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, text_encoder, optimizer, train_dataloader, lr_scheduler
-        )
-    else:
-        unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-            unet, optimizer, train_dataloader, lr_scheduler
-        )
-
-    # For mixed precision training we cast all non-trainable weigths (vae, non-lora text_encoder and non-lora unet) to half-precision
-    # as these weights are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move vae and text_encoder to device and cast to weight_dtype
-    if vae is not None:
-        vae.to(accelerator.device, dtype=weight_dtype)
-
-    if not args.train_text_encoder and text_encoder is not None:
-        text_encoder.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        tracker_config = vars(args)
-        tracker_config.pop("validation_images")
-        accelerator.init_trackers("dreambooth", config=tracker_config)
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num batches each epoch = {len(train_dataloader)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the mos recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1] if len(dirs) > 0 else None
-
-        if path is None:
-            accelerator.print(
-                f"Checkpoint '{args.resume_from_checkpoint}' does not exist. Starting a new training run."
-            )
-            args.resume_from_checkpoint = None
-        else:
-            accelerator.print(f"Resuming from checkpoint {path}")
-            accelerator.load_state(os.path.join(args.output_dir, path))
-            global_step = int(path.split("-")[1])
-
-            resume_global_step = global_step * args.gradient_accumulation_steps
-            first_epoch = global_step // num_update_steps_per_epoch
-            resume_step = resume_global_step % (num_update_steps_per_epoch * args.gradient_accumulation_steps)
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    for epoch in range(first_epoch, args.num_train_epochs):
-        unet.train()
-        if args.train_text_encoder:
-            text_encoder.train()
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(unet):
-                pixel_values = batch["pixel_values"].to(dtype=weight_dtype)
-
-                if vae is not None:
-                    # Convert images to latent space
-                    model_input = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
-                    model_input = model_input * vae.config.scaling_factor
-                else:
-                    model_input = pixel_values
-
-                # Sample noise that we'll add to the model input
-                if args.offset_noise:
-                    noise = torch.randn_like(model_input) + 0.1 * torch.randn(
-                        model_input.shape[0], model_input.shape[1], 1, 1, device=model_input.device
-                    )
-                else:
-                    noise = torch.randn_like(model_input)
-                bsz, channels, height, width = model_input.shape
-                # Sample a random timestep for each image
-                timesteps = torch.randint(
-                    0, noise_scheduler.config.num_train_timesteps, (bsz,), device=model_input.device
-                )
-                timesteps = timesteps.long()
-
-                # Add noise to the model input according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_model_input = noise_scheduler.add_noise(model_input, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                if args.pre_compute_text_embeddings:
-                    encoder_hidden_states = batch["input_ids"]
-                else:
-                    encoder_hidden_states = encode_prompt(
-                        text_encoder,
-                        batch["input_ids"],
-                        batch["attention_mask"],
-                        text_encoder_use_attention_mask=args.text_encoder_use_attention_mask,
-                    )
-
-                if accelerator.unwrap_model(unet).config.in_channels == channels * 2:
-                    noisy_model_input = torch.cat([noisy_model_input, noisy_model_input], dim=1)
-
-                if args.class_labels_conditioning == "timesteps":
-                    class_labels = timesteps
-                else:
-                    class_labels = None
-
-                # Predict the noise residual
-                model_pred = unet(
-                    noisy_model_input, timesteps, encoder_hidden_states, class_labels=class_labels
-                ).sample
-
-                if model_pred.shape[1] == 6:
-                    model_pred, _ = torch.chunk(model_pred, 2, dim=1)
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(model_input, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                if args.with_prior_preservation:
-                    # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
-                    model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
-                    target, target_prior = torch.chunk(target, 2, dim=0)
-
-                    # Compute instance loss
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                    # Compute prior loss
-                    prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
-
-                    # Add the prior loss to the instance loss.
-                    loss = loss + args.prior_loss_weight * prior_loss
-                else:
-                    loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-                if accelerator.sync_gradients:
-                    params_to_clip = (
-                        itertools.chain(unet.parameters(), text_encoder.parameters())
-                        if args.train_text_encoder
-                        else unet.parameters()
-                    )
-                    accelerator.clip_grad_norm_(params_to_clip, args.max_grad_norm)
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad(set_to_none=args.set_grads_to_none)
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-
-                if accelerator.is_main_process:
-                    if global_step % args.checkpointing_steps == 0:
-                        # _before_ saving state, check if this save would set us over the `checkpoints_total_limit`
-                        if args.checkpoints_total_limit is not None:
-                            checkpoints = os.listdir(args.output_dir)
-                            checkpoints = [d for d in checkpoints if d.startswith("checkpoint")]
-                            checkpoints = sorted(checkpoints, key=lambda x: int(x.split("-")[1]))
-
-                            # before we save the new checkpoint, we need to have at _most_ `checkpoints_total_limit - 1` checkpoints
-                            if len(checkpoints) >= args.checkpoints_total_limit:
-                                num_to_remove = len(checkpoints) - args.checkpoints_total_limit + 1
-                                removing_checkpoints = checkpoints[0:num_to_remove]
-
-                                logger.info(
-                                    f"{len(checkpoints)} checkpoints already exist, removing {len(removing_checkpoints)} checkpoints"
-                                )
-                                logger.info(f"removing checkpoints: {', '.join(removing_checkpoints)}")
-
-                                for removing_checkpoint in removing_checkpoints:
-                                    removing_checkpoint = os.path.join(args.output_dir, removing_checkpoint)
-                                    shutil.rmtree(removing_checkpoint)
-
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-                    images = []
-
-                    if args.validation_prompt is not None and global_step % args.validation_steps == 0:
-                        images = log_validation(
-                            text_encoder,
-                            tokenizer,
-                            unet,
-                            vae,
-                            args,
-                            accelerator,
-                            weight_dtype,
-                            epoch,
-                            validation_prompt_encoder_hidden_states,
-                            validation_prompt_negative_prompt_embeds,
-                        )
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-    # Create the pipeline using using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        pipeline_args = {}
-
-        if text_encoder is not None:
-            pipeline_args["text_encoder"] = accelerator.unwrap_model(text_encoder)
-
-        if args.skip_save_text_encoder:
-            pipeline_args["text_encoder"] = None
-
-        pipeline = DiffusionPipeline.from_pretrained(
-            args.pretrained_model_name_or_path,
-            unet=accelerator.unwrap_model(unet),
-            revision=args.revision,
-            **pipeline_args,
-        )
-
-        # We train on the simplified learning objective. If we were previously predicting a variance, we need the scheduler to ignore it
-        scheduler_args = {}
-
-        if "variance_type" in pipeline.scheduler.config:
-            variance_type = pipeline.scheduler.config.variance_type
-
-            if variance_type in ["learned", "learned_range"]:
-                variance_type = "fixed_small"
-
-            scheduler_args["variance_type"] = variance_type
-
-        pipeline.scheduler = pipeline.scheduler.from_config(pipeline.scheduler.config, **scheduler_args)
-
-        pipeline.save_pretrained(args.output_dir)
-
-        if args.push_to_hub:
-            save_model_card(
-                repo_id,
-                images=images,
-                base_model=args.pretrained_model_name_or_path,
-                train_text_encoder=args.train_text_encoder,
-                prompt=args.instance_prompt,
-                repo_folder=args.output_dir,
-                pipeline=pipeline,
-            )
-            upload_folder(
-                repo_id=repo_id,
-                folder_path=args.output_dir,
-                commit_message="End of training",
-                ignore_patterns=["step_*", "epoch_*"],
-            )
-
-    accelerator.end_training()
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/tests/models/test_attention_processor.py

@@ -1,119 +0,0 @@
-import tempfile
-import unittest
-
-import numpy as np
-import torch
-
-from diffusers import DiffusionPipeline
-from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
-
-
-class AttnAddedKVProcessorTests(unittest.TestCase):
-    def get_constructor_arguments(self, only_cross_attention: bool = False):
-        query_dim = 10
-
-        if only_cross_attention:
-            cross_attention_dim = 12
-        else:
-            # when only cross attention is not set, the cross attention dim must be the same as the query dim
-            cross_attention_dim = query_dim
-
-        return {
-            "query_dim": query_dim,
-            "cross_attention_dim": cross_attention_dim,
-            "heads": 2,
-            "dim_head": 4,
-            "added_kv_proj_dim": 6,
-            "norm_num_groups": 1,
-            "only_cross_attention": only_cross_attention,
-            "processor": AttnAddedKVProcessor(),
-        }
-
-    def get_forward_arguments(self, query_dim, added_kv_proj_dim):
-        batch_size = 2
-
-        hidden_states = torch.rand(batch_size, query_dim, 3, 2)
-        encoder_hidden_states = torch.rand(batch_size, 4, added_kv_proj_dim)
-        attention_mask = None
-
-        return {
-            "hidden_states": hidden_states,
-            "encoder_hidden_states": encoder_hidden_states,
-            "attention_mask": attention_mask,
-        }
-
-    def test_only_cross_attention(self):
-        # self and cross attention
-
-        torch.manual_seed(0)
-
-        constructor_args = self.get_constructor_arguments(only_cross_attention=False)
-        attn = Attention(**constructor_args)
-
-        self.assertTrue(attn.to_k is not None)
-        self.assertTrue(attn.to_v is not None)
-
-        forward_args = self.get_forward_arguments(
-            query_dim=constructor_args["query_dim"], added_kv_proj_dim=constructor_args["added_kv_proj_dim"]
-        )
-
-        self_and_cross_attn_out = attn(**forward_args)
-
-        # only self attention
-
-        torch.manual_seed(0)
-
-        constructor_args = self.get_constructor_arguments(only_cross_attention=True)
-        attn = Attention(**constructor_args)
-
-        self.assertTrue(attn.to_k is None)
-        self.assertTrue(attn.to_v is None)
-
-        forward_args = self.get_forward_arguments(
-            query_dim=constructor_args["query_dim"], added_kv_proj_dim=constructor_args["added_kv_proj_dim"]
-        )
-
-        only_cross_attn_out = attn(**forward_args)
-
-        self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all())
-
-
-class DeprecatedAttentionBlockTests(unittest.TestCase):
-    def test_conversion_when_using_device_map(self):
-        pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None)
-
-        pre_conversion = pipe(
-            "foo",
-            num_inference_steps=2,
-            generator=torch.Generator("cpu").manual_seed(0),
-            output_type="np",
-        ).images
-
-        # the initial conversion succeeds
-        pipe = DiffusionPipeline.from_pretrained(
-            "hf-internal-testing/tiny-stable-diffusion-pipe", device_map="sequential", safety_checker=None
-        )
-
-        conversion = pipe(
-            "foo",
-            num_inference_steps=2,
-            generator=torch.Generator("cpu").manual_seed(0),
-            output_type="np",
-        ).images
-
-        with tempfile.TemporaryDirectory() as tmpdir:
-            # save the converted model
-            pipe.save_pretrained(tmpdir)
-
-            # can also load the converted weights
-            pipe = DiffusionPipeline.from_pretrained(tmpdir, device_map="sequential", safety_checker=None)
-
-        after_conversion = pipe(
-            "foo",
-            num_inference_steps=2,
-            generator=torch.Generator("cpu").manual_seed(0),
-            output_type="np",
-        ).images
-
-        self.assertTrue(np.allclose(pre_conversion, conversion, atol=1e-5))
-        self.assertTrue(np.allclose(conversion, after_conversion, atol=1e-5))

spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_r50-d8_480x480_80k_pascal_context.py

@@ -1,9 +0,0 @@
-_base_ = [
-    '../_base_/models/fcn_r50-d8.py', '../_base_/datasets/pascal_context.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_80k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=60),
-    auxiliary_head=dict(num_classes=60),
-    test_cfg=dict(mode='slide', crop_size=(480, 480), stride=(320, 320)))
-optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)

spaces/Anonymous-sub/Rerender/ControlNet/annotator/uniformer/mmcv/ops/carafe.py

@@ -1,287 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.autograd import Function
-from torch.nn.modules.module import Module
-
-from ..cnn import UPSAMPLE_LAYERS, normal_init, xavier_init
-from ..utils import ext_loader
-
-ext_module = ext_loader.load_ext('_ext', [
-    'carafe_naive_forward', 'carafe_naive_backward', 'carafe_forward',
-    'carafe_backward'
-])
-
-
-class CARAFENaiveFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, masks, kernel_size, group_size, scale_factor):
-        return g.op(
-            'mmcv::MMCVCARAFENaive',
-            features,
-            masks,
-            kernel_size_i=kernel_size,
-            group_size_i=group_size,
-            scale_factor_f=scale_factor)
-
-    @staticmethod
-    def forward(ctx, features, masks, kernel_size, group_size, scale_factor):
-        assert scale_factor >= 1
-        assert masks.size(1) == kernel_size * kernel_size * group_size
-        assert masks.size(-1) == features.size(-1) * scale_factor
-        assert masks.size(-2) == features.size(-2) * scale_factor
-        assert features.size(1) % group_size == 0
-        assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1
-        ctx.kernel_size = kernel_size
-        ctx.group_size = group_size
-        ctx.scale_factor = scale_factor
-        ctx.feature_size = features.size()
-        ctx.mask_size = masks.size()
-
-        n, c, h, w = features.size()
-        output = features.new_zeros((n, c, h * scale_factor, w * scale_factor))
-        ext_module.carafe_naive_forward(
-            features,
-            masks,
-            output,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        if features.requires_grad or masks.requires_grad:
-            ctx.save_for_backward(features, masks)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        assert grad_output.is_cuda
-
-        features, masks = ctx.saved_tensors
-        kernel_size = ctx.kernel_size
-        group_size = ctx.group_size
-        scale_factor = ctx.scale_factor
-
-        grad_input = torch.zeros_like(features)
-        grad_masks = torch.zeros_like(masks)
-        ext_module.carafe_naive_backward(
-            grad_output.contiguous(),
-            features,
-            masks,
-            grad_input,
-            grad_masks,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        return grad_input, grad_masks, None, None, None
-
-
-carafe_naive = CARAFENaiveFunction.apply
-
-
-class CARAFENaive(Module):
-
-    def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFENaive, self).__init__()
-
-        assert isinstance(kernel_size, int) and isinstance(
-            group_size, int) and isinstance(scale_factor, int)
-        self.kernel_size = kernel_size
-        self.group_size = group_size
-        self.scale_factor = scale_factor
-
-    def forward(self, features, masks):
-        return carafe_naive(features, masks, self.kernel_size, self.group_size,
-                            self.scale_factor)
-
-
-class CARAFEFunction(Function):
-
-    @staticmethod
-    def symbolic(g, features, masks, kernel_size, group_size, scale_factor):
-        return g.op(
-            'mmcv::MMCVCARAFE',
-            features,
-            masks,
-            kernel_size_i=kernel_size,
-            group_size_i=group_size,
-            scale_factor_f=scale_factor)
-
-    @staticmethod
-    def forward(ctx, features, masks, kernel_size, group_size, scale_factor):
-        assert scale_factor >= 1
-        assert masks.size(1) == kernel_size * kernel_size * group_size
-        assert masks.size(-1) == features.size(-1) * scale_factor
-        assert masks.size(-2) == features.size(-2) * scale_factor
-        assert features.size(1) % group_size == 0
-        assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1
-        ctx.kernel_size = kernel_size
-        ctx.group_size = group_size
-        ctx.scale_factor = scale_factor
-        ctx.feature_size = features.size()
-        ctx.mask_size = masks.size()
-
-        n, c, h, w = features.size()
-        output = features.new_zeros((n, c, h * scale_factor, w * scale_factor))
-        routput = features.new_zeros(output.size(), requires_grad=False)
-        rfeatures = features.new_zeros(features.size(), requires_grad=False)
-        rmasks = masks.new_zeros(masks.size(), requires_grad=False)
-        ext_module.carafe_forward(
-            features,
-            masks,
-            rfeatures,
-            routput,
-            rmasks,
-            output,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-
-        if features.requires_grad or masks.requires_grad:
-            ctx.save_for_backward(features, masks, rfeatures)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        assert grad_output.is_cuda
-
-        features, masks, rfeatures = ctx.saved_tensors
-        kernel_size = ctx.kernel_size
-        group_size = ctx.group_size
-        scale_factor = ctx.scale_factor
-
-        rgrad_output = torch.zeros_like(grad_output, requires_grad=False)
-        rgrad_input_hs = torch.zeros_like(grad_output, requires_grad=False)
-        rgrad_input = torch.zeros_like(features, requires_grad=False)
-        rgrad_masks = torch.zeros_like(masks, requires_grad=False)
-        grad_input = torch.zeros_like(features, requires_grad=False)
-        grad_masks = torch.zeros_like(masks, requires_grad=False)
-        ext_module.carafe_backward(
-            grad_output.contiguous(),
-            rfeatures,
-            masks,
-            rgrad_output,
-            rgrad_input_hs,
-            rgrad_input,
-            rgrad_masks,
-            grad_input,
-            grad_masks,
-            kernel_size=kernel_size,
-            group_size=group_size,
-            scale_factor=scale_factor)
-        return grad_input, grad_masks, None, None, None
-
-
-carafe = CARAFEFunction.apply
-
-
-class CARAFE(Module):
-    """ CARAFE: Content-Aware ReAssembly of FEatures
-
-    Please refer to https://arxiv.org/abs/1905.02188 for more details.
-
-    Args:
-        kernel_size (int): reassemble kernel size
-        group_size (int): reassemble group size
-        scale_factor (int): upsample ratio
-
-    Returns:
-        upsampled feature map
-    """
-
-    def __init__(self, kernel_size, group_size, scale_factor):
-        super(CARAFE, self).__init__()
-
-        assert isinstance(kernel_size, int) and isinstance(
-            group_size, int) and isinstance(scale_factor, int)
-        self.kernel_size = kernel_size
-        self.group_size = group_size
-        self.scale_factor = scale_factor
-
-    def forward(self, features, masks):
-        return carafe(features, masks, self.kernel_size, self.group_size,
-                      self.scale_factor)
-
-
-@UPSAMPLE_LAYERS.register_module(name='carafe')
-class CARAFEPack(nn.Module):
-    """A unified package of CARAFE upsampler that contains: 1) channel
-    compressor 2) content encoder 3) CARAFE op.
-
-    Official implementation of ICCV 2019 paper
-    CARAFE: Content-Aware ReAssembly of FEatures
-    Please refer to https://arxiv.org/abs/1905.02188 for more details.
-
-    Args:
-        channels (int): input feature channels
-        scale_factor (int): upsample ratio
-        up_kernel (int): kernel size of CARAFE op
-        up_group (int): group size of CARAFE op
-        encoder_kernel (int): kernel size of content encoder
-        encoder_dilation (int): dilation of content encoder
-        compressed_channels (int): output channels of channels compressor
-
-    Returns:
-        upsampled feature map
-    """
-
-    def __init__(self,
-                 channels,
-                 scale_factor,
-                 up_kernel=5,
-                 up_group=1,
-                 encoder_kernel=3,
-                 encoder_dilation=1,
-                 compressed_channels=64):
-        super(CARAFEPack, self).__init__()
-        self.channels = channels
-        self.scale_factor = scale_factor
-        self.up_kernel = up_kernel
-        self.up_group = up_group
-        self.encoder_kernel = encoder_kernel
-        self.encoder_dilation = encoder_dilation
-        self.compressed_channels = compressed_channels
-        self.channel_compressor = nn.Conv2d(channels, self.compressed_channels,
-                                            1)
-        self.content_encoder = nn.Conv2d(
-            self.compressed_channels,
-            self.up_kernel * self.up_kernel * self.up_group *
-            self.scale_factor * self.scale_factor,
-            self.encoder_kernel,
-            padding=int((self.encoder_kernel - 1) * self.encoder_dilation / 2),
-            dilation=self.encoder_dilation,
-            groups=1)
-        self.init_weights()
-
-    def init_weights(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                xavier_init(m, distribution='uniform')
-        normal_init(self.content_encoder, std=0.001)
-
-    def kernel_normalizer(self, mask):
-        mask = F.pixel_shuffle(mask, self.scale_factor)
-        n, mask_c, h, w = mask.size()
-        # use float division explicitly,
-        # to void inconsistency while exporting to onnx
-        mask_channel = int(mask_c / float(self.up_kernel**2))
-        mask = mask.view(n, mask_channel, -1, h, w)
-
-        mask = F.softmax(mask, dim=2, dtype=mask.dtype)
-        mask = mask.view(n, mask_c, h, w).contiguous()
-
-        return mask
-
-    def feature_reassemble(self, x, mask):
-        x = carafe(x, mask, self.up_kernel, self.up_group, self.scale_factor)
-        return x
-
-    def forward(self, x):
-        compressed_x = self.channel_compressor(x)
-        mask = self.content_encoder(compressed_x)
-        mask = self.kernel_normalizer(mask)
-
-        x = self.feature_reassemble(x, mask)
-        return x

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

@@ -1,429 +0,0 @@
-import json
-import re
-import requests
-import os
-import time
-import random
-import string
-from requests_toolbelt.multipart.encoder import MultipartEncoder
-
-absolute_path = os.path.dirname(__file__)
-base_url = "http://127.0.0.1:23456"
-
-
-# 映射表
-def voice_speakers():
-    url = f"{base_url}/voice/speakers"
-
-    res = requests.post(url=url)
-    json = res.json()
-    for i in json:
-        print(i)
-        for j in json[i]:
-            print(j)
-    return json
-
-
-# 语音合成 voice vits
-def voice_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50, save_audio=True,
-               save_path=None):
-    fields = {
-        "text": text,
-        "id": str(id),
-        "format": format,
-        "lang": lang,
-        "length": str(length),
-        "noise": str(noise),
-        "noisew": str(noisew),
-        "max": str(max)
-    }
-    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-    m = MultipartEncoder(fields=fields, boundary=boundary)
-    headers = {"Content-Type": m.content_type}
-    url = f"{base_url}/voice/vits"
-
-    res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-def voice_vits_streaming(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50,
-                         save_audio=True, save_path=None):
-    fields = {
-        "text": text,
-        "id": str(id),
-        "format": format,
-        "lang": lang,
-        "length": str(length),
-        "noise": str(noise),
-        "noisew": str(noisew),
-        "max": str(max),
-        "streaming": 'True'
-    }
-    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-    m = MultipartEncoder(fields=fields, boundary=boundary)
-    headers = {"Content-Type": m.content_type}
-    url = f"{base_url}/voice"
-
-    res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-def voice_vits_streaming(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50,
-                         save_path=None):
-    fields = {
-        "text": text,
-        "id": str(id),
-        "format": format,
-        "lang": lang,
-        "length": str(length),
-        "noise": str(noise),
-        "noisew": str(noisew),
-        "max": str(max),
-        "streaming": 'True'
-    }
-    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-    m = MultipartEncoder(fields=fields, boundary=boundary)
-    headers = {"Content-Type": m.content_type}
-    url = f"{base_url}/voice"
-
-    res = requests.post(url=url, data=m, headers=headers, stream=True)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    audio = res.content
-
-    def get_file_size_from_bytes(byte_data):
-        file_size_offset = 4
-        file_size_length = 4
-
-        try:
-            file_size_bytes = byte_data[file_size_offset:file_size_offset + file_size_length]
-            file_size = int.from_bytes(file_size_bytes, byteorder='little')
-            return file_size + 8
-        except IndexError:
-            return None
-
-    audio = None
-    p = 0
-    audio_size = None
-    audios = []
-
-    for chunk in res.iter_content(chunk_size=1024):
-        if audio is None:
-            audio = chunk
-        else:
-            audio += chunk
-
-        p += len(chunk)
-        if audio_size is not None:
-            if p >= audio_size:
-                p = p - audio_size
-                audios.append(audio[:audio_size])
-                audio = audio[audio_size:]
-                audio_size = get_file_size_from_bytes(audio)
-        else:
-            audio_size = get_file_size_from_bytes(audio)
-    for i, audio in enumerate(audios):
-        with open(f"{path[:-4]}-{i}.wav", "wb") as f:
-            f.write(audio)
-
-        print(f"{path[:-4]}-{i}.wav")
-    return path
-
-
-# 语音转换 hubert-vits
-def voice_hubert_vits(upload_path, id, format="wav", length=1, noise=0.667, noisew=0.8, save_audio=True,
-                      save_path=None):
-    upload_name = os.path.basename(upload_path)
-    upload_type = f'audio/{upload_name.split(".")[1]}'  # wav,ogg
-
-    with open(upload_path, 'rb') as upload_file:
-        fields = {
-            "upload": (upload_name, upload_file, upload_type),
-            "id": str(id),
-            "format": format,
-            "length": str(length),
-            "noise": str(noise),
-            "noisew": str(noisew),
-        }
-        boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-        m = MultipartEncoder(fields=fields, boundary=boundary)
-        headers = {"Content-Type": m.content_type}
-        url = f"{base_url}/voice/hubert-vits"
-
-        res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-# 维度情感模型 w2v2-vits
-def voice_w2v2_vits(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50, emotion=0,
-                    save_audio=True, save_path=None):
-    fields = {
-        "text": text,
-        "id": str(id),
-        "format": format,
-        "lang": lang,
-        "length": str(length),
-        "noise": str(noise),
-        "noisew": str(noisew),
-        "max": str(max),
-        "emotion": str(emotion)
-    }
-    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-    m = MultipartEncoder(fields=fields, boundary=boundary)
-    headers = {"Content-Type": m.content_type}
-    url = f"{base_url}/voice/w2v2-vits"
-
-    res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-# 语音转换 同VITS模型内角色之间的音色转换
-def voice_conversion(upload_path, original_id, target_id, save_audio=True, save_path=None):
-    upload_name = os.path.basename(upload_path)
-    upload_type = f'audio/{upload_name.split(".")[1]}'  # wav,ogg
-
-    with open(upload_path, 'rb') as upload_file:
-        fields = {
-            "upload": (upload_name, upload_file, upload_type),
-            "original_id": str(original_id),
-            "target_id": str(target_id),
-        }
-        boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-        m = MultipartEncoder(fields=fields, boundary=boundary)
-
-        headers = {"Content-Type": m.content_type}
-        url = f"{base_url}/voice/conversion"
-
-        res = requests.post(url=url, data=m, headers=headers)
-
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-def voice_ssml(ssml, save_audio=True, save_path=None):
-    fields = {
-        "ssml": ssml,
-    }
-    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-    m = MultipartEncoder(fields=fields, boundary=boundary)
-    headers = {"Content-Type": m.content_type}
-    url = f"{base_url}/voice/ssml"
-
-    res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-def voice_dimensional_emotion(upload_path, save_audio=True,
-                              save_path=None):
-    upload_name = os.path.basename(upload_path)
-    upload_type = f'audio/{upload_name.split(".")[1]}'  # wav,ogg
-
-    with open(upload_path, 'rb') as upload_file:
-        fields = {
-            "upload": (upload_name, upload_file, upload_type),
-        }
-        boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-        m = MultipartEncoder(fields=fields, boundary=boundary)
-        headers = {"Content-Type": m.content_type}
-        url = f"{base_url}/voice/dimension-emotion"
-
-        res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-def vits_json(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50,
-              save_path=None):
-    fields = {
-        "text": text,
-        "id": str(id),
-        "format": format,
-        "lang": lang,
-        "length": str(length),
-        "noise": str(noise),
-        "noisew": str(noisew),
-        "max": str(max)
-    }
-    f = json.dumps(fields)
-    url = f"{base_url}/voice"
-    header = {"Content-Type": 'application/json'}
-    res = requests.post(url=url, data=f, headers=header)
-
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-
-    with open(path, "wb") as f:
-        f.write(res.content)
-    print(path)
-    return path
-
-
-# Bert_vits2
-def voice_bert_vits2(text, id=0, format="wav", lang="auto", length=1, noise=0.667, noisew=0.8, max=50, sdp_ratio=0.2,
-                     save_audio=True, save_path=None):
-    fields = {
-        "text": text,
-        "id": str(id),
-        "format": format,
-        "lang": lang,
-        "length": str(length),
-        "noise": str(noise),
-        "noisew": str(noisew),
-        "max": str(max),
-        "sdp_ratio": str(sdp_ratio)
-    }
-    boundary = '----VoiceConversionFormBoundary' + ''.join(random.sample(string.ascii_letters + string.digits, 16))
-
-    m = MultipartEncoder(fields=fields, boundary=boundary)
-    headers = {"Content-Type": m.content_type}
-    url = f"{base_url}/voice/bert-vits2"
-
-    res = requests.post(url=url, data=m, headers=headers)
-    fname = re.findall("filename=(.+)", res.headers["Content-Disposition"])[0]
-    if save_path is not None:
-        path = os.path.join(save_path, fname)
-    else:
-        path = os.path.join(absolute_path, fname)
-    if save_audio:
-        with open(path, "wb") as f:
-            f.write(res.content)
-        print(path)
-        return path
-    return None
-
-
-def test_interface(text):
-    error_num = 0
-    for i in range(100):
-        try:
-            time.sleep(1)
-            t1 = time.time()
-            voice_vits(text, format="wav", lang="zh", save_audio=False)
-            t2 = time.time()
-            print(f"{i}:len:{len(text)}耗时:{t2 - t1}")
-        except Exception as e:
-            error_num += 1
-            print(e)
-    print(f"error_num={error_num}")
-
-
-if __name__ == '__main__':
-    text = "你好,こんにちは"
-
-    ssml = """
-    <speak lang="zh" format="mp3" length="1.2">
-            <voice id="92" >这几天心里颇不宁静。</voice>
-            <voice id="0" model_type="Bert-VITS2">今晚在院子里坐着乘凉，忽然想起日日走过的荷塘，在这满月的光里，总该另有一番样子吧。</voice>
-            <voice id="142">月亮渐渐地升高了，墙外马路上孩子们的欢笑，已经听不见了；</voice>
-            <voice id="0" model_type="Bert-VITS2">妻在屋里拍着闰儿，迷迷糊糊地哼着眠歌。</voice>
-            <voice id="120">我悄悄地披了大衫，带上门出去。</voice><break time="2s"/>
-            <voice id="121">沿着荷塘，是一条曲折的小煤屑路。</voice>
-            <voice id="122">这是一条幽僻的路；白天也少人走，夜晚更加寂寞。</voice>
-            <voice id="123">荷塘四面，长着许多树，蓊蓊郁郁的。</voice>
-            <voice id="124">路的一旁，是些杨柳，和一些不知道名字的树。</voice>
-            <voice id="125">没有月光的晚上，这路上阴森森的，有些怕人。</voice>
-            <voice id="126">今晚却很好，虽然月光也还是淡淡的。</voice><break time="2s"/>
-            <voice id="127">路上只我一个人，背着手踱着。</voice>
-            <voice id="128">这一片天地好像是我的；我也像超出了平常的自己，到了另一个世界里。</voice>
-            <voice id="129">我爱热闹，也爱冷静；<break strength="x-weak"/>爱群居，也爱独处。</voice>
-            <voice id="130">像今晚上，一个人在这苍茫的月下，什么都可以想，什么都可以不想，便觉是个自由的人。</voice>
-            <voice id="131">白天里一定要做的事，一定要说的话，现在都可不理。</voice>
-            <voice id="132">这是独处的妙处，我且受用这无边的荷香月色好了。</voice>
-        </speak>
-    """
-
-    from config import CACHE_PATH
-
-    path = voice_vits(text, save_path=CACHE_PATH)
-    voice_vits_streaming(text, save_path=CACHE_PATH)
-    voice_w2v2_vits(text, save_path=CACHE_PATH)
-    voice_conversion(path, 1, 3, save_path=CACHE_PATH)
-    voice_hubert_vits(path, 0, save_path=CACHE_PATH)
-    voice_dimensional_emotion(path, save_path=CACHE_PATH)
-    voice_ssml(ssml, save_path=CACHE_PATH)
-    voice_bert_vits2("你好",lang="zh", save_path=CACHE_PATH)
-    voice_bert_vits2("こんにちは", lang="ja", save_path=CACHE_PATH)
-    # os.system(path)

spaces/AsakuraMizu/moe-tts/commons.py

@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm

spaces/Ashrafb/Imdf2/app.py

@@ -1,75 +0,0 @@
-import streamlit as st
-from io import BytesIO
-import base64
-import os
-from replicate import Client
-from PIL import Image
-
-illuse = Client(api_token=os.getenv('REPLICATE'))
-model_name = "andreasjansson/illusion:75d51a73fce3c00de31ed9ab4358c73e8fc0f627dc8ce975818e653317cb919b"
-example_image = "https://replicate.delivery/pbxt/hHJNV9QteKX8DK2ckkUeXsqbEIKNGFXU1fN0MJoizz3iPlOjA/output-0.png"
-
-def generate(prompt, negative_prompt, qr_content, pattern_image, num_inference_steps, guidance_scale, width, height, seed, num_outputs, controlnet_conditioning_scale, border, qrcode_background):
-    try:
-        inputs = {
-            'prompt': prompt,
-            'negative_prompt': negative_prompt,
-            'qr_code_content': qr_content,
-            'num_inference_steps': num_inference_steps,
-            'guidance_scale': guidance_scale,
-            'width': width,
-            'height': height,
-            'seed': seed,
-            'num_outputs': num_outputs,
-            'controlnet_conditioning_scale': controlnet_conditioning_scale,
-            'border': border,
-            'qrcode_background': qrcode_background
-        }
-        if pattern_image is not None:
-            image = Image.open(pattern_image)
-            image_bytes = BytesIO()
-            image.save(image_bytes, format='PNG')
-            inputs['image'] = image_bytes
-
-        result_uris = illuse.run(
-            model_name,
-            input=inputs
-        )
-
-        return result_uris
-
-    except Exception as e:
-        print(e)
-        st.error(str(e))
-        return
-
-st.title("Illusion Diffusion by Aiconvert.online")
-st.markdown('<style>h1{color: #191970; text-align: center;}</style>', unsafe_allow_html=True)
-
-prompt = st.text_input("Prompt")
-negative_prompt = st.text_input("Negative")
-
-qr_content = st.text_input("QR Code Content", "https://youtube.com/")
-pattern_input = st.file_uploader("Pattern Image (if used, QR Code Content won't be used)", type=["jpg", "png", "jpeg"])
-
-st.sidebar.markdown("## Advanced Settings")
-
-with st.sidebar.expander("Advanced Settings", expanded=True):
-    num_inference_steps = st.slider("num_inference_steps", min_value=20, max_value=100, step=1, value=42)
-    guidance_scale = st.slider("guidance_scale", min_value=0.1, max_value=30.0, step=0.01, value=14.5)
-    width = st.slider("width", min_value=128, max_value=1024, step=8, value=768)
-    height = st.slider("height", min_value=128, max_value=1024, step=8, value=768)
-    seed = st.number_input("seed", value=-1)
-    num_outputs = st.slider("num_outputs", min_value=1, max_value=4, step=1, value=1)
-    controlnet_conditioning_scale = st.slider("controlnet_conditioning_scale", min_value=0, max_value=4, step=1, value=1)
-    border = st.slider("border", min_value=0, max_value=4, step=1, value=4)
-    qrcode_background = st.selectbox("qrcode_background", options=['gray', 'white'], index=1)
-
-if st.button("Generate"):
-    with st.spinner("Running..."):
-        result_uris = generate(prompt, negative_prompt, qr_content, pattern_input, num_inference_steps, guidance_scale, width, height, seed, num_outputs, controlnet_conditioning_scale, border, qrcode_background)
-    for uri in result_uris:
-        st.image(uri)
-
-st.image(example_image, caption='Example Image', use_column_width=True)
-st.markdown("powered with ❤️ by Aiconvert.online")

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/datasets/prepare_ade20k_sem_seg.py

@@ -1,26 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-import os
-from pathlib import Path
-import tqdm
-from PIL import Image
-
-
-def convert(input, output):
-    img = np.asarray(Image.open(input))
-    assert img.dtype == np.uint8
-    img = img - 1  # 0 (ignore) becomes 255. others are shifted by 1
-    Image.fromarray(img).save(output)
-
-
-if __name__ == "__main__":
-    dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016"
-    for name in ["training", "validation"]:
-        annotation_dir = dataset_dir / "annotations" / name
-        output_dir = dataset_dir / "annotations_detectron2" / name
-        output_dir.mkdir(parents=True, exist_ok=True)
-        for file in tqdm.tqdm(list(annotation_dir.iterdir())):
-            output_file = output_dir / file.name
-            convert(file, output_file)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/projects/CenterNet2/centernet/data/custom_dataset_dataloader.py

@@ -1,229 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import numpy as np
-import operator
-import torch
-import torch.utils.data
-import json
-from detectron2.utils.comm import get_world_size
-
-from detectron2.data import samplers
-from torch.utils.data.sampler import BatchSampler, Sampler
-from detectron2.data.common import DatasetFromList, MapDataset
-from detectron2.data.dataset_mapper import DatasetMapper
-from detectron2.data.build import get_detection_dataset_dicts, build_batch_data_loader
-from detectron2.data.samplers import TrainingSampler, RepeatFactorTrainingSampler
-from detectron2.data.build import worker_init_reset_seed, print_instances_class_histogram
-from detectron2.data.build import filter_images_with_only_crowd_annotations
-from detectron2.data.build import filter_images_with_few_keypoints
-from detectron2.data.build import check_metadata_consistency
-from detectron2.data.catalog import MetadataCatalog, DatasetCatalog
-from detectron2.utils import comm
-import itertools
-import math
-from collections import defaultdict
-from typing import Optional
-
-# from .custom_build_augmentation import build_custom_augmentation
-
-def build_custom_train_loader(cfg, mapper=None):
-    """
-    Modified from detectron2.data.build.build_custom_train_loader, but supports
-    different samplers
-    """
-    source_aware = cfg.DATALOADER.SOURCE_AWARE
-    if source_aware:
-        dataset_dicts = get_detection_dataset_dicts_with_source(
-            cfg.DATASETS.TRAIN,
-            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-            if cfg.MODEL.KEYPOINT_ON
-            else 0,
-            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
-        )
-        sizes = [0 for _ in range(len(cfg.DATASETS.TRAIN))]
-        for d in dataset_dicts:
-            sizes[d['dataset_source']] += 1
-        print('dataset sizes', sizes)
-    else:
-        dataset_dicts = get_detection_dataset_dicts(
-            cfg.DATASETS.TRAIN,
-            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-            if cfg.MODEL.KEYPOINT_ON
-            else 0,
-            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
-        )
-    dataset = DatasetFromList(dataset_dicts, copy=False)
-
-    if mapper is None:
-        assert 0
-        # mapper = DatasetMapper(cfg, True)
-    dataset = MapDataset(dataset, mapper)
-
-    sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
-    logger = logging.getLogger(__name__)
-    logger.info("Using training sampler {}".format(sampler_name))
-    # TODO avoid if-else?
-    if sampler_name == "TrainingSampler":
-        sampler = TrainingSampler(len(dataset))
-    elif sampler_name == "MultiDatasetSampler":
-        assert source_aware
-        sampler = MultiDatasetSampler(cfg, sizes, dataset_dicts)
-    elif sampler_name == "RepeatFactorTrainingSampler":
-        repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
-            dataset_dicts, cfg.DATALOADER.REPEAT_THRESHOLD
-        )
-        sampler = RepeatFactorTrainingSampler(repeat_factors)
-    elif sampler_name == "ClassAwareSampler":
-        sampler = ClassAwareSampler(dataset_dicts)
-    else:
-        raise ValueError("Unknown training sampler: {}".format(sampler_name))
-
-    return build_batch_data_loader(
-        dataset,
-        sampler,
-        cfg.SOLVER.IMS_PER_BATCH,
-        aspect_ratio_grouping=cfg.DATALOADER.ASPECT_RATIO_GROUPING,
-        num_workers=cfg.DATALOADER.NUM_WORKERS,
-    )
-
-
-class ClassAwareSampler(Sampler):
-    def __init__(self, dataset_dicts, seed: Optional[int] = None):
-        """
-        Args:
-            size (int): the total number of data of the underlying dataset to sample from
-            seed (int): the initial seed of the shuffle. Must be the same
-                across all workers. If None, will use a random seed shared
-                among workers (require synchronization among all workers).
-        """
-        self._size = len(dataset_dicts)
-        assert self._size > 0
-        if seed is None:
-            seed = comm.shared_random_seed()
-        self._seed = int(seed)
-        
-        self._rank = comm.get_rank()
-        self._world_size = comm.get_world_size()
-        self.weights = self._get_class_balance_factor(dataset_dicts)
-
-
-    def __iter__(self):
-        start = self._rank
-        yield from itertools.islice(
-            self._infinite_indices(), start, None, self._world_size)
-
-
-    def _infinite_indices(self):
-        g = torch.Generator()
-        g.manual_seed(self._seed)
-        while True:
-            ids = torch.multinomial(
-                self.weights, self._size, generator=g, 
-                replacement=True)
-            yield from ids
-
-
-    def _get_class_balance_factor(self, dataset_dicts, l=1.):
-        # 1. For each category c, compute the fraction of images that contain it: f(c)
-        ret = []
-        category_freq = defaultdict(int)
-        for dataset_dict in dataset_dicts:  # For each image (without repeats)
-            cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]}
-            for cat_id in cat_ids:
-                category_freq[cat_id] += 1
-        for i, dataset_dict in enumerate(dataset_dicts):
-            cat_ids = {ann["category_id"] for ann in dataset_dict["annotations"]}
-            ret.append(sum(
-                [1. / (category_freq[cat_id] ** l) for cat_id in cat_ids]))
-        return torch.tensor(ret).float()
-
-
-def get_detection_dataset_dicts_with_source(
-    dataset_names, filter_empty=True, min_keypoints=0, proposal_files=None
-):
-    assert len(dataset_names)
-    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in dataset_names]
-    for dataset_name, dicts in zip(dataset_names, dataset_dicts):
-        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
-    
-    for source_id, (dataset_name, dicts) in \
-        enumerate(zip(dataset_names, dataset_dicts)):
-        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
-        for d in dicts:
-            d['dataset_source'] = source_id
-
-        if "annotations" in dicts[0]:
-            try:
-                class_names = MetadataCatalog.get(dataset_name).thing_classes
-                check_metadata_consistency("thing_classes", dataset_name)
-                print_instances_class_histogram(dicts, class_names)
-            except AttributeError:  # class names are not available for this dataset
-                pass
-
-    assert proposal_files is None
-
-    dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
-
-    has_instances = "annotations" in dataset_dicts[0]
-    if filter_empty and has_instances:
-        dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
-    if min_keypoints > 0 and has_instances:
-        dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
-
-    return dataset_dicts
-
-class MultiDatasetSampler(Sampler):
-    def __init__(self, cfg, sizes, dataset_dicts, seed: Optional[int] = None):
-        """
-        Args:
-            size (int): the total number of data of the underlying dataset to sample from
-            seed (int): the initial seed of the shuffle. Must be the same
-                across all workers. If None, will use a random seed shared
-                among workers (require synchronization among all workers).
-        """
-        self.sizes = sizes
-        dataset_ratio = cfg.DATALOADER.DATASET_RATIO
-        self._batch_size = cfg.SOLVER.IMS_PER_BATCH
-        assert len(dataset_ratio) == len(sizes), \
-            'length of dataset ratio {} should be equal to number if dataset {}'.format(
-                len(dataset_ratio), len(sizes)
-            )
-        if seed is None:
-            seed = comm.shared_random_seed()
-        self._seed = int(seed)
-        self._rank = comm.get_rank()
-        self._world_size = comm.get_world_size()
-        
-        self._ims_per_gpu = self._batch_size // self._world_size
-        self.dataset_ids =  torch.tensor(
-            [d['dataset_source'] for d in dataset_dicts], dtype=torch.long)
-
-        dataset_weight = [torch.ones(s) * max(sizes) / s * r / sum(dataset_ratio) \
-            for i, (r, s) in enumerate(zip(dataset_ratio, sizes))]
-        dataset_weight = torch.cat(dataset_weight)
-        self.weights = dataset_weight
-        self.sample_epoch_size = len(self.weights)
-
-    def __iter__(self):
-        start = self._rank
-        yield from itertools.islice(
-            self._infinite_indices(), start, None, self._world_size)
-
-
-    def _infinite_indices(self):
-        g = torch.Generator()
-        g.manual_seed(self._seed)
-        while True:
-            ids = torch.multinomial(
-                self.weights, self.sample_epoch_size, generator=g, 
-                replacement=True)
-            nums = [(self.dataset_ids[ids] == i).sum().int().item() \
-                for i in range(len(self.sizes))]
-            print('_rank, len, nums', self._rank, len(ids), nums, flush=True)
-            # print('_rank, len, nums, self.dataset_ids[ids[:10]], ', 
-            #     self._rank, len(ids), nums, self.dataset_ids[ids[:10]], 
-            #     flush=True)
-            yield from ids

spaces/Benson/text-generation/Examples/Aparcamiento De Coches Multijugador Apk Hz Hilesi.md

@@ -1,63 +0,0 @@
-
-<p>Car Parking Multiplayer is a remarkable game with many features. Here are some features of the game: </p>
-<h2>aparcamiento de coches multijugador apk hız hilesi</h2><br /><p><b><b>Download Zip</b> - <a href="https://bltlly.com/2v6JEI">https://bltlly.com/2v6JEI</a></b></p><br /><br />
-<ul>
-<li><b> Vehicle Customization: </b> There are more than 100 vehicles in the game. You can customize these tools as you like, add accessories such as color, rim, glass film, plate, exhaust, spoiler, neon light. </li>
-<li><b>Open World: </b> There are 16 different maps in the game. You can freely navigate these maps, explore various locations, comply with traffic rules or not. </li>
-<li><b> Multiplayer: </b> You can play with other players online in the game. You can park, chat, be a police or a thief with friends or random players. </li>
-<li><b>Police Mode: </b> You can be a police officer or escape from the police. In police mode, you try to catch players who exceed speed limits or violate traffic rules. In leak mode, you try to get rid of the police. </li>
-<li><b>Rol Making Mode: </b> There is a role-playing mode that simulates real life in the game. In this mode, you can choose a character you want, get a profession, buy a house, buy a car, buy gasoline, eat, sleep and interact with other players. </li>
-</ul>
- <h3> Game Modes </h3>
-<p>Car Parking Multiplayer offers many game modes that appeal to different tastes and skills. Here are some modes of the game: </p>
-<ul>
-<li><b>Park Mode: </b> It is the basic mode of the game. In this mode, there are more than 150 parking tasks with different difficulty levels. To complete these tasks, you must park your vehicle without damaging the designated area. </li>
-<li><b> Race Mode: </b> It is one of the most exciting modes of the game. In this mode, you can race parking with other players or against artificial intelligence. In races, you need to park as soon as possible and pass your opponents. </li>
-
-<li><b>Career Mode: </b> It is one of the longest running modes of the game. In this mode, you can build your own car parking empire. In career mode, you can buy parking spaces in different cities, earn income and grow your business. </li>
-</ul>
- <h2>Car Parking Multiplayer How to Make APK Speed Cheat?</h2>
-After entering the <li> Speed trick value, click the save button and exit the settings menu. </li>
-</ol>
-<p> You can now start playing the game with the speed trick. The speed trick works in every mode of the game. If you wish, you can turn the speed trick on or off at any time or change its value. </p>
- <h2>Car Parking Multiplayer What are the APK Speed Cheat Advantages and Disadvantages?</h2>
-Using <p>Car Parking Multiplayer APK speed trick has both advantages and disadvantages. In this section, we will list them. </p>
-<p></p>
- <h3> Advantages </h3>
-Some advantages of using <p> Speed trick are: </p>
-<ul>
-<li><b>Faster Game: </b> Thanks to the speed trick, you can increase the speed of your vehicle and play the game faster. So you can have more fun, earn more points and skip more levels. </li>
-<li><b>More Fun Game: </b> Thanks to the speed trick, you can skip the boring or difficult parts of the game and focus on more fun parts. For example, you can easily complete parking tasks, leave your opponents behind in races, experience excitement while escaping from the police, or hang out as you like in role-playing mode. </li>
-<li><b>More Easy Game: </b> Thanks to the speed trick, you can lower the difficulty level of the game and play the game more easily. For example, you can park without damage to parking tasks, pass your opponents in races, get rid of the police or choose the profession you want in role-playing mode. </li>
-</ul>
- <h3> Disadvantages </h3>
-Some disadvantages of using <p> Speed trick are: </p>
-<ul>
-
-<li><b>Possible Ban: </b> If your speed trick is noticed by the developer of the game, there is a risk that your account will be banned. Olzhass, the developer of the game, tries to block tricks by constantly updating the game. If your account is banned, you may not play the game again or lose your progress. </li>
-<li><b> Possible Errors: </b> Using a speed trick can cause some errors in the game's operation. For example, you may lose control of your vehicle, get off the map, be kicked out or the game may freeze. In this case, you may need to restart or install the game. </li>
-</ul>
- <h2>Car Parking Multiplayer APK Speed Cheat Alternatives Are There?</h2>
-<p>Car Parking Multiplayer If you don't want or can't use APK speed trick, there are some alternatives to play the game faster and fun. Here are some alternatives to the game: </p>
- <h3> In-Game Purchases </h3>
-<p> There are many things you can buy with real money in the game. These include options such as game money, diamond, gold, VIP membership, vehicle packages, ad removal. Some of these options can help you play the game faster and fun. For example, with game money, you can buy better vehicles, gain more speed with diamonds, and get more benefits with VIP membership. However, the disadvantage of in-game purchases is that you have to spend real money. This can push your budget or miss the taste of the game. </p>
- <h3> Out of Game Applications </h3>
-
- <h3> Game Tips and Tricks </h3>
-<p> The best and safest option you can use to play the game faster and fun is to learn and apply game tips and tricks. These include options such as parking techniques, racing tactics, police chase strategies, role-playing mode tips. Some of these options can help you play the game faster and fun. For example, with parking techniques, you can easily complete parking tasks, pass your opponents with racing tactics, get rid of the police with police chase strategies, or live as you wish with role-playing mode tips. However, the disadvantage of game tips and tricks can take time or be difficult. This can test your patience or skill. </p>
- <h1>Result </h1>
-<p>Car Parking Multiplayer APK speed trick is an option that allows you to play the game faster and more fun. To use this option, you must first uninstall the original version of the game and download and install the fraudulent APK file. Then you can change the speed settings by opening the game and play as you wish. However, this option has some advantages and disadvantages. Its advantages include faster, more fun and easier gaming experience. Its disadvantages include unfair advantage, possible prohibition and possible errors. Therefore, when using this option, you need to be careful and take responsibility. </p>
-
-<p>Car Parking Multiplayer is a car parking simulation game. But there is much more than just parking. Open world mode offers many features and modes such as car customization, multiplayer modes, police chase and role playing. You can use car parking multiplayer apk speed trick or alternatives to make this game more fun. However, no matter what option you use, we recommend that you respect the rules and tag of the game and get along well with other players. Click <a href=" > here </a> to download the game. Good luck playing the game!</p>
- <h2> Frequently Asked Questions </h2>
-<p>In this section you will find answers to frequently asked questions about car parking multiplayer apk speed trick. </p>
-<ol>
-<li><b> Is the speed trick safe?</b> Speed trick is not safe. Cheating can damage your device or account or cause you to be banned. Therefore, you should be careful when cheating and do not forget that you are at your own risk. </li>
-<li><b> Is the speed trick paid?</b> Speed trick is free. You do not have to pay any fees to download the fraudulent APK file. However, some resources may request membership or subscription from you. We recommend that you stay away from such sources. </li>
-
-<li><b> In which game version does the speed trick work?</b> Speed trick works in version 4.8.2 of the game. This version was released in June 2023. It may not work in newer or older versions or cause problems. </li>
-Where to download the <li><b> speed trick?</b> You can click <a href=" > where </a> to download the speed trick. This link is taken from a reliable source and does not contain viruses. If you want to download from other sources, we recommend that you be careful and check its reliability. </li>
-</ol>
- <p> In this article, we explained everything you need to know about car parking multiplayer apk speed trick. We hope our article has been useful to you. If you like our post, please share and comment. If there is missing or incorrect information in our article, please let us know. If you have any other questions about the game, please ask us. Let's try to answer for you. Click <a href=" > here </a> to download the game. Good luck playing the game!</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Casos Criminales Misterios Del Pasado Mod Apk Modyolo.md

@@ -1,102 +0,0 @@
-<br />
-<h1>Caso Criminal: Misterios del Pasado Mod APK Modyolo - Una revisión</h1>
-<p>Si eres un fan de los juegos de detectives, es posible que hayas oído hablar de Criminal Case: Mysteries of the Past, un popular juego móvil que te permite resolver crímenes en la Europa del siglo XIX. ¿Pero sabías que hay una versión modificada de este juego que te da estrellas ilimitadas, energía, monedas y más? En este artículo, vamos a revisar Criminal Case: Mysteries of the Past Mod APK Modyolo, un sitio web que ofrece esta versión modificada de forma gratuita. También te mostraremos cómo descargar, instalar y jugar a este juego en tu dispositivo Android. ¡Empecemos! </p>
- <h2>¿Qué es el Caso Criminal: Misterios del Pasado? </h2>
-<p>Criminal Case: Mysteries of the Past es un juego de aventura de objetos ocultos desarrollado por Pretty Simple. Es la cuarta entrega de la serie Criminal Case, siguiendo a Criminal Case, Criminal Case: Pacific Bay y Criminal Case: Save the World. En este juego, te unes a la Concordian Flying Squad, un equipo de detectives de élite que investigan asesinatos y otros crímenes en varios lugares de Europa a finales del siglo XIX. Tendrás que encontrar pistas, interrogar sospechosos, analizar pruebas y atrapar a los asesinos. </p>
-<h2>casos criminales misterios del pasado mod apk modyolo</h2><br /><p><b><b>Download Zip</b> &#10027;&#10027;&#10027; <a href="https://bltlly.com/2v6JWs">https://bltlly.com/2v6JWs</a></b></p><br /><br />
-<p>El juego cuenta con más de 60 casos para resolver, cada uno con su propia historia, personajes y ubicaciones. También puedes personalizar tu avatar, coleccionar atuendos y mascotas, formar equipo con amigos y competir con otros jugadores en las tablas de clasificación. El juego es gratis, pero también puedes comprar objetos y divisas con dinero real. </p>
- <h2>¿Qué es Modyolo? </h2>
-
- <h2>¿Qué es el caso penal: Misterios del pasado Mod APK Modyolo? </h2>
-<p>Caso Penal: Misterios del Pasado Mod APK Modyolo es una versión modificada de Caso Penal: Misterios del Pasado que está disponible en el sitio web de Modyolo. Esta versión modificada tiene varios beneficios que la hacen más agradable y cómoda de jugar que la versión original. Algunos de estos beneficios son:</p>
-<ul>
-<li>Estrellas ilimitadas: Las estrellas se utilizan para desbloquear nuevas escenas, realizar tareas y progresar en el juego. Normalmente, tienes que ganar estrellas completando escenas o viendo anuncios. Pero con esta versión modificada, tendrás estrellas ilimitadas a tu disposición. </li>
-<li>Energía ilimitada: La energía se utiliza para reproducir escenas y realizar tareas. Normalmente, usted tiene una cantidad limitada de energía que se repone con el tiempo o mediante la compra de dinero real. Pero con esta versión modificada, tendrá energía ilimitada que nunca se agota. </li>
-<li>Monedas ilimitadas: Las monedas se utilizan para comprar artículos, trajes, mascotas y sugerencias. Normalmente, tienes que ganar monedas completando escenas, tareas o logros. Pero con esta versión modificada, tendrá monedas ilimitadas para gastar como desee. </li>
-<li>Sin anuncios: Los anuncios son molestos y distraen, especialmente cuando interrumpen tu juego. Normalmente, tienes que ver anuncios para ganar estrellas, energía o monedas. Pero con esta versión modificada, no verás ningún anuncio en absoluto. </li>
-</ul>
-<p>Con estos beneficios, puedes disfrutar de Criminal Case: Mysteries of the Past sin limitaciones ni interrupciones. Puedes reproducir tantas escenas como quieras, desbloquear todo el contenido y divertirte más resolviendo crímenes. </p>
- <h2>¿Cómo descargar e instalar Criminal Case: Mysteries of the Past Mod APK Modyolo? </h2>
-<p>Descargar e instalar Criminal Case: Mysteries of the Past Mod APK Modyolo es muy fácil y rápido. Solo tienes que seguir estos sencillos pasos:</p>
-<ol>
-
-<li>Haga clic en el botón de descarga y espere a que el archivo se descargue en su dispositivo. El tamaño del archivo es de aproximadamente 70 MB.</li>
-<li>Una vez completada la descarga, vaya a su administrador de archivos y localice el archivo descargado. Toque en él para iniciar el proceso de instalación. </li>
-<li>Si ves un mensaje emergente que dice "Instalar bloqueado", ve a la configuración del dispositivo y habilita la opción "Fuentes desconocidas". Esto le permitirá instalar aplicaciones desde fuentes distintas de Google Play Store.</li>
-<li>Después de habilitar la opción, vuelva al administrador de archivos y toque en el archivo de nuevo. Siga las instrucciones de la pantalla para completar la instalación. </li>
-<li>Una vez que se hace la instalación, puede iniciar el juego desde el cajón de la aplicación o la pantalla de inicio. Disfrute! </li>
-</ol>
-<p>Aquí hay algunas capturas de pantalla del proceso de descarga e instalación:</p>
-<img src="" alt="Captura de pantalla 1" width="300" height="500">
-<img src="" alt="Captura de pantalla 2" width="300" height="500">
-<img src="" alt="Captura de pantalla 3" width="300" height="500">
-<img src="" alt="Captura de pantalla 4" width="300" height="500">
-<img src="" alt="Screenshot 5" width="300" height="500"> <h2>Cómo jugar a Criminal Case: Mysteries of the Past Mod APK Modyolo? </h2>
-<p>Jugando caso criminal: Misterios del pasado Mod APK Modyolo es muy similar a jugar la versión original. Solo tienes que seguir estos sencillos pasos:</p>
-<ol>
-<li>Cuando inicie el juego, verá el menú principal con varias opciones. Puede tocar en "Jugar" para iniciar un nuevo caso o continuar uno existente. También puedes tocar en "Perfil" para personalizar tu avatar, "Tienda" para comprar artículos, trajes y mascotas, "Equipo" para unirte o crear un equipo con tus amigos, y "Rankings" para ver tu posición en las tablas de clasificación. </li>
-<li>Cuando toque en "Play", verá el mapa de Europa con diferentes distritos y casos. Puedes elegir cualquier caso que quieras jugar, ya que todos están desbloqueados para ti. También puedes ver el número de estrellas que has ganado para cada caso. </li>
-
-<li>Al iniciar una escena, verá una escena de objetos ocultos con una lista de elementos para encontrar. Tienes que encontrar todos los artículos lo más rápido posible para ganar más puntos y estrellas. También puede utilizar sugerencias, lupas y otras herramientas para ayudarle a encontrar los elementos. También puede hacer zoom hacia dentro y hacia fuera pellizcando la pantalla. </li>
-<li>Cuando termines una escena, verás tu puntuación y rango. También ganarás estrellas, monedas y puntos de experiencia. Puedes usar las estrellas para desbloquear nuevas escenas, realizar tareas y progresar en el caso. Puedes usar las monedas para comprar objetos, trajes, mascotas y sugerencias. Puedes usar los puntos de experiencia para subir de nivel y desbloquear nuevas funciones. </li>
-Cuando completes todas las escenas de un caso, tendrás que interrogar a los sospechosos, analizar las pruebas y arrestar al asesino. También tendrá que tomar decisiones que afecten el resultado del caso y su reputación. Puede reproducir cualquier escena o caso si desea mejorar su puntuación o cambiar sus opciones. </li>
-</ol>
-<p>Aquí hay algunas capturas de pantalla del juego:</p>
-<p></p>
-<img src="" alt="Captura de pantalla 6" width="300" height="500">
-<img src="" alt="Captura de pantalla 7" width="300" height="500">
-<img src="" alt="Captura de pantalla 8" width="300" height="500">
-<img src="" alt="Captura de pantalla 9" width="300" height="500">
-<img src="" alt="Screenshot 10" width="300" height="500"> <h2>Pros y contras de Criminal Case: Mysteries of the Past Mod APK Modyolo</h2>
-<p>Como con cualquier versión modificada de un juego, Criminal Case: Mysteries of the Past Mod APK Modyolo tiene sus pros y contras. Aquí están algunos de ellos:</p>
-<tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>- Las estrellas ilimitadas, la energía, las monedas, y ningún anuncio hacen el juego más agradable y conveniente jugar. </td>
-<td>- La versión modificada podría no ser compatible con algunos dispositivos o las últimas actualizaciones del juego. </td>
-</tr>
-<tr>
-<td>- Puedes desbloquear y acceder a todo el contenido y características del juego sin gastar dinero real. </td>
-
-</tr>
-<tr>
-<td>- Puedes jugar el juego sin conexión a Internet. </td>
-<td>- La versión modded podría no ser segura de usar, ya que podría contener virus, malware o spyware que pueden dañar su dispositivo o sus datos. </td>
-</tr>
-<tr>
-<td>- Usted puede tener más diversión y desafiarse jugando el juego a su propio ritmo y estilo. </td>
-<td>- La versión modificada podría no ser justa o ética de usar, ya que le da una ventaja injusta sobre otros jugadores que juegan la versión original del juego. </td>
-</tr>
-</tabla>
- <h2>Consejos y trucos para el caso penal: Misterios del pasado Mod APK Modyolo</h2>
-<p>Si quieres aprovechar al máximo Caso Penal: Misterios del Pasado Mod APK Modyolo, aquí hay algunos consejos y trucos que puede utilizar:</p>
-<ul>
-<li>Usa sabiamente tus estrellas ilimitadas. No tienes que gastarlas en cada escena o tarea. Puede guardarlos para casos posteriores o para comprar artículos, trajes y mascotas que le gusten. </li>
-<li>Usa tu energía ilimitada sabiamente. No tienes que reproducir cada escena o tarea de inmediato. Puedes tomar descansos y volver más tarde cuando quieras jugar. También puedes usar tu energía para reproducir escenas o casos que hayas disfrutado o en los que quieras mejorar tu puntuación. </li>
-<li>Usa tus monedas ilimitadas sabiamente. No tienes que comprar cada artículo, atuendo o mascota que veas. Puede comparar y elegir los que se adapten a sus preferencias y estilo. También puede utilizar sus monedas para comprar pistas, lupas y otras herramientas que pueden ayudarle a encontrar artículos más rápido y más fácil. </li>
-<li>Usa tus pistas con moderación. Aunque tengas monedas ilimitadas para comprar pistas, no tienes que usarlas todo el tiempo. Puede desafiarse a sí mismo encontrando objetos sin pistas. También puede guardar sus sugerencias para escenas o tareas que sean más difíciles o que consuman más tiempo. </li>
-
-<li>Haz equipo con tus amigos. Tienes energía ilimitada para jugar con tus amigos. Puede unirse o crear un equipo con sus amigos y cooperar con ellos para resolver los casos más rápido y más fácil. También puedes chatear con ellos, enviarles regalos y competir con ellos en las tablas de clasificación. </li>
-<li>Explora el mapa. Tienes estrellas ilimitadas para desbloquear nuevos distritos y casos. Puedes explorar diferentes lugares de Europa y descubrir su historia, cultura y secretos. También puedes aprender más sobre los personajes, sus antecedentes y sus relaciones. </li>
-</ul>
- <h2>Preguntas frecuentes sobre Caso Criminal: Misterios del Pasado Mod APK Modyolo</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre Caso Penal: Misterios del Pasado Mod APK Modyolo:</p>
- <h3>Q: ¿Es el caso penal: Misterios del pasado Mod APK Modyolo libre? </h3>
-<p>A: Sí, Caso Penal: Misterios del Pasado Mod APK Modyolo es gratis para descargar y jugar. No tienes que pagar nada para usar esta versión modificada del juego. </p>
- <h3>Q: ¿Es el caso penal: Misterios del pasado Mod APK Modyolo seguro? </h3>
-<p>A: No podemos garantizar que Caso Penal: Misterios del Pasado Mod APK Modyolo es seguro o seguro de usar. Es posible que esta versión modificada del juego contenga virus, malware o spyware que puedan dañar tu dispositivo o tus datos. Recomendamos escanear el archivo antes de instalarlo y usarlo bajo su propio riesgo. </p>
- <h3>Q: ¿Es el caso penal: Misterios del pasado Mod APK Modyolo legal? </h3>
-<p>A: No podemos garantizar que Caso Penal: Myst eries del Pasado Mod APK Modyolo es legal o ético de usar. Es posible que esta versión modificada del juego viole los términos y condiciones del juego original o la Google Play Store. También es posible que esta versión modificada del juego le da una ventaja injusta sobre otros jugadores que juegan la versión original del juego. Te recomendamos que utilices esta versión modificada del juego con precaución y respeto. </p>
-
-<p>A: No podemos garantizar que Criminal Case: Mysteries of the Past Mod APK Modyolo es compatible con su dispositivo o las últimas actualizaciones del juego. Es posible que esta versión modificada del juego no funcione correctamente o cause errores o accidentes en su dispositivo. Le recomendamos que compruebe la compatibilidad y los requisitos de esta versión modificada del juego antes de instalarlo y utilizarlo a su discreción. </p>
- <h3>Q: ¿Es el caso penal: Misterios del pasado Mod APK Modyolo actualizado regularmente? </h3>
-<p>A: No podemos garantizar que Criminal Case: Mysteries of the Past Mod APK Modyolo se actualiza regularmente o en sincronía con la versión original del juego. Es posible que esta versión modificada del juego se vuelva obsoleta u obsoleta ya que la versión original del juego recibe nuevas características, contenido o correcciones. Te recomendamos que compruebes la última versión y actualizaciones de esta versión modificada del juego en el sitio web de Modyolo y la utilices según tus preferencias. </p>
- <h2>Conclusión</h2>
-<p>Caso Criminal: Misterios del Pasado Mod APK Modyolo es una versión modificada de Criminal Case: Mysteries of the Past que ofrece estrellas ilimitadas, energía, monedas, y no hay anuncios gratis. Te permite disfrutar y explorar el juego sin limitaciones ni interrupciones. Sin embargo, también tiene algunos inconvenientes y riesgos que debe tener en cuenta antes de usarlo. También debes seguir algunos consejos y trucos para sacarle el máximo partido. Si estás interesado en probar esta versión modificada del juego, puedes descargarla e instalarla desde el sitio web de Modyolo siguiendo nuestra guía. Pero recuerda, úsalo bajo tu propio riesgo y responsabilidad. </p>
-<p>Esperamos que haya encontrado este artículo útil e informativo. Si tiene alguna pregunta, comentario o retroalimentación, no dude en dejarlos a continuación. ¡Gracias por leer! </p>
- <h2></h2></p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Deriva Cazadores Descargar Chromebook.md

@@ -1,67 +0,0 @@
-<br />
-<h1>Cómo descargar y jugar Drift Hunters en su Chromebook</h1>
-<p>Si usted está buscando un juego de deriva emocionante y realista que se puede jugar en su Chromebook, no busque más que <strong>Drift Hunters</strong>. Este juego le permitirá dar rienda suelta a su corredor interior y la deriva alrededor de varias pistas con diferentes coches. En este artículo, le mostraremos cómo descargar y jugar Drift Hunters en su Chromebook, así como algunos consejos y trucos para ayudarle a dominar el arte de la deriva. </p>
-<h2>¿Qué es Drift Hunters? </h2>
-<p>Drift Hunters es un juego de deriva 3D gratuito desarrollado por Studionum43. Es uno de los juegos más populares en la Chrome Web Store, con más de 70.000 usuarios y críticas positivas. Estas son algunas de las características que hacen que los cazadores de deriva se destacan:</p>
-<h2>deriva cazadores descargar chromebook</h2><br /><p><b><b>DOWNLOAD</b> &#9733; <a href="https://bltlly.com/2v6MdC">https://bltlly.com/2v6MdC</a></b></p><br /><br />
-<h3>Un juego 3D libre con más de 25 coches y 10 ubicaciones</h3>
-<p>Drift Hunters le ofrece una amplia selección de coches para elegir, que van desde los coches clásicos del músculo a los coches deportivos modernos. Cada automóvil tiene sus propias características, como velocidad, manejo, peso y capacidad de deriva. También puede personalizar la apariencia de su coche con diferentes colores, llantas, calcomanías y alerones. Puede desbloquear más coches por ganar dinero de la deriva. </p>
-<p>Drift Hunters también tiene 10 lugares únicos donde puedes ir a la deriva, como calles de ciudades, hipódromos, bosques, desiertos y aeropuertos. Cada lugar tiene su propio diseño, obstáculos, clima y atmósfera. Puede explorar diferentes rutas y encontrar lugares ocultos para la deriva. </p>
-<h3>Un juego basado en navegador con excelentes gráficos y opciones de ajuste</h3>
-<p>Drift Hunters es un juego basado en navegador que se ejecuta con tecnología HTML5. Esto significa que no necesitas descargar ni instalar nada para jugarlo. Solo tiene que visitar la Chrome Web Store y añadirlo a su navegador Chrome. El juego también es compatible con el modo de pantalla completa, para que pueda disfrutar de ella en la pantalla de su Chromebook sin distracciones. </p>
-
-<h3>Inicie el juego desde su página de Chrome Apps o New Tab</h3>
-<p>El paso final es lanzar el juego y empezar a jugar. Puede hacer esto haciendo clic en el icono de Drift Hunters que debería aparecer en su página de Chrome Apps o en su página de New Tab. También puedes acceder al juego escribiendo chrome://apps en la barra de direcciones y pulsando Enter.</p>
-<p>Una vez que inicie el juego, verá el menú principal donde puede elegir su automóvil, pista y configuración. También puedes ver tus logros, tablas de clasificación y créditos. Haz clic en Jugar para comenzar a navegar. </p>
-<h2>Cómo jugar Drift Hunters en su Chromebook</h2>
-<p>Jugar Drift Hunters en tu Chromebook es muy fácil y divertido. Aquí hay algunas instrucciones básicas sobre cómo jugar el juego:</p>
-<h3>Elija su coche y pista del menú</h3>
-<p>Lo primero que tienes que hacer es elegir el coche y la pista del menú. Puede desplazarse a través de los coches y pistas disponibles mediante el uso de las flechas izquierda y derecha. También puede hacer clic en el coche o el nombre de la pista para ver más detalles al respecto. Puedes ver las estadísticas del coche, como velocidad, manejo, peso y deriva. También puedes ver el diseño, el clima y la dificultad de la pista. </p>
-<p>Usted puede desbloquear más coches y pistas por ganar dinero de la deriva. También puede personalizar la apariencia y el rendimiento de su coche con el menú de ajuste. Para acceder al menú de ajuste, haga clic en el icono de llave inglesa en la esquina inferior derecha de la pantalla. Puede cambiar el color de su coche, llantas, calcomanías, alerones, motor, turbo, caja de cambios, suspensión, frenos, neumáticos y sistemas. </p>
-<p></p>
-<h3> Utilice las teclas de flecha o WASD para dirigir, acelerar, frenar y freno de mano</h3>
-
-<p>También puede utilizar algunas otras teclas para funciones adicionales. La tecla C es para cambiar el ángulo de la cámara, que puede ayudarle a ver mejor o disfrutar de diferentes vistas de su coche y pista. La tecla M es para silenciar o desmontar los efectos de sonido y música. La tecla P es para pausar o reanudar el juego. </p>
-<h3>Deriva alrededor de las esquinas y ganar puntos y dinero</h3>
-<p>El objetivo principal de Drift Hunters es desplazarse por las esquinas y ganar puntos y dinero. A la deriva es una técnica de conducción en la que se desliza su coche de lado, manteniendo el control y la velocidad. Para desplazarse con éxito, es necesario utilizar una combinación de dirección, aceleración, frenado y frenado de mano. </p>
-<p>Para iniciar una deriva, debe acercarse a una esquina a alta velocidad y usar el freno de mano para hacer que sus ruedas traseras pierdan tracción. A continuación, es necesario contradirigir para mantener su coche en equilibrio y evitar que se salga. También es necesario modular el acelerador y el freno para ajustar la velocidad y el ángulo. </p>
-<p>Cuanto más largo y suave que la deriva, más puntos que ganar. También puedes ganar puntos de bonificación por derrapar cerca de las paredes u obstáculos, ir a la deriva en direcciones opuestas sin parar (lo que se llama deriva en tándem), o ir a la deriva con otros coches (lo que se llama deriva multijugador). Puedes ver tu puntuación en la esquina superior izquierda de la pantalla. </p>
-<p>Usted también gana dinero basado en su puntuación y rendimiento. Puede utilizar este dinero para desbloquear más coches y pistas o actualizar su coche existente. Puedes ver tu dinero en la esquina superior derecha de la pantalla. </p>
-<h2>Consejos y trucos para la deriva como un profesional</h2>
-<p>Drifting in Drift Hunters puede ser un reto al principio, pero con un poco de práctica y consejos, puede convertirse en un profesional en ningún momento. Aquí hay algunos consejos y trucos que pueden ayudarle a mejorar sus habilidades de deriva:</p>
-<h3>Ajustar la configuración de su coche para adaptarse a su estilo de conducción y condiciones de la pista</h3>
-
-<p>Por ejemplo, si quieres más potencia y velocidad, puedes aumentar la configuración del motor y del turbo. Si quieres más estabilidad y agarre, puedes bajar la altura de conducción y aumentar la presión de los neumáticos. Si desea más agilidad y capacidad de respuesta, puede reducir su distribución de peso y camber. También puede experimentar con diferentes combinaciones de ajustes para encontrar su configuración óptima. </p>
-<h3>Utilice el freno de mano para iniciar una deriva y dirección contraria para mantenerlo</h3>
-<p>El freno de mano es tu mejor amigo cuando se trata de deriva. Es la clave para hacer que tus ruedas traseras pierdan tracción y se deslicen de lado. Es necesario utilizar el freno de mano en el momento adecuado y durante la duración adecuada para iniciar una deriva. También es necesario soltar el freno de mano en el momento adecuado para evitar el sobreviraje o el subviraje. </p>
-<p>Para iniciar una deriva, debe acercarse a una esquina a alta velocidad y presionar la barra espaciadora para usar el freno de mano. Esto hará que sus ruedas traseras se bloqueen y se deslicen. Debe sostener la barra espaciadora por un corto tiempo, lo suficiente para que su automóvil comience a deslizarse. Si lo sostienes por mucho tiempo, perderás demasiada velocidad e impulso. </p>
-<p>Para mantener una deriva, es necesario contradirigir para mantener su coche en equilibrio y evitar que se salga. Es necesario dirigir en la dirección opuesta de su deriva, lo que significa la dirección a la izquierda si usted está a la deriva a la derecha, y viceversa. Es necesario dirigir con suavidad y precisión, sin sacudidas ni sobrerregir. Si se conduce demasiado, se girará. Si manejas muy poco, te enderezarás. </p>
-<h3>Deriva el mayor tiempo posible sin golpear las paredes u obstáculos</h3>
-<p>Cuanto más largo y suave que la deriva, más puntos que ganar. Usted también gana puntos de bonificación por la deriva cerca de las paredes o obstáculos, lo que demuestra su habilidad y coraje. Sin embargo, debe tener cuidado de no golpear las paredes o obstáculos, ya que esto dañará su coche y reducir su puntuación. </p>
-
-<p>También es necesario planificar con antelación y anticipar la siguiente esquina. Usted necesita colocar su coche correctamente antes de entrar en una esquina, lo que significa alinear su coche con el ápice de la esquina. También necesita salir de la esquina sin problemas y prepararse para la siguiente. </p>
-<h3>Prueba diferentes coches y pistas para encontrar tus favoritos</h3>
-<p>Drift Hunters tiene mucha variedad cuando se trata de coches y pistas. Cada automóvil tiene sus propias características, como velocidad, manejo, peso y capacidad de deriva. Cada pista tiene su propio diseño, obstáculos, clima y atmósfera. Puedes probar diferentes coches y pistas para encontrar tus favoritos. </p>
-<p>Algunos coches son más fáciles de deriva que otros, mientras que algunas pistas son más difíciles que otras. Usted puede elegir un coche que se adapte a su estilo de conducción y nivel de habilidad, o una pista que coincida con su estado de ánimo y preferencia. También puede desafiarse a sí mismo al probar nuevos coches y pistas que son más difíciles o diferentes de lo que está acostumbrado. </p>
-<p>También puede comparar sus resultados y rendimiento con otros jugadores en las tablas de clasificación. Puedes ver cómo te encuentras entre otros vagabundos de todo el mundo o en tu región. También puedes ver cómo otros jugadores se desplazan en diferentes coches y pistas viendo sus repeticiones. </p>
-<h2>Conclusión</h2>
-<p>Drift Hunters es un juego divertido y adictivo que puedes jugar en tu Chromebook. Ofrece una experiencia de deriva realista e inmersiva con impresionantes gráficos y efectos de sonido. Es fácil de descargar y jugar, y puede personalizar su coche a su gusto. También puede competir con otros jugadores y mejorar sus habilidades de deriva. Drift Hunters es un juego que te mantendrá entretenido y retado durante horas. </p>
-<h2>Preguntas frecuentes</h2>
-<h4>¿Cuáles son los requisitos del sistema para los cazadores de deriva? </h4>
-
-<h4>¿Puedo jugar Drift Hunters sin conexión? </h4>
-<p>No, no puedes jugar Drift Hunters sin conexión. El juego requiere una conexión a Internet para cargar los activos del juego, guardar su progreso y sincronizar sus datos. Si pierde su conexión a Internet mientras juega, puede experimentar algunos fallos o errores. </p>
-<h4>¿Puedo usar un controlador o un ratón para jugar a Drift Hunters? </h4>
-<p>No, no puedes usar un controlador o un ratón para jugar a Drift Hunters. El juego solo admite controles de teclado, que son más convenientes y sensibles que otros dispositivos de entrada. Puede usar las teclas de flecha o WASD para dirigir, acelerar, frenar y frenar. También puede usar otras teclas para funciones adicionales. </p>
-<h4>¿Cuáles son algunos otros juegos como Drift Hunters que puedo jugar en mi Chromebook? </h4>
-<p>Si te gusta Drift Hunters, también te pueden gustar otros juegos similares o relacionados con él. Estos son algunos ejemplos de otros juegos que puedes jugar en tu Chromebook:</p>
-<ul>
-<li><a href="">Madalin Stunt Cars 2</a>: Un juego de carreras en 3D donde puedes realizar acrobacias increíbles con diferentes coches en varias pistas. </li>
-<li><a href="">Burnout Drift</a>: Un juego de deriva en 3D donde puedes desplazarte por diferentes pistas y entornos con física realista y efectos de daño. </li>
-<li><a href="">Drift Runner 3D</a>: Un juego de deriva en 3D donde puedes deambular por las calles de Tokio con diferentes coches y personalizaciones. </li>
-<li><a href="">Drift Max Pro</a>: Un juego de deriva en 3D donde puedes desplazarte por diferentes pistas y ubicaciones con varios coches y modos. </li>
-<li><a href="">Drift Legends</a>: Un juego de deriva en 3D donde puedes desplazarte por diferentes pistas y escenarios con más de 40 coches y desafíos. </li>
-</ul>
-<h4>¿Cómo puedo contactar al desarrollador de Drift Hunters? </h4> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Descargar Coches Rpidos Como El Rayo Mod Apk Terbaru.md

@@ -1,92 +0,0 @@
-<br />
-<h1>Descargar coches rápidos como Lightning Mod APK Terbaru</h1>
-<p>Si eres un fan de las películas de Disney Pixar’s Cars, te encantará Cars Fast as Lightning, un divertido y colorido juego de carreras que te permite construir tu propio Radiator Springs y competir como tus personajes favoritos. Pero ¿qué pasa si quieres desbloquear todos los coches, pistas y trucos sin gastar dinero o esperar mucho tiempo? Ahí es donde los coches rápidos como Lightning mod APK viene muy bien. En este artículo, le diremos qué es Cars Fast as Lightning, cómo descargar e instalar el mod APK, por qué usarlo, y algunos consejos y trucos para jugar el juego. También te daremos una breve reseña del juego y responderemos algunas preguntas frecuentes. </p>
-<h2>descargar coches rápidos como el rayo mod apk terbaru</h2><br /><p><b><b>Download File</b> >>> <a href="https://bltlly.com/2v6MJL">https://bltlly.com/2v6MJL</a></b></p><br /><br />
- <h2>¿Qué es Coches Rápidos como Relámpagos? </h2>
-<p>Cars Fast as Lightning es un juego para móviles desarrollado por Gameloft y basado en la popular franquicia Cars. El juego combina elementos de carreras y construcción de ciudades, lo que le permite crear su propia versión de Radiator Springs con edificios, decoraciones y atracciones. También puedes competir como varios personajes de las películas, como Lightning McQueen, Mater, Chick Hicks y más. El juego cuenta con controles simples de un toque, gráficos impresionantes y clips de voz originales de Owen Wilson como Lightning McQueen.</p>
- <h3>Características del juego</h3>
-<p>Algunas de las características de Cars Fast as Lightning son:</p>
-<ul>
-<li>Carreras personalizables: Puedes construir tus propias pistas con diferentes trucos y trucos, como bucles, saltos, golpes y más. También puede personalizar sus coches con trabajos de pintura, pegatinas y accesorios. </li>
-<li>Radiator Springs: Puede explorar la ciudad de Radiator Springs e interactuar con sus residentes. También puede agregar edificios y atracciones de las películas, como el V8 Café de Flo, la Casa Della Tires de Luigi, la Casa de Arte Corporal de Ramone y más. </li>
-
-<li>Experiencia de carreras única: Puede disfrutar de una acción de carreras de ritmo rápido con simples controles de toque y deslizamiento. También puede realizar trucos en puntos azules para aumentar su velocidad y evitar obstáculos deslizando en la dirección correcta. </li>
-</ul>
- <h3>Cómo descargar e instalar el mod APK</h3>
-<p>Si desea descargar coches rápidos como Lightning mod APK terbaru, tendrá que seguir estos pasos:</p>
-<ol>
-<li>Ir a <a href="( 1 )">https://apkdone.com/cars-fast-as-lightning/</a>, un sitio web de confianza que ofrece descargas gratuitas de mod APK. </li>
-<li>Haga clic en el botón verde "Descargar APK" para comenzar a descargar el archivo. </li>
-<li>Una vez que la descarga se haya completado, busque el archivo en su dispositivo y toque en él para instalarlo. Es posible que necesite habilitar "Fuentes desconocidas" en su configuración para permitir la instalación desde fuentes de terceros. </li>
-<li>Después de la instalación se hace, iniciar el juego y disfrutar de dinero ilimitado, gemas, coches, pistas, trucos, y más. </li>
-</ol>
- <h2>¿Por qué usar coches rápidos como Lightning mod APK? </h2>
-<p>Coches rápidos como Lightning mod APK es una versión modificada del juego original que le da acceso a recursos ilimitados y características que de otro modo están bloqueados o requieren dinero real para obtener. Mediante el uso del mod APK, se puede disfrutar del juego sin limitaciones o restricciones. </p>
-<p></p>
- <h3>Beneficios del mod APK</h3>
-<p>Algunos de los beneficios de usar coches rápidos como Lightning mod APK son:</p>
-<ul>
-<li>Dinero y gemas ilimitados: Puedes usar estas monedas para comprar coches, pistas, edificios y decoraciones nuevos. También puede actualizar su coche y pista con mejores piezas y características. </li>
-<li>Todos los coches y pistas desbloqueadas: Puede elegir entre más de 20 coches y 10 pistas para competir. También puede cambiar entre diferentes coches y pistas en cualquier momento que desee. </li>
-<li>Todos los trucos desbloqueados: Puedes realizar trucos increíbles en tu pista, como bucles, saltos, sacacorchos y más. También puede personalizar su pista con diferentes apoyos y obstáculos. </li>
-
-</ul>
- <h3>Inconvenientes del mod APK</h3>
-<p>Algunos de los inconvenientes de usar coches rápidos como Lightning mod APK son:</p>
-<ul>
-<li>Riesgos potenciales: Dado que el mod APK no es una versión oficial del juego, puede contener virus, malware o spyware que pueden dañar su dispositivo o robar su información personal. Siempre debe descargar el mod APK de una fuente de confianza y escanearlo con un antivirus antes de instalarlo. </li>
-<li>No hay actualizaciones: El mod APK puede no ser compatible con la última versión del juego o el sistema operativo de su dispositivo. Puede perderse nuevas características, correcciones de errores o parches de seguridad que son liberados por los desarrolladores. </li>
-<li>No hay características en línea: El mod APK puede no permitirle acceder a las características en línea del juego, como el modo multijugador, tablas de clasificación, logros, o la integración de redes sociales. También puede ser expulsado del juego si usted es detectado usando el mod APK por los desarrolladores. </li>
-</ul>
- <h2>Consejos y trucos para jugar coches rápidos como un rayo</h2>
-<p>Si quieres mejorar tus habilidades y divertirte más jugando Cars Fast as Lightning, aquí tienes algunos consejos y trucos que puedes usar:</p>
- <h3>Aumenta tu velocidad con trucos</h3>
-<p>Uno de los aspectos más importantes de las carreras en Cars Fast as Lightning es realizar trucos en puntos azules en su pista. Estos trucos te darán un impulso de velocidad que puede ayudarte a ganar la carrera. Para realizar un truco, debes tocar la pantalla cuando alcances un punto azul. El truco variará según el tipo de spot y el coche que uses. Por ejemplo, algunos spots te harán hacer un bucle, un salto o un giro. Algunos coches también tendrán trucos únicos, como la deriva de Lightning McQueen o el gancho de remolque de Mater. Trate de realizar tantos trucos como sea posible para aumentar su velocidad y puntuación. </p>
- <h3>Actualizar su coche y la pista</h3>
-
- <h3>Desafiar a otros coches y recoger pegatinas</h3>
-<p>Para desbloquear nuevos coches y pistas en Cars Fast as Lightning, es necesario desafiar a otros coches de las películas y ganar carreras contra ellos. Cada coche tiene su propia personalidad y nivel de dificultad. También puedes recoger pegatinas de cada coche completando ciertas tareas o logros. Por ejemplo, para conseguir la pegatina de Mater, necesitas ganar una carrera contra él con un truco de gancho de remolque. Pegatinas le ayudará a desbloquear nuevos coches y pistas más rápido. </p>
- <h3>Completa misiones y recoge monedas</h3>
-<p>Además de las carreras, también puedes completar misiones en Cars Fast as Lightning para ganar monedas y gemas adicionales. Las misiones son tareas que necesitas hacer en el juego, como construir una cierta atracción, realizar un cierto truco o ganar un cierto número de carreras. Puedes comprobar tus misiones tocando el icono del portapapeles en la esquina superior izquierda de la pantalla. Completar misiones también aumentará tu nivel y desbloqueará nuevas características en el juego. </p>
- <h3>Despierta coches dormidos y enviarlos a las atracciones</h3>
-<p>En Cars Fast as Lightning, también puedes construir tu propio Radiator Springs con varios edificios y atracciones de las películas. Estos edificios y atracciones generarán monedas para usted con el tiempo. Sin embargo, algunos de ellos requerirán coches somnolientos para operarlos. Los coches somnolientos son los coches que no están activos en las carreras o la construcción. Usted puede despertar coches soñolientos tocando en ellos y enviarlos a una atracción que coincida con su color. Por ejemplo, puedes enviar a Red el camión de bomberos al V8 Café de Flo o a Luigi a la Casa Della Tires de Luigi. Esto los hará felices y te hará ganar más monedas . </p>
- <h2>Coches rápidos como rayo juego de revisión</h2>
-
- <h3>Pros y contras del juego</h3>
-<p>Algunos de los pros y contras de Cars Fast as Lightning son:</p>
-<tabla>
-<tr>
-<th>Pros</th>
-<th>Contras</th>
-</tr>
-<tr>
-<td>Divertido y colorido juego de carreras</td>
-<td>Requiere conexión a Internet</td>
-</tr>
-<tr>
-<td>Coches y pistas personalizables</td>
-<td>Puede ser repetitivo y aburrido</td>
-</tr>
-<tr>
-<td>Construcción de la ciudad de Radiator Springs</td>
-<td>Tiene compras y anuncios en la aplicación</td>
-</tr>
-<tr>
-<td>Personajes de coches y clips de voz</td>
-<td>Ocupa mucho espacio de almacenamiento</td>
-</tr>
-<tr>
-<td>Controles simples e intuitivos</td>
-<td>Carece de modo multijugador en línea</td>
-</tr>
-</tabla>
- <h3>Valoraciones y comentarios de los usuarios</h3>
-<p>Cars Fast as Lightning ha recibido calificaciones y comentarios positivos de los usuarios en Google Play Store y App Store. El juego tiene una calificación promedio de 4.3 de 5 estrellas en Google Play Store, basado en más de 1.5 millones de comentarios. El juego tiene una calificación promedio de 4.6 de 5 estrellas en App Store, basado en más de 17 mil comentarios. Algunos de los comentarios de los usuarios son:</p>
-
-<p>Cars Fast as Lightning es un juego móvil que te permite competir como tus personajes favoritos de las películas de Cars y construir tu propio Radiator Springs. El juego tiene un montón de características y contenido, tales como coches personalizables y pistas, Radiator Springs ciudad-edificio, Coches personajes y clips de voz, controles simples e intuitivos, acción de carreras divertido y colorido, etc. El juego también tiene algunos inconvenientes, como requerir conexión a Internet, ser repetitivo y aburrido, tener compras y anuncios en la aplicación, ocupar mucho espacio de almacenamiento, carecer de modo multijugador en línea, etc.</p>
- <h3>Resumen del artículo</h3>
-<p>En este artículo, le hemos dicho lo que es Cars Fast as Lightning, cómo descargar e instalar el mod APK terbaru, ¿por qué usarlo, y algunos consejos y trucos para jugar el juego. También te hemos dado una breve reseña del juego y respondido algunas preguntas frecuentes. </p>
- <h3>Preguntas frecuentes</h3>
-<p>Aquí están algunas de las preguntas más frecuentes sobre Cars Fast as Lightning:</p> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/dateutil/rrule.py

@@ -1,1737 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-The rrule module offers a small, complete, and very fast, implementation of
-the recurrence rules documented in the
-`iCalendar RFC <https://tools.ietf.org/html/rfc5545>`_,
-including support for caching of results.
-"""
-import calendar
-import datetime
-import heapq
-import itertools
-import re
-import sys
-from functools import wraps
-# For warning about deprecation of until and count
-from warnings import warn
-
-from six import advance_iterator, integer_types
-
-from six.moves import _thread, range
-
-from ._common import weekday as weekdaybase
-
-try:
-    from math import gcd
-except ImportError:
-    from fractions import gcd
-
-__all__ = ["rrule", "rruleset", "rrulestr",
-           "YEARLY", "MONTHLY", "WEEKLY", "DAILY",
-           "HOURLY", "MINUTELY", "SECONDLY",
-           "MO", "TU", "WE", "TH", "FR", "SA", "SU"]
-
-# Every mask is 7 days longer to handle cross-year weekly periods.
-M366MASK = tuple([1]*31+[2]*29+[3]*31+[4]*30+[5]*31+[6]*30 +
-                 [7]*31+[8]*31+[9]*30+[10]*31+[11]*30+[12]*31+[1]*7)
-M365MASK = list(M366MASK)
-M29, M30, M31 = list(range(1, 30)), list(range(1, 31)), list(range(1, 32))
-MDAY366MASK = tuple(M31+M29+M31+M30+M31+M30+M31+M31+M30+M31+M30+M31+M31[:7])
-MDAY365MASK = list(MDAY366MASK)
-M29, M30, M31 = list(range(-29, 0)), list(range(-30, 0)), list(range(-31, 0))
-NMDAY366MASK = tuple(M31+M29+M31+M30+M31+M30+M31+M31+M30+M31+M30+M31+M31[:7])
-NMDAY365MASK = list(NMDAY366MASK)
-M366RANGE = (0, 31, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335, 366)
-M365RANGE = (0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304, 334, 365)
-WDAYMASK = [0, 1, 2, 3, 4, 5, 6]*55
-del M29, M30, M31, M365MASK[59], MDAY365MASK[59], NMDAY365MASK[31]
-MDAY365MASK = tuple(MDAY365MASK)
-M365MASK = tuple(M365MASK)
-
-FREQNAMES = ['YEARLY', 'MONTHLY', 'WEEKLY', 'DAILY', 'HOURLY', 'MINUTELY', 'SECONDLY']
-
-(YEARLY,
- MONTHLY,
- WEEKLY,
- DAILY,
- HOURLY,
- MINUTELY,
- SECONDLY) = list(range(7))
-
-# Imported on demand.
-easter = None
-parser = None
-
-
-class weekday(weekdaybase):
-    """
-    This version of weekday does not allow n = 0.
-    """
-    def __init__(self, wkday, n=None):
-        if n == 0:
-            raise ValueError("Can't create weekday with n==0")
-
-        super(weekday, self).__init__(wkday, n)
-
-
-MO, TU, WE, TH, FR, SA, SU = weekdays = tuple(weekday(x) for x in range(7))
-
-
-def _invalidates_cache(f):
-    """
-    Decorator for rruleset methods which may invalidate the
-    cached length.
-    """
-    @wraps(f)
-    def inner_func(self, *args, **kwargs):
-        rv = f(self, *args, **kwargs)
-        self._invalidate_cache()
-        return rv
-
-    return inner_func
-
-
-class rrulebase(object):
-    def __init__(self, cache=False):
-        if cache:
-            self._cache = []
-            self._cache_lock = _thread.allocate_lock()
-            self._invalidate_cache()
-        else:
-            self._cache = None
-            self._cache_complete = False
-            self._len = None
-
-    def __iter__(self):
-        if self._cache_complete:
-            return iter(self._cache)
-        elif self._cache is None:
-            return self._iter()
-        else:
-            return self._iter_cached()
-
-    def _invalidate_cache(self):
-        if self._cache is not None:
-            self._cache = []
-            self._cache_complete = False
-            self._cache_gen = self._iter()
-
-            if self._cache_lock.locked():
-                self._cache_lock.release()
-
-        self._len = None
-
-    def _iter_cached(self):
-        i = 0
-        gen = self._cache_gen
-        cache = self._cache
-        acquire = self._cache_lock.acquire
-        release = self._cache_lock.release
-        while gen:
-            if i == len(cache):
-                acquire()
-                if self._cache_complete:
-                    break
-                try:
-                    for j in range(10):
-                        cache.append(advance_iterator(gen))
-                except StopIteration:
-                    self._cache_gen = gen = None
-                    self._cache_complete = True
-                    break
-                release()
-            yield cache[i]
-            i += 1
-        while i < self._len:
-            yield cache[i]
-            i += 1
-
-    def __getitem__(self, item):
-        if self._cache_complete:
-            return self._cache[item]
-        elif isinstance(item, slice):
-            if item.step and item.step < 0:
-                return list(iter(self))[item]
-            else:
-                return list(itertools.islice(self,
-                                             item.start or 0,
-                                             item.stop or sys.maxsize,
-                                             item.step or 1))
-        elif item >= 0:
-            gen = iter(self)
-            try:
-                for i in range(item+1):
-                    res = advance_iterator(gen)
-            except StopIteration:
-                raise IndexError
-            return res
-        else:
-            return list(iter(self))[item]
-
-    def __contains__(self, item):
-        if self._cache_complete:
-            return item in self._cache
-        else:
-            for i in self:
-                if i == item:
-                    return True
-                elif i > item:
-                    return False
-        return False
-
-    # __len__() introduces a large performance penalty.
-    def count(self):
-        """ Returns the number of recurrences in this set. It will have go
-            trough the whole recurrence, if this hasn't been done before. """
-        if self._len is None:
-            for x in self:
-                pass
-        return self._len
-
-    def before(self, dt, inc=False):
-        """ Returns the last recurrence before the given datetime instance. The
-            inc keyword defines what happens if dt is an occurrence. With
-            inc=True, if dt itself is an occurrence, it will be returned. """
-        if self._cache_complete:
-            gen = self._cache
-        else:
-            gen = self
-        last = None
-        if inc:
-            for i in gen:
-                if i > dt:
-                    break
-                last = i
-        else:
-            for i in gen:
-                if i >= dt:
-                    break
-                last = i
-        return last
-
-    def after(self, dt, inc=False):
-        """ Returns the first recurrence after the given datetime instance. The
-            inc keyword defines what happens if dt is an occurrence. With
-            inc=True, if dt itself is an occurrence, it will be returned.  """
-        if self._cache_complete:
-            gen = self._cache
-        else:
-            gen = self
-        if inc:
-            for i in gen:
-                if i >= dt:
-                    return i
-        else:
-            for i in gen:
-                if i > dt:
-                    return i
-        return None
-
-    def xafter(self, dt, count=None, inc=False):
-        """
-        Generator which yields up to `count` recurrences after the given
-        datetime instance, equivalent to `after`.
-
-        :param dt:
-            The datetime at which to start generating recurrences.
-
-        :param count:
-            The maximum number of recurrences to generate. If `None` (default),
-            dates are generated until the recurrence rule is exhausted.
-
-        :param inc:
-            If `dt` is an instance of the rule and `inc` is `True`, it is
-            included in the output.
-
-        :yields: Yields a sequence of `datetime` objects.
-        """
-
-        if self._cache_complete:
-            gen = self._cache
-        else:
-            gen = self
-
-        # Select the comparison function
-        if inc:
-            comp = lambda dc, dtc: dc >= dtc
-        else:
-            comp = lambda dc, dtc: dc > dtc
-
-        # Generate dates
-        n = 0
-        for d in gen:
-            if comp(d, dt):
-                if count is not None:
-                    n += 1
-                    if n > count:
-                        break
-
-                yield d
-
-    def between(self, after, before, inc=False, count=1):
-        """ Returns all the occurrences of the rrule between after and before.
-        The inc keyword defines what happens if after and/or before are
-        themselves occurrences. With inc=True, they will be included in the
-        list, if they are found in the recurrence set. """
-        if self._cache_complete:
-            gen = self._cache
-        else:
-            gen = self
-        started = False
-        l = []
-        if inc:
-            for i in gen:
-                if i > before:
-                    break
-                elif not started:
-                    if i >= after:
-                        started = True
-                        l.append(i)
-                else:
-                    l.append(i)
-        else:
-            for i in gen:
-                if i >= before:
-                    break
-                elif not started:
-                    if i > after:
-                        started = True
-                        l.append(i)
-                else:
-                    l.append(i)
-        return l
-
-
-class rrule(rrulebase):
-    """
-    That's the base of the rrule operation. It accepts all the keywords
-    defined in the RFC as its constructor parameters (except byday,
-    which was renamed to byweekday) and more. The constructor prototype is::
-
-            rrule(freq)
-
-    Where freq must be one of YEARLY, MONTHLY, WEEKLY, DAILY, HOURLY, MINUTELY,
-    or SECONDLY.
-
-    .. note::
-        Per RFC section 3.3.10, recurrence instances falling on invalid dates
-        and times are ignored rather than coerced:
-
-            Recurrence rules may generate recurrence instances with an invalid
-            date (e.g., February 30) or nonexistent local time (e.g., 1:30 AM
-            on a day where the local time is moved forward by an hour at 1:00
-            AM).  Such recurrence instances MUST be ignored and MUST NOT be
-            counted as part of the recurrence set.
-
-        This can lead to possibly surprising behavior when, for example, the
-        start date occurs at the end of the month:
-
-        >>> from dateutil.rrule import rrule, MONTHLY
-        >>> from datetime import datetime
-        >>> start_date = datetime(2014, 12, 31)
-        >>> list(rrule(freq=MONTHLY, count=4, dtstart=start_date))
-        ... # doctest: +NORMALIZE_WHITESPACE
-        [datetime.datetime(2014, 12, 31, 0, 0),
-         datetime.datetime(2015, 1, 31, 0, 0),
-         datetime.datetime(2015, 3, 31, 0, 0),
-         datetime.datetime(2015, 5, 31, 0, 0)]
-
-    Additionally, it supports the following keyword arguments:
-
-    :param dtstart:
-        The recurrence start. Besides being the base for the recurrence,
-        missing parameters in the final recurrence instances will also be
-        extracted from this date. If not given, datetime.now() will be used
-        instead.
-    :param interval:
-        The interval between each freq iteration. For example, when using
-        YEARLY, an interval of 2 means once every two years, but with HOURLY,
-        it means once every two hours. The default interval is 1.
-    :param wkst:
-        The week start day. Must be one of the MO, TU, WE constants, or an
-        integer, specifying the first day of the week. This will affect
-        recurrences based on weekly periods. The default week start is got
-        from calendar.firstweekday(), and may be modified by
-        calendar.setfirstweekday().
-    :param count:
-        If given, this determines how many occurrences will be generated.
-
-        .. note::
-            As of version 2.5.0, the use of the keyword ``until`` in conjunction
-            with ``count`` is deprecated, to make sure ``dateutil`` is fully
-            compliant with `RFC-5545 Sec. 3.3.10 <https://tools.ietf.org/
-            html/rfc5545#section-3.3.10>`_. Therefore, ``until`` and ``count``
-            **must not** occur in the same call to ``rrule``.
-    :param until:
-        If given, this must be a datetime instance specifying the upper-bound
-        limit of the recurrence. The last recurrence in the rule is the greatest
-        datetime that is less than or equal to the value specified in the
-        ``until`` parameter.
-
-        .. note::
-            As of version 2.5.0, the use of the keyword ``until`` in conjunction
-            with ``count`` is deprecated, to make sure ``dateutil`` is fully
-            compliant with `RFC-5545 Sec. 3.3.10 <https://tools.ietf.org/
-            html/rfc5545#section-3.3.10>`_. Therefore, ``until`` and ``count``
-            **must not** occur in the same call to ``rrule``.
-    :param bysetpos:
-        If given, it must be either an integer, or a sequence of integers,
-        positive or negative. Each given integer will specify an occurrence
-        number, corresponding to the nth occurrence of the rule inside the
-        frequency period. For example, a bysetpos of -1 if combined with a
-        MONTHLY frequency, and a byweekday of (MO, TU, WE, TH, FR), will
-        result in the last work day of every month.
-    :param bymonth:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the months to apply the recurrence to.
-    :param bymonthday:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the month days to apply the recurrence to.
-    :param byyearday:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the year days to apply the recurrence to.
-    :param byeaster:
-        If given, it must be either an integer, or a sequence of integers,
-        positive or negative. Each integer will define an offset from the
-        Easter Sunday. Passing the offset 0 to byeaster will yield the Easter
-        Sunday itself. This is an extension to the RFC specification.
-    :param byweekno:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the week numbers to apply the recurrence to. Week numbers
-        have the meaning described in ISO8601, that is, the first week of
-        the year is that containing at least four days of the new year.
-    :param byweekday:
-        If given, it must be either an integer (0 == MO), a sequence of
-        integers, one of the weekday constants (MO, TU, etc), or a sequence
-        of these constants. When given, these variables will define the
-        weekdays where the recurrence will be applied. It's also possible to
-        use an argument n for the weekday instances, which will mean the nth
-        occurrence of this weekday in the period. For example, with MONTHLY,
-        or with YEARLY and BYMONTH, using FR(+1) in byweekday will specify the
-        first friday of the month where the recurrence happens. Notice that in
-        the RFC documentation, this is specified as BYDAY, but was renamed to
-        avoid the ambiguity of that keyword.
-    :param byhour:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the hours to apply the recurrence to.
-    :param byminute:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the minutes to apply the recurrence to.
-    :param bysecond:
-        If given, it must be either an integer, or a sequence of integers,
-        meaning the seconds to apply the recurrence to.
-    :param cache:
-        If given, it must be a boolean value specifying to enable or disable
-        caching of results. If you will use the same rrule instance multiple
-        times, enabling caching will improve the performance considerably.
-     """
-    def __init__(self, freq, dtstart=None,
-                 interval=1, wkst=None, count=None, until=None, bysetpos=None,
-                 bymonth=None, bymonthday=None, byyearday=None, byeaster=None,
-                 byweekno=None, byweekday=None,
-                 byhour=None, byminute=None, bysecond=None,
-                 cache=False):
-        super(rrule, self).__init__(cache)
-        global easter
-        if not dtstart:
-            if until and until.tzinfo:
-                dtstart = datetime.datetime.now(tz=until.tzinfo).replace(microsecond=0)
-            else:
-                dtstart = datetime.datetime.now().replace(microsecond=0)
-        elif not isinstance(dtstart, datetime.datetime):
-            dtstart = datetime.datetime.fromordinal(dtstart.toordinal())
-        else:
-            dtstart = dtstart.replace(microsecond=0)
-        self._dtstart = dtstart
-        self._tzinfo = dtstart.tzinfo
-        self._freq = freq
-        self._interval = interval
-        self._count = count
-
-        # Cache the original byxxx rules, if they are provided, as the _byxxx
-        # attributes do not necessarily map to the inputs, and this can be
-        # a problem in generating the strings. Only store things if they've
-        # been supplied (the string retrieval will just use .get())
-        self._original_rule = {}
-
-        if until and not isinstance(until, datetime.datetime):
-            until = datetime.datetime.fromordinal(until.toordinal())
-        self._until = until
-
-        if self._dtstart and self._until:
-            if (self._dtstart.tzinfo is not None) != (self._until.tzinfo is not None):
-                # According to RFC5545 Section 3.3.10:
-                # https://tools.ietf.org/html/rfc5545#section-3.3.10
-                #
-                # > If the "DTSTART" property is specified as a date with UTC
-                # > time or a date with local time and time zone reference,
-                # > then the UNTIL rule part MUST be specified as a date with
-                # > UTC time.
-                raise ValueError(
-                    'RRULE UNTIL values must be specified in UTC when DTSTART '
-                    'is timezone-aware'
-                )
-
-        if count is not None and until:
-            warn("Using both 'count' and 'until' is inconsistent with RFC 5545"
-                 " and has been deprecated in dateutil. Future versions will "
-                 "raise an error.", DeprecationWarning)
-
-        if wkst is None:
-            self._wkst = calendar.firstweekday()
-        elif isinstance(wkst, integer_types):
-            self._wkst = wkst
-        else:
-            self._wkst = wkst.weekday
-
-        if bysetpos is None:
-            self._bysetpos = None
-        elif isinstance(bysetpos, integer_types):
-            if bysetpos == 0 or not (-366 <= bysetpos <= 366):
-                raise ValueError("bysetpos must be between 1 and 366, "
-                                 "or between -366 and -1")
-            self._bysetpos = (bysetpos,)
-        else:
-            self._bysetpos = tuple(bysetpos)
-            for pos in self._bysetpos:
-                if pos == 0 or not (-366 <= pos <= 366):
-                    raise ValueError("bysetpos must be between 1 and 366, "
-                                     "or between -366 and -1")
-
-        if self._bysetpos:
-            self._original_rule['bysetpos'] = self._bysetpos
-
-        if (byweekno is None and byyearday is None and bymonthday is None and
-                byweekday is None and byeaster is None):
-            if freq == YEARLY:
-                if bymonth is None:
-                    bymonth = dtstart.month
-                    self._original_rule['bymonth'] = None
-                bymonthday = dtstart.day
-                self._original_rule['bymonthday'] = None
-            elif freq == MONTHLY:
-                bymonthday = dtstart.day
-                self._original_rule['bymonthday'] = None
-            elif freq == WEEKLY:
-                byweekday = dtstart.weekday()
-                self._original_rule['byweekday'] = None
-
-        # bymonth
-        if bymonth is None:
-            self._bymonth = None
-        else:
-            if isinstance(bymonth, integer_types):
-                bymonth = (bymonth,)
-
-            self._bymonth = tuple(sorted(set(bymonth)))
-
-            if 'bymonth' not in self._original_rule:
-                self._original_rule['bymonth'] = self._bymonth
-
-        # byyearday
-        if byyearday is None:
-            self._byyearday = None
-        else:
-            if isinstance(byyearday, integer_types):
-                byyearday = (byyearday,)
-
-            self._byyearday = tuple(sorted(set(byyearday)))
-            self._original_rule['byyearday'] = self._byyearday
-
-        # byeaster
-        if byeaster is not None:
-            if not easter:
-                from dateutil import easter
-            if isinstance(byeaster, integer_types):
-                self._byeaster = (byeaster,)
-            else:
-                self._byeaster = tuple(sorted(byeaster))
-
-            self._original_rule['byeaster'] = self._byeaster
-        else:
-            self._byeaster = None
-
-        # bymonthday
-        if bymonthday is None:
-            self._bymonthday = ()
-            self._bynmonthday = ()
-        else:
-            if isinstance(bymonthday, integer_types):
-                bymonthday = (bymonthday,)
-
-            bymonthday = set(bymonthday)            # Ensure it's unique
-
-            self._bymonthday = tuple(sorted(x for x in bymonthday if x > 0))
-            self._bynmonthday = tuple(sorted(x for x in bymonthday if x < 0))
-
-            # Storing positive numbers first, then negative numbers
-            if 'bymonthday' not in self._original_rule:
-                self._original_rule['bymonthday'] = tuple(
-                    itertools.chain(self._bymonthday, self._bynmonthday))
-
-        # byweekno
-        if byweekno is None:
-            self._byweekno = None
-        else:
-            if isinstance(byweekno, integer_types):
-                byweekno = (byweekno,)
-
-            self._byweekno = tuple(sorted(set(byweekno)))
-
-            self._original_rule['byweekno'] = self._byweekno
-
-        # byweekday / bynweekday
-        if byweekday is None:
-            self._byweekday = None
-            self._bynweekday = None
-        else:
-            # If it's one of the valid non-sequence types, convert to a
-            # single-element sequence before the iterator that builds the
-            # byweekday set.
-            if isinstance(byweekday, integer_types) or hasattr(byweekday, "n"):
-                byweekday = (byweekday,)
-
-            self._byweekday = set()
-            self._bynweekday = set()
-            for wday in byweekday:
-                if isinstance(wday, integer_types):
-                    self._byweekday.add(wday)
-                elif not wday.n or freq > MONTHLY:
-                    self._byweekday.add(wday.weekday)
-                else:
-                    self._bynweekday.add((wday.weekday, wday.n))
-
-            if not self._byweekday:
-                self._byweekday = None
-            elif not self._bynweekday:
-                self._bynweekday = None
-
-            if self._byweekday is not None:
-                self._byweekday = tuple(sorted(self._byweekday))
-                orig_byweekday = [weekday(x) for x in self._byweekday]
-            else:
-                orig_byweekday = ()
-
-            if self._bynweekday is not None:
-                self._bynweekday = tuple(sorted(self._bynweekday))
-                orig_bynweekday = [weekday(*x) for x in self._bynweekday]
-            else:
-                orig_bynweekday = ()
-
-            if 'byweekday' not in self._original_rule:
-                self._original_rule['byweekday'] = tuple(itertools.chain(
-                    orig_byweekday, orig_bynweekday))
-
-        # byhour
-        if byhour is None:
-            if freq < HOURLY:
-                self._byhour = {dtstart.hour}
-            else:
-                self._byhour = None
-        else:
-            if isinstance(byhour, integer_types):
-                byhour = (byhour,)
-
-            if freq == HOURLY:
-                self._byhour = self.__construct_byset(start=dtstart.hour,
-                                                      byxxx=byhour,
-                                                      base=24)
-            else:
-                self._byhour = set(byhour)
-
-            self._byhour = tuple(sorted(self._byhour))
-            self._original_rule['byhour'] = self._byhour
-
-        # byminute
-        if byminute is None:
-            if freq < MINUTELY:
-                self._byminute = {dtstart.minute}
-            else:
-                self._byminute = None
-        else:
-            if isinstance(byminute, integer_types):
-                byminute = (byminute,)
-
-            if freq == MINUTELY:
-                self._byminute = self.__construct_byset(start=dtstart.minute,
-                                                        byxxx=byminute,
-                                                        base=60)
-            else:
-                self._byminute = set(byminute)
-
-            self._byminute = tuple(sorted(self._byminute))
-            self._original_rule['byminute'] = self._byminute
-
-        # bysecond
-        if bysecond is None:
-            if freq < SECONDLY:
-                self._bysecond = ((dtstart.second,))
-            else:
-                self._bysecond = None
-        else:
-            if isinstance(bysecond, integer_types):
-                bysecond = (bysecond,)
-
-            self._bysecond = set(bysecond)
-
-            if freq == SECONDLY:
-                self._bysecond = self.__construct_byset(start=dtstart.second,
-                                                        byxxx=bysecond,
-                                                        base=60)
-            else:
-                self._bysecond = set(bysecond)
-
-            self._bysecond = tuple(sorted(self._bysecond))
-            self._original_rule['bysecond'] = self._bysecond
-
-        if self._freq >= HOURLY:
-            self._timeset = None
-        else:
-            self._timeset = []
-            for hour in self._byhour:
-                for minute in self._byminute:
-                    for second in self._bysecond:
-                        self._timeset.append(
-                            datetime.time(hour, minute, second,
-                                          tzinfo=self._tzinfo))
-            self._timeset.sort()
-            self._timeset = tuple(self._timeset)
-
-    def __str__(self):
-        """
-        Output a string that would generate this RRULE if passed to rrulestr.
-        This is mostly compatible with RFC5545, except for the
-        dateutil-specific extension BYEASTER.
-        """
-
-        output = []
-        h, m, s = [None] * 3
-        if self._dtstart:
-            output.append(self._dtstart.strftime('DTSTART:%Y%m%dT%H%M%S'))
-            h, m, s = self._dtstart.timetuple()[3:6]
-
-        parts = ['FREQ=' + FREQNAMES[self._freq]]
-        if self._interval != 1:
-            parts.append('INTERVAL=' + str(self._interval))
-
-        if self._wkst:
-            parts.append('WKST=' + repr(weekday(self._wkst))[0:2])
-
-        if self._count is not None:
-            parts.append('COUNT=' + str(self._count))
-
-        if self._until:
-            parts.append(self._until.strftime('UNTIL=%Y%m%dT%H%M%S'))
-
-        if self._original_rule.get('byweekday') is not None:
-            # The str() method on weekday objects doesn't generate
-            # RFC5545-compliant strings, so we should modify that.
-            original_rule = dict(self._original_rule)
-            wday_strings = []
-            for wday in original_rule['byweekday']:
-                if wday.n:
-                    wday_strings.append('{n:+d}{wday}'.format(
-                        n=wday.n,
-                        wday=repr(wday)[0:2]))
-                else:
-                    wday_strings.append(repr(wday))
-
-            original_rule['byweekday'] = wday_strings
-        else:
-            original_rule = self._original_rule
-
-        partfmt = '{name}={vals}'
-        for name, key in [('BYSETPOS', 'bysetpos'),
-                          ('BYMONTH', 'bymonth'),
-                          ('BYMONTHDAY', 'bymonthday'),
-                          ('BYYEARDAY', 'byyearday'),
-                          ('BYWEEKNO', 'byweekno'),
-                          ('BYDAY', 'byweekday'),
-                          ('BYHOUR', 'byhour'),
-                          ('BYMINUTE', 'byminute'),
-                          ('BYSECOND', 'bysecond'),
-                          ('BYEASTER', 'byeaster')]:
-            value = original_rule.get(key)
-            if value:
-                parts.append(partfmt.format(name=name, vals=(','.join(str(v)
-                                                             for v in value))))
-
-        output.append('RRULE:' + ';'.join(parts))
-        return '\n'.join(output)
-
-    def replace(self, **kwargs):
-        """Return new rrule with same attributes except for those attributes given new
-           values by whichever keyword arguments are specified."""
-        new_kwargs = {"interval": self._interval,
-                      "count": self._count,
-                      "dtstart": self._dtstart,
-                      "freq": self._freq,
-                      "until": self._until,
-                      "wkst": self._wkst,
-                      "cache": False if self._cache is None else True }
-        new_kwargs.update(self._original_rule)
-        new_kwargs.update(kwargs)
-        return rrule(**new_kwargs)
-
-    def _iter(self):
-        year, month, day, hour, minute, second, weekday, yearday, _ = \
-            self._dtstart.timetuple()
-
-        # Some local variables to speed things up a bit
-        freq = self._freq
-        interval = self._interval
-        wkst = self._wkst
-        until = self._until
-        bymonth = self._bymonth
-        byweekno = self._byweekno
-        byyearday = self._byyearday
-        byweekday = self._byweekday
-        byeaster = self._byeaster
-        bymonthday = self._bymonthday
-        bynmonthday = self._bynmonthday
-        bysetpos = self._bysetpos
-        byhour = self._byhour
-        byminute = self._byminute
-        bysecond = self._bysecond
-
-        ii = _iterinfo(self)
-        ii.rebuild(year, month)
-
-        getdayset = {YEARLY: ii.ydayset,
-                     MONTHLY: ii.mdayset,
-                     WEEKLY: ii.wdayset,
-                     DAILY: ii.ddayset,
-                     HOURLY: ii.ddayset,
-                     MINUTELY: ii.ddayset,
-                     SECONDLY: ii.ddayset}[freq]
-
-        if freq < HOURLY:
-            timeset = self._timeset
-        else:
-            gettimeset = {HOURLY: ii.htimeset,
-                          MINUTELY: ii.mtimeset,
-                          SECONDLY: ii.stimeset}[freq]
-            if ((freq >= HOURLY and
-                 self._byhour and hour not in self._byhour) or
-                (freq >= MINUTELY and
-                 self._byminute and minute not in self._byminute) or
-                (freq >= SECONDLY and
-                 self._bysecond and second not in self._bysecond)):
-                timeset = ()
-            else:
-                timeset = gettimeset(hour, minute, second)
-
-        total = 0
-        count = self._count
-        while True:
-            # Get dayset with the right frequency
-            dayset, start, end = getdayset(year, month, day)
-
-            # Do the "hard" work ;-)
-            filtered = False
-            for i in dayset[start:end]:
-                if ((bymonth and ii.mmask[i] not in bymonth) or
-                    (byweekno and not ii.wnomask[i]) or
-                    (byweekday and ii.wdaymask[i] not in byweekday) or
-                    (ii.nwdaymask and not ii.nwdaymask[i]) or
-                    (byeaster and not ii.eastermask[i]) or
-                    ((bymonthday or bynmonthday) and
-                     ii.mdaymask[i] not in bymonthday and
-                     ii.nmdaymask[i] not in bynmonthday) or
-                    (byyearday and
-                     ((i < ii.yearlen and i+1 not in byyearday and
-                       -ii.yearlen+i not in byyearday) or
-                      (i >= ii.yearlen and i+1-ii.yearlen not in byyearday and
-                       -ii.nextyearlen+i-ii.yearlen not in byyearday)))):
-                    dayset[i] = None
-                    filtered = True
-
-            # Output results
-            if bysetpos and timeset:
-                poslist = []
-                for pos in bysetpos:
-                    if pos < 0:
-                        daypos, timepos = divmod(pos, len(timeset))
-                    else:
-                        daypos, timepos = divmod(pos-1, len(timeset))
-                    try:
-                        i = [x for x in dayset[start:end]
-                             if x is not None][daypos]
-                        time = timeset[timepos]
-                    except IndexError:
-                        pass
-                    else:
-                        date = datetime.date.fromordinal(ii.yearordinal+i)
-                        res = datetime.datetime.combine(date, time)
-                        if res not in poslist:
-                            poslist.append(res)
-                poslist.sort()
-                for res in poslist:
-                    if until and res > until:
-                        self._len = total
-                        return
-                    elif res >= self._dtstart:
-                        if count is not None:
-                            count -= 1
-                            if count < 0:
-                                self._len = total
-                                return
-                        total += 1
-                        yield res
-            else:
-                for i in dayset[start:end]:
-                    if i is not None:
-                        date = datetime.date.fromordinal(ii.yearordinal + i)
-                        for time in timeset:
-                            res = datetime.datetime.combine(date, time)
-                            if until and res > until:
-                                self._len = total
-                                return
-                            elif res >= self._dtstart:
-                                if count is not None:
-                                    count -= 1
-                                    if count < 0:
-                                        self._len = total
-                                        return
-
-                                total += 1
-                                yield res
-
-            # Handle frequency and interval
-            fixday = False
-            if freq == YEARLY:
-                year += interval
-                if year > datetime.MAXYEAR:
-                    self._len = total
-                    return
-                ii.rebuild(year, month)
-            elif freq == MONTHLY:
-                month += interval
-                if month > 12:
-                    div, mod = divmod(month, 12)
-                    month = mod
-                    year += div
-                    if month == 0:
-                        month = 12
-                        year -= 1
-                    if year > datetime.MAXYEAR:
-                        self._len = total
-                        return
-                ii.rebuild(year, month)
-            elif freq == WEEKLY:
-                if wkst > weekday:
-                    day += -(weekday+1+(6-wkst))+self._interval*7
-                else:
-                    day += -(weekday-wkst)+self._interval*7
-                weekday = wkst
-                fixday = True
-            elif freq == DAILY:
-                day += interval
-                fixday = True
-            elif freq == HOURLY:
-                if filtered:
-                    # Jump to one iteration before next day
-                    hour += ((23-hour)//interval)*interval
-
-                if byhour:
-                    ndays, hour = self.__mod_distance(value=hour,
-                                                      byxxx=self._byhour,
-                                                      base=24)
-                else:
-                    ndays, hour = divmod(hour+interval, 24)
-
-                if ndays:
-                    day += ndays
-                    fixday = True
-
-                timeset = gettimeset(hour, minute, second)
-            elif freq == MINUTELY:
-                if filtered:
-                    # Jump to one iteration before next day
-                    minute += ((1439-(hour*60+minute))//interval)*interval
-
-                valid = False
-                rep_rate = (24*60)
-                for j in range(rep_rate // gcd(interval, rep_rate)):
-                    if byminute:
-                        nhours, minute = \
-                            self.__mod_distance(value=minute,
-                                                byxxx=self._byminute,
-                                                base=60)
-                    else:
-                        nhours, minute = divmod(minute+interval, 60)
-
-                    div, hour = divmod(hour+nhours, 24)
-                    if div:
-                        day += div
-                        fixday = True
-                        filtered = False
-
-                    if not byhour or hour in byhour:
-                        valid = True
-                        break
-
-                if not valid:
-                    raise ValueError('Invalid combination of interval and ' +
-                                     'byhour resulting in empty rule.')
-
-                timeset = gettimeset(hour, minute, second)
-            elif freq == SECONDLY:
-                if filtered:
-                    # Jump to one iteration before next day
-                    second += (((86399 - (hour * 3600 + minute * 60 + second))
-                                // interval) * interval)
-
-                rep_rate = (24 * 3600)
-                valid = False
-                for j in range(0, rep_rate // gcd(interval, rep_rate)):
-                    if bysecond:
-                        nminutes, second = \
-                            self.__mod_distance(value=second,
-                                                byxxx=self._bysecond,
-                                                base=60)
-                    else:
-                        nminutes, second = divmod(second+interval, 60)
-
-                    div, minute = divmod(minute+nminutes, 60)
-                    if div:
-                        hour += div
-                        div, hour = divmod(hour, 24)
-                        if div:
-                            day += div
-                            fixday = True
-
-                    if ((not byhour or hour in byhour) and
-                            (not byminute or minute in byminute) and
-                            (not bysecond or second in bysecond)):
-                        valid = True
-                        break
-
-                if not valid:
-                    raise ValueError('Invalid combination of interval, ' +
-                                     'byhour and byminute resulting in empty' +
-                                     ' rule.')
-
-                timeset = gettimeset(hour, minute, second)
-
-            if fixday and day > 28:
-                daysinmonth = calendar.monthrange(year, month)[1]
-                if day > daysinmonth:
-                    while day > daysinmonth:
-                        day -= daysinmonth
-                        month += 1
-                        if month == 13:
-                            month = 1
-                            year += 1
-                            if year > datetime.MAXYEAR:
-                                self._len = total
-                                return
-                        daysinmonth = calendar.monthrange(year, month)[1]
-                    ii.rebuild(year, month)
-
-    def __construct_byset(self, start, byxxx, base):
-        """
-        If a `BYXXX` sequence is passed to the constructor at the same level as
-        `FREQ` (e.g. `FREQ=HOURLY,BYHOUR={2,4,7},INTERVAL=3`), there are some
-        specifications which cannot be reached given some starting conditions.
-
-        This occurs whenever the interval is not coprime with the base of a
-        given unit and the difference between the starting position and the
-        ending position is not coprime with the greatest common denominator
-        between the interval and the base. For example, with a FREQ of hourly
-        starting at 17:00 and an interval of 4, the only valid values for
-        BYHOUR would be {21, 1, 5, 9, 13, 17}, because 4 and 24 are not
-        coprime.
-
-        :param start:
-            Specifies the starting position.
-        :param byxxx:
-            An iterable containing the list of allowed values.
-        :param base:
-            The largest allowable value for the specified frequency (e.g.
-            24 hours, 60 minutes).
-
-        This does not preserve the type of the iterable, returning a set, since
-        the values should be unique and the order is irrelevant, this will
-        speed up later lookups.
-
-        In the event of an empty set, raises a :exception:`ValueError`, as this
-        results in an empty rrule.
-        """
-
-        cset = set()
-
-        # Support a single byxxx value.
-        if isinstance(byxxx, integer_types):
-            byxxx = (byxxx, )
-
-        for num in byxxx:
-            i_gcd = gcd(self._interval, base)
-            # Use divmod rather than % because we need to wrap negative nums.
-            if i_gcd == 1 or divmod(num - start, i_gcd)[1] == 0:
-                cset.add(num)
-
-        if len(cset) == 0:
-            raise ValueError("Invalid rrule byxxx generates an empty set.")
-
-        return cset
-
-    def __mod_distance(self, value, byxxx, base):
-        """
-        Calculates the next value in a sequence where the `FREQ` parameter is
-        specified along with a `BYXXX` parameter at the same "level"
-        (e.g. `HOURLY` specified with `BYHOUR`).
-
-        :param value:
-            The old value of the component.
-        :param byxxx:
-            The `BYXXX` set, which should have been generated by
-            `rrule._construct_byset`, or something else which checks that a
-            valid rule is present.
-        :param base:
-            The largest allowable value for the specified frequency (e.g.
-            24 hours, 60 minutes).
-
-        If a valid value is not found after `base` iterations (the maximum
-        number before the sequence would start to repeat), this raises a
-        :exception:`ValueError`, as no valid values were found.
-
-        This returns a tuple of `divmod(n*interval, base)`, where `n` is the
-        smallest number of `interval` repetitions until the next specified
-        value in `byxxx` is found.
-        """
-        accumulator = 0
-        for ii in range(1, base + 1):
-            # Using divmod() over % to account for negative intervals
-            div, value = divmod(value + self._interval, base)
-            accumulator += div
-            if value in byxxx:
-                return (accumulator, value)
-
-
-class _iterinfo(object):
-    __slots__ = ["rrule", "lastyear", "lastmonth",
-                 "yearlen", "nextyearlen", "yearordinal", "yearweekday",
-                 "mmask", "mrange", "mdaymask", "nmdaymask",
-                 "wdaymask", "wnomask", "nwdaymask", "eastermask"]
-
-    def __init__(self, rrule):
-        for attr in self.__slots__:
-            setattr(self, attr, None)
-        self.rrule = rrule
-
-    def rebuild(self, year, month):
-        # Every mask is 7 days longer to handle cross-year weekly periods.
-        rr = self.rrule
-        if year != self.lastyear:
-            self.yearlen = 365 + calendar.isleap(year)
-            self.nextyearlen = 365 + calendar.isleap(year + 1)
-            firstyday = datetime.date(year, 1, 1)
-            self.yearordinal = firstyday.toordinal()
-            self.yearweekday = firstyday.weekday()
-
-            wday = datetime.date(year, 1, 1).weekday()
-            if self.yearlen == 365:
-                self.mmask = M365MASK
-                self.mdaymask = MDAY365MASK
-                self.nmdaymask = NMDAY365MASK
-                self.wdaymask = WDAYMASK[wday:]
-                self.mrange = M365RANGE
-            else:
-                self.mmask = M366MASK
-                self.mdaymask = MDAY366MASK
-                self.nmdaymask = NMDAY366MASK
-                self.wdaymask = WDAYMASK[wday:]
-                self.mrange = M366RANGE
-
-            if not rr._byweekno:
-                self.wnomask = None
-            else:
-                self.wnomask = [0]*(self.yearlen+7)
-                # no1wkst = firstwkst = self.wdaymask.index(rr._wkst)
-                no1wkst = firstwkst = (7-self.yearweekday+rr._wkst) % 7
-                if no1wkst >= 4:
-                    no1wkst = 0
-                    # Number of days in the year, plus the days we got
-                    # from last year.
-                    wyearlen = self.yearlen+(self.yearweekday-rr._wkst) % 7
-                else:
-                    # Number of days in the year, minus the days we
-                    # left in last year.
-                    wyearlen = self.yearlen-no1wkst
-                div, mod = divmod(wyearlen, 7)
-                numweeks = div+mod//4
-                for n in rr._byweekno:
-                    if n < 0:
-                        n += numweeks+1
-                    if not (0 < n <= numweeks):
-                        continue
-                    if n > 1:
-                        i = no1wkst+(n-1)*7
-                        if no1wkst != firstwkst:
-                            i -= 7-firstwkst
-                    else:
-                        i = no1wkst
-                    for j in range(7):
-                        self.wnomask[i] = 1
-                        i += 1
-                        if self.wdaymask[i] == rr._wkst:
-                            break
-                if 1 in rr._byweekno:
-                    # Check week number 1 of next year as well
-                    # TODO: Check -numweeks for next year.
-                    i = no1wkst+numweeks*7
-                    if no1wkst != firstwkst:
-                        i -= 7-firstwkst
-                    if i < self.yearlen:
-                        # If week starts in next year, we
-                        # don't care about it.
-                        for j in range(7):
-                            self.wnomask[i] = 1
-                            i += 1
-                            if self.wdaymask[i] == rr._wkst:
-                                break
-                if no1wkst:
-                    # Check last week number of last year as
-                    # well. If no1wkst is 0, either the year
-                    # started on week start, or week number 1
-                    # got days from last year, so there are no
-                    # days from last year's last week number in
-                    # this year.
-                    if -1 not in rr._byweekno:
-                        lyearweekday = datetime.date(year-1, 1, 1).weekday()
-                        lno1wkst = (7-lyearweekday+rr._wkst) % 7
-                        lyearlen = 365+calendar.isleap(year-1)
-                        if lno1wkst >= 4:
-                            lno1wkst = 0
-                            lnumweeks = 52+(lyearlen +
-                                            (lyearweekday-rr._wkst) % 7) % 7//4
-                        else:
-                            lnumweeks = 52+(self.yearlen-no1wkst) % 7//4
-                    else:
-                        lnumweeks = -1
-                    if lnumweeks in rr._byweekno:
-                        for i in range(no1wkst):
-                            self.wnomask[i] = 1
-
-        if (rr._bynweekday and (month != self.lastmonth or
-                                year != self.lastyear)):
-            ranges = []
-            if rr._freq == YEARLY:
-                if rr._bymonth:
-                    for month in rr._bymonth:
-                        ranges.append(self.mrange[month-1:month+1])
-                else:
-                    ranges = [(0, self.yearlen)]
-            elif rr._freq == MONTHLY:
-                ranges = [self.mrange[month-1:month+1]]
-            if ranges:
-                # Weekly frequency won't get here, so we may not
-                # care about cross-year weekly periods.
-                self.nwdaymask = [0]*self.yearlen
-                for first, last in ranges:
-                    last -= 1
-                    for wday, n in rr._bynweekday:
-                        if n < 0:
-                            i = last+(n+1)*7
-                            i -= (self.wdaymask[i]-wday) % 7
-                        else:
-                            i = first+(n-1)*7
-                            i += (7-self.wdaymask[i]+wday) % 7
-                        if first <= i <= last:
-                            self.nwdaymask[i] = 1
-
-        if rr._byeaster:
-            self.eastermask = [0]*(self.yearlen+7)
-            eyday = easter.easter(year).toordinal()-self.yearordinal
-            for offset in rr._byeaster:
-                self.eastermask[eyday+offset] = 1
-
-        self.lastyear = year
-        self.lastmonth = month
-
-    def ydayset(self, year, month, day):
-        return list(range(self.yearlen)), 0, self.yearlen
-
-    def mdayset(self, year, month, day):
-        dset = [None]*self.yearlen
-        start, end = self.mrange[month-1:month+1]
-        for i in range(start, end):
-            dset[i] = i
-        return dset, start, end
-
-    def wdayset(self, year, month, day):
-        # We need to handle cross-year weeks here.
-        dset = [None]*(self.yearlen+7)
-        i = datetime.date(year, month, day).toordinal()-self.yearordinal
-        start = i
-        for j in range(7):
-            dset[i] = i
-            i += 1
-            # if (not (0 <= i < self.yearlen) or
-            #    self.wdaymask[i] == self.rrule._wkst):
-            # This will cross the year boundary, if necessary.
-            if self.wdaymask[i] == self.rrule._wkst:
-                break
-        return dset, start, i
-
-    def ddayset(self, year, month, day):
-        dset = [None] * self.yearlen
-        i = datetime.date(year, month, day).toordinal() - self.yearordinal
-        dset[i] = i
-        return dset, i, i + 1
-
-    def htimeset(self, hour, minute, second):
-        tset = []
-        rr = self.rrule
-        for minute in rr._byminute:
-            for second in rr._bysecond:
-                tset.append(datetime.time(hour, minute, second,
-                                          tzinfo=rr._tzinfo))
-        tset.sort()
-        return tset
-
-    def mtimeset(self, hour, minute, second):
-        tset = []
-        rr = self.rrule
-        for second in rr._bysecond:
-            tset.append(datetime.time(hour, minute, second, tzinfo=rr._tzinfo))
-        tset.sort()
-        return tset
-
-    def stimeset(self, hour, minute, second):
-        return (datetime.time(hour, minute, second,
-                tzinfo=self.rrule._tzinfo),)
-
-
-class rruleset(rrulebase):
-    """ The rruleset type allows more complex recurrence setups, mixing
-    multiple rules, dates, exclusion rules, and exclusion dates. The type
-    constructor takes the following keyword arguments:
-
-    :param cache: If True, caching of results will be enabled, improving
-                  performance of multiple queries considerably. """
-
-    class _genitem(object):
-        def __init__(self, genlist, gen):
-            try:
-                self.dt = advance_iterator(gen)
-                genlist.append(self)
-            except StopIteration:
-                pass
-            self.genlist = genlist
-            self.gen = gen
-
-        def __next__(self):
-            try:
-                self.dt = advance_iterator(self.gen)
-            except StopIteration:
-                if self.genlist[0] is self:
-                    heapq.heappop(self.genlist)
-                else:
-                    self.genlist.remove(self)
-                    heapq.heapify(self.genlist)
-
-        next = __next__
-
-        def __lt__(self, other):
-            return self.dt < other.dt
-
-        def __gt__(self, other):
-            return self.dt > other.dt
-
-        def __eq__(self, other):
-            return self.dt == other.dt
-
-        def __ne__(self, other):
-            return self.dt != other.dt
-
-    def __init__(self, cache=False):
-        super(rruleset, self).__init__(cache)
-        self._rrule = []
-        self._rdate = []
-        self._exrule = []
-        self._exdate = []
-
-    @_invalidates_cache
-    def rrule(self, rrule):
-        """ Include the given :py:class:`rrule` instance in the recurrence set
-            generation. """
-        self._rrule.append(rrule)
-
-    @_invalidates_cache
-    def rdate(self, rdate):
-        """ Include the given :py:class:`datetime` instance in the recurrence
-            set generation. """
-        self._rdate.append(rdate)
-
-    @_invalidates_cache
-    def exrule(self, exrule):
-        """ Include the given rrule instance in the recurrence set exclusion
-            list. Dates which are part of the given recurrence rules will not
-            be generated, even if some inclusive rrule or rdate matches them.
-        """
-        self._exrule.append(exrule)
-
-    @_invalidates_cache
-    def exdate(self, exdate):
-        """ Include the given datetime instance in the recurrence set
-            exclusion list. Dates included that way will not be generated,
-            even if some inclusive rrule or rdate matches them. """
-        self._exdate.append(exdate)
-
-    def _iter(self):
-        rlist = []
-        self._rdate.sort()
-        self._genitem(rlist, iter(self._rdate))
-        for gen in [iter(x) for x in self._rrule]:
-            self._genitem(rlist, gen)
-        exlist = []
-        self._exdate.sort()
-        self._genitem(exlist, iter(self._exdate))
-        for gen in [iter(x) for x in self._exrule]:
-            self._genitem(exlist, gen)
-        lastdt = None
-        total = 0
-        heapq.heapify(rlist)
-        heapq.heapify(exlist)
-        while rlist:
-            ritem = rlist[0]
-            if not lastdt or lastdt != ritem.dt:
-                while exlist and exlist[0] < ritem:
-                    exitem = exlist[0]
-                    advance_iterator(exitem)
-                    if exlist and exlist[0] is exitem:
-                        heapq.heapreplace(exlist, exitem)
-                if not exlist or ritem != exlist[0]:
-                    total += 1
-                    yield ritem.dt
-                lastdt = ritem.dt
-            advance_iterator(ritem)
-            if rlist and rlist[0] is ritem:
-                heapq.heapreplace(rlist, ritem)
-        self._len = total
-
-
-
-
-class _rrulestr(object):
-    """ Parses a string representation of a recurrence rule or set of
-    recurrence rules.
-
-    :param s:
-        Required, a string defining one or more recurrence rules.
-
-    :param dtstart:
-        If given, used as the default recurrence start if not specified in the
-        rule string.
-
-    :param cache:
-        If set ``True`` caching of results will be enabled, improving
-        performance of multiple queries considerably.
-
-    :param unfold:
-        If set ``True`` indicates that a rule string is split over more
-        than one line and should be joined before processing.
-
-    :param forceset:
-        If set ``True`` forces a :class:`dateutil.rrule.rruleset` to
-        be returned.
-
-    :param compatible:
-        If set ``True`` forces ``unfold`` and ``forceset`` to be ``True``.
-
-    :param ignoretz:
-        If set ``True``, time zones in parsed strings are ignored and a naive
-        :class:`datetime.datetime` object is returned.
-
-    :param tzids:
-        If given, a callable or mapping used to retrieve a
-        :class:`datetime.tzinfo` from a string representation.
-        Defaults to :func:`dateutil.tz.gettz`.
-
-    :param tzinfos:
-        Additional time zone names / aliases which may be present in a string
-        representation.  See :func:`dateutil.parser.parse` for more
-        information.
-
-    :return:
-        Returns a :class:`dateutil.rrule.rruleset` or
-        :class:`dateutil.rrule.rrule`
-    """
-
-    _freq_map = {"YEARLY": YEARLY,
-                 "MONTHLY": MONTHLY,
-                 "WEEKLY": WEEKLY,
-                 "DAILY": DAILY,
-                 "HOURLY": HOURLY,
-                 "MINUTELY": MINUTELY,
-                 "SECONDLY": SECONDLY}
-
-    _weekday_map = {"MO": 0, "TU": 1, "WE": 2, "TH": 3,
-                    "FR": 4, "SA": 5, "SU": 6}
-
-    def _handle_int(self, rrkwargs, name, value, **kwargs):
-        rrkwargs[name.lower()] = int(value)
-
-    def _handle_int_list(self, rrkwargs, name, value, **kwargs):
-        rrkwargs[name.lower()] = [int(x) for x in value.split(',')]
-
-    _handle_INTERVAL = _handle_int
-    _handle_COUNT = _handle_int
-    _handle_BYSETPOS = _handle_int_list
-    _handle_BYMONTH = _handle_int_list
-    _handle_BYMONTHDAY = _handle_int_list
-    _handle_BYYEARDAY = _handle_int_list
-    _handle_BYEASTER = _handle_int_list
-    _handle_BYWEEKNO = _handle_int_list
-    _handle_BYHOUR = _handle_int_list
-    _handle_BYMINUTE = _handle_int_list
-    _handle_BYSECOND = _handle_int_list
-
-    def _handle_FREQ(self, rrkwargs, name, value, **kwargs):
-        rrkwargs["freq"] = self._freq_map[value]
-
-    def _handle_UNTIL(self, rrkwargs, name, value, **kwargs):
-        global parser
-        if not parser:
-            from dateutil import parser
-        try:
-            rrkwargs["until"] = parser.parse(value,
-                                             ignoretz=kwargs.get("ignoretz"),
-                                             tzinfos=kwargs.get("tzinfos"))
-        except ValueError:
-            raise ValueError("invalid until date")
-
-    def _handle_WKST(self, rrkwargs, name, value, **kwargs):
-        rrkwargs["wkst"] = self._weekday_map[value]
-
-    def _handle_BYWEEKDAY(self, rrkwargs, name, value, **kwargs):
-        """
-        Two ways to specify this: +1MO or MO(+1)
-        """
-        l = []
-        for wday in value.split(','):
-            if '(' in wday:
-                # If it's of the form TH(+1), etc.
-                splt = wday.split('(')
-                w = splt[0]
-                n = int(splt[1][:-1])
-            elif len(wday):
-                # If it's of the form +1MO
-                for i in range(len(wday)):
-                    if wday[i] not in '+-0123456789':
-                        break
-                n = wday[:i] or None
-                w = wday[i:]
-                if n:
-                    n = int(n)
-            else:
-                raise ValueError("Invalid (empty) BYDAY specification.")
-
-            l.append(weekdays[self._weekday_map[w]](n))
-        rrkwargs["byweekday"] = l
-
-    _handle_BYDAY = _handle_BYWEEKDAY
-
-    def _parse_rfc_rrule(self, line,
-                         dtstart=None,
-                         cache=False,
-                         ignoretz=False,
-                         tzinfos=None):
-        if line.find(':') != -1:
-            name, value = line.split(':')
-            if name != "RRULE":
-                raise ValueError("unknown parameter name")
-        else:
-            value = line
-        rrkwargs = {}
-        for pair in value.split(';'):
-            name, value = pair.split('=')
-            name = name.upper()
-            value = value.upper()
-            try:
-                getattr(self, "_handle_"+name)(rrkwargs, name, value,
-                                               ignoretz=ignoretz,
-                                               tzinfos=tzinfos)
-            except AttributeError:
-                raise ValueError("unknown parameter '%s'" % name)
-            except (KeyError, ValueError):
-                raise ValueError("invalid '%s': %s" % (name, value))
-        return rrule(dtstart=dtstart, cache=cache, **rrkwargs)
-
-    def _parse_date_value(self, date_value, parms, rule_tzids,
-                          ignoretz, tzids, tzinfos):
-        global parser
-        if not parser:
-            from dateutil import parser
-
-        datevals = []
-        value_found = False
-        TZID = None
-
-        for parm in parms:
-            if parm.startswith("TZID="):
-                try:
-                    tzkey = rule_tzids[parm.split('TZID=')[-1]]
-                except KeyError:
-                    continue
-                if tzids is None:
-                    from . import tz
-                    tzlookup = tz.gettz
-                elif callable(tzids):
-                    tzlookup = tzids
-                else:
-                    tzlookup = getattr(tzids, 'get', None)
-                    if tzlookup is None:
-                        msg = ('tzids must be a callable, mapping, or None, '
-                               'not %s' % tzids)
-                        raise ValueError(msg)
-
-                TZID = tzlookup(tzkey)
-                continue
-
-            # RFC 5445 3.8.2.4: The VALUE parameter is optional, but may be found
-            # only once.
-            if parm not in {"VALUE=DATE-TIME", "VALUE=DATE"}:
-                raise ValueError("unsupported parm: " + parm)
-            else:
-                if value_found:
-                    msg = ("Duplicate value parameter found in: " + parm)
-                    raise ValueError(msg)
-                value_found = True
-
-        for datestr in date_value.split(','):
-            date = parser.parse(datestr, ignoretz=ignoretz, tzinfos=tzinfos)
-            if TZID is not None:
-                if date.tzinfo is None:
-                    date = date.replace(tzinfo=TZID)
-                else:
-                    raise ValueError('DTSTART/EXDATE specifies multiple timezone')
-            datevals.append(date)
-
-        return datevals
-
-    def _parse_rfc(self, s,
-                   dtstart=None,
-                   cache=False,
-                   unfold=False,
-                   forceset=False,
-                   compatible=False,
-                   ignoretz=False,
-                   tzids=None,
-                   tzinfos=None):
-        global parser
-        if compatible:
-            forceset = True
-            unfold = True
-
-        TZID_NAMES = dict(map(
-            lambda x: (x.upper(), x),
-            re.findall('TZID=(?P<name>[^:]+):', s)
-        ))
-        s = s.upper()
-        if not s.strip():
-            raise ValueError("empty string")
-        if unfold:
-            lines = s.splitlines()
-            i = 0
-            while i < len(lines):
-                line = lines[i].rstrip()
-                if not line:
-                    del lines[i]
-                elif i > 0 and line[0] == " ":
-                    lines[i-1] += line[1:]
-                    del lines[i]
-                else:
-                    i += 1
-        else:
-            lines = s.split()
-        if (not forceset and len(lines) == 1 and (s.find(':') == -1 or
-                                                  s.startswith('RRULE:'))):
-            return self._parse_rfc_rrule(lines[0], cache=cache,
-                                         dtstart=dtstart, ignoretz=ignoretz,
-                                         tzinfos=tzinfos)
-        else:
-            rrulevals = []
-            rdatevals = []
-            exrulevals = []
-            exdatevals = []
-            for line in lines:
-                if not line:
-                    continue
-                if line.find(':') == -1:
-                    name = "RRULE"
-                    value = line
-                else:
-                    name, value = line.split(':', 1)
-                parms = name.split(';')
-                if not parms:
-                    raise ValueError("empty property name")
-                name = parms[0]
-                parms = parms[1:]
-                if name == "RRULE":
-                    for parm in parms:
-                        raise ValueError("unsupported RRULE parm: "+parm)
-                    rrulevals.append(value)
-                elif name == "RDATE":
-                    for parm in parms:
-                        if parm != "VALUE=DATE-TIME":
-                            raise ValueError("unsupported RDATE parm: "+parm)
-                    rdatevals.append(value)
-                elif name == "EXRULE":
-                    for parm in parms:
-                        raise ValueError("unsupported EXRULE parm: "+parm)
-                    exrulevals.append(value)
-                elif name == "EXDATE":
-                    exdatevals.extend(
-                        self._parse_date_value(value, parms,
-                                               TZID_NAMES, ignoretz,
-                                               tzids, tzinfos)
-                    )
-                elif name == "DTSTART":
-                    dtvals = self._parse_date_value(value, parms, TZID_NAMES,
-                                                    ignoretz, tzids, tzinfos)
-                    if len(dtvals) != 1:
-                        raise ValueError("Multiple DTSTART values specified:" +
-                                         value)
-                    dtstart = dtvals[0]
-                else:
-                    raise ValueError("unsupported property: "+name)
-            if (forceset or len(rrulevals) > 1 or rdatevals
-                    or exrulevals or exdatevals):
-                if not parser and (rdatevals or exdatevals):
-                    from dateutil import parser
-                rset = rruleset(cache=cache)
-                for value in rrulevals:
-                    rset.rrule(self._parse_rfc_rrule(value, dtstart=dtstart,
-                                                     ignoretz=ignoretz,
-                                                     tzinfos=tzinfos))
-                for value in rdatevals:
-                    for datestr in value.split(','):
-                        rset.rdate(parser.parse(datestr,
-                                                ignoretz=ignoretz,
-                                                tzinfos=tzinfos))
-                for value in exrulevals:
-                    rset.exrule(self._parse_rfc_rrule(value, dtstart=dtstart,
-                                                      ignoretz=ignoretz,
-                                                      tzinfos=tzinfos))
-                for value in exdatevals:
-                    rset.exdate(value)
-                if compatible and dtstart:
-                    rset.rdate(dtstart)
-                return rset
-            else:
-                return self._parse_rfc_rrule(rrulevals[0],
-                                             dtstart=dtstart,
-                                             cache=cache,
-                                             ignoretz=ignoretz,
-                                             tzinfos=tzinfos)
-
-    def __call__(self, s, **kwargs):
-        return self._parse_rfc(s, **kwargs)
-
-
-rrulestr = _rrulestr()
-
-# vim:ts=4:sw=4:et

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/cli/__init__.py

@@ -1,4 +0,0 @@
-"""Subpackage containing all of pip's command line interface related code
-"""
-
-# This file intentionally does not import submodules

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/colorama/__init__.py

@@ -1,7 +0,0 @@
-# Copyright Jonathan Hartley 2013. BSD 3-Clause license, see LICENSE file.
-from .initialise import init, deinit, reinit, colorama_text, just_fix_windows_console
-from .ansi import Fore, Back, Style, Cursor
-from .ansitowin32 import AnsiToWin32
-
-__version__ = '0.4.6'
-

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/idna/compat.py

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

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/TensorMask/tensormask/arch.py

@@ -1,904 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import copy
-import logging
-import math
-from typing import List
-import torch
-import torch.nn.functional as F
-from fvcore.nn import sigmoid_focal_loss_star_jit, smooth_l1_loss
-from torch import nn
-
-from detectron2.layers import ShapeSpec, batched_nms, cat, paste_masks_in_image
-from detectron2.modeling.anchor_generator import DefaultAnchorGenerator
-from detectron2.modeling.backbone import build_backbone
-from detectron2.modeling.box_regression import Box2BoxTransform
-from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
-from detectron2.modeling.meta_arch.retinanet import (
-    permute_all_cls_and_box_to_N_HWA_K_and_concat,
-    permute_to_N_HWA_K,
-)
-from detectron2.structures import Boxes, ImageList, Instances
-from detectron2.utils.logger import log_first_n
-
-from tensormask.layers import SwapAlign2Nat
-
-__all__ = ["TensorMask"]
-
-
-def _assignment_rule(
-    gt_boxes,
-    anchor_boxes,
-    unit_lengths,
-    min_anchor_size,
-    scale_thresh=2.0,
-    spatial_thresh=1.0,
-    uniqueness_on=True,
-):
-    """
-    Given two lists of boxes of N ground truth boxes and M anchor boxes,
-    compute the assignment between the two, following the assignment rules in
-    https://arxiv.org/abs/1903.12174.
-    The box order must be (xmin, ymin, xmax, ymax), so please make sure to convert
-    to BoxMode.XYXY_ABS before calling this function.
-
-    Args:
-        gt_boxes, anchor_boxes (Boxes): two Boxes. Contains N & M boxes/anchors, respectively.
-        unit_lengths (Tensor): Contains the unit lengths of M anchor boxes.
-        min_anchor_size (float): Minimum size of the anchor, in pixels
-        scale_thresh (float): The `scale` threshold: the maximum size of the anchor
-                              should not be greater than scale_thresh x max(h, w) of
-                              the ground truth box.
-        spatial_thresh (float): The `spatial` threshold: the l2 distance between the
-                              center of the anchor and the ground truth box should not
-                              be greater than spatial_thresh x u where u is the unit length.
-
-    Returns:
-        matches (Tensor[int64]): a vector of length M, where matches[i] is a matched
-                ground-truth index in [0, N)
-        match_labels (Tensor[int8]): a vector of length M, where pred_labels[i] indicates
-            whether a prediction is a true or false positive or ignored
-    """
-    gt_boxes, anchor_boxes = gt_boxes.tensor, anchor_boxes.tensor
-    N = gt_boxes.shape[0]
-    M = anchor_boxes.shape[0]
-    if N == 0 or M == 0:
-        return (
-            gt_boxes.new_full((N,), 0, dtype=torch.int64),
-            gt_boxes.new_full((N,), -1, dtype=torch.int8),
-        )
-
-    # Containment rule
-    lt = torch.min(gt_boxes[:, None, :2], anchor_boxes[:, :2])  # [N,M,2]
-    rb = torch.max(gt_boxes[:, None, 2:], anchor_boxes[:, 2:])  # [N,M,2]
-    union = cat([lt, rb], dim=2)  # [N,M,4]
-
-    dummy_gt_boxes = torch.zeros_like(gt_boxes)
-    anchor = dummy_gt_boxes[:, None, :] + anchor_boxes[:, :]  # [N,M,4]
-
-    contain_matrix = torch.all(union == anchor, dim=2)  # [N,M]
-
-    # Centrality rule, scale
-    gt_size_lower = torch.max(gt_boxes[:, 2:] - gt_boxes[:, :2], dim=1)[0]  # [N]
-    gt_size_upper = gt_size_lower * scale_thresh  # [N]
-    # Fall back for small objects
-    gt_size_upper[gt_size_upper < min_anchor_size] = min_anchor_size
-    # Due to sampling of locations, the anchor sizes are deducted with sampling strides
-    anchor_size = (
-        torch.max(anchor_boxes[:, 2:] - anchor_boxes[:, :2], dim=1)[0] - unit_lengths
-    )  # [M]
-
-    size_diff_upper = gt_size_upper[:, None] - anchor_size  # [N,M]
-    scale_matrix = size_diff_upper >= 0  # [N,M]
-
-    # Centrality rule, spatial
-    gt_center = (gt_boxes[:, 2:] + gt_boxes[:, :2]) / 2  # [N,2]
-    anchor_center = (anchor_boxes[:, 2:] + anchor_boxes[:, :2]) / 2  # [M,2]
-    offset_center = gt_center[:, None, :] - anchor_center[:, :]  # [N,M,2]
-    offset_center /= unit_lengths[:, None]  # [N,M,2]
-    spatial_square = spatial_thresh * spatial_thresh
-    spatial_matrix = torch.sum(offset_center * offset_center, dim=2) <= spatial_square
-
-    assign_matrix = (contain_matrix & scale_matrix & spatial_matrix).int()
-
-    # assign_matrix is N (gt) x M (predicted)
-    # Max over gt elements (dim 0) to find best gt candidate for each prediction
-    matched_vals, matches = assign_matrix.max(dim=0)
-    match_labels = matches.new_full(matches.size(), 1, dtype=torch.int8)
-
-    match_labels[matched_vals == 0] = 0
-    match_labels[matched_vals == 1] = 1
-
-    # find all the elements that match to ground truths multiple times
-    not_unique_idxs = assign_matrix.sum(dim=0) > 1
-    if uniqueness_on:
-        match_labels[not_unique_idxs] = 0
-    else:
-        match_labels[not_unique_idxs] = -1
-
-    return matches, match_labels
-
-
-# TODO make the paste_mask function in d2 core support mask list
-def _paste_mask_lists_in_image(masks, boxes, image_shape, threshold=0.5):
-    """
-    Paste a list of masks that are of various resolutions (e.g., 28 x 28) into an image.
-    The location, height, and width for pasting each mask is determined by their
-    corresponding bounding boxes in boxes.
-
-    Args:
-        masks (list(Tensor)): A list of Tensor of shape (1, Hmask_i, Wmask_i).
-                            Values are in [0, 1]. The list length, Bimg, is the
-                            number of detected object instances in the image.
-        boxes (Boxes): A Boxes of length Bimg. boxes.tensor[i] and masks[i] correspond
-                            to the same object instance.
-        image_shape (tuple): height, width
-        threshold (float): A threshold in [0, 1] for converting the (soft) masks to
-            binary masks.
-
-    Returns:
-        img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the
-        number of detected object instances and Himage, Wimage are the image width
-        and height. img_masks[i] is a binary mask for object instance i.
-    """
-    if len(masks) == 0:
-        return torch.empty((0, 1) + image_shape, dtype=torch.uint8)
-
-    # Loop over masks groups. Each group has the same mask prediction size.
-    img_masks = []
-    ind_masks = []
-    mask_sizes = torch.tensor([m.shape[-1] for m in masks])
-    unique_sizes = torch.unique(mask_sizes)
-    for msize in unique_sizes.tolist():
-        cur_ind = torch.where(mask_sizes == msize)[0]
-        ind_masks.append(cur_ind)
-
-        cur_masks = cat([masks[i] for i in cur_ind])
-        cur_boxes = boxes[cur_ind]
-        img_masks.append(paste_masks_in_image(cur_masks, cur_boxes, image_shape, threshold))
-
-    img_masks = cat(img_masks)
-    ind_masks = cat(ind_masks)
-
-    img_masks_out = torch.empty_like(img_masks)
-    img_masks_out[ind_masks, :, :] = img_masks
-
-    return img_masks_out
-
-
-def _postprocess(results, result_mask_info, output_height, output_width, mask_threshold=0.5):
-    """
-    Post-process the output boxes for TensorMask.
-    The input images are often resized when entering an object detector.
-    As a result, we often need the outputs of the detector in a different
-    resolution from its inputs.
-
-    This function will postprocess the raw outputs of TensorMask
-    to produce outputs according to the desired output resolution.
-
-    Args:
-        results (Instances): the raw outputs from the detector.
-            `results.image_size` contains the input image resolution the detector sees.
-            This object might be modified in-place. Note that it does not contain the field
-            `pred_masks`, which is provided by another input `result_masks`.
-        result_mask_info (list[Tensor], Boxes): a pair of two items for mask related results.
-                The first item is a list of #detection tensors, each is the predicted masks.
-                The second item is the anchors corresponding to the predicted masks.
-        output_height, output_width: the desired output resolution.
-
-    Returns:
-        Instances: the postprocessed output from the model, based on the output resolution
-    """
-    scale_x, scale_y = (output_width / results.image_size[1], output_height / results.image_size[0])
-    results = Instances((output_height, output_width), **results.get_fields())
-
-    output_boxes = results.pred_boxes
-    output_boxes.tensor[:, 0::2] *= scale_x
-    output_boxes.tensor[:, 1::2] *= scale_y
-    output_boxes.clip(results.image_size)
-
-    inds_nonempty = output_boxes.nonempty()
-    results = results[inds_nonempty]
-    result_masks, result_anchors = result_mask_info
-    if result_masks:
-        result_anchors.tensor[:, 0::2] *= scale_x
-        result_anchors.tensor[:, 1::2] *= scale_y
-        result_masks = [x for (i, x) in zip(inds_nonempty.tolist(), result_masks) if i]
-        results.pred_masks = _paste_mask_lists_in_image(
-            result_masks,
-            result_anchors[inds_nonempty],
-            results.image_size,
-            threshold=mask_threshold,
-        )
-    return results
-
-
-class TensorMaskAnchorGenerator(DefaultAnchorGenerator):
-    """
-    For a set of image sizes and feature maps, computes a set of anchors for TensorMask.
-    It also computes the unit lengths and indexes for each anchor box.
-    """
-
-    def grid_anchors_with_unit_lengths_and_indexes(self, grid_sizes):
-        anchors = []
-        unit_lengths = []
-        indexes = []
-        for lvl, (size, stride, base_anchors) in enumerate(
-            zip(grid_sizes, self.strides, self.cell_anchors)
-        ):
-            grid_height, grid_width = size
-            device = base_anchors.device
-            shifts_x = torch.arange(
-                0, grid_width * stride, step=stride, dtype=torch.float32, device=device
-            )
-            shifts_y = torch.arange(
-                0, grid_height * stride, step=stride, dtype=torch.float32, device=device
-            )
-            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
-            shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=2)
-            # Stack anchors in shapes of (HWA, 4)
-            cur_anchor = (shifts[:, :, None, :] + base_anchors.view(1, 1, -1, 4)).view(-1, 4)
-            anchors.append(cur_anchor)
-            unit_lengths.append(
-                torch.full((cur_anchor.shape[0],), stride, dtype=torch.float32, device=device)
-            )
-            # create mask indexes using mesh grid
-            shifts_l = torch.full((1,), lvl, dtype=torch.int64, device=device)
-            shifts_i = torch.zeros((1,), dtype=torch.int64, device=device)
-            shifts_h = torch.arange(0, grid_height, dtype=torch.int64, device=device)
-            shifts_w = torch.arange(0, grid_width, dtype=torch.int64, device=device)
-            shifts_a = torch.arange(0, base_anchors.shape[0], dtype=torch.int64, device=device)
-            grids = torch.meshgrid(shifts_l, shifts_i, shifts_h, shifts_w, shifts_a)
-
-            indexes.append(torch.stack(grids, dim=5).view(-1, 5))
-
-        return anchors, unit_lengths, indexes
-
-    def forward(self, features):
-        """
-        Returns:
-            list[list[Boxes]]: a list of #image elements. Each is a list of #feature level Boxes.
-                The Boxes contains anchors of this image on the specific feature level.
-            list[list[Tensor]]: a list of #image elements. Each is a list of #feature level tensors.
-                The tensor contains strides, or unit lengths for the anchors.
-            list[list[Tensor]]: a list of #image elements. Each is a list of #feature level tensors.
-                The Tensor contains indexes for the anchors, with the last dimension meaning
-                (L, N, H, W, A), where L is level, I is image (not set yet), H is height,
-                W is width, and A is anchor.
-        """
-        num_images = len(features[0])
-        grid_sizes = [feature_map.shape[-2:] for feature_map in features]
-        anchors_list, lengths_list, indexes_list = self.grid_anchors_with_unit_lengths_and_indexes(
-            grid_sizes
-        )
-
-        # Convert anchors from Tensor to Boxes
-        anchors_per_im = [Boxes(x) for x in anchors_list]
-
-        anchors = [copy.deepcopy(anchors_per_im) for _ in range(num_images)]
-        unit_lengths = [copy.deepcopy(lengths_list) for _ in range(num_images)]
-        indexes = [copy.deepcopy(indexes_list) for _ in range(num_images)]
-
-        return anchors, unit_lengths, indexes
-
-
-@META_ARCH_REGISTRY.register()
-class TensorMask(nn.Module):
-    """
-    TensorMask model. Creates FPN backbone, anchors and a head for classification
-    and box regression. Calculates and applies proper losses to class, box, and
-    masks.
-    """
-
-    def __init__(self, cfg):
-        super().__init__()
-
-        # get the deice of the model
-        self.device = torch.device(cfg.MODEL.DEVICE)
-
-        # fmt: off
-        self.num_classes              = cfg.MODEL.TENSOR_MASK.NUM_CLASSES
-        self.in_features              = cfg.MODEL.TENSOR_MASK.IN_FEATURES
-        self.anchor_sizes             = cfg.MODEL.ANCHOR_GENERATOR.SIZES
-        self.num_levels               = len(cfg.MODEL.ANCHOR_GENERATOR.SIZES)
-        # Loss parameters:
-        self.focal_loss_alpha         = cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_ALPHA
-        self.focal_loss_gamma         = cfg.MODEL.TENSOR_MASK.FOCAL_LOSS_GAMMA
-        # Inference parameters:
-        self.score_threshold          = cfg.MODEL.TENSOR_MASK.SCORE_THRESH_TEST
-        self.topk_candidates          = cfg.MODEL.TENSOR_MASK.TOPK_CANDIDATES_TEST
-        self.nms_threshold            = cfg.MODEL.TENSOR_MASK.NMS_THRESH_TEST
-        self.detections_im            = cfg.TEST.DETECTIONS_PER_IMAGE
-        # Mask parameters:
-        self.mask_on                  = cfg.MODEL.MASK_ON
-        self.mask_loss_weight         = cfg.MODEL.TENSOR_MASK.MASK_LOSS_WEIGHT
-        self.mask_pos_weight          = torch.tensor(cfg.MODEL.TENSOR_MASK.POSITIVE_WEIGHT,
-                                                     dtype=torch.float32,
-                                                     device=self.device)
-        self.bipyramid_on             = cfg.MODEL.TENSOR_MASK.BIPYRAMID_ON
-        # fmt: on
-
-        # build the backbone
-        self.backbone = build_backbone(cfg)
-
-        backbone_shape = self.backbone.output_shape()
-        feature_shapes = [backbone_shape[f] for f in self.in_features]
-        feature_strides = [x.stride for x in feature_shapes]
-        # build anchors
-        self.anchor_generator = TensorMaskAnchorGenerator(cfg, feature_shapes)
-        self.num_anchors = self.anchor_generator.num_cell_anchors[0]
-        anchors_min_level = cfg.MODEL.ANCHOR_GENERATOR.SIZES[0]
-        self.mask_sizes = [size // feature_strides[0] for size in anchors_min_level]
-        self.min_anchor_size = min(anchors_min_level) - feature_strides[0]
-
-        # head of the TensorMask
-        self.head = TensorMaskHead(
-            cfg, self.num_levels, self.num_anchors, self.mask_sizes, feature_shapes
-        )
-        # box transform
-        self.box2box_transform = Box2BoxTransform(weights=cfg.MODEL.TENSOR_MASK.BBOX_REG_WEIGHTS)
-        pixel_mean = torch.Tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(3, 1, 1)
-        pixel_std = torch.Tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(3, 1, 1)
-        self.normalizer = lambda x: (x - pixel_mean) / pixel_std
-        self.to(self.device)
-
-    def forward(self, batched_inputs):
-        """
-        Args:
-            batched_inputs: a list, batched outputs of :class:`DetectionTransform` .
-                Each item in the list contains the inputs for one image.
-            For now, each item in the list is a dict that contains:
-                image: Tensor, image in (C, H, W) format.
-                instances: Instances
-                Other information that's included in the original dicts, such as:
-                    "height", "width" (int): the output resolution of the model, used in inference.
-                        See :meth:`postprocess` for details.
-         Returns:
-            losses (dict[str: Tensor]): mapping from a named loss to a tensor
-                storing the loss. Used during training only.
-        """
-        images = self.preprocess_image(batched_inputs)
-        if "instances" in batched_inputs[0]:
-            gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-        elif "targets" in batched_inputs[0]:
-            log_first_n(
-                logging.WARN, "'targets' in the model inputs is now renamed to 'instances'!", n=10
-            )
-            gt_instances = [x["targets"].to(self.device) for x in batched_inputs]
-        else:
-            gt_instances = None
-
-        features = self.backbone(images.tensor)
-        features = [features[f] for f in self.in_features]
-        # apply the TensorMask head
-        pred_logits, pred_deltas, pred_masks = self.head(features)
-        # generate anchors based on features, is it image specific?
-        anchors, unit_lengths, indexes = self.anchor_generator(features)
-
-        if self.training:
-            # get ground truths for class labels and box targets, it will label each anchor
-            gt_class_info, gt_delta_info, gt_mask_info, num_fg = self.get_ground_truth(
-                anchors, unit_lengths, indexes, gt_instances
-            )
-            # compute the loss
-            return self.losses(
-                gt_class_info,
-                gt_delta_info,
-                gt_mask_info,
-                num_fg,
-                pred_logits,
-                pred_deltas,
-                pred_masks,
-            )
-        else:
-            # do inference to get the output
-            results = self.inference(pred_logits, pred_deltas, pred_masks, anchors, indexes, images)
-            processed_results = []
-            for results_im, input_im, image_size in zip(
-                results, batched_inputs, images.image_sizes
-            ):
-                height = input_im.get("height", image_size[0])
-                width = input_im.get("width", image_size[1])
-                # this is to do post-processing with the image size
-                result_box, result_mask = results_im
-                r = _postprocess(result_box, result_mask, height, width)
-                processed_results.append({"instances": r})
-            return processed_results
-
-    def losses(
-        self,
-        gt_class_info,
-        gt_delta_info,
-        gt_mask_info,
-        num_fg,
-        pred_logits,
-        pred_deltas,
-        pred_masks,
-    ):
-        """
-        Args:
-            For `gt_class_info`, `gt_delta_info`, `gt_mask_info` and `num_fg` parameters, see
-                :meth:`TensorMask.get_ground_truth`.
-            For `pred_logits`, `pred_deltas` and `pred_masks`, see
-                :meth:`TensorMaskHead.forward`.
-
-        Returns:
-            losses (dict[str: Tensor]): mapping from a named loss to a scalar tensor
-                storing the loss. Used during training only. The potential dict keys are:
-                "loss_cls", "loss_box_reg" and "loss_mask".
-        """
-        gt_classes_target, gt_valid_inds = gt_class_info
-        gt_deltas, gt_fg_inds = gt_delta_info
-        gt_masks, gt_mask_inds = gt_mask_info
-        loss_normalizer = torch.tensor(max(1, num_fg), dtype=torch.float32, device=self.device)
-
-        # classification and regression
-        pred_logits, pred_deltas = permute_all_cls_and_box_to_N_HWA_K_and_concat(
-            pred_logits, pred_deltas, self.num_classes
-        )
-        loss_cls = (
-            sigmoid_focal_loss_star_jit(
-                pred_logits[gt_valid_inds],
-                gt_classes_target[gt_valid_inds],
-                alpha=self.focal_loss_alpha,
-                gamma=self.focal_loss_gamma,
-                reduction="sum",
-            )
-            / loss_normalizer
-        )
-
-        if num_fg == 0:
-            loss_box_reg = pred_deltas.sum() * 0
-        else:
-            loss_box_reg = (
-                smooth_l1_loss(pred_deltas[gt_fg_inds], gt_deltas, beta=0.0, reduction="sum")
-                / loss_normalizer
-            )
-        losses = {"loss_cls": loss_cls, "loss_box_reg": loss_box_reg}
-
-        # mask prediction
-        if self.mask_on:
-            loss_mask = 0
-            for lvl in range(self.num_levels):
-                cur_level_factor = 2 ** lvl if self.bipyramid_on else 1
-                for anc in range(self.num_anchors):
-                    cur_gt_mask_inds = gt_mask_inds[lvl][anc]
-                    if cur_gt_mask_inds is None:
-                        loss_mask += pred_masks[lvl][anc][0, 0, 0, 0] * 0
-                    else:
-                        cur_mask_size = self.mask_sizes[anc] * cur_level_factor
-                        # TODO maybe there are numerical issues when mask sizes are large
-                        cur_size_divider = torch.tensor(
-                            self.mask_loss_weight / (cur_mask_size ** 2),
-                            dtype=torch.float32,
-                            device=self.device,
-                        )
-
-                        cur_pred_masks = pred_masks[lvl][anc][
-                            cur_gt_mask_inds[:, 0],  # N
-                            :,  # V x U
-                            cur_gt_mask_inds[:, 1],  # H
-                            cur_gt_mask_inds[:, 2],  # W
-                        ]
-
-                        loss_mask += F.binary_cross_entropy_with_logits(
-                            cur_pred_masks.view(-1, cur_mask_size, cur_mask_size),  # V, U
-                            gt_masks[lvl][anc].to(dtype=torch.float32),
-                            reduction="sum",
-                            weight=cur_size_divider,
-                            pos_weight=self.mask_pos_weight,
-                        )
-            losses["loss_mask"] = loss_mask / loss_normalizer
-        return losses
-
-    @torch.no_grad()
-    def get_ground_truth(self, anchors, unit_lengths, indexes, targets):
-        """
-        Args:
-            anchors (list[list[Boxes]]): a list of N=#image elements. Each is a
-                list of #feature level Boxes. The Boxes contains anchors of
-                this image on the specific feature level.
-            unit_lengths (list[list[Tensor]]): a list of N=#image elements. Each is a
-                list of #feature level Tensor. The tensor contains unit lengths for anchors of
-                this image on the specific feature level.
-            indexes (list[list[Tensor]]): a list of N=#image elements. Each is a
-                list of #feature level Tensor. The tensor contains the 5D index of
-                each anchor, the second dimension means (L, N, H, W, A), where L
-                is level, I is image, H is height, W is width, and A is anchor.
-            targets (list[Instances]): a list of N `Instances`s. The i-th
-                `Instances` contains the ground-truth per-instance annotations
-                for the i-th input image.  Specify `targets` during training only.
-
-        Returns:
-            gt_class_info (Tensor, Tensor): A pair of two tensors for classification.
-                The first one is an integer tensor of shape (R, #classes) storing ground-truth
-                labels for each anchor. R is the total number of anchors in the batch.
-                The second one is an integer tensor of shape (R,), to indicate which
-                anchors are valid for loss computation, which anchors are not.
-            gt_delta_info (Tensor, Tensor): A pair of two tensors for boxes.
-                The first one, of shape (F, 4). F=#foreground anchors.
-                The last dimension represents ground-truth box2box transform
-                targets (dx, dy, dw, dh) that map each anchor to its matched ground-truth box.
-                Only foreground anchors have values in this tensor. Could be `None` if F=0.
-                The second one, of shape (R,), is an integer tensor indicating which anchors
-                are foreground ones used for box regression. Could be `None` if F=0.
-            gt_mask_info (list[list[Tensor]], list[list[Tensor]]): A pair of two lists for masks.
-                The first one is a list of P=#feature level elements. Each is a
-                list of A=#anchor tensors. Each tensor contains the ground truth
-                masks of the same size and for the same feature level. Could be `None`.
-                The second one is a list of P=#feature level elements. Each is a
-                list of A=#anchor tensors. Each tensor contains the location of the ground truth
-                masks of the same size and for the same feature level. The second dimension means
-                (N, H, W), where N is image, H is height, and W is width. Could be `None`.
-            num_fg (int): F=#foreground anchors, used later for loss normalization.
-        """
-        gt_classes = []
-        gt_deltas = []
-        gt_masks = [[[] for _ in range(self.num_anchors)] for _ in range(self.num_levels)]
-        gt_mask_inds = [[[] for _ in range(self.num_anchors)] for _ in range(self.num_levels)]
-
-        anchors = [Boxes.cat(anchors_i) for anchors_i in anchors]
-        unit_lengths = [cat(unit_lengths_i) for unit_lengths_i in unit_lengths]
-        indexes = [cat(indexes_i) for indexes_i in indexes]
-
-        num_fg = 0
-        for i, (anchors_im, unit_lengths_im, indexes_im, targets_im) in enumerate(
-            zip(anchors, unit_lengths, indexes, targets)
-        ):
-            # Initialize all
-            gt_classes_i = torch.full_like(
-                unit_lengths_im, self.num_classes, dtype=torch.int64, device=self.device
-            )
-            # Ground truth classes
-            has_gt = len(targets_im) > 0
-            if has_gt:
-                # Compute the pairwise matrix
-                gt_matched_inds, anchor_labels = _assignment_rule(
-                    targets_im.gt_boxes, anchors_im, unit_lengths_im, self.min_anchor_size
-                )
-                # Find the foreground instances
-                fg_inds = anchor_labels == 1
-                fg_anchors = anchors_im[fg_inds]
-                num_fg += len(fg_anchors)
-                # Find the ground truths for foreground instances
-                gt_fg_matched_inds = gt_matched_inds[fg_inds]
-                # Assign labels for foreground instances
-                gt_classes_i[fg_inds] = targets_im.gt_classes[gt_fg_matched_inds]
-                # Anchors with label -1 are ignored, others are left as negative
-                gt_classes_i[anchor_labels == -1] = -1
-
-                # Boxes
-                # Ground truth box regression, only for foregrounds
-                matched_gt_boxes = targets_im[gt_fg_matched_inds].gt_boxes
-                # Compute box regression offsets for foregrounds only
-                gt_deltas_i = self.box2box_transform.get_deltas(
-                    fg_anchors.tensor, matched_gt_boxes.tensor
-                )
-                gt_deltas.append(gt_deltas_i)
-
-                # Masks
-                if self.mask_on:
-                    # Compute masks for each level and each anchor
-                    matched_indexes = indexes_im[fg_inds, :]
-                    for lvl in range(self.num_levels):
-                        ids_lvl = matched_indexes[:, 0] == lvl
-                        if torch.any(ids_lvl):
-                            cur_level_factor = 2 ** lvl if self.bipyramid_on else 1
-                            for anc in range(self.num_anchors):
-                                ids_lvl_anchor = ids_lvl & (matched_indexes[:, 4] == anc)
-                                if torch.any(ids_lvl_anchor):
-                                    gt_masks[lvl][anc].append(
-                                        targets_im[
-                                            gt_fg_matched_inds[ids_lvl_anchor]
-                                        ].gt_masks.crop_and_resize(
-                                            fg_anchors[ids_lvl_anchor].tensor,
-                                            self.mask_sizes[anc] * cur_level_factor,
-                                        )
-                                    )
-                                    # Select (N, H, W) dimensions
-                                    gt_mask_inds_lvl_anc = matched_indexes[ids_lvl_anchor, 1:4]
-                                    # Set the image index to the current image
-                                    gt_mask_inds_lvl_anc[:, 0] = i
-                                    gt_mask_inds[lvl][anc].append(gt_mask_inds_lvl_anc)
-            gt_classes.append(gt_classes_i)
-
-        # Classes and boxes
-        gt_classes = cat(gt_classes)
-        gt_valid_inds = gt_classes >= 0
-        gt_fg_inds = gt_valid_inds & (gt_classes < self.num_classes)
-        gt_classes_target = torch.zeros(
-            (gt_classes.shape[0], self.num_classes), dtype=torch.float32, device=self.device
-        )
-        gt_classes_target[gt_fg_inds, gt_classes[gt_fg_inds]] = 1
-        gt_deltas = cat(gt_deltas) if gt_deltas else None
-
-        # Masks
-        gt_masks = [[cat(mla) if mla else None for mla in ml] for ml in gt_masks]
-        gt_mask_inds = [[cat(ila) if ila else None for ila in il] for il in gt_mask_inds]
-        return (
-            (gt_classes_target, gt_valid_inds),
-            (gt_deltas, gt_fg_inds),
-            (gt_masks, gt_mask_inds),
-            num_fg,
-        )
-
-    def inference(self, pred_logits, pred_deltas, pred_masks, anchors, indexes, images):
-        """
-        Arguments:
-            pred_logits, pred_deltas, pred_masks: Same as the output of:
-                meth:`TensorMaskHead.forward`
-            anchors, indexes: Same as the input of meth:`TensorMask.get_ground_truth`
-            images (ImageList): the input images
-
-        Returns:
-            results (List[Instances]): a list of #images elements.
-        """
-        assert len(anchors) == len(images)
-        results = []
-
-        pred_logits = [permute_to_N_HWA_K(x, self.num_classes) for x in pred_logits]
-        pred_deltas = [permute_to_N_HWA_K(x, 4) for x in pred_deltas]
-
-        pred_logits = cat(pred_logits, dim=1)
-        pred_deltas = cat(pred_deltas, dim=1)
-
-        for img_idx, (anchors_im, indexes_im) in enumerate(zip(anchors, indexes)):
-            # Get the size of the current image
-            image_size = images.image_sizes[img_idx]
-
-            logits_im = pred_logits[img_idx]
-            deltas_im = pred_deltas[img_idx]
-
-            if self.mask_on:
-                masks_im = [[mla[img_idx] for mla in ml] for ml in pred_masks]
-            else:
-                masks_im = [None] * self.num_levels
-            results_im = self.inference_single_image(
-                logits_im,
-                deltas_im,
-                masks_im,
-                Boxes.cat(anchors_im),
-                cat(indexes_im),
-                tuple(image_size),
-            )
-            results.append(results_im)
-        return results
-
-    def inference_single_image(
-        self, pred_logits, pred_deltas, pred_masks, anchors, indexes, image_size
-    ):
-        """
-        Single-image inference. Return bounding-box detection results by thresholding
-        on scores and applying non-maximum suppression (NMS).
-
-        Arguments:
-            pred_logits (list[Tensor]): list of #feature levels. Each entry contains
-                tensor of size (AxHxW, K)
-            pred_deltas (list[Tensor]): Same shape as 'pred_logits' except that K becomes 4.
-            pred_masks (list[list[Tensor]]): List of #feature levels, each is a list of #anchors.
-                Each entry contains tensor of size (M_i*M_i, H, W). `None` if mask_on=False.
-            anchors (list[Boxes]): list of #feature levels. Each entry contains
-                a Boxes object, which contains all the anchors for that
-                image in that feature level.
-            image_size (tuple(H, W)): a tuple of the image height and width.
-
-        Returns:
-            Same as `inference`, but for only one image.
-        """
-        pred_logits = pred_logits.flatten().sigmoid_()
-        # We get top locations across all levels to accelerate the inference speed,
-        # which does not seem to affect the accuracy.
-        # First select values above the threshold
-        logits_top_idxs = torch.where(pred_logits > self.score_threshold)[0]
-        # Then get the top values
-        num_topk = min(self.topk_candidates, logits_top_idxs.shape[0])
-        pred_prob, topk_idxs = pred_logits[logits_top_idxs].sort(descending=True)
-        # Keep top k scoring values
-        pred_prob = pred_prob[:num_topk]
-        # Keep top k values
-        top_idxs = logits_top_idxs[topk_idxs[:num_topk]]
-
-        # class index
-        cls_idxs = top_idxs % self.num_classes
-        # HWA index
-        top_idxs //= self.num_classes
-        # predict boxes
-        pred_boxes = self.box2box_transform.apply_deltas(
-            pred_deltas[top_idxs], anchors[top_idxs].tensor
-        )
-        # apply nms
-        keep = batched_nms(pred_boxes, pred_prob, cls_idxs, self.nms_threshold)
-        # pick the top ones
-        keep = keep[: self.detections_im]
-
-        results = Instances(image_size)
-        results.pred_boxes = Boxes(pred_boxes[keep])
-        results.scores = pred_prob[keep]
-        results.pred_classes = cls_idxs[keep]
-
-        # deal with masks
-        result_masks, result_anchors = [], None
-        if self.mask_on:
-            # index and anchors, useful for masks
-            top_indexes = indexes[top_idxs]
-            top_anchors = anchors[top_idxs]
-            result_indexes = top_indexes[keep]
-            result_anchors = top_anchors[keep]
-            # Get masks and do sigmoid
-            for lvl, _, h, w, anc in result_indexes.tolist():
-                cur_size = self.mask_sizes[anc] * (2 ** lvl if self.bipyramid_on else 1)
-                result_masks.append(
-                    torch.sigmoid(pred_masks[lvl][anc][:, h, w].view(1, cur_size, cur_size))
-                )
-
-        return results, (result_masks, result_anchors)
-
-    def preprocess_image(self, batched_inputs):
-        """
-        Normalize, pad and batch the input images.
-        """
-        images = [x["image"].to(self.device) for x in batched_inputs]
-        images = [self.normalizer(x) for x in images]
-        images = ImageList.from_tensors(images, self.backbone.size_divisibility)
-        return images
-
-
-class TensorMaskHead(nn.Module):
-    def __init__(self, cfg, num_levels, num_anchors, mask_sizes, input_shape: List[ShapeSpec]):
-        """
-        TensorMask head.
-        """
-        super().__init__()
-        # fmt: off
-        self.in_features        = cfg.MODEL.TENSOR_MASK.IN_FEATURES
-        in_channels             = input_shape[0].channels
-        num_classes             = cfg.MODEL.TENSOR_MASK.NUM_CLASSES
-        cls_channels            = cfg.MODEL.TENSOR_MASK.CLS_CHANNELS
-        num_convs               = cfg.MODEL.TENSOR_MASK.NUM_CONVS
-        # box parameters
-        bbox_channels           = cfg.MODEL.TENSOR_MASK.BBOX_CHANNELS
-        # mask parameters
-        self.mask_on            = cfg.MODEL.MASK_ON
-        self.mask_sizes         = mask_sizes
-        mask_channels           = cfg.MODEL.TENSOR_MASK.MASK_CHANNELS
-        self.align_on           = cfg.MODEL.TENSOR_MASK.ALIGNED_ON
-        self.bipyramid_on       = cfg.MODEL.TENSOR_MASK.BIPYRAMID_ON
-        # fmt: on
-
-        # class subnet
-        cls_subnet = []
-        cur_channels = in_channels
-        for _ in range(num_convs):
-            cls_subnet.append(
-                nn.Conv2d(cur_channels, cls_channels, kernel_size=3, stride=1, padding=1)
-            )
-            cur_channels = cls_channels
-            cls_subnet.append(nn.ReLU())
-
-        self.cls_subnet = nn.Sequential(*cls_subnet)
-        self.cls_score = nn.Conv2d(
-            cur_channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1
-        )
-        modules_list = [self.cls_subnet, self.cls_score]
-
-        # box subnet
-        bbox_subnet = []
-        cur_channels = in_channels
-        for _ in range(num_convs):
-            bbox_subnet.append(
-                nn.Conv2d(cur_channels, bbox_channels, kernel_size=3, stride=1, padding=1)
-            )
-            cur_channels = bbox_channels
-            bbox_subnet.append(nn.ReLU())
-
-        self.bbox_subnet = nn.Sequential(*bbox_subnet)
-        self.bbox_pred = nn.Conv2d(
-            cur_channels, num_anchors * 4, kernel_size=3, stride=1, padding=1
-        )
-        modules_list.extend([self.bbox_subnet, self.bbox_pred])
-
-        # mask subnet
-        if self.mask_on:
-            mask_subnet = []
-            cur_channels = in_channels
-            for _ in range(num_convs):
-                mask_subnet.append(
-                    nn.Conv2d(cur_channels, mask_channels, kernel_size=3, stride=1, padding=1)
-                )
-                cur_channels = mask_channels
-                mask_subnet.append(nn.ReLU())
-
-            self.mask_subnet = nn.Sequential(*mask_subnet)
-            modules_list.append(self.mask_subnet)
-            for mask_size in self.mask_sizes:
-                cur_mask_module = "mask_pred_%02d" % mask_size
-                self.add_module(
-                    cur_mask_module,
-                    nn.Conv2d(
-                        cur_channels, mask_size * mask_size, kernel_size=1, stride=1, padding=0
-                    ),
-                )
-                modules_list.append(getattr(self, cur_mask_module))
-            if self.align_on:
-                if self.bipyramid_on:
-                    for lvl in range(num_levels):
-                        cur_mask_module = "align2nat_%02d" % lvl
-                        lambda_val = 2 ** lvl
-                        setattr(self, cur_mask_module, SwapAlign2Nat(lambda_val))
-                    # Also the fusing layer, stay at the same channel size
-                    mask_fuse = [
-                        nn.Conv2d(cur_channels, cur_channels, kernel_size=3, stride=1, padding=1),
-                        nn.ReLU(),
-                    ]
-                    self.mask_fuse = nn.Sequential(*mask_fuse)
-                    modules_list.append(self.mask_fuse)
-                else:
-                    self.align2nat = SwapAlign2Nat(1)
-
-        # Initialization
-        for modules in modules_list:
-            for layer in modules.modules():
-                if isinstance(layer, nn.Conv2d):
-                    torch.nn.init.normal_(layer.weight, mean=0, std=0.01)
-                    torch.nn.init.constant_(layer.bias, 0)
-
-        # Use prior in model initialization to improve stability
-        bias_value = -math.log((1 - 0.01) / 0.01)
-        torch.nn.init.constant_(self.cls_score.bias, bias_value)
-
-    def forward(self, features):
-        """
-        Arguments:
-            features (list[Tensor]): FPN feature map tensors in high to low resolution.
-                Each tensor in the list correspond to different feature levels.
-
-        Returns:
-            pred_logits (list[Tensor]): #lvl tensors, each has shape (N, AxK, Hi, Wi).
-                The tensor predicts the classification probability
-                at each spatial position for each of the A anchors and K object
-                classes.
-            pred_deltas (list[Tensor]): #lvl tensors, each has shape (N, Ax4, Hi, Wi).
-                The tensor predicts 4-vector (dx,dy,dw,dh) box
-                regression values for every anchor. These values are the
-                relative offset between the anchor and the ground truth box.
-            pred_masks (list(list[Tensor])): #lvl list of tensors, each is a list of
-                A tensors of shape (N, M_{i,a}, Hi, Wi).
-                The tensor predicts a dense set of M_ixM_i masks at every location.
-        """
-        pred_logits = [self.cls_score(self.cls_subnet(x)) for x in features]
-        pred_deltas = [self.bbox_pred(self.bbox_subnet(x)) for x in features]
-
-        pred_masks = None
-        if self.mask_on:
-            mask_feats = [self.mask_subnet(x) for x in features]
-
-            if self.bipyramid_on:
-                mask_feat_high_res = mask_feats[0]
-                H, W = mask_feat_high_res.shape[-2:]
-                mask_feats_up = []
-                for lvl, mask_feat in enumerate(mask_feats):
-                    lambda_val = 2.0 ** lvl
-                    mask_feat_up = mask_feat
-                    if lvl > 0:
-                        mask_feat_up = F.interpolate(
-                            mask_feat, scale_factor=lambda_val, mode="bilinear", align_corners=False
-                        )
-                    mask_feats_up.append(
-                        self.mask_fuse(mask_feat_up[:, :, :H, :W] + mask_feat_high_res)
-                    )
-                mask_feats = mask_feats_up
-
-            pred_masks = []
-            for lvl, mask_feat in enumerate(mask_feats):
-                cur_masks = []
-                for mask_size in self.mask_sizes:
-                    cur_mask_module = getattr(self, "mask_pred_%02d" % mask_size)
-                    cur_mask = cur_mask_module(mask_feat)
-                    if self.align_on:
-                        if self.bipyramid_on:
-                            cur_mask_module = getattr(self, "align2nat_%02d" % lvl)
-                            cur_mask = cur_mask_module(cur_mask)
-                        else:
-                            cur_mask = self.align2nat(cur_mask)
-                    cur_masks.append(cur_mask)
-                pred_masks.append(cur_masks)
-        return pred_logits, pred_deltas, pred_masks

spaces/CVPR/LIVE/pybind11/tests/test_eval_call.py

@@ -1,5 +0,0 @@
-# -*- coding: utf-8 -*-
-# This file is called from 'test_eval.py'
-
-if 'call_test2' in locals():
-    call_test2(y)  # noqa: F821 undefined name

spaces/CVPR/LIVE/thrust/testing/unittest/assertions.h

@@ -1,593 +0,0 @@
-#pragma once
-
-#include <thrust/complex.h>
-#include <thrust/host_vector.h>
-#include <thrust/device_vector.h>
-#include <thrust/iterator/iterator_traits.h>
-#include <thrust/detail/type_traits.h>
-
-#include <unittest/exceptions.h>
-#include <unittest/util.h>
-
-#define ASSERT_EQUAL_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)           unittest::assert_equal((X),(Y), FILE_,  LINE_)
-#define ASSERT_EQUAL_QUIET_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)     unittest::assert_equal_quiet((X),(Y), FILE_, LINE_)
-#define ASSERT_NOT_EQUAL_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)       unittest::assert_not_equal((X),(Y), FILE_,  LINE_)
-#define ASSERT_NOT_EQUAL_QUIET_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_) unittest::assert_not_equal_quiet((X),(Y), FILE_, LINE_)
-#define ASSERT_LEQUAL_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)          unittest::assert_lequal((X),(Y), FILE_,  LINE_)
-#define ASSERT_GEQUAL_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)          unittest::assert_gequal((X),(Y), FILE_,  LINE_)
-#define ASSERT_LESS_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)            unittest::assert_less((X),(Y), FILE_,  LINE_)
-#define ASSERT_GREATER_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)         unittest::assert_greater((X),(Y), FILE_,  LINE_)
-#define ASSERT_ALMOST_EQUAL_WITH_FILE_AND_LINE(X,Y,FILE_,LINE_)    unittest::assert_almost_equal((X),(Y), FILE_, LINE_)
-#define ASSERT_EQUAL_RANGES_WITH_FILE_AND_LINE(X,Y,Z,FILE_,LINE_)  unittest::assert_equal((X),(Y),(Z), FILE_,  LINE_)
-
-#define ASSERT_THROWS_WITH_FILE_AND_LINE(                                     \
-  EXPR, EXCEPTION_TYPE, FILE_, LINE_                                          \
-)                                                                             \
-  {                                                                           \
-    unittest::threw_status THRUST_PP_CAT2(__s, LINE_)                         \
-      = unittest::did_not_throw;                                              \
-    try { EXPR; }                                                             \
-    catch (EXCEPTION_TYPE const&)                                             \
-    { THRUST_PP_CAT2(__s, LINE_) = unittest::threw_right_type; }              \
-    catch (...)                                                               \
-    { THRUST_PP_CAT2(__s, LINE_) = unittest::threw_wrong_type; }              \
-    unittest::check_assert_throws(                                            \
-      THRUST_PP_CAT2(__s, LINE_), THRUST_PP_STRINGIZE(EXCEPTION_TYPE)         \
-    , FILE_, LINE_                                                            \
-    );                                                                        \
-  }                                                                           \
-  /**/
-
-#define ASSERT_THROWS_EQUAL_WITH_FILE_AND_LINE(                               \
-  EXPR, EXCEPTION_TYPE, VALUE, FILE_, LINE_                                   \
-)                                                                             \
-  {                                                                           \
-    unittest::threw_status THRUST_PP_CAT2(__s, LINE_)                         \
-      = unittest::did_not_throw;                                              \
-    try { EXPR; }                                                             \
-    catch (EXCEPTION_TYPE const& THRUST_PP_CAT2(__e, LINE_))                  \
-    {                                                                         \
-      if (VALUE == THRUST_PP_CAT2(__e, LINE_))                                \
-        THRUST_PP_CAT2(__s, LINE_)                                            \
-          = unittest::threw_right_type;                                       \
-      else                                                                    \
-        THRUST_PP_CAT2(__s, LINE_)                                            \
-          = unittest::threw_right_type_but_wrong_value;                       \
-    }                                                                         \
-    catch (...) { THRUST_PP_CAT2(__s, LINE_) = unittest::threw_wrong_type; }  \
-    unittest::check_assert_throws(                                            \
-      THRUST_PP_CAT2(__s, LINE_), THRUST_PP_STRINGIZE(EXCEPTION_TYPE)         \
-    , FILE_, LINE_                                                            \
-    );                                                                        \
-  }                                                                           \
-  /**/
-
-#define KNOWN_FAILURE_WITH_FILE_AND_LINE(FILE_, LINE_)                                  \
-  { unittest::UnitTestKnownFailure f; f << "[" << FILE_ ":" << LINE_ << "]"; throw f; } \
-  /**/
-
-#define ASSERT_EQUAL(X,Y)           ASSERT_EQUAL_WITH_FILE_AND_LINE((X),(Y), __FILE__,  __LINE__)
-#define ASSERT_EQUAL_QUIET(X,Y)     ASSERT_EQUAL_QUIET_WITH_FILE_AND_LINE((X),(Y), __FILE__, __LINE__)
-#define ASSERT_NOT_EQUAL(X,Y)       ASSERT_NOT_EQUAL_WITH_FILE_AND_LINE((X),(Y), __FILE__,  __LINE__)
-#define ASSERT_NOT_EQUAL_QUIET(X,Y) ASSERT_NOT_EQUAL_QUIET_WITH_FILE_AND_LINE((X),(Y), __FILE__, __LINE__)
-#define ASSERT_LEQUAL(X,Y)          ASSERT_LEQUAL_WITH_FILE_AND_LINE((X),(Y), __FILE__,  __LINE__)
-#define ASSERT_GEQUAL(X,Y)          ASSERT_GEQUAL_WITH_FILE_AND_LINE((X),(Y), __FILE__,  __LINE__)
-#define ASSERT_LESS(X,Y)            ASSERT_LESS_WITH_FILE_AND_LINE((X),(Y), __FILE__,  __LINE__)
-#define ASSERT_GREATER(X,Y)         ASSERT_GREATER_WITH_FILE_AND_LINE((X),(Y), __FILE__,  __LINE__)
-#define ASSERT_ALMOST_EQUAL(X,Y)    ASSERT_ALMOST_EQUAL_WITH_FILE_AND_LINE((X),(Y), __FILE__, __LINE__)
-#define ASSERT_EQUAL_RANGES(X,Y,Z)  ASSERT_EQUAL_WITH_FILE_AND_LINE((X),(Y),(Z), __FILE__,  __LINE__)
-
-#define ASSERT_THROWS(EXPR, EXCEPTION_TYPE)                                   \
-  ASSERT_THROWS_WITH_FILE_AND_LINE(EXPR, EXCEPTION_TYPE, __FILE__, __LINE__)  \
-  /**/
-
-#define ASSERT_THROWS_EQUAL(EXPR, EXCEPTION_TYPE, VALUE)                                  \
-  ASSERT_THROWS_EQUAL_WITH_FILE_AND_LINE(EXPR, EXCEPTION_TYPE, VALUE, __FILE__, __LINE__) \
-  /**/
-
-#define KNOWN_FAILURE KNOWN_FAILURE_WITH_FILE_AND_LINE(__FILE__, __LINE__)
-
-namespace unittest
-{
-
-size_t const MAX_OUTPUT_LINES = 10;
-
-double const DEFAULT_RELATIVE_TOL = 1e-4;
-double const DEFAULT_ABSOLUTE_TOL = 1e-4;
-
-template<typename T>
-  struct value_type
-{
-  typedef typename thrust::detail::remove_const<
-    typename thrust::detail::remove_reference<
-      T
-    >::type
-  >::type type;
-};
-
-template<typename T>
-  struct value_type< thrust::device_reference<T> >
-{
-  typedef typename value_type<T>::type type;
-};
-
-////
-// check scalar values
-template <typename T1, typename T2>
-void assert_equal(T1 a, T2 b,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a == b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are not equal: " << a << " " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-void assert_equal(char a, char b,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a == b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are not equal: " << int(a) << " " << int(b);
-        f << " [type='" << type_name<char>() << "']";
-        throw f;
-    }
-}
-
-// sometimes its not possible to << a type
-template <typename T1, typename T2>
-void assert_equal_quiet(const T1& a, const T2& b,
-                        const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a == b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are not equal";
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-////
-// check scalar values
-template <typename T1, typename T2>
-void assert_not_equal(T1 a, T2 b,
-                      const std::string& filename = "unknown", int lineno = -1)
-{
-    if(a == b){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are equal: " << a << " " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-void assert_not_equal(char a, char b,
-                      const std::string& filename = "unknown", int lineno = -1)
-{
-    if(a == b){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are equal: " << int(a) << " " << int(b);
-        f << " [type='" << type_name<char>() << "']";
-        throw f;
-    }
-}
-
-// sometimes its not possible to << a type
-template <typename T1, typename T2>
-void assert_not_equal_quiet(const T1& a, const T2& b,
-                            const std::string& filename = "unknown", int lineno = -1)
-{
-    if(a == b){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are equal";
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-template <typename T1, typename T2>
-void assert_less(T1 a, T2 b,
-                 const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a < b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << a << " is greater or equal to " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-void assert_less(char a, char b,
-                 const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a < b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << int(a) << " is greater than or equal to " << int(b);
-        f << " [type='" << type_name<char>() << "']";
-        throw f;
-    }
-}
-
-template <typename T1, typename T2>
-void assert_greater(T1 a, T2 b,
-                    const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a > b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << a << " is less than or equal to " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-void assert_greater(char a, char b,
-                    const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a > b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << int(a) << " is less than or equal to " << int(b);
-        f << " [type='" << type_name<char>() << "']";
-        throw f;
-    }
-}
-
-template <typename T1, typename T2>
-void assert_lequal(T1 a, T2 b,
-                   const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a <= b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << a << " is greater than " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-void assert_lequal(char a, char b,
-                   const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a <= b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << int(a) << " is greater than " << int(b);
-        f << " [type='" << type_name<char>() << "']";
-        throw f;
-    }
-}
-
-template <typename T1, typename T2>
-void assert_gequal(T1 a, T2 b,
-                   const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a >= b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << a << " is less than " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-void assert_gequal(char a, char b,
-                   const std::string& filename = "unknown", int lineno = -1)
-{
-    if(!(a >= b)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << int(a) << " is less than " << int(b);
-        f << " [type='" << type_name<char>() << "']";
-        throw f;
-    }
-}
-
-// define our own abs() because std::abs() isn't portable for all types for some reason
-template<typename T>
-  T abs(const T &x)
-{
-  return x > 0 ? x : -x;
-}
-
-
-inline
-bool almost_equal(const double& a, const double& b, const double& a_tol, const double& r_tol)
-{
-    if(abs(a - b) > r_tol * (abs(a) + abs(b)) + a_tol)
-        return false;
-    else
-        return true;
-}
-
-template <typename T1, typename T2>
-void assert_almost_equal(T1 a, T2 b,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         double a_tol = DEFAULT_ABSOLUTE_TOL, double r_tol = DEFAULT_RELATIVE_TOL)
-
-{
-    if(!almost_equal(a, b, a_tol, r_tol)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are not approximately equal: " << (double) a << " " << (double) b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-
-template <typename T1, typename T2>
-void assert_almost_equal(thrust::complex<T1> a, thrust::complex<T2> b,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         double a_tol = DEFAULT_ABSOLUTE_TOL, double r_tol = DEFAULT_RELATIVE_TOL)
-
-{
-  if(!almost_equal(a.real(), b.real(), a_tol, r_tol)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are not approximately equal: " <<  a << " " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-
-template <typename T1, typename T2>
-  void assert_almost_equal(const thrust::complex<T1>& a, const std::complex<T2>& b,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         double a_tol = DEFAULT_ABSOLUTE_TOL, double r_tol = DEFAULT_RELATIVE_TOL)
-
-{
-  if(!almost_equal(a.real(), b.real(), a_tol, r_tol)){
-        unittest::UnitTestFailure f;
-        f << "[" << filename << ":" << lineno << "] ";
-        f << "values are not approximately equal: " <<  a << " " << b;
-        f << " [type='" << type_name<T1>() << "']";
-        throw f;
-    }
-}
-
-template <typename T>
-class almost_equal_to
-{
-    public:
-        double a_tol, r_tol;
-        almost_equal_to(double _a_tol = DEFAULT_ABSOLUTE_TOL, double _r_tol = DEFAULT_RELATIVE_TOL) : a_tol(_a_tol), r_tol(_r_tol) {}
-        bool operator()(const T& a, const T& b) const {
-            return almost_equal((double) a, (double) b, a_tol, r_tol);
-        }
-};
-
-
-template <typename T>
-class almost_equal_to<thrust::complex<T> >
-{
-    public:
-        double a_tol, r_tol;
-        almost_equal_to(double _a_tol = DEFAULT_ABSOLUTE_TOL, double _r_tol = DEFAULT_RELATIVE_TOL) : a_tol(_a_tol), r_tol(_r_tol) {}
-        bool operator()(const thrust::complex<T>& a, const thrust::complex<T>& b) const {
-            return almost_equal((double) a.real(), (double) b.real(), a_tol, r_tol) 
-                && almost_equal((double) a.imag(), (double) b.imag(), a_tol, r_tol);
-        }
-};
-
-////
-// check sequences
-
-template <typename ForwardIterator1, typename ForwardIterator2, typename BinaryPredicate>
-void assert_equal(ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2, BinaryPredicate op,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    typedef typename thrust::iterator_difference<ForwardIterator1>::type difference_type;
-    typedef typename thrust::iterator_value<ForwardIterator1>::type InputType;
-    
-    bool failure = false;
-
-    difference_type length1 = thrust::distance(first1, last1);
-    difference_type length2 = thrust::distance(first2, last2);
-    
-    difference_type min_length = thrust::min(length1, length2);
-
-    unittest::UnitTestFailure f;
-    f << "[" << filename << ":" << lineno << "] ";
-
-    // check lengths
-    if (length1 != length2)
-    {
-      failure = true;
-      f << "Sequences have different sizes (" << length1 << " != " << length2 << ")\n";
-    }
-
-    // check values
-    
-    size_t mismatches = 0;
-
-    for (difference_type i = 0; i < min_length; i++)
-    {
-      if(!op(*first1, *first2))
-      {
-        if (mismatches == 0)
-        {
-          failure = true;
-          f << "Sequences are not equal [type='" << type_name<InputType>() << "']\n";
-          f << "--------------------------------\n";
-        }
-
-        mismatches++;
-
-        if(mismatches <= MAX_OUTPUT_LINES)
-        {
-          if (sizeof(InputType) == 1)
-            f << "  [" << i << "] " << *first1 + InputType() << "  " << *first2 + InputType() << "\n"; // unprintable chars are a problem
-          else
-            f << "  [" << i << "] " << *first1 << "  " << *first2 << "\n";
-        }
-      }
-
-      first1++;
-      first2++;
-    }
-
-    if (mismatches > 0)
-    {
-      if(mismatches > MAX_OUTPUT_LINES)
-          f << "  (output limit reached)\n";
-      f << "--------------------------------\n";
-      f << "Sequences differ at " << mismatches << " of " << min_length << " positions" << "\n";
-    }
-    else if (length1 != length2)
-    {
-      f << "Sequences agree through " << min_length << " positions [type='" << type_name<InputType>() << "']\n";
-    }
-
-    if (failure)
-      throw f;
-}
-
-template <typename ForwardIterator1, typename ForwardIterator2>
-void assert_equal(ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    typedef typename thrust::iterator_traits<ForwardIterator1>::value_type InputType;
-    assert_equal(first1, last1, first2, last2, thrust::equal_to<InputType>(), filename, lineno);
-}
-
-
-template <typename ForwardIterator1, typename ForwardIterator2>
-void assert_almost_equal(ForwardIterator1 first1, ForwardIterator1 last1, ForwardIterator2 first2, ForwardIterator2 last2,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         const double a_tol = DEFAULT_ABSOLUTE_TOL, const double r_tol = DEFAULT_RELATIVE_TOL)
-{
-    typedef typename thrust::iterator_traits<ForwardIterator1>::value_type InputType;
-    assert_equal(first1, last1, first2, last2, almost_equal_to<InputType>(a_tol, r_tol), filename, lineno);
-}
-
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_equal(const thrust::host_vector<T,Alloc1>& A, const thrust::host_vector<T,Alloc2>& B,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    assert_equal(A.begin(), A.end(), B.begin(), B.end(), filename, lineno);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_almost_equal(const thrust::host_vector<T,Alloc1>& A, const thrust::host_vector<T,Alloc2>& B,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         const double a_tol = DEFAULT_ABSOLUTE_TOL, const double r_tol = DEFAULT_RELATIVE_TOL)
-{
-    assert_almost_equal(A.begin(), A.end(), B.begin(), B.end(), filename, lineno, a_tol, r_tol);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_equal(const thrust::host_vector<T,Alloc1>& A, const thrust::device_vector<T,Alloc2>& B,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    thrust::host_vector<T,Alloc1> B_host = B;
-    assert_equal(A, B_host, filename, lineno);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_equal(const thrust::device_vector<T,Alloc1>& A, const thrust::host_vector<T,Alloc2>& B,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    thrust::host_vector<T,Alloc2> A_host = A;
-    assert_equal(A_host, B, filename, lineno);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_equal(const thrust::device_vector<T,Alloc1>& A, const thrust::device_vector<T,Alloc2>& B,
-                  const std::string& filename = "unknown", int lineno = -1)
-{
-    thrust::host_vector<T> A_host = A;
-    thrust::host_vector<T> B_host = B;
-    assert_equal(A_host, B_host, filename, lineno);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_almost_equal(const thrust::host_vector<T,Alloc1>& A, const thrust::device_vector<T,Alloc2>& B,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         const double a_tol = DEFAULT_ABSOLUTE_TOL, const double r_tol = DEFAULT_RELATIVE_TOL)
-{
-    thrust::host_vector<T,Alloc1> B_host = B;
-    assert_almost_equal(A, B_host, filename, lineno, a_tol, r_tol);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_almost_equal(const thrust::device_vector<T,Alloc1>& A, const thrust::host_vector<T,Alloc2>& B,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         const double a_tol = DEFAULT_ABSOLUTE_TOL, const double r_tol = DEFAULT_RELATIVE_TOL)
-{
-    thrust::host_vector<T,Alloc2> A_host = A;
-    assert_almost_equal(A_host, B, filename, lineno, a_tol, r_tol);
-}
-
-template <typename T, typename Alloc1, typename Alloc2>
-void assert_almost_equal(const thrust::device_vector<T,Alloc1>& A, const thrust::device_vector<T,Alloc2>& B,
-                         const std::string& filename = "unknown", int lineno = -1,
-                         const double a_tol = DEFAULT_ABSOLUTE_TOL, const double r_tol = DEFAULT_RELATIVE_TOL)
-{
-    thrust::host_vector<T> A_host = A;
-    thrust::host_vector<T> B_host = B;
-    assert_almost_equal(A_host, B_host, filename, lineno, a_tol, r_tol);
-}
-
-enum threw_status
-{
-  did_not_throw
-, threw_wrong_type
-, threw_right_type_but_wrong_value
-, threw_right_type
-};
-
-void check_assert_throws(
-  threw_status s
-, std::string const& exception_name
-, std::string const& file_name = "unknown"
-, int line_number = -1
-)
-{
-  switch (s)
-  {
-    case did_not_throw:
-    {
-      unittest::UnitTestFailure f;
-      f << "[" << file_name << ":" << line_number << "] did not throw anything";
-      throw f;
-    }
-    case threw_wrong_type:
-    {
-      unittest::UnitTestFailure f;
-      f << "[" << file_name << ":" << line_number << "] did not throw an "
-        << "object of type " << exception_name;
-      throw f;
-    }
-    case threw_right_type_but_wrong_value:
-    {
-      unittest::UnitTestFailure f;
-      f << "[" << file_name << ":" << line_number << "] threw an object of the "
-        << "correct type (" << exception_name << ") but wrong value";
-      throw f;
-    }
-    case threw_right_type:
-      break;
-    default:
-    {
-      unittest::UnitTestFailure f;
-      f << "[" << file_name << ":" << line_number << "] encountered an "
-        << "unknown error";
-      throw f;
-    }
-  }
-}
-
-}; //end namespace unittest

spaces/CVPR/WALT/configs/_base_/schedules/schedule_1x.py

@@ -1,11 +0,0 @@
-# optimizer
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=500,
-    warmup_ratio=0.001,
-    step=[8, 11])
-runner = dict(type='EpochBasedRunner', max_epochs=12)

spaces/ChenyangSi/FreeU/__init__.py

@@ -1 +0,0 @@
-from free_lunch_utils import register_upblock2d, register_free_upblock2d, register_crossattn_upblock2d, register_free_crossattn_upblock2d

spaces/ChrisCaviar/ControlNet-v1-1/app_softedge.py

@@ -1,110 +0,0 @@
-#!/usr/bin/env python
-
-import gradio as gr
-
-from utils import randomize_seed_fn
-
-
-def create_demo(process, max_images=12, default_num_images=3):
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image()
-                prompt = gr.Textbox(label='Prompt')
-                run_button = gr.Button('Run')
-                with gr.Accordion('Advanced options', open=False):
-                    preprocessor_name = gr.Radio(label='Preprocessor',
-                                                 choices=[
-                                                     'HED',
-                                                     'PidiNet',
-                                                     'HED safe',
-                                                     'PidiNet safe',
-                                                     'None',
-                                                 ],
-                                                 type='value',
-                                                 value='PidiNet')
-                    num_samples = gr.Slider(label='Number of images',
-                                            minimum=1,
-                                            maximum=max_images,
-                                            value=default_num_images,
-                                            step=1)
-                    image_resolution = gr.Slider(label='Image resolution',
-                                                 minimum=256,
-                                                 maximum=512,
-                                                 value=512,
-                                                 step=256)
-                    preprocess_resolution = gr.Slider(
-                        label='Preprocess resolution',
-                        minimum=128,
-                        maximum=512,
-                        value=512,
-                        step=1)
-                    num_steps = gr.Slider(label='Number of steps',
-                                          minimum=1,
-                                          maximum=100,
-                                          value=20,
-                                          step=1)
-                    guidance_scale = gr.Slider(label='Guidance scale',
-                                               minimum=0.1,
-                                               maximum=30.0,
-                                               value=9.0,
-                                               step=0.1)
-                    seed = gr.Slider(label='Seed',
-                                     minimum=0,
-                                     maximum=1000000,
-                                     step=1,
-                                     value=0,
-                                     randomize=True)
-                    randomize_seed = gr.Checkbox(label='Randomize seed',
-                                                 value=True)
-                    a_prompt = gr.Textbox(
-                        label='Additional prompt',
-                        value='best quality, extremely detailed')
-                    n_prompt = gr.Textbox(
-                        label='Negative prompt',
-                        value=
-                        'longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality'
-                    )
-            with gr.Column():
-                result = gr.Gallery(label='Output', show_label=False).style(
-                    columns=2, object_fit='scale-down')
-        inputs = [
-            image,
-            prompt,
-            a_prompt,
-            n_prompt,
-            num_samples,
-            image_resolution,
-            preprocess_resolution,
-            num_steps,
-            guidance_scale,
-            seed,
-            preprocessor_name,
-        ]
-        prompt.submit(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-        )
-        run_button.click(
-            fn=randomize_seed_fn,
-            inputs=[seed, randomize_seed],
-            outputs=seed,
-        ).then(
-            fn=process,
-            inputs=inputs,
-            outputs=result,
-            api_name='scribble',
-        )
-    return demo
-
-
-if __name__ == '__main__':
-    from model import Model
-    model = Model(task_name='softedge')
-    demo = create_demo(model.process_softedge)
-    demo.queue().launch()