Update parquet files (step 18 of 121)

spaces/101-5/gpt4free/g4f/.v1/unfinished/openprompt/mail.py

@@ -1,111 +0,0 @@
-import email
-
-import requests
-
-
-class MailClient:
-
-    def __init__(self):
-        self.username = None
-        self.token = None
-        self.raw = None
-        self.mailids = None
-        self.mails = None
-        self.mail = None
-
-    def create(self, force=False):
-        headers = {
-            'accept': 'application/json',
-        }
-
-        if self.username:
-            pass
-        else:
-            self.response = requests.put(
-                'https://www.developermail.com/api/v1/mailbox', headers=headers)
-            self.response = self.response.json()
-            self.username = self.response['result']['name']
-            self.token = self.response['result']['token']
-
-        return {'username': self.username, 'token': self.token}
-
-    def destroy(self):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.delete(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}', headers=headers)
-        self.response = self.response.json()
-        self.username = None
-        self.token = None
-        return self.response
-
-    def newtoken(self):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.put(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/token', headers=headers)
-        self.response = self.response.json()
-        self.token = self.response['result']['token']
-        return {'username': self.username, 'token': self.token}
-
-    def getmailids(self):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-
-        self.response = requests.get(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}', headers=headers)
-        self.response = self.response.json()
-        self.mailids = self.response['result']
-        return self.mailids
-
-    def getmails(self, mailids: list = None):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-            'Content-Type': 'application/json',
-        }
-
-        if mailids is None:
-            mailids = self.mailids
-
-        data = str(mailids)
-
-        self.response = requests.post(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/messages', headers=headers, data=data)
-        self.response = self.response.json()
-        self.mails = self.response['result']
-        return self.mails
-
-    def getmail(self, mailid: str, raw=False):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.get(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/messages/{mailid}', headers=headers)
-        self.response = self.response.json()
-        self.mail = self.response['result']
-        if raw is False:
-            self.mail = email.message_from_string(self.mail)
-        return self.mail
-
-    def delmail(self, mailid: str):
-        headers = {
-            'accept': 'application/json',
-            'X-MailboxToken': self.token,
-        }
-        self.response = requests.delete(
-            f'https://www.developermail.com/api/v1/mailbox/{self.username}/messages/{mailid}', headers=headers)
-        self.response = self.response.json()
-        return self.response
-
-
-client = MailClient()
-client.newtoken()
-print(client.getmails())

spaces/101-5/gpt4free/g4f/models.py

@@ -1,201 +0,0 @@
-from g4f import Provider
-
-
-class Model:
-    class model:
-        name: str
-        base_provider: str
-        best_provider: str
-
-    class gpt_35_turbo:
-        name: str = 'gpt-3.5-turbo'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Forefront
-
-    class gpt_4:
-        name: str = 'gpt-4'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Bing
-        best_providers: list = [Provider.Bing, Provider.Lockchat]
-
-    class claude_instant_v1_100k:
-        name: str = 'claude-instant-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_instant_v1:
-        name: str = 'claude-instant-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1_100k:
-        name: str = 'claude-v1-100k'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class claude_v1:
-        name: str = 'claude-v1'
-        base_provider: str = 'anthropic'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class alpaca_7b:
-        name: str = 'alpaca-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class stablelm_tuned_alpha_7b:
-        name: str = 'stablelm-tuned-alpha-7b'
-        base_provider: str = 'replicate'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloom:
-        name: str = 'bloom'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class bloomz:
-        name: str = 'bloomz'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_t5_xxl:
-        name: str = 'flan-t5-xxl'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class flan_ul2:
-        name: str = 'flan-ul2'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class gpt_neox_20b:
-        name: str = 'gpt-neox-20b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class oasst_sft_4_pythia_12b_epoch_35:
-        name: str = 'oasst-sft-4-pythia-12b-epoch-3.5'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class santacoder:
-        name: str = 'santacoder'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_medium_nightly:
-        name: str = 'command-medium-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class command_xlarge_nightly:
-        name: str = 'command-xlarge-nightly'
-        base_provider: str = 'cohere'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_cushman_001:
-        name: str = 'code-cushman-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class code_davinci_002:
-        name: str = 'code-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_ada_001:
-        name: str = 'text-ada-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_babbage_001:
-        name: str = 'text-babbage-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_curie_001:
-        name: str = 'text-curie-001'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_002:
-        name: str = 'text-davinci-002'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-
-    class text_davinci_003:
-        name: str = 'text-davinci-003'
-        base_provider: str = 'openai'
-        best_provider: Provider.Provider = Provider.Vercel
-        
-    class palm:
-        name: str = 'palm'
-        base_provider: str = 'google'
-        best_provider: Provider.Provider = Provider.Bard
-        
-            
-    """    'falcon-40b': Model.falcon_40b,
-    'falcon-7b': Model.falcon_7b,
-    'llama-13b': Model.llama_13b,"""
-    
-    class falcon_40b:
-        name: str = 'falcon-40b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-    class falcon_7b:
-        name: str = 'falcon-7b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-        
-    class llama_13b:
-        name: str = 'llama-13b'
-        base_provider: str = 'huggingface'
-        best_provider: Provider.Provider = Provider.H2o
-    
-class ModelUtils:
-    convert: dict = {
-        'gpt-3.5-turbo': Model.gpt_35_turbo,
-        'gpt-4': Model.gpt_4,
-        
-        'claude-instant-v1-100k': Model.claude_instant_v1_100k,
-        'claude-v1-100k': Model.claude_v1_100k,
-        'claude-instant-v1': Model.claude_instant_v1,
-        'claude-v1': Model.claude_v1,
-        
-        'alpaca-7b': Model.alpaca_7b,
-        'stablelm-tuned-alpha-7b': Model.stablelm_tuned_alpha_7b,
-        
-        'bloom': Model.bloom,
-        'bloomz': Model.bloomz,
-        
-        'flan-t5-xxl': Model.flan_t5_xxl,
-        'flan-ul2': Model.flan_ul2,
-        
-        'gpt-neox-20b': Model.gpt_neox_20b,
-        'oasst-sft-4-pythia-12b-epoch-3.5': Model.oasst_sft_4_pythia_12b_epoch_35,
-        'santacoder': Model.santacoder,
-        
-        'command-medium-nightly': Model.command_medium_nightly,
-        'command-xlarge-nightly': Model.command_xlarge_nightly,
-        
-        'code-cushman-001': Model.code_cushman_001,
-        'code-davinci-002': Model.code_davinci_002,
-        
-        'text-ada-001': Model.text_ada_001,
-        'text-babbage-001': Model.text_babbage_001,
-        'text-curie-001': Model.text_curie_001,
-        'text-davinci-002': Model.text_davinci_002,
-        'text-davinci-003': Model.text_davinci_003,
-        
-        'palm2': Model.palm,
-        'palm': Model.palm,
-        'google': Model.palm,
-        'google-bard': Model.palm,
-        'google-palm': Model.palm,
-        'bard': Model.palm,
-        
-        'falcon-40b': Model.falcon_40b,
-        'falcon-7b': Model.falcon_7b,
-        'llama-13b': Model.llama_13b,
-    }

spaces/1acneusushi/gradio-2dmoleculeeditor/data/Bluesoleil Activation Serial Number Free Download and Install Guide.md

@@ -1,125 +0,0 @@
-<br />
-<h1>How to Get BlueSoleil Activation Serial Number for Free</h1>
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-<h2>What is BlueSoleil and why do you need it?</h2>
-<h3>BlueSoleil is a Bluetooth software that allows you to connect your devices with your computer</h3>
-<p>BlueSoleil is a software that enables your computer to communicate with Bluetooth devices such as phones, headsets, keyboards, mice, printers, cameras, etc. It supports various Bluetooth protocols and profiles, such as A2DP, AVRCP, BIP, DUN, FTP, HID, OPP, PAN, PBAP, SPP, etc. It also supports multiple Bluetooth dongles and adapters.</p>
-<h3>You need BlueSoleil to transfer data, make calls, listen to music, and more</h3>
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-<ul>
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-</ul>
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-<h2>How to get BlueSoleil activation serial number for free?</h2>
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spaces/1acneusushi/gradio-2dmoleculeeditor/data/Chess Position Trainer 5 Crack A Comprehensive Review of the Features and Benefits of this Chess Software.md

@@ -1,110 +0,0 @@
-
-<h1>Chess Position Trainer 5 Crack: How to Download and Use It</h1>
- <p>If you are a chess enthusiast who wants to improve your game, you might have heard of Chess Position Trainer 5, a professional chess software that helps you build your own repertoire, train your openings, tactics and endgames, and analyze your games and positions with a powerful engine. But what if you don't want to pay for the software? Is there a way to get it for free? In this article, we will show you how to download and use Chess Position Trainer 5 crack, as well as the risks and drawbacks of doing so.</p>
- <h2>What is Chess Position Trainer 5?</h2>
- <p>Chess Position Trainer 5 is a chess training software that was developed by chesspositiontrainer.com in 2019. It is designed to help chess players of all levels create, manage and study their chess opening repertoire. It also offers features such as flash-card based training, blindfold mode, position explorer, engine analysis, game import/export, statistics, and more. Chess Position Trainer 5 claims to be a major time saver for your daily work and make it fun to train your chess openings. It supports multiple languages such as English, Deutsch, Français, Nederlands, Italiano, Português.</p>
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- <p>Some of the features and benefits of Chess Position Trainer 5 are:</p>
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- <p>While using Chess Position Trainer 5 crack might seem like a good idea to save money and enjoy the software's features, it also comes with many risks and drawbacks that you should be aware of. Here are some of them:</p>
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-<li>A: Chess Position Trainer 5 costs $39.95 for the Pro version and $29.95 for the Standard version. You can also download a free version with limited features from the official website.</li>
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-<li>A: You can update Chess Position Trainer 5 by downloading the latest version from the official website and installing it over your existing version. You don't need to uninstall your previous version or enter your license key again.</li>
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-<li>A: You can contact Chess Position Trainer 5 support by sending an email to [email protected] or by filling out a contact form on the official website. You can also visit the blog or forum sections on the website for more information and tips.</li>
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-<li>A: You can learn more about Chess Position Trainer 5 by visiting the official website where you can find features, screenshots, videos, manuals, testimonials, FAQs, and more.</li>
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-<h1>Cubase 12: A Complete Guide for Beginners</h1>
- <p>If you are looking for a powerful and versatile music production software, you might want to check out Cubase 12. Cubase is one of the most popular and trusted DAWs (Digital Audio Workstations) in the world, used by professional composers, producers, engineers, and musicians across all genres and styles. Whether you want to create your own music, record and edit audio, mix and master your tracks, or collaborate with other artists, Cubase can help you achieve your musical goals.</p>
- <p>In this article, we will give you a complete overview of Cubase 12, its main features and benefits, and how to get started with it. We will also show you some tips and tricks on how to use some of the new and improved tools in Cubase 12, such as MIDI Remote integration, VariAudio and Scale Assistant, Sampler Track 2, MixConsole, and more. By the end of this article, you will have a better understanding of what Cubase 12 can do for you and how to use it effectively.</p>
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- <h2>MIDI Remote Integration</h2>
- <p>MIDI controllers are essential devices for any music production setup. They allow you to control various parameters and functions in your software with physical knobs, faders, buttons, pads, keys, or pedals. In Cubase 12, integrating your MIDI controller has become easier than ever. Thanks to the new concept of MIDI Remote Scripts, Cubase will automatically detect your device and map the controls accordingly. If there is no script for your device yet, you can easily create your own with the MIDI Controller Surface Editor. You can then conveniently connect controls and parameters with the Mapping Assistant.</p>
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- <ol>
-<li>Connect your MIDI controller to your computer via USB or MIDI cable.</li>
-<li>Launch Cubase 12 and create or open a project.</li>
-<li>Go to Studio > Studio Setup.</li>
-<li>Click on the + button in the top left corner and select your MIDI controller from the list.</li>
-<li>If there is a script available for your device, it will be automatically loaded. If not, you can create one using the MIDI Controller Surface Editor.</li>
-<li>Click on Apply and OK.</li>
-</ol>
- <h3>How to use MIDI Remote Scripts and Mapping Assistant to customize your control surface?</h3>
- <h3>How to record and edit MIDI data with Cubase 12?</h3>
- <p>To record and edit MIDI data with Cubase 12, follow these steps:</p>
- <ol>
-<li>Create a MIDI track and assign it to a VST instrument or an external MIDI device.</li>
-<li>Set the input and output routing for the MIDI track in the Inspector.</li>
-<li>Enable the Record Enable button on the MIDI track.</li>
-<li>Set the left and right locators to define the recording range.</li>
-<li>Click the Record button on the Transport panel or press * on the numeric keypad to start recording.</li>
-<li>Play your MIDI controller or use the On-Screen Keyboard to enter MIDI notes.</li>
-<li>Click the Stop button on the Transport panel or press 0 on the numeric keypad to stop recording.</li>
-<li>The recorded MIDI data will appear as a MIDI part on the MIDI track. You can double-click it to open it in the Key Editor, where you can edit the notes, velocities, lengths, and other parameters.</li>
-</ol>
- <h2>VariAudio and Scale Assistant</h2>
- <p>VariAudio is a powerful tool for editing and correcting the pitch of your vocals and monophonic instruments. It allows you to analyze, modify, and quantize the pitch of each note in an audio file, as well as adjust its formant, vibrato, and volume. You can also use VariAudio to create harmonies, melodies, or effects from your audio material. Scale Assistant is a new feature in Cubase 12 that helps you set and follow the scale of your project. It can automatically detect the scale of your audio or MIDI data, or let you choose one from a list of presets or custom scales. You can then use Scale Assistant to snap your notes to the scale, transpose them, or color-code them according to their harmonic function.</p>
- <h3>How to use VariAudio to edit and correct the pitch of your vocals and monophonic instruments?</h3>
- <p>To use VariAudio to edit and correct the pitch of your vocals and monophonic instruments, follow these steps:</p>
- <ol>
-<li>Select an audio event that contains vocals or monophonic instruments in the Project window.</li>
-<li>Open the Sample Editor by double-clicking the audio event.</li>
-<li>In the Inspector, click on the VariAudio tab.</li>
-<li>Click on Edit VariAudio to analyze the pitch of the audio event. The audio event will be divided into segments, each representing a note. The segments will be displayed as blocks on a piano roll grid.</li>
-<li>To correct the pitch of a segment, drag it up or down until it snaps to a semitone. You can also use the Quantize Pitch slider to automatically adjust all segments to the nearest semitone.</li>
-<li>To modify the pitch of a segment, drag its handles left or right to change its start or end position. You can also drag its center line up or down to bend its pitch curve.</li>
-<li>To adjust the formant of a segment, use the Formant Shift slider. This will change the timbre of the segment without affecting its pitch.</li>
-<li>To adjust the vibrato of a segment, use the Straighten Pitch slider. This will reduce or increase the amount of pitch variation within the segment.</li>
-<li>To adjust the volume of a segment, use the Volume slider. This will change the amplitude of the segment without affecting its dynamics.</li>
-</ol>
- <h3>How to use Scale Assistant to set and follow the scale of your project?</h3>
- <h3>How to use Scale Assistant to set and follow the scale of your project?</h3>
- <p>To use Scale Assistant to set and follow the scale of your project, follow these steps:</p>
- <ol>
-<li>Open the Key Editor by double-clicking a MIDI part or an audio event with VariAudio segments.</li>
-<li>In the Inspector, click on the Scale Assistant tab.</li>
-<li>To detect the scale of your data, click on Detect Scale. Cubase will analyze the notes or segments and suggest a scale that matches them. You can also choose a different scale from the drop-down menu.</li>
-<li>To snap your notes or segments to the scale, enable Snap Pitch Editing. This will prevent you from moving them outside the scale. You can also use the Snap Live Input button to snap the notes that you play on your MIDI controller to the scale.</li>
-<li>To transpose your notes or segments to a different key, use the Transpose slider. This will shift them up or down by semitones while keeping them in the scale.</li>
-<li>To color-code your notes or segments according to their harmonic function, enable Colorize Pitch Editing. This will assign different colors to tonic, dominant, subdominant, and other degrees of the scale.</li>
-</ol>
- <h2>Sampler Track 2</h2>
- <p>Sampler Track 2 is a new and improved version of the Sampler Track, a tool that allows you to create and use sampler tracks in Cubase 12. A sampler track is a track that contains a sample, which is a short audio file that can be played back at different pitches and speeds. You can use sampler tracks to create melodies, rhythms, effects, or sound design elements from any audio material. Sampler Track 2 introduces some new features and enhancements, such as Slice mode, LFOs, and Sample Library Browser.</p>
- <h3>How to create and use Sampler Tracks in Cubase 12?</h3>
- <p>To create and use Sampler Tracks in Cubase 12, follow these steps:</p>
- <ol>
-<li>Right-click on an empty space in the Project window and select Add Track > Sampler Track.</li>
-<li>A new sampler track will be created with an empty sampler zone. To load a sample into the sampler zone, drag and drop an audio file from the MediaBay, the Project window, or your file explorer.</li>
-<li>The sample will be mapped across the entire keyboard range. You can play it with your MIDI controller or use the On-Screen Keyboard to trigger it.</li>
-<li>You can adjust the pitch, volume, filter, envelope, and modulation settings of the sample in the Sampler Control section.</li>
-<li>You can also add effects to the sample in the Insert Effects section.</li>
-<li>To record MIDI data for the sampler track, follow the same steps as for recording MIDI data for any other track.</li>
-</ol>
- <h3>How to use the new Slice mode and LFOs to manipulate your samples?</h3>
- <p>To use the new Slice mode and LFOs to manipulate your samples, follow these steps:</p>
- <ol>
-<li>In the Sampler Control section, click on the Slice button to enable Slice mode. This will divide your sample into smaller segments based on its transients or beats.</li>
-<li>You can adjust the sensitivity of the slicing algorithm with the Threshold slider. You can also manually add or remove slices with the + and - buttons.</li>
-<li>Each slice will be assigned to a different key on your keyboard. You can play them individually or in any order you like.</li>
-<li>You can also edit each slice separately by clicking on it and adjusting its pitch, volume, filter, envelope, and modulation settings.</li>
-<li>To add LFOs (Low Frequency Oscillators) to your sample or slices, click on the LFO button in the Modulation section. This will open a new window where you can choose from different waveforms, rates, depths, and destinations for your LFOs.</li>
-<li>You can use LFOs to modulate various parameters of your sample or slices, such as pitch, volume, filter cutoff, pan, etc. This can create interesting rhythmic or dynamic effects.</li>
-</ol>
- <h3>How to use the new Sample Library Browser to find and load samples?</h3>
- <p>To use the new Sample Library Browser to find and load samples To use the new Sample Library Browser to find and load samples, follow these steps: <ol>
-<li>In the Sampler Control section, click on the Browse button to open the Sample Library Browser.</li>
-<li>The Sample Library Browser shows a list of categories and subcategories of samples that are available on your computer or online. You can also use the search field to find samples by name, keyword, or tag.</li>
-<li>To preview a sample, click on the Play button next to it. You can adjust the volume and pitch of the preview with the sliders below.</li>
-<li>To load a sample into the sampler zone, double-click on it or drag and drop it onto the sampler zone. The sample will replace the existing one in the sampler zone.</li>
-<li>You can also load multiple samples into different slices by selecting them and dragging and dropping them onto the sampler zone. This will create a new slice for each sample and assign them to consecutive keys on your keyboard.</li>
-</ol>
- <h2>MixConsole</h2>
- <p>MixConsole is the main mixing environment in Cubase 12. It allows you to control the level, pan, solo/mute status, routing, effects, and other parameters of your audio and MIDI channels. You can also use MixConsole to create groups, sends, inserts, and busses for your channels. MixConsole features a flexible and customizable layout that lets you adjust the size, position, and visibility of different sections and elements. You can also open multiple MixConsole windows with different configurations and views. MixConsole also introduces some new and improved features in Cubase 12, such as Spectral Comparison EQ, Channel Strip modules, Snapshots, History, and more.</p>
- <h3>How to use the MixConsole to mix your tracks and apply effects?</h3>
- <p>To use the MixConsole to mix your tracks and apply effects, follow these steps:</p>
- <ol>
-<li>Open the MixConsole window by pressing F3 or selecting Studio > MixConsole from the menu bar.</li>
-<li>The MixConsole window shows all the channels in your project in the same order as in the track list. You can use the scroll bar or the channel overview to navigate to different channels. You can also use the Visibility tab in the left zone to show or hide individual channels or channel types.</li>
-<li>To adjust the level of a channel, drag its fader up or down. To adjust the pan of a channel, drag its pan knob left or right. To solo or mute a channel, click on its S or M button. To enable or disable record for a channel, click on its R button.</li>
-<li>To set up the input and output routing for a channel, click on its input or output selector in the Inspector or in the Routing section. You can choose from different options depending on the type of channel. For example, you can route an audio channel to an audio input or output device, a group channel, an FX channel, a VCA fader, etc.</li>
-<li>To apply effects to a channel, you can use either inserts or sends. Inserts are effects that are applied directly to the signal of a channel. Sends are effects that are applied to a copy of the signal of a channel and then mixed with other signals in an FX channel. To add an insert effect to a channel, click on an empty slot in the Inserts section and select an effect from the list. To add a send effect to a channel, click on an empty slot in the Sends section and select an FX channel from the list. You can adjust the amount of send level with the knob next to the slot.</li>
-<h3>How to use the new Spectral Comparison EQ to clean up overlapping frequencies?</h3>
- <p>The Spectral Comparison EQ is a new feature in Cubase 12 that allows you to compare and adjust the frequency spectrum of two different channels. This can help you identify and eliminate any overlapping or clashing frequencies that might cause muddiness or interference in your mix. To use the Spectral Comparison EQ, follow these steps:</p>
- <ol>
-<li>Select a channel that you want to compare with another channel. This will be the reference channel.</li>
-<li>Open the Channel Settings window by clicking on the e button on the channel.</li>
-<li>In the Channel Settings window, click on the EQ tab to open the Equalizer section.</li>
-<li>In the Equalizer section, click on the Spectral Comparison button to enable it. This will open a new window where you can see the frequency spectrum of the reference channel and the comparison channel.</li>
-<li>To select a comparison channel, click on the drop-down menu next to the Spectral Comparison button and choose another channel from the list. You can also use the arrow buttons to cycle through different channels.</li>
-<li>You can adjust the opacity and color of the frequency spectrum of each channel with the sliders below. You can also switch between different views, such as Combined, Side by Side, or Overlay.</li>
-<li>To adjust the EQ of the reference channel, use the EQ bands and knobs as usual. You can also use the EQ Curve Display to drag and drop points on the curve. You will see how your adjustments affect the frequency spectrum of both channels in real time.</li>
-<li>To adjust the EQ of the comparison channel, click on the Swap button next to the Spectral Comparison button. This will switch the roles of the reference and comparison channels. You can then use the same steps as above to modify the EQ of the comparison channel.</li>
-</ol>
- <h3>How to use the Channel Strip modules to shape your sound?</h3>
- <p>The Channel Strip is a section in the Channel Settings window that contains various modules that can help you shape your sound. These modules include Gate, Compressor, Limiter, Envelope Shaper, Saturation, Magneto III, Standard Clipper, Tape Saturation, and Tube Saturation. You can use these modules to control the dynamics, tone, saturation, and distortion of your signal. To use the Channel Strip modules, follow these steps:</p>
- <ol>
-<li>Open the Channel Settings window by clicking on the e button on a channel.</li>
-<li>In the Channel Settings window, click on the Strip tab to open the Channel Strip section.</li>
-<li>In the Channel Strip section, you can see a list of modules that are available for your channel. You can enable or disable each module by clicking on its power button.</li>
-<h3>How to use the Channel Strip modules to shape your sound?</h3>
- <p>The Channel Strip is a section in the Channel Settings window that contains various modules that can help you shape your sound. These modules include Gate, Compressor, Limiter, Envelope Shaper, Saturation, Magneto III, Standard Clipper, Tape Saturation, and Tube Saturation. You can use these modules to control the dynamics, tone, saturation, and distortion of your signal. To use the Channel Strip modules, follow these steps:</p>
- <ol>
-<li>Open the Channel Settings window by clicking on the e button on a channel.</li>
-<li>In the Channel Settings window, click on the Strip tab to open the Channel Strip section.</li>
-<li>In the Channel Strip section, you can see a list of modules that are available for your channel. You can enable or disable each module by clicking on its power button.</li>
-<li>You can also reorder or replace each module by dragging and dropping it to a different slot or clicking on its name and selecting another module from the list.</li>
-<li>To adjust the settings of each module, use the knobs and buttons below its name. You can also use the Module Display to see a graphical representation of the module's parameters and curves.</li>
-<li>You can also use the Presets menu to load or save presets for each module or for the entire channel strip.</li>
-</ol>
- <h2>Conclusion</h2>
- <p>Cubase 12 is a powerful and versatile music production software that offers a wide range of features and tools for creating, recording, editing, mixing, and mastering your music. Whether you are a beginner or a professional, Cubase 12 can help you achieve your musical goals with ease and efficiency. In this article, we have given you a complete overview of Cubase 12, its main features and benefits, and how to get started with it. We have also shown you some tips and tricks on how to use some of the new and improved tools in Cubase 12, such as MIDI Remote integration, VariAudio and Scale Assistant, Sampler Track 2, MixConsole, and more. We hope you have found this article useful and informative.</p>
- <p>If you want to learn more about Cubase 12, you can visit the official website of Steinberg, where you can find more tutorials, guides, videos, forums, and support. You can also download a free trial version of Cubase 12 and try it for yourself. Cubase 12 is available in three editions: Pro, Artist, and Elements. Each edition has different features and prices to suit your needs and budget. You can compare the editions and buy Cubase 12 online from Steinberg's online shop or from authorized dealers.</p>
- <p>Thank you for reading this article. We hope you have enjoyed it and learned something new. Now it's time for you to try Cubase 12 and unleash your creativity. Happy music making!</p>
- <h2>FAQs</h2>
- <h3>What are the system requirements for Cubase 12?</h3>
- <p>The system requirements for Cubase 12 are as follows:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Windows 10 (64-bit) or macOS Big Sur (11) / Catalina (10.15)</th>
-</tr>
-<tr>
-<th>CPU</th>
-<th>64-bit Intel or AMD multi-core processor (Intel i5 or faster recommended)</th>
-</tr>
-<tr>
-<th>RAM</th>
-<th>8 GB minimum (16 GB or more recommended)</th>
-</tr>
-<tr>
-<th>Hard Disk Space</th>
-<th>30 GB free space</th>
-</tr>
-<tr>
-<th>Graphics Card</th>
-<th>Graphics card with DirectX 10 and WDDM 1.1 support (Windows only)</th>
-</tr>
-<tr>
-<th>Audio Interface</th>
-<th>ASIO compatible audio hardware recommended</th>
-</tr>
-<tr>
-<th>MIDI Interface</th>
-<th>MIDI input/output device (optional)</th>
-</tr>
-<tr>
-<th>Internet Connection</th>
-<th>An internet connection is required for activation, account setup, access to manual and personal/product registration.</th>
-</tr>
-</table>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <p>Cubase Pro is the most comprehensive edition of Cubase 12, offering all the features and tools that Cubase has to offer. It is designed for professional composers, producers, engineers, and musicians who need advanced functionality and flexibility. Cubase Pro supports up to 256 physical inputs and outputs, unlimited audio, instrument, and MIDI tracks, 8 VST instruments with over 3400 sounds, 75 audio effects, 18 MIDI effects, 5.1 surround sound, full score editing and printing, advanced audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, SpectraLayers One integration, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room, VCA faders, Channel Strip modules, Spectral Comparison EQ, Frequency 2 EQ, SuperVision metering suite, Video Export Render, Eucon support, and more.</p>
- <p>Cubase Artist is a more affordable edition of Cubase 12, offering most of the features and tools that Cubase Pro has to offer. It is designed for aspiring composers, producers, engineers, and musicians who need a reliable and versatile music production software. Cubase Artist supports up to 32 physical inputs and outputs, unlimited audio and MIDI tracks, 64 instrument tracks, 8 VST instruments with over 3400 sounds, 64 audio effects, 18 MIDI effects, full score editing and printing, basic audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room (limited), VCA faders (limited), Channel Strip modules (limited), Spectral Comparison EQ (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), Eucon support (limited), and more.</p>
- <p>Cubase Elements is the most basic edition of Cubase 12, Cubase Elements is the most basic edition of Cubase 12, offering the essential features and tools that Cubase has to offer. It is designed for beginners and hobbyists who want to learn and enjoy music production. Cubase Elements supports up to 24 physical inputs and outputs, 48 audio tracks, 64 MIDI tracks, 24 instrument tracks, 3 VST instruments with over 1000 sounds, 44 audio effects, 18 MIDI effects, basic score editing and printing, basic audio editing and processing, Sampler Track 2, Scale Assistant, MixConsole Snapshots (limited), Channel Strip modules (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), and more.</p>
- <p>As you can see, Cubase 12 offers different editions to suit your needs and budget. You can compare the editions and their features in more detail on the Steinberg website. You can also upgrade from one edition to another at any time by purchasing an update or upgrade license.</p>
- <h2>FAQs</h2>
- <h3>What are the system requirements for Cubase 12?</h3>
- <p>The system requirements for Cubase 12 are as follows:</p>
- <table>
-<tr>
-<th>Operating System</th>
-<th>Windows 10 (64-bit) or macOS Big Sur (11) / Catalina (10.15)</th>
-</tr>
-<tr>
-<th>CPU</th>
-<th>64-bit Intel or AMD multi-core processor (Intel i5 or faster recommended)</th>
-</tr>
-<tr>
-<th>RAM</th>
-<th>8 GB minimum (16 GB or more recommended)</th>
-</tr>
-<tr>
-<th>Hard Disk Space</th>
-<th>30 GB free space</th>
-</tr>
-<tr>
-<th>Graphics Card</th>
-<th>Graphics card with DirectX 10 and WDDM 1.1 support (Windows only)</th>
-</tr>
-<tr>
-<th>Audio Interface</th>
-<th>ASIO compatible audio hardware recommended</th>
-</tr>
-<tr>
-<th>MIDI Interface</th>
-<th>MIDI input/output device (optional)</th>
-</tr>
-<tr>
-<th>Internet Connection</th>
-<th>An internet connection is required for activation, account setup, access to manual and personal/product registration.</th>
-</tr>
-</table>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <h3>What are the differences between Cubase Pro, Artist, and Elements?</h3>
- <p>Cubase Pro is the most comprehensive edition of Cubase 12, offering all the features and tools that Cubase has to offer. It is designed for professional composers, producers, engineers, and musicians who need advanced functionality and flexibility. Cubase Pro supports up to 256 physical inputs and outputs, unlimited audio, instrument, and MIDI tracks, 8 VST instruments with over 3400 sounds, 75 audio effects, 18 MIDI effects, 5.1 surround sound, full score editing and printing, advanced audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, SpectraLayers One integration, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room, VCA faders, Channel Strip modules, Spectral Comparison EQ, Frequency 2 EQ, SuperVision metering suite, Video Export Render, Eucon support, and more.</p>
- <p>Cubase Artist is a more affordable edition of Cubase 12, offering most of the features and tools that Cubase Pro has to offer. It is designed for aspiring composers, producers, engineers, and musicians who need a reliable and versatile music production software. Cubase Artist supports up to 32 physical inputs and outputs, unlimited audio and MIDI tracks, 64 instrument tracks, 8 VST instruments with over 3400 sounds, 64 audio effects, 18 MIDI effects, full score editing and printing, basic audio editing and processing, VariAudio 3, Audio Alignment, ARA 2 support, Sampler Track 2, Scale Assistant, Global Tracks, MixConsole Snapshots, Control Room (limited), VCA faders (limited), Channel Strip modules (limited), Spectral Comparison EQ (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), Eucon support (limited), and more.</p>
- <p>Cubase Elements is the most basic edition of Cubase 12, offering the essential features and tools that Cubase has to offer. It is designed for beginners and hobbyists who want to learn and enjoy music production. Cubase Elements supports up to 24 physical inputs and outputs, 48 audio tracks, 64 MIDI tracks, 24 instrument tracks, 3 VST instruments with over 1000 sounds, 44 audio effects, 18 MIDI effects, basic score editing and printing, basic audio editing and processing, Sampler Track 2, Scale Assistant, MixConsole Snapshots (limited), Channel Strip modules (limited), Frequency 2 EQ (limited), SuperVision metering suite (limited), Video Export Render (limited), and more.</p>
- <p>As you can see, Cubase 12 offers different editions to suit your needs and budget. You can compare the editions and their features in more detail on the Steinberg website. You can also upgrade from one edition to another at any time by purchasing an update or upgrade license.</p>
- <h3>How much does Cubase 12 cost and where can I buy it?</h3>
- <p>The prices of Cubase 12 vary depending on the edition and the region. You can check the current prices on the Steinberg online shop or on authorized dealers' websites. Here are some approximate prices for reference:</p>
- <table>
-<tr>
-<th>Edition</th>
-<th>Price</th>
-</tr>
-<tr>
-<td>Cubase Pro</td>
-<td>$559.99 USD / €559 EUR / £499 GBP</td>
-</tr>
-<tr>
-<td>Cubase Artist</td>
-<td>$309.99 USD / €309 EUR / £269 GBP</td>
-</tr>
-<tr>
-<td>Cubase Elements</td>
-<td>$99.99 USD / €99 EUR / £85 GBP</td>
-</tr>
-</table>
- <p>You can buy Cubase 12 online from the Steinberg online shop or from authorized dealers. You can also buy a boxed version of Cubase 12 from some retailers. When you buy Cubase 12 online or in a box, you will receive a download access code that you can use to download the software from the Steinberg Download Assistant. You will also need a USB-eLicenser or a Soft-eLicenser to activate your license.</p>
- <h3>Where can I find more tutorials and guides for Cubase 12?</h3>
- <p>If you want to learn more about Cubase 12, you can find many tutorials and guides on the official website of Steinberg, where you can access the online help, the operation manual, the quick start guide, the video tutorials, the forums, and the support section. You can also find many tutorials and guides on YouTube, where you can watch videos from Steinberg's channel or from other users. You can also find many tutorials and guides on blogs, <h3>Where can I find more tutorials and guides for Cubase 12?</h3>
- <p>If you want to learn more about Cubase 12, you can find many tutorials and guides on the official website of Steinberg, where you can access the online help, the operation manual, the quick start guide, the video tutorials, the forums, and the support section. You can also find many tutorials and guides on YouTube, where you can watch videos from Steinberg's channel or from other users. You can also find many tutorials and guides on blogs, magazines, books, podcasts, or online courses that cover Cubase 12 or music production in general. Here are some examples of resources that you can check out:</p>
- <ul>
-<li>[Cubase 12 Tutorials]: A series of videos from Steinberg that introduce the new features and improvements in Cubase 12.</li>
-<li>[Cubase Academy]: An online course from Ask.Video that teaches you how to use Cubase 12 from beginner to advanced level.</li>
-<li>[Cubase 12 Explained]: A video tutorial from Groove3 that covers the basics and essentials of Cubase 12.</li>
-<li>[Cubase 12 Tips & Tricks]: A blog post from MusicTech that shares some useful tips and tricks for working with Cubase 12.</li>
-<li>[Cubase 12 Review]: A magazine article from Sound On Sound that gives an in-depth review of Cubase 12 and its features.</li>
-</ul>
- <h3>How can I contact Steinberg for support and feedback?</h3>
- <p>If you have any questions, issues, or feedback regarding Cubase 12 or any other Steinberg product, you can contact Steinberg for support and feedback through various channels. You can use the following methods to get in touch with Steinberg:</p>
- <ul>
-<li>[MySteinberg]: A personal online account where you can manage your licenses, products, downloads, updates, registration, and support requests.</li>
-<li>[Steinberg Support]: A section on the Steinberg website where you can find answers to frequently asked questions, submit support tickets, report bugs, request features, and access manuals and documentation.</li>
-<li>[Steinberg Forums]: A community platform where you can interact with other users and experts, ask questions, share tips, exchange ideas, and give feedback.</li>
-<li>[Steinberg YouTube Channel]: A video platform where you can watch tutorials, guides, demos, interviews, and live streams from Steinberg.</li>
-<li>[Steinberg Social Media]: A network of social media channels where you can follow Steinberg on Facebook, Twitter, Instagram, LinkedIn, and SoundCloud.</li>
-</ul></p> 197e85843d<br />
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- <h1>Cookie Run: Kingdom APK 2022 - A Sweet Adventure Game for Android</h1>
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- <p>Do you love cookies? Do you love adventure games? If you answered yes to both questions, then you will love Cookie Run: Kingdom APK 2022. This is a new game from Devsisters Corporation, the makers of the popular Cookie Run series. In this game, you can build your own cookie kingdom, meet your favorite tiny heroes, explore a vast and colorful world, and enjoy a captivating story and gameplay.</p>
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- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about Cookie Run: Kingdom APK 2022:</p>
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-<li><b>What are some tips and tricks for playing Cookie Run: Kingdom APK 2022?</b></li>
-<p>Some tips and tricks for playing Cookie Run: Kingdom APK 2022 are:</p>
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-<li>Use different types of cookies for different situations. For example, use tank cookies to absorb damage, healer cookies to restore health, or DPS cookies to deal damage.</li>
-<li>Upgrade your buildings and facilities regularly to increase your production, storage, defense, and income.</li>
-<li>Join or create a guild to chat with other players, exchange gifts, request help, or participate in guild wars.</li>
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-<li><b>How can I get more cookies and crystals in Cookie Run: Kingdom APK 2022?</b></li>
-<p>Some ways to get more cookies and crystals in Cookie Run: Kingdom APK 2022 are:</p>
-<ul>
-<li>Log in every day to claim your daily login bonus, which may include cookies and crystals.</li>
-<li>Watch ads or complete surveys to earn free crystals.</li>
-<li>Invite your friends to play the game and get referral rewards, such as cookies and crystals.</li>
-<li>Purchase them with real money from the shop.</li>
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-<li><b>How can I join or create a guild in Cookie Run: Kingdom APK 2022?</b></li>
-<p>To join or create a guild in Cookie Run: Kingdom APK 2022, you need to:</p>
-<ul>
-<li>Reach level 10 in the game.</li>
-<li>Tap on the guild icon on the bottom right corner of the screen.</li>
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-<li><b>How can I contact the developers or report a bug in Cookie Run: Kingdom APK 2022?</b></li>
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-<h1>Hay Day <h1>Hay Day Diamond Mod APK: A Guide for Beginners</h1>
- <p>Do you love farming games? Do you want to create your own dream farm and enjoy the rural life? If yes, then you should try Hay Day, one of the most popular and addictive farming simulators on mobile devices. Hay Day is a game where you can grow crops, raise animals, trade goods, and interact with other players. You can also customize your farm with various decorations and buildings. However, to do all these things, you need diamonds and coins, the two main currencies in the game. Diamonds are especially hard to come by, and they are used for speeding up processes, buying special items, and unlocking features. That's why many players look for ways to get more diamonds without spending real money. One of the ways is to use Hay Day Diamond Mod APK, a modified version of the game that gives you unlimited diamonds and coins. In this article, we will tell you everything you need to know about Hay Day Diamond Mod APK, including its features, how to download and install it, and its pros and cons. Let's get started!</p>
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- <p>Hay Day is a game that simulates the life of a farmer. You start with a small piece of land and a few resources, and your goal is to turn it into a prosperous farm. You can do this by:</p>
- <h3>Build a Farm</h3>
- <p>One of the main aspects of Hay Day is building your farm. You can choose from a variety of buildings and structures to place on your land, such as barns, silos, mills, bakeries, dairy factories, sugar mills, popcorn pots, pie ovens, cake ovens, juice presses, ice cream makers, candy machines, jam makers, honey extractors, looms, sewing machines, tailors, smelters, jewelers, fishing boats, fishing nets, fishing lures, duck salons, duck traps, lobster pools, lobster tails, sushi bars, soup kitchens, salad bars, sandwich bars, barbecue grills, pizza ovens, pasta makers, and more. Each building has a different function and produces different goods. You can also upgrade your buildings to increase their capacity and efficiency. You can also decorate your farm with various items, such as fences, paths, trees, flowers, ponds, fountains, statues, benches, signs, and more. You can also change the layout of your farm anytime you want. Building and decorating your farm is a fun and creative way to express yourself and make your farm unique.</p>
- <h3>Crops to Harvest and Grow</h3>
- <p>Another important aspect of Hay Day is growing crops. You can plant various seeds on your fields, such as wheat, corn, carrots, soybeans, sugarcane, indigo, tomatoes, potatoes, strawberries, pumpkins, cotton, rice, chili peppers, cacao, coffee beans, tea leaves, and more. You can also grow fruits on your trees, such as apples, cherries, pears, plums, lemons, oranges, bananas, coconuts, olives, grapes, and more. You can also grow flowers on your bushes, such as roses, tulips, lilies, lavender, jasmine, and more. You can also grow mushrooms on your logs.</p>
- <p>Growing crops is not only a source of income but also a source of ingredients for your buildings. You can use your crops to make various products, such as bread, butter, cheese, cream, milk, sugar, flour, popcorn, pies, cakes, juices, ice cream, candy, jam, honey, wool, fabric, clothes, metal bars, jewelry, fish, lobster, sushi, soup, salad, sandwich, barbecue, pizza, pasta, and more. You can also sell your crops and products to other players or to the roadside shop for coins. You can also use them to fulfill orders from the truck or the boat for coins and experience points. You can also donate them to your neighborhood or to the town visitors for rewards.</p>
- <p>Growing crops is not only rewarding but also challenging. You have to water your crops regularly to make them grow faster. You also have to protect them from pests and diseases. You also have to harvest them before they wither and replant new seeds. You also have to manage your storage space and your resources wisely. Growing crops is a skill that requires patience and strategy.</p>
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- <h3>Animals</h3>
- <p>Another essential aspect of Hay Day is raising animals. You can keep various animals on your farm, such as chickens, cows, pigs, sheep, goats, horses, donkeys, rabbits, cats, dogs, and more. You can also keep bees in your beehives and catch fish in your fishing area. Each animal has a different function and produces different goods. You can also feed them and pet them to make them happy and productive.</p>
- <p>Raising animals is not only a source of income but also a source of fun. You can use your animal products to make various products, such as eggs, bacon, ham, wool, cheese, cream, milk, honey, fish fillets, lobster tails, and more. You can also sell your animal products to other players or to the roadside shop for coins. You can also use them to fulfill orders from the truck or the boat for coins and experience points. You can also donate them to your neighborhood or to the town visitors for rewards.</p>
- <p>Raising animals is not only fun but also challenging. You have to feed your animals regularly to make them produce goods. You also have to collect their goods before they expire and store them in your barn or silo. You also have to protect them from foxes and other predators. You also have to manage your storage space and your resources wisely. Raising animals is a skill that requires care and attention.</p>
- <h3>Places to Visit</h3>
- <p>Another exciting aspect of Hay Day is visiting other places. You can travel to different locations in the game world, such as the town, the mine, the valley, the derby, and more. Each place has a different purpose and offers different activities. You can also meet new characters and discover new stories.</p>
- <p>Visiting places is not only a source of adventure but also a source of rewards. You can use your places to earn coins, diamonds, vouchers, boosters, expansion materials, decoration materials, and more. You can also complete tasks and quests to earn rewards. You can also participate in events and competitions to earn rewards.</p>
- <p>Visiting places is not only rewarding but also challenging. You have to unlock new places by reaching certain levels or completing certain requirements. You also have to upgrade your places by spending coins or diamonds. You also have to maintain your places by repairing or cleaning them. You also have to manage your time and energy wisely. Visiting places is a skill that requires exploration and curiosity.</p>
- <h3>Play with Friends and Neighbors</h3>
- <p>Another enjoyable aspect of Hay Day is playing with friends and neighbors. You can connect with other players from around the world through various ways, such as Facebook, Game Center, Google Play Games, Supercell ID, or friend codes. You can also join or create a neighborhood with up to 30 members.</p>
- <p>Playing with friends and neighbors is not only a source of socialization but also a source of cooperation. You can chat with other players and share tips and tricks. You can also trade goods and resources with other players through the roadside shop or the newspaper. You can also help other players by reviving their crops or filling their crates. You can also request or donate goods or resources from your neighborhood members.</p>
- <p>Playing with friends and neighbors is not only cooperative but also competitive. You can compete with other players or neighborhoods in various ways, such as the derby, the valley, the global leaderboard, or the seasonal leaderboard. You can also earn trophies and badges for your achievements. You can also show off your farm and your products to other players.</p>
- <p>Playing with friends and neighbors is a skill that requires communication and collaboration.</p>
- <h2>Hay Day Diamond Mod APK Features</h2>
- <p>Now that we have covered the original game and its features, let's move on to Hay Day Diamond Mod APK and its features. Hay Day Diamond Mod APK is a modified version of the game that gives you unlimited diamonds and coins. It also removes ads from the game. It is not an official version of the game, but it is created by third-party developers who modify the original game files.</p>
- <p>Hay Day Diamond Mod APK has many features that make it different from the original game. Here are some of them:</p>
- <h3>Unlimited Diamonds</h3>
- <p>The most obvious feature of Hay Day Diamond Mod APK is unlimited diamonds. Diamonds are the premium currency in Hay Day, and they are very hard to get in the original game. You can only get them by leveling up, completing achievements, watching ads, or buying them with real money. However, in Hay Day Diamond Mod APK, you can get unlimited diamonds for free. You can use them to speed up processes, buy special items, unlock features, and more. You can also use them to buy coins, vouchers, boosters, expansion materials, decoration materials, and more. You can also use them to skip tasks and quests, or to retry events and competitions. With unlimited diamonds, you can enjoy the game without any limitations or restrictions.</p>
- <h3>Unlimited Coins</h3>
- <p>Another feature of Hay Day Diamond Mod APK is unlimited coins. Coins are the basic currency in Hay Day, and they are used for buying and selling goods and resources. You can earn coins by selling your crops and products to other players or to the roadside shop. You can also earn coins by fulfilling orders from the truck or the boat. You can also earn coins by helping other players or by completing tasks and quests. However, in Hay Day Diamond Mod APK, you can get unlimited coins for free. You can use them to buy more items and resources from the shop or from other players. You can also use them to upgrade your buildings and structures. You can also use them to expand your land and your fishing area. You can also use them to decorate your farm and your town. With unlimited coins, you can buy anything you want and need in the game.</p>
- <h3>No Ads</h3>
- <p>Another feature of Hay Day Diamond Mod APK is no ads. Ads are annoying and distracting, and they can ruin your gaming experience. In the original game, you have to watch ads to get some rewards, such as diamonds, vouchers, boosters, or expansion materials. However, in Hay Day Diamond Mod APK, you don't have to watch any ads at all. You can get all the rewards without wasting your time or data. You can also enjoy the game without any interruptions or pop-ups. With no ads, you can improve your gaming experience and focus on the fun.</p>
- <h2>How to Download and Install Hay Day Diamond Mod APK</h2>
- <p>Now that we have covered the features of Hay Day Diamond Mod APK, let's see how to download and install it on your device. Before we do that, let's first go over some requirements and precautions that you need to follow.</p>
- <h3>Requirements</h3>
- <p>To download and install Hay Day Diamond Mod APK on your device, you need to meet some minimum requirements. These are:</p>
- <ul>
-<li>Your device must have Android 4.1 or higher version.</li>
-<li>Your device must have at least 1 GB of RAM and 100 MB of free storage space.</li>
-<li>Your device must have a stable internet connection.</li>
-<li>Your device must allow installation of apps from unknown sources. To enable this option, go to Settings > Security > Unknown Sources and toggle it on.</li>
-<li>You must uninstall the original Hay Day game from your device if you have it installed already.</li>
-</ul>
- <p>If you meet these requirements, you are ready to download and install Hay Day Diamond Mod APK on your device.</p>
- <h3>Download Link</h3>
- <p>To download Hay Day Diamond Mod APK on your device, you need to find a reliable and safe download link for it. There are many websites that claim to offer Hay Day Diamond Mod APK for free, but not all of them are trustworthy. Some of them may contain viruses or malware that can harm your device or steal your personal information. Some of them may also provide fake or outdated versions of the game that may not work properly or may cause errors.</p>
- <p>To avoid these risks, you should only download Hay Day Diamond Mod APK from a reputable source that has positive reviews and feedback from other users. One such source is [HayDayModAPK.com], a website that provides the latest version of Hay Day Diamond Mod APK for free. This website is secure and verified by Google Play Protect. It also has a user-friendly interface and fast download speed.</p>
- <p>To download Hay Day Diamond Mod APK from [HayDayModAPK.com], follow these steps:</p>
- <ol>
-<li>Go to [HayDayModAPK.com] using your browser.</li>
-<li>Click on the Download button on the homepage.</li>
-<li>Wait for the download to start automatically. If it doesn't start, click on the link that says "Click here if the download doesn't start automatically".</li>
-<li>Wait for the download to finish.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded Hay Day Diamond Mod APK on your device.</p>
- <h3>Installation Process</h3>
- <p>To install Hay Day Diamond Mod APK on your device, follow these steps:</p>
- <ol>
-<li>Locate the downloaded file in your file manager or download folder. The file name should be HayDayModAPK.apk.</li>
-<li>Tap on the file to open it.</li>
-<li>A pop-up window will appear asking you to install the app. Tap on Install.</li>
-<li>Wait for the installation to complete.</li>
-<li>A pop-up window will appear asking you to open the app. Tap on Open.</li>
-<li>Allow the app to access your device's resources and permissions.</li>
-<li>Wait for the game to load and update.</li>
-<li>Enjoy playing Hay Day Diamond Mod APK with unlimited diamonds and coins!</li>
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- <p>Congratulations! You have successfully installed Hay Day Diamond Mod APK on your device.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Hay Day Diamond Mod APK is a modified version of Hay Day that gives you unlimited diamonds and coins. It also removes ads from the game. It is not an official version of the game, but it is created by third-party developers who modify the original game files. It has many features that make it different from the original game, such as unlimited diamonds, unlimited coins, and no ads. It also has some requirements and precautions that you need to follow, such as uninstalling the original game, enabling unknown sources, finding a reliable download link, and following the installation process.</p>
- <p>Hay Day Diamond Mod APK is a great way to enjoy Hay Day without any limitations or restrictions. You can build your farm, grow crops, raise animals, visit places, play with friends and neighbors, and more. You can also buy anything you want and need in the game. You can also speed up processes, buy special items, unlock features, and more. You can also enjoy the game without any interruptions or pop-ups. Hay Day Diamond Mod APK is a fun and addictive farming simulator that you should try.</p>
- <p>However, Hay Day Diamond Mod APK also has some disadvantages that you should be aware of. For example, it is not an official version of the game, so it may not be compatible with some devices or updates. It may also cause errors or glitches in the game. It may also violate the terms and conditions of the original game, so it may result in a ban or a penalty from Supercell. It may also expose your device or personal information to viruses or malware from untrusted sources. Hay Day Diamond Mod APK is a risky and unofficial version of Hay Day that you should use at your own discretion.</p>
- <p>Hay Day Diamond Mod APK is a guide for beginners who want to know more about this modified version of Hay Day. We hope that this article has helped you understand what Hay Day Diamond Mod APK is, what are its features, how to download and install it, and what are its pros and cons. We hope that you have enjoyed reading this article and that you have learned something new. Thank you for your time and attention.</p>
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- <p>Here are some frequently asked questions about Hay Day Diamond Mod APK:</p>
- <h4>Q: Is Hay Day Diamond Mod APK safe to use?</h4>
- <p>A: Hay Day Diamond Mod APK is not an official version of Hay Day, so it may not be safe to use. It may contain viruses or malware that can harm your device or steal your personal information. It may also cause errors or glitches in the game. It may also violate the terms and conditions of the original game, so it may result in a ban or a penalty from Supercell. You should only download Hay Day Diamond Mod APK from a reputable source that has positive reviews and feedback from other users. You should also scan the file with an antivirus software before installing it. You should also backup your data before using it.</p>
- <h4>Q: How do I update Hay Day Diamond Mod APK?</h4>
- <p>A: Hay Day Diamond Mod APK may not be compatible with some updates of the original game, so it may not work properly or at all after an update. You should check the website where you downloaded Hay Day Diamond Mod APK for any updates or new versions of the mod apk. You should also uninstall the old version of Hay Day Diamond Mod APK before installing the new one. You should also backup your data before updating it.</p>
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-<li>Using cheats and hacks: There are some websites and apps that claim to offer cheats and hacks for Hay Day, such as generators, tools, scripts, or codes. These cheats and hacks promise to give you unlimited diamonds and coins in Hay Day for free. However, these cheats and hacks are also risky and unofficial, and they may not work properly or at all. They may also contain viruses or malware that can harm your device or steal your personal information. They may also cause errors or glitches in the game. They may also violate the terms and conditions of the original game, so they may result in a ban or a penalty from Supercell. You should only use cheats and hacks from a reputable source that has positive reviews and feedback from other users. You should also scan the file with an antivirus software before using it. You should also backup your data before using it.</li>
-<li>Using tips and tricks: There are some websites and blogs that offer tips and tricks for Hay Day, such as guides, tutorials, strategies, or secrets. These tips and tricks can help you earn more diamonds and coins in Hay Day by playing smartly and efficiently. They can also help you improve your skills and knowledge of the game. These tips and tricks are not risky or unofficial, and they can work well if you follow them correctly. However, these tips and tricks may not be enough to give you unlimited diamonds and coins in Hay Day, and they may take some time and effort to apply. You should only use tips and tricks from a reliable source that has accurate and updated information.</li>
-<li>Using real money: The simplest and safest way to get more diamonds and coins in Hay Day is to use real money. You can buy diamonds and coins from the in-game shop using your credit card, debit card, PayPal account, Google Play account, Apple ID account, or other payment methods. You can also buy gift cards or vouchers that can be redeemed for diamonds and coins in the game. Buying diamonds and coins with real money is not risky or unofficial, and it can give you instant access to any item or feature in the game. However, buying diamonds and coins with real money can be expensive and addictive, and it may not be affordable or feasible for everyone.</li>
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spaces/AI-ZTH-03-23/6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5/README.md

@@ -1,12 +0,0 @@
----
-title: 6.AI.Dashboard.Wiki.Chat.Cognitive.HTML5
-emoji: 🦀
-colorFrom: gray
-colorTo: gray
-sdk: static
-pinned: false
-license: mit
-duplicated_from: awacke1/AI.Dashboard.Wiki.Chat.Cognitive.HTML5
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/AIFILMS/StyleGANEX/models/stylegan2/lpips/pretrained_networks.py

@@ -1,181 +0,0 @@
-from collections import namedtuple
-import torch
-from torchvision import models as tv
-from IPython import embed
-
-class squeezenet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(squeezenet, self).__init__()
-        pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.slice6 = torch.nn.Sequential()
-        self.slice7 = torch.nn.Sequential()
-        self.N_slices = 7
-        for x in range(2):
-            self.slice1.add_module(str(x), pretrained_features[x])
-        for x in range(2,5):
-            self.slice2.add_module(str(x), pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), pretrained_features[x])
-        for x in range(10, 11):
-            self.slice5.add_module(str(x), pretrained_features[x])
-        for x in range(11, 12):
-            self.slice6.add_module(str(x), pretrained_features[x])
-        for x in range(12, 13):
-            self.slice7.add_module(str(x), pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        h = self.slice6(h)
-        h_relu6 = h
-        h = self.slice7(h)
-        h_relu7 = h
-        vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
-        out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
-
-        return out
-
-
-class alexnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(alexnet, self).__init__()
-        alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(2):
-            self.slice1.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(2, 5):
-            self.slice2.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(5, 8):
-            self.slice3.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(8, 10):
-            self.slice4.add_module(str(x), alexnet_pretrained_features[x])
-        for x in range(10, 12):
-            self.slice5.add_module(str(x), alexnet_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1 = h
-        h = self.slice2(h)
-        h_relu2 = h
-        h = self.slice3(h)
-        h_relu3 = h
-        h = self.slice4(h)
-        h_relu4 = h
-        h = self.slice5(h)
-        h_relu5 = h
-        alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
-        out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
-
-        return out
-
-class vgg16(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True):
-        super(vgg16, self).__init__()
-        vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
-        self.slice1 = torch.nn.Sequential()
-        self.slice2 = torch.nn.Sequential()
-        self.slice3 = torch.nn.Sequential()
-        self.slice4 = torch.nn.Sequential()
-        self.slice5 = torch.nn.Sequential()
-        self.N_slices = 5
-        for x in range(4):
-            self.slice1.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(4, 9):
-            self.slice2.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(9, 16):
-            self.slice3.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(16, 23):
-            self.slice4.add_module(str(x), vgg_pretrained_features[x])
-        for x in range(23, 30):
-            self.slice5.add_module(str(x), vgg_pretrained_features[x])
-        if not requires_grad:
-            for param in self.parameters():
-                param.requires_grad = False
-
-    def forward(self, X):
-        h = self.slice1(X)
-        h_relu1_2 = h
-        h = self.slice2(h)
-        h_relu2_2 = h
-        h = self.slice3(h)
-        h_relu3_3 = h
-        h = self.slice4(h)
-        h_relu4_3 = h
-        h = self.slice5(h)
-        h_relu5_3 = h
-        vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
-        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
-
-        return out
-
-
-
-class resnet(torch.nn.Module):
-    def __init__(self, requires_grad=False, pretrained=True, num=18):
-        super(resnet, self).__init__()
-        if(num==18):
-            self.net = tv.resnet18(pretrained=pretrained)
-        elif(num==34):
-            self.net = tv.resnet34(pretrained=pretrained)
-        elif(num==50):
-            self.net = tv.resnet50(pretrained=pretrained)
-        elif(num==101):
-            self.net = tv.resnet101(pretrained=pretrained)
-        elif(num==152):
-            self.net = tv.resnet152(pretrained=pretrained)
-        self.N_slices = 5
-
-        self.conv1 = self.net.conv1
-        self.bn1 = self.net.bn1
-        self.relu = self.net.relu
-        self.maxpool = self.net.maxpool
-        self.layer1 = self.net.layer1
-        self.layer2 = self.net.layer2
-        self.layer3 = self.net.layer3
-        self.layer4 = self.net.layer4
-
-    def forward(self, X):
-        h = self.conv1(X)
-        h = self.bn1(h)
-        h = self.relu(h)
-        h_relu1 = h
-        h = self.maxpool(h)
-        h = self.layer1(h)
-        h_conv2 = h
-        h = self.layer2(h)
-        h_conv3 = h
-        h = self.layer3(h)
-        h_conv4 = h
-        h = self.layer4(h)
-        h_conv5 = h
-
-        outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
-        out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
-
-        return out

spaces/ALSv/FSW/roop/processors/frame/face_swapper.py

@@ -1,88 +0,0 @@
-from typing import Any, List, Callable
-import cv2
-import insightface
-import threading
-
-import roop.globals
-import roop.processors.frame.core
-from roop.core import update_status
-from roop.face_analyser import get_one_face, get_many_faces
-from roop.typing import Face, Frame
-from roop.utilities import conditional_download, resolve_relative_path, is_image, is_video
-
-FACE_SWAPPER = None
-THREAD_LOCK = threading.Lock()
-NAME = 'ROOP.FACE-SWAPPER'
-
-
-def get_face_swapper() -> Any:
-    global FACE_SWAPPER
-
-    with THREAD_LOCK:
-        if FACE_SWAPPER is None:
-            model_path = resolve_relative_path('../models/inswapper_128.onnx')
-            FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.execution_providers)
-    return FACE_SWAPPER
-
-
-def pre_check() -> bool:
-    download_directory_path = resolve_relative_path('../models')
-    conditional_download(download_directory_path, ['https://huggingface.co/Apex-X/inswapper_128.onnx/resolve/main/inswapper_128.onnx'])
-    return True
-
-
-def pre_start() -> bool:
-    if not is_image(roop.globals.source_path):
-        update_status('Select an image for source path.', NAME)
-        return False
-    elif not get_one_face(cv2.imread(roop.globals.source_path)):
-        update_status('No face in source path detected.', NAME)
-        return False
-    if not is_image(roop.globals.target_path) and not is_video(roop.globals.target_path):
-        update_status('Select an image or video for target path.', NAME)
-        return False
-    return True
-
-
-def post_process() -> None:
-    global FACE_SWAPPER
-
-    FACE_SWAPPER = None
-
-
-def swap_face(source_face: Face, target_face: Face, temp_frame: Frame) -> Frame:
-    return get_face_swapper().get(temp_frame, target_face, source_face, paste_back=True)
-
-
-def process_frame(source_face: Face, temp_frame: Frame) -> Frame:
-    if roop.globals.many_faces:
-        many_faces = get_many_faces(temp_frame)
-        if many_faces:
-            for target_face in many_faces:
-                temp_frame = swap_face(source_face, target_face, temp_frame)
-    else:
-        target_face = get_one_face(temp_frame)
-        if target_face:
-            temp_frame = swap_face(source_face, target_face, temp_frame)
-    return temp_frame
-
-
-def process_frames(source_path: str, temp_frame_paths: List[str], update: Callable[[], None]) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    for temp_frame_path in temp_frame_paths:
-        temp_frame = cv2.imread(temp_frame_path)
-        result = process_frame(source_face, temp_frame)
-        cv2.imwrite(temp_frame_path, result)
-        if update:
-            update()
-
-
-def process_image(source_path: str, target_path: str, output_path: str) -> None:
-    source_face = get_one_face(cv2.imread(source_path))
-    target_frame = cv2.imread(target_path)
-    result = process_frame(source_face, target_frame)
-    cv2.imwrite(output_path, result)
-
-
-def process_video(source_path: str, temp_frame_paths: List[str]) -> None:
-    roop.processors.frame.core.process_video(source_path, temp_frame_paths, process_frames)

spaces/ATang0729/Forecast4Muses/Model/Model6/Model6_2_ProfileRecogition/mmpretrain/configs/_base_/models/resnetv1d152.py

@@ -1,17 +0,0 @@
-# model settings
-model = dict(
-    type='ImageClassifier',
-    backbone=dict(
-        type='ResNetV1d',
-        depth=152,
-        num_stages=4,
-        out_indices=(3, ),
-        style='pytorch'),
-    neck=dict(type='GlobalAveragePooling'),
-    head=dict(
-        type='LinearClsHead',
-        num_classes=1000,
-        in_channels=2048,
-        loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
-        topk=(1, 5),
-    ))

spaces/AchyuthGamer/OpenGPT-Chat-UI/src/lib/server/websearch/searchWeb.ts

@@ -1,63 +0,0 @@
-import { SERPAPI_KEY, SERPER_API_KEY } from "$env/static/private";
-
-import { getJson } from "serpapi";
-import type { GoogleParameters } from "serpapi";
-
-// Show result as JSON
-export async function searchWeb(query: string) {
-	if (SERPER_API_KEY) {
-		return await searchWebSerper(query);
-	}
-	if (SERPAPI_KEY) {
-		return await searchWebSerpApi(query);
-	}
-	throw new Error("No Serper.dev or SerpAPI key found");
-}
-
-export async function searchWebSerper(query: string) {
-	const params = {
-		q: query,
-		hl: "en",
-		gl: "us",
-	};
-
-	const response = await fetch("https://google.serper.dev/search", {
-		method: "POST",
-		body: JSON.stringify(params),
-		headers: {
-			"x-api-key": SERPER_API_KEY,
-			"Content-type": "application/json; charset=UTF-8",
-		},
-	});
-
-	/* eslint-disable @typescript-eslint/no-explicit-any */
-	const data = (await response.json()) as Record<string, any>;
-
-	if (!response.ok) {
-		throw new Error(
-			data["message"] ??
-				`Serper API returned error code ${response.status} - ${response.statusText}`
-		);
-	}
-
-	return {
-		organic_results: data["organic"] ?? [],
-		knowledge_graph: data["knowledgeGraph"] ?? null,
-		answer_box: data["answerBox"] ?? null,
-	};
-}
-
-export async function searchWebSerpApi(query: string) {
-	const params = {
-		q: query,
-		hl: "en",
-		gl: "us",
-		google_domain: "google.com",
-		api_key: SERPAPI_KEY,
-	} satisfies GoogleParameters;
-
-	// Show result as JSON
-	const response = await getJson("google", params);
-
-	return response;
-}

spaces/AgentVerse/agentVerse/agentverse/environments/__init__.py

@@ -1,17 +0,0 @@
-from typing import Dict
-from agentverse.registry import Registry
-
-
-env_registry = Registry(name="EnvironmentRegistry")
-
-
-from .base import BaseEnvironment, BaseRule
-
-# from .basic import PipelineEnvironment
-from .simulation_env.basic import BasicEnvironment
-from .simulation_env.pokemon import PokemonEnvironment
-from .simulation_env.prisoner_dilemma import PrisonerDilemmaEnvironment
-from .simulation_env.sde_team import SdeTeamEnvironment
-from .simulation_env.sde_team_given_tests import SdeTeamGivenTestsEnvironment
-
-from .tasksolving_env.basic import BasicEnvironment

spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/statesroundrectangle/Factory.js

@@ -1,13 +0,0 @@
-import StatesRoundRectangle from './StatesRoundRectangle.js';
-import ObjectFactory from '../ObjectFactory.js';
-import SetValue from '../../../plugins/utils/object/SetValue.js';
-
-ObjectFactory.register('statesRoundRectangle', function (config) {
-    var gameObject = new StatesRoundRectangle(this.scene, config);
-    this.scene.add.existing(gameObject);
-    return gameObject;
-});
-
-SetValue(window, 'RexPlugins.UI.StatesRoundRectangle', StatesRoundRectangle);
-
-export default StatesRoundRectangle;

spaces/Aitor/CVchat/README.md

@@ -1,13 +0,0 @@
----
-title: CVchat
-emoji: 🌍
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
-license: cc-by-4.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Amrrs/podscript/README.md

@@ -1,13 +0,0 @@
----
-title: Podscript
-emoji: 📚
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.3.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

spaces/Androidonnxfork/CivitAi-to-Diffusers/diffusers/docs/source/en/api/models/unet2d.md

@@ -1,13 +0,0 @@
-# UNet2DModel
-
-The [UNet](https://huggingface.co/papers/1505.04597) model was originally introduced by Ronneberger et al for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it's number of dimensions and whether it is a conditional model or not. This is a 2D UNet model.
-
-The abstract from the paper is:
-
-*There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net.*
-
-## UNet2DModel
-[[autodoc]] UNet2DModel
-
-## UNet2DOutput
-[[autodoc]] models.unet_2d.UNet2DOutput

spaces/Andy1621/uniformer_image_detection/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py

@@ -1,215 +0,0 @@
-import mmcv
-import numpy as np
-import torch
-
-from ..builder import BBOX_CODERS
-from .base_bbox_coder import BaseBBoxCoder
-
-
-@BBOX_CODERS.register_module()
-class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
-    """Legacy Delta XYWH BBox coder used in MMDet V1.x.
-
-    Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2,
-    y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh)
-    back to original bbox (x1, y1, x2, y2).
-
-    Note:
-        The main difference between :class`LegacyDeltaXYWHBBoxCoder` and
-        :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and
-        height calculation. We suggest to only use this coder when testing with
-        MMDet V1.x models.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Args:
-        target_means (Sequence[float]): denormalizing means of target for
-            delta coordinates
-        target_stds (Sequence[float]): denormalizing standard deviation of
-            target for delta coordinates
-    """
-
-    def __init__(self,
-                 target_means=(0., 0., 0., 0.),
-                 target_stds=(1., 1., 1., 1.)):
-        super(BaseBBoxCoder, self).__init__()
-        self.means = target_means
-        self.stds = target_stds
-
-    def encode(self, bboxes, gt_bboxes):
-        """Get box regression transformation deltas that can be used to
-        transform the ``bboxes`` into the ``gt_bboxes``.
-
-        Args:
-            bboxes (torch.Tensor): source boxes, e.g., object proposals.
-            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
-                ground-truth boxes.
-
-        Returns:
-            torch.Tensor: Box transformation deltas
-        """
-        assert bboxes.size(0) == gt_bboxes.size(0)
-        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
-        encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means,
-                                           self.stds)
-        return encoded_bboxes
-
-    def decode(self,
-               bboxes,
-               pred_bboxes,
-               max_shape=None,
-               wh_ratio_clip=16 / 1000):
-        """Apply transformation `pred_bboxes` to `boxes`.
-
-        Args:
-            boxes (torch.Tensor): Basic boxes.
-            pred_bboxes (torch.Tensor): Encoded boxes with shape
-            max_shape (tuple[int], optional): Maximum shape of boxes.
-                Defaults to None.
-            wh_ratio_clip (float, optional): The allowed ratio between
-                width and height.
-
-        Returns:
-            torch.Tensor: Decoded boxes.
-        """
-        assert pred_bboxes.size(0) == bboxes.size(0)
-        decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means,
-                                           self.stds, max_shape, wh_ratio_clip)
-
-        return decoded_bboxes
-
-
-@mmcv.jit(coderize=True)
-def legacy_bbox2delta(proposals,
-                      gt,
-                      means=(0., 0., 0., 0.),
-                      stds=(1., 1., 1., 1.)):
-    """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner.
-
-    We usually compute the deltas of x, y, w, h of proposals w.r.t ground
-    truth bboxes to get regression target.
-    This is the inverse function of `delta2bbox()`
-
-    Args:
-        proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
-        gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-
-    Returns:
-        Tensor: deltas with shape (N, 4), where columns represent dx, dy,
-            dw, dh.
-    """
-    assert proposals.size() == gt.size()
-
-    proposals = proposals.float()
-    gt = gt.float()
-    px = (proposals[..., 0] + proposals[..., 2]) * 0.5
-    py = (proposals[..., 1] + proposals[..., 3]) * 0.5
-    pw = proposals[..., 2] - proposals[..., 0] + 1.0
-    ph = proposals[..., 3] - proposals[..., 1] + 1.0
-
-    gx = (gt[..., 0] + gt[..., 2]) * 0.5
-    gy = (gt[..., 1] + gt[..., 3]) * 0.5
-    gw = gt[..., 2] - gt[..., 0] + 1.0
-    gh = gt[..., 3] - gt[..., 1] + 1.0
-
-    dx = (gx - px) / pw
-    dy = (gy - py) / ph
-    dw = torch.log(gw / pw)
-    dh = torch.log(gh / ph)
-    deltas = torch.stack([dx, dy, dw, dh], dim=-1)
-
-    means = deltas.new_tensor(means).unsqueeze(0)
-    stds = deltas.new_tensor(stds).unsqueeze(0)
-    deltas = deltas.sub_(means).div_(stds)
-
-    return deltas
-
-
-@mmcv.jit(coderize=True)
-def legacy_delta2bbox(rois,
-                      deltas,
-                      means=(0., 0., 0., 0.),
-                      stds=(1., 1., 1., 1.),
-                      max_shape=None,
-                      wh_ratio_clip=16 / 1000):
-    """Apply deltas to shift/scale base boxes in the MMDet V1.x manner.
-
-    Typically the rois are anchor or proposed bounding boxes and the deltas are
-    network outputs used to shift/scale those boxes.
-    This is the inverse function of `bbox2delta()`
-
-    Args:
-        rois (Tensor): Boxes to be transformed. Has shape (N, 4)
-        deltas (Tensor): Encoded offsets with respect to each roi.
-            Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when
-            rois is a grid of anchors. Offset encoding follows [1]_.
-        means (Sequence[float]): Denormalizing means for delta coordinates
-        stds (Sequence[float]): Denormalizing standard deviation for delta
-            coordinates
-        max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
-        wh_ratio_clip (float): Maximum aspect ratio for boxes.
-
-    Returns:
-        Tensor: Boxes with shape (N, 4), where columns represent
-            tl_x, tl_y, br_x, br_y.
-
-    References:
-        .. [1] https://arxiv.org/abs/1311.2524
-
-    Example:
-        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 0.,  0.,  1.,  1.],
-        >>>                      [ 5.,  5.,  5.,  5.]])
-        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
-        >>>                        [  1.,   1.,   1.,   1.],
-        >>>                        [  0.,   0.,   2.,  -1.],
-        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
-        >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32))
-        tensor([[0.0000, 0.0000, 1.5000, 1.5000],
-                [0.0000, 0.0000, 5.2183, 5.2183],
-                [0.0000, 0.1321, 7.8891, 0.8679],
-                [5.3967, 2.4251, 6.0033, 3.7749]])
-    """
-    means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4)
-    stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4)
-    denorm_deltas = deltas * stds + means
-    dx = denorm_deltas[:, 0::4]
-    dy = denorm_deltas[:, 1::4]
-    dw = denorm_deltas[:, 2::4]
-    dh = denorm_deltas[:, 3::4]
-    max_ratio = np.abs(np.log(wh_ratio_clip))
-    dw = dw.clamp(min=-max_ratio, max=max_ratio)
-    dh = dh.clamp(min=-max_ratio, max=max_ratio)
-    # Compute center of each roi
-    px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx)
-    py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy)
-    # Compute width/height of each roi
-    pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw)
-    ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh)
-    # Use exp(network energy) to enlarge/shrink each roi
-    gw = pw * dw.exp()
-    gh = ph * dh.exp()
-    # Use network energy to shift the center of each roi
-    gx = px + pw * dx
-    gy = py + ph * dy
-    # Convert center-xy/width/height to top-left, bottom-right
-
-    # The true legacy box coder should +- 0.5 here.
-    # However, current implementation improves the performance when testing
-    # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP)
-    x1 = gx - gw * 0.5
-    y1 = gy - gh * 0.5
-    x2 = gx + gw * 0.5
-    y2 = gy + gh * 0.5
-    if max_shape is not None:
-        x1 = x1.clamp(min=0, max=max_shape[1] - 1)
-        y1 = y1.clamp(min=0, max=max_shape[0] - 1)
-        x2 = x2.clamp(min=0, max=max_shape[1] - 1)
-        y2 = y2.clamp(min=0, max=max_shape[0] - 1)
-    bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas)
-    return bboxes

spaces/Andy1621/uniformer_image_segmentation/configs/dnlnet/dnl_r101-d8_769x769_80k_cityscapes.py

@@ -1,2 +0,0 @@
-_base_ = './dnl_r50-d8_769x769_80k_cityscapes.py'
-model = dict(pretrained='open-mmlab://resnet101_v1c', backbone=dict(depth=101))

spaces/AnishKumbhar/ChatBot/text-generation-webui-main/docs/README.md

@@ -1,21 +0,0 @@
-# text-generation-webui documentation
-
-## Table of contents
-
-* [Audio Notification](Audio-Notification.md)
-* [Chat mode](Chat-mode.md)
-* [DeepSpeed](DeepSpeed.md)
-* [Docker](Docker.md)
-* [ExLlama](ExLlama.md)
-* [Extensions](Extensions.md)
-* [GPTQ models (4 bit mode)](GPTQ-models-(4-bit-mode).md)
-* [LLaMA model](LLaMA-model.md)
-* [llama.cpp](llama.cpp.md)
-* [LoRA](LoRA.md)
-* [Low VRAM guide](Low-VRAM-guide.md)
-* [RWKV model](RWKV-model.md)
-* [Spell book](Spell-book.md)
-* [System requirements](System-requirements.md)
-* [Training LoRAs](Training-LoRAs.md)
-* [Windows installation guide](Windows-installation-guide.md)
-* [WSL installation guide](WSL-installation-guide.md)

spaces/Anonymous-123/ImageNet-Editing/object_removal/TFill/README.md

@@ -1,147 +0,0 @@
-# TFill
-[paper](https://openaccess.thecvf.com/content/CVPR2022/html/Zheng_Bridging_Global_Context_Interactions_for_High-Fidelity_Image_Completion_CVPR_2022_paper.html) | [arXiv](https://arxiv.org/abs/2104.00845) | [Project](https://chuanxiaz.com/tfill/) | [Video](https://www.youtube.com/watch?v=efB1fw0jiLs&feature=youtu.be)
-
-This repository implements the training, testing and editing tools for "Bridging Global Context Interactions for High-Fidelity Image Completion (CVPR2022, scores: 1, 1, 2, 2)" by [Chuanxia Zheng](https://www.chuanxiaz.com), [Tat-Jen Cham](https://personal.ntu.edu.sg/astjcham/), [Jianfei Cai](https://jianfei-cai.github.io/) and [Dinh Phung](https://research.monash.edu/en/persons/dinh-phung). Given masked images, the proposed **TFill** model is able to generate high-fidelity plausible results on various settings.
-
-## Examples
-![teaser](images/example.png)
-
-## Object Removal
-![teaser](images/tfill_removal.gif)
-
-## Object Repair
-![teaser](images/tfill_repair.gif)
-
-## Framework
-We propose the two-stages image completion framework, where the upper content inference network (TFill-*Coarse*) generates semantically correct content using a transformer encoder to directly capture the global context information; the lower appearance refinement network (TFill-*refined*) copies global visible and generated features to holes. 
-
-![teaser](images/framework.png)
-
-
-
-# Getting started
-
-- Clone this repo:
-
-```
-git clone https://github.com/lyndonzheng/TFill
-cd TFill
-```
-## Requirements
-The original model is trained and evaluated with Pytorch v1.9.1, which cannot be visited in current [PyTorch](https://pytorch.org/get-started/previous-versions/). Therefore, we create a new environment with Pytorch v1.10.0 to test the model, where the performance is the same. 
-
-A suitable [conda](https://conda.io/) environment named `Tfill` can be created and activated with:
-
-```
-conda env create -f environment.yaml
-conda activate TFill
-```
-## Runing pretrained models
-Download the pre-trained models using the following links ([CelebA-HQ](https://drive.google.com/drive/folders/1ntbVDjJ7-nAt4nLGuu7RNi3QpLfh40gk?usp=sharing), [FFHQ](https://drive.google.com/drive/folders/1xuAsShrw9wI5Be0sQka3vZEsfwnq0pPT?usp=sharing), [ImageNet](https://drive.google.com/drive/folders/1B4RswBUD6_jXAu3MVz3LtuNfoV4wTmGf?usp=sharing), [Plcases2](https://drive.google.com/drive/folders/154ikacQ8A2JLC8iIGda8jiZN-ysL1xh5?usp=sharing)
-) and put them under```checkpoints/``` directory. It should have the following structure:
-
-```
-./checkpoints/
-├── celeba
-│   ├── latest_net_D.pth
-│   ├── latest_net_D_Ref.pth
-│   ├── latest_net_E.pth
-│   ├── latest_net_G.pth
-│   ├── latest_net_G_Ref.pth
-│   ├── latest_net_T.pth
-├── ffhq
-│   ├── ...
-├── ...
-```
-
-- Test the model
-```
-sh ./scripts/test.sh
-```
-For different models, the users just need to modify lines 2-4, including ```name```,```img_file```,```mask_file```. For instance, we can replace the *celeba* to *imagenet*.
-
-The default results will be stored under the ```results/``` folder, in which:
-
-- ```examples/```: shows original and masked images;
-- ```img_out/```: shows upsampled *Coarse* outputs;
-- ```img_ref_out/```: shows the final *Refined* outputs.
-
-## Datasets
-- ```face dataset```: 
-  - 24,183 training images and  2,824 test images from [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) and use the algorithm of [Growing GANs](https://github.com/tkarras/progressive_growing_of_gans) to get the high-resolution CelebA-HQ dataset.
-  - 60,000 training images and 10,000 test images from [FFHQ](https://github.com/NVlabs/ffhq-dataset) provided by [StyleGAN](https://github.com/NVlabs/stylegan).
-- ```natural scenery```: original training and val images from [Places2](http://places2.csail.mit.edu/).
-- ```object``` original training images from [ImageNet](http://www.image-net.org/).
-
-## Traning
-
-- Train a model (two stage: *Coarse* and *Refinement*)
-```
-sh ./scripts/train.sh
-```
-The default setting is for the top *Coarse* training. The users just need to replace the *coarse* with *refine* at line 6. Then, the model can continue training for high-resolution image completion.
-More hyper-parameter can be in ```options/```. 
-
-The coarse results using transformer and restrictive CNN is impressive, which provides plausible results for both **foreground** objects and **background** scene.
-
-![teaser](images/center_imagenet.jpg)
-![teaser](images/center_places2.jpg)
-
-# GUI
-The GUI operation is similar to our previous GUI in [PIC](https://github.com/lyndonzheng/Pluralistic-Inpainting), where steps are also the same.
-
-Basic usage is:
-
-```
-sh ./scripts/ui.sh 
-```
-In ```gui/ui_model.py```, users can modify the ```img_root```(line 30) and the corresponding ```img_files```(line 31) to randomly edit images from the testing dataset.
-
-## Editing Examples
-
-- **Results (original, output) for face editing**
-
-![teaser](images/free_face.jpg)
-
-- **Results (original, masked input, output) for nature scene editing**
-
-![teaser](images/free_nature.jpg)
-
-## Next
-- Higher-resolution pluralistic image completion
-
-## License
-This work is licensed under a MIT License.
-
-This software is for educational and academic research purpose only. If you wish to obtain a commercial royalty bearing license to this software, please contact us at [email protected].
-
-## Citation
-
-The code also uses our previous [PIC](https://github.com/lyndonzheng/Pluralistic-Inpainting). If you use this code for your research, please cite our papers.
-```
-@InProceedings{Zheng_2022_CVPR,
-    author    = {Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei and Phung, Dinh},
-    title     = {Bridging Global Context Interactions for High-Fidelity Image Completion},
-    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
-    month     = {June},
-    year      = {2022},
-    pages     = {11512-11522}
-}
-
-@inproceedings{zheng2019pluralistic,
-  title={Pluralistic Image Completion},
-  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
-  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
-  pages={1438--1447},
-  year={2019}
-}
-
-@article{zheng2021pluralistic,
-  title={Pluralistic Free-From Image Completion},
-  author={Zheng, Chuanxia and Cham, Tat-Jen and Cai, Jianfei},
-  journal={International Journal of Computer Vision},
-  pages={1--20},
-  year={2021},
-  publisher={Springer}
-}
-```

spaces/Anonymous-sub/Rerender/ControlNet/annotator/midas/midas/transforms.py

@@ -1,234 +0,0 @@
-import numpy as np
-import cv2
-import math
-
-
-def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
-    """Rezise the sample to ensure the given size. Keeps aspect ratio.
-
-    Args:
-        sample (dict): sample
-        size (tuple): image size
-
-    Returns:
-        tuple: new size
-    """
-    shape = list(sample["disparity"].shape)
-
-    if shape[0] >= size[0] and shape[1] >= size[1]:
-        return sample
-
-    scale = [0, 0]
-    scale[0] = size[0] / shape[0]
-    scale[1] = size[1] / shape[1]
-
-    scale = max(scale)
-
-    shape[0] = math.ceil(scale * shape[0])
-    shape[1] = math.ceil(scale * shape[1])
-
-    # resize
-    sample["image"] = cv2.resize(
-        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
-    )
-
-    sample["disparity"] = cv2.resize(
-        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
-    )
-    sample["mask"] = cv2.resize(
-        sample["mask"].astype(np.float32),
-        tuple(shape[::-1]),
-        interpolation=cv2.INTER_NEAREST,
-    )
-    sample["mask"] = sample["mask"].astype(bool)
-
-    return tuple(shape)
-
-
-class Resize(object):
-    """Resize sample to given size (width, height).
-    """
-
-    def __init__(
-        self,
-        width,
-        height,
-        resize_target=True,
-        keep_aspect_ratio=False,
-        ensure_multiple_of=1,
-        resize_method="lower_bound",
-        image_interpolation_method=cv2.INTER_AREA,
-    ):
-        """Init.
-
-        Args:
-            width (int): desired output width
-            height (int): desired output height
-            resize_target (bool, optional):
-                True: Resize the full sample (image, mask, target).
-                False: Resize image only.
-                Defaults to True.
-            keep_aspect_ratio (bool, optional):
-                True: Keep the aspect ratio of the input sample.
-                Output sample might not have the given width and height, and
-                resize behaviour depends on the parameter 'resize_method'.
-                Defaults to False.
-            ensure_multiple_of (int, optional):
-                Output width and height is constrained to be multiple of this parameter.
-                Defaults to 1.
-            resize_method (str, optional):
-                "lower_bound": Output will be at least as large as the given size.
-                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
-                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
-                Defaults to "lower_bound".
-        """
-        self.__width = width
-        self.__height = height
-
-        self.__resize_target = resize_target
-        self.__keep_aspect_ratio = keep_aspect_ratio
-        self.__multiple_of = ensure_multiple_of
-        self.__resize_method = resize_method
-        self.__image_interpolation_method = image_interpolation_method
-
-    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
-        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if max_val is not None and y > max_val:
-            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        if y < min_val:
-            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
-
-        return y
-
-    def get_size(self, width, height):
-        # determine new height and width
-        scale_height = self.__height / height
-        scale_width = self.__width / width
-
-        if self.__keep_aspect_ratio:
-            if self.__resize_method == "lower_bound":
-                # scale such that output size is lower bound
-                if scale_width > scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "upper_bound":
-                # scale such that output size is upper bound
-                if scale_width < scale_height:
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            elif self.__resize_method == "minimal":
-                # scale as least as possbile
-                if abs(1 - scale_width) < abs(1 - scale_height):
-                    # fit width
-                    scale_height = scale_width
-                else:
-                    # fit height
-                    scale_width = scale_height
-            else:
-                raise ValueError(
-                    f"resize_method {self.__resize_method} not implemented"
-                )
-
-        if self.__resize_method == "lower_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, min_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, min_val=self.__width
-            )
-        elif self.__resize_method == "upper_bound":
-            new_height = self.constrain_to_multiple_of(
-                scale_height * height, max_val=self.__height
-            )
-            new_width = self.constrain_to_multiple_of(
-                scale_width * width, max_val=self.__width
-            )
-        elif self.__resize_method == "minimal":
-            new_height = self.constrain_to_multiple_of(scale_height * height)
-            new_width = self.constrain_to_multiple_of(scale_width * width)
-        else:
-            raise ValueError(f"resize_method {self.__resize_method} not implemented")
-
-        return (new_width, new_height)
-
-    def __call__(self, sample):
-        width, height = self.get_size(
-            sample["image"].shape[1], sample["image"].shape[0]
-        )
-
-        # resize sample
-        sample["image"] = cv2.resize(
-            sample["image"],
-            (width, height),
-            interpolation=self.__image_interpolation_method,
-        )
-
-        if self.__resize_target:
-            if "disparity" in sample:
-                sample["disparity"] = cv2.resize(
-                    sample["disparity"],
-                    (width, height),
-                    interpolation=cv2.INTER_NEAREST,
-                )
-
-            if "depth" in sample:
-                sample["depth"] = cv2.resize(
-                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
-                )
-
-            sample["mask"] = cv2.resize(
-                sample["mask"].astype(np.float32),
-                (width, height),
-                interpolation=cv2.INTER_NEAREST,
-            )
-            sample["mask"] = sample["mask"].astype(bool)
-
-        return sample
-
-
-class NormalizeImage(object):
-    """Normlize image by given mean and std.
-    """
-
-    def __init__(self, mean, std):
-        self.__mean = mean
-        self.__std = std
-
-    def __call__(self, sample):
-        sample["image"] = (sample["image"] - self.__mean) / self.__std
-
-        return sample
-
-
-class PrepareForNet(object):
-    """Prepare sample for usage as network input.
-    """
-
-    def __init__(self):
-        pass
-
-    def __call__(self, sample):
-        image = np.transpose(sample["image"], (2, 0, 1))
-        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
-
-        if "mask" in sample:
-            sample["mask"] = sample["mask"].astype(np.float32)
-            sample["mask"] = np.ascontiguousarray(sample["mask"])
-
-        if "disparity" in sample:
-            disparity = sample["disparity"].astype(np.float32)
-            sample["disparity"] = np.ascontiguousarray(disparity)
-
-        if "depth" in sample:
-            depth = sample["depth"].astype(np.float32)
-            sample["depth"] = np.ascontiguousarray(depth)
-
-        return sample

spaces/Apex-X/ROOPOK/roop/face_reference.py

@@ -1,21 +0,0 @@
-from typing import Optional
-
-from roop.typing import Face
-
-FACE_REFERENCE = None
-
-
-def get_face_reference() -> Optional[Face]:
-    return FACE_REFERENCE
-
-
-def set_face_reference(face: Face) -> None:
-    global FACE_REFERENCE
-
-    FACE_REFERENCE = face
-
-
-def clear_face_reference() -> None:
-    global FACE_REFERENCE
-
-    FACE_REFERENCE = None

spaces/Apex-X/Tm/roop/typing.py

@@ -1,7 +0,0 @@
-from typing import Any
-
-from insightface.app.common import Face
-import numpy
-
-Face = Face
-Frame = numpy.ndarray[Any, Any]

spaces/Artgor/digit-draw-detect/pages/about.py

@@ -1,25 +0,0 @@
-import streamlit as st
-
-text = """
-## A brief history of the project.
-The first idea of the project was conceived in the summer of 2017. I completed cs231n course and wanted to put my skills to the test. So I studied Flask and many other tools from scratch and made an [app](https://github.com/Erlemar/digit-draw-recognize) for recognizing handwritten digits. It had two models - a simple feed-forward neural net written in NumPy and a CNN written in Tensorflow. One fun feature of this app was online learning: the model continuously improved its predictions based on new inputs (though this did sometimes lead to incorrect predictions).
-
-In 2019 I decided to update the [project](https://github.com/Erlemar/digit-draw-predict): I trained the new neural net in PyTorch and used cv2 to detect separate digits (people often drew multiple digits). More than that, the model had 11 classes - I made a separate class for "junk", as people often drew things for fun: animals, objects, or words.
-
-The first two versions were deployed on Heroku's free plan, but in 2022 these plans were discontinued. I didn't want my project to die because of nostalgia, so I developed a new version and deployed it differently. The current version has an object detection model (yolo3 written from scratch) and 12 classes (digits, junk, and **censored**).
-If you want to know what does **censored** means, just try to draw something 😉
-
-Initially, I considered deploying the app on Streamlit Cloud, but its computational limits were too low, so now the model is live on HuggingFace Spaces.
-
-### Links with additional information:
-
-* [Project page on my personal website](https://andlukyane.com/project/drawn-digits-prediction)
-* [A dataset with the digits and bounding boxes on Kaggle](https://www.kaggle.com/datasets/artgor/handwritten-digits-and-bounding-boxes)
-* [Training code](https://github.com/Erlemar/pytorch_tempest_pet_)
-* [Blogpost on my personal website](https://andlukyane.com/blog/a-third-life-of-a-personal-project)
-* [Blogpost on medium](https://towardsdatascience.com/the-third-life-of-a-personal-pet-project-for-handwritten-digit-recognition-fd908dc8e7a1)
-* [Russian blogpost on habr](https://habr.com/ru/company/ods/blog/707046/)
-* [Project code on GitHub](https://github.com/Erlemar/digit-draw-detect)
-"""
-
-st.markdown(text, unsafe_allow_html=True)

spaces/Asifpa6/emotion-analyzer-app/app.py

@@ -1,66 +0,0 @@
-import streamlit as st
-import pandas as pd
-from emotion_analysis import get_emotion
-import base64
-
-
-def read_data(file_path):
-    file_extension = file_path.split('.')[-1].lower()
-    
-    if file_extension == 'xlsx' or file_extension == 'xls':
-        data = pd.read_excel(file_path)
-    elif file_extension == 'csv':
-        data = pd.read_csv(file_path)
-    else:
-        raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.")
-    
-    return data
-
-
-# Streamlit app
-def main():
-    st.title("Text Emotion Detection")
-    menu = ["Input Text", "Batch Processing"]
-    option = st.sidebar.radio("Select an option", menu)
-
-
-    if option == "Input Text":
-        text = st.text_area("Enter your text:")
-        if st.button("Submit"):
-            if text.strip() != "":
-                emotion_detail, confidence_score = get_emotion(text)
-                st.write("Detected Emotion")
-                st.write(f"{emotion_detail[0]} - {confidence_score}")
-            else:
-                st.write("Please enter some text.")
-
-    elif option == "Batch Processing":
-        uploaded_file = st.file_uploader("Upload CSV or Excel file", type=["csv", "xlsx"])
-        
-        if uploaded_file is not None:
-            file_name = uploaded_file.name
-            file_extension = file_name.split('.')[-1].lower()
-            file_name = uploaded_file.name
-            if file_extension == 'xlsx' or file_extension == 'xls':
-                dataframe = pd.read_excel(uploaded_file)
-            elif file_extension == 'csv':
-                dataframe = pd.read_csv(uploaded_file)
-            else:
-                raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.")
-            # dataframe = pd.read_excel(uploaded_file)
-            if "text" not in dataframe.columns:
-                st.write("CSV file should have a 'text' column.")
-            else:
-                dataframe["emotion"], dataframe["confidence"] = zip(*dataframe["text"].map(get_emotion))
-                st.write("Detected Emotions")
-                st.write(dataframe)
-                # Download button
-                csv = dataframe.to_csv(index=False)
-                b64 = base64.b64encode(csv.encode()).decode()  # Convert DataFrame to CSV string
-                href = f'<a href="data:file/csv;base64,{b64}" download="processed_data.csv">Download</a>'
-                st.markdown(href, unsafe_allow_html=True)
-        else:
-            pass
-
-if __name__ == '__main__':
-    main()

spaces/Asmithayellow/Asmi/app.py

@@ -1,34 +0,0 @@
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
-
-if __name__ == "__main__":
-    demo.launch() #To create a public link, set `share=True` in `launch()`. To enable errors and logs, set `debug=True` in `launch()`.

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

@@ -1,115 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# This library is free software; you can redistribute it and/or
-# modify it under the terms of the GNU Lesser General Public
-# License as published by the Free Software Foundation; either
-# version 2.1 of the License, or (at your option) any later version.
-#
-# This library is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
-# Lesser General Public License for more details.
-#
-# You should have received a copy of the GNU Lesser General Public
-# License along with this library; if not, write to the Free Software
-# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
-# 02110-1301  USA
-######################### END LICENSE BLOCK #########################
-
-from typing import List, Union
-
-from .charsetgroupprober import CharSetGroupProber
-from .charsetprober import CharSetProber
-from .enums import InputState
-from .resultdict import ResultDict
-from .universaldetector import UniversalDetector
-from .version import VERSION, __version__
-
-__all__ = ["UniversalDetector", "detect", "detect_all", "__version__", "VERSION"]
-
-
-def detect(
-    byte_str: Union[bytes, bytearray], should_rename_legacy: bool = False
-) -> ResultDict:
-    """
-    Detect the encoding of the given byte string.
-
-    :param byte_str:     The byte sequence to examine.
-    :type byte_str:      ``bytes`` or ``bytearray``
-    :param should_rename_legacy:  Should we rename legacy encodings
-                                  to their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    if not isinstance(byte_str, bytearray):
-        if not isinstance(byte_str, bytes):
-            raise TypeError(
-                f"Expected object of type bytes or bytearray, got: {type(byte_str)}"
-            )
-        byte_str = bytearray(byte_str)
-    detector = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    detector.feed(byte_str)
-    return detector.close()
-
-
-def detect_all(
-    byte_str: Union[bytes, bytearray],
-    ignore_threshold: bool = False,
-    should_rename_legacy: bool = False,
-) -> List[ResultDict]:
-    """
-    Detect all the possible encodings of the given byte string.
-
-    :param byte_str:          The byte sequence to examine.
-    :type byte_str:           ``bytes`` or ``bytearray``
-    :param ignore_threshold:  Include encodings that are below
-                              ``UniversalDetector.MINIMUM_THRESHOLD``
-                              in results.
-    :type ignore_threshold:   ``bool``
-    :param should_rename_legacy:  Should we rename legacy encodings
-                                  to their more modern equivalents?
-    :type should_rename_legacy:   ``bool``
-    """
-    if not isinstance(byte_str, bytearray):
-        if not isinstance(byte_str, bytes):
-            raise TypeError(
-                f"Expected object of type bytes or bytearray, got: {type(byte_str)}"
-            )
-        byte_str = bytearray(byte_str)
-
-    detector = UniversalDetector(should_rename_legacy=should_rename_legacy)
-    detector.feed(byte_str)
-    detector.close()
-
-    if detector.input_state == InputState.HIGH_BYTE:
-        results: List[ResultDict] = []
-        probers: List[CharSetProber] = []
-        for prober in detector.charset_probers:
-            if isinstance(prober, CharSetGroupProber):
-                probers.extend(p for p in prober.probers)
-            else:
-                probers.append(prober)
-        for prober in probers:
-            if ignore_threshold or prober.get_confidence() > detector.MINIMUM_THRESHOLD:
-                charset_name = prober.charset_name or ""
-                lower_charset_name = charset_name.lower()
-                # Use Windows encoding name instead of ISO-8859 if we saw any
-                # extra Windows-specific bytes
-                if lower_charset_name.startswith("iso-8859") and detector.has_win_bytes:
-                    charset_name = detector.ISO_WIN_MAP.get(
-                        lower_charset_name, charset_name
-                    )
-                # Rename legacy encodings with superset encodings if asked
-                if should_rename_legacy:
-                    charset_name = detector.LEGACY_MAP.get(
-                        charset_name.lower(), charset_name
-                    )
-                results.append(
-                    {
-                        "encoding": charset_name,
-                        "confidence": prober.get_confidence(),
-                        "language": prober.language,
-                    }
-                )
-        if len(results) > 0:
-            return sorted(results, key=lambda result: -result["confidence"])
-
-    return [detector.result]

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

@@ -1,131 +0,0 @@
-"""Module containing bug report helper(s)."""
-
-import json
-import platform
-import ssl
-import sys
-
-from pip._vendor import idna
-from pip._vendor import urllib3
-
-from . import __version__ as requests_version
-
-charset_normalizer = None
-
-try:
-    from pip._vendor import chardet
-except ImportError:
-    chardet = None
-
-try:
-    from pip._vendor.urllib3.contrib import pyopenssl
-except ImportError:
-    pyopenssl = None
-    OpenSSL = None
-    cryptography = None
-else:
-    import cryptography
-    import OpenSSL
-
-
-def _implementation():
-    """Return a dict with the Python implementation and version.
-
-    Provide both the name and the version of the Python implementation
-    currently running. For example, on CPython 3.10.3 it will return
-    {'name': 'CPython', 'version': '3.10.3'}.
-
-    This function works best on CPython and PyPy: in particular, it probably
-    doesn't work for Jython or IronPython. Future investigation should be done
-    to work out the correct shape of the code for those platforms.
-    """
-    implementation = platform.python_implementation()
-
-    if implementation == "CPython":
-        implementation_version = platform.python_version()
-    elif implementation == "PyPy":
-        implementation_version = "{}.{}.{}".format(
-            sys.pypy_version_info.major,
-            sys.pypy_version_info.minor,
-            sys.pypy_version_info.micro,
-        )
-        if sys.pypy_version_info.releaselevel != "final":
-            implementation_version = "".join(
-                [implementation_version, sys.pypy_version_info.releaselevel]
-            )
-    elif implementation == "Jython":
-        implementation_version = platform.python_version()  # Complete Guess
-    elif implementation == "IronPython":
-        implementation_version = platform.python_version()  # Complete Guess
-    else:
-        implementation_version = "Unknown"
-
-    return {"name": implementation, "version": implementation_version}
-
-
-def info():
-    """Generate information for a bug report."""
-    try:
-        platform_info = {
-            "system": platform.system(),
-            "release": platform.release(),
-        }
-    except OSError:
-        platform_info = {
-            "system": "Unknown",
-            "release": "Unknown",
-        }
-
-    implementation_info = _implementation()
-    urllib3_info = {"version": urllib3.__version__}
-    charset_normalizer_info = {"version": None}
-    chardet_info = {"version": None}
-    if charset_normalizer:
-        charset_normalizer_info = {"version": charset_normalizer.__version__}
-    if chardet:
-        chardet_info = {"version": chardet.__version__}
-
-    pyopenssl_info = {
-        "version": None,
-        "openssl_version": "",
-    }
-    if OpenSSL:
-        pyopenssl_info = {
-            "version": OpenSSL.__version__,
-            "openssl_version": f"{OpenSSL.SSL.OPENSSL_VERSION_NUMBER:x}",
-        }
-    cryptography_info = {
-        "version": getattr(cryptography, "__version__", ""),
-    }
-    idna_info = {
-        "version": getattr(idna, "__version__", ""),
-    }
-
-    system_ssl = ssl.OPENSSL_VERSION_NUMBER
-    system_ssl_info = {"version": f"{system_ssl:x}" if system_ssl is not None else ""}
-
-    return {
-        "platform": platform_info,
-        "implementation": implementation_info,
-        "system_ssl": system_ssl_info,
-        "using_pyopenssl": pyopenssl is not None,
-        "using_charset_normalizer": chardet is None,
-        "pyOpenSSL": pyopenssl_info,
-        "urllib3": urllib3_info,
-        "chardet": chardet_info,
-        "charset_normalizer": charset_normalizer_info,
-        "cryptography": cryptography_info,
-        "idna": idna_info,
-        "requests": {
-            "version": requests_version,
-        },
-    }
-
-
-def main():
-    """Pretty-print the bug information as JSON."""
-    print(json.dumps(info(), sort_keys=True, indent=2))
-
-
-if __name__ == "__main__":
-    main()

spaces/Ataturk-Chatbot/HuggingFaceChat/venv/lib/python3.11/site-packages/pkg_resources/_vendor/importlib_resources/_compat.py

@@ -1,98 +0,0 @@
-# flake8: noqa
-
-import abc
-import sys
-import pathlib
-from contextlib import suppress
-
-if sys.version_info >= (3, 10):
-    from zipfile import Path as ZipPath  # type: ignore
-else:
-    from ..zipp import Path as ZipPath  # type: ignore
-
-
-try:
-    from typing import runtime_checkable  # type: ignore
-except ImportError:
-
-    def runtime_checkable(cls):  # type: ignore
-        return cls
-
-
-try:
-    from typing import Protocol  # type: ignore
-except ImportError:
-    Protocol = abc.ABC  # type: ignore
-
-
-class TraversableResourcesLoader:
-    """
-    Adapt loaders to provide TraversableResources and other
-    compatibility.
-
-    Used primarily for Python 3.9 and earlier where the native
-    loaders do not yet implement TraversableResources.
-    """
-
-    def __init__(self, spec):
-        self.spec = spec
-
-    @property
-    def path(self):
-        return self.spec.origin
-
-    def get_resource_reader(self, name):
-        from . import readers, _adapters
-
-        def _zip_reader(spec):
-            with suppress(AttributeError):
-                return readers.ZipReader(spec.loader, spec.name)
-
-        def _namespace_reader(spec):
-            with suppress(AttributeError, ValueError):
-                return readers.NamespaceReader(spec.submodule_search_locations)
-
-        def _available_reader(spec):
-            with suppress(AttributeError):
-                return spec.loader.get_resource_reader(spec.name)
-
-        def _native_reader(spec):
-            reader = _available_reader(spec)
-            return reader if hasattr(reader, 'files') else None
-
-        def _file_reader(spec):
-            try:
-                path = pathlib.Path(self.path)
-            except TypeError:
-                return None
-            if path.exists():
-                return readers.FileReader(self)
-
-        return (
-            # native reader if it supplies 'files'
-            _native_reader(self.spec)
-            or
-            # local ZipReader if a zip module
-            _zip_reader(self.spec)
-            or
-            # local NamespaceReader if a namespace module
-            _namespace_reader(self.spec)
-            or
-            # local FileReader
-            _file_reader(self.spec)
-            # fallback - adapt the spec ResourceReader to TraversableReader
-            or _adapters.CompatibilityFiles(self.spec)
-        )
-
-
-def wrap_spec(package):
-    """
-    Construct a package spec with traversable compatibility
-    on the spec/loader/reader.
-
-    Supersedes _adapters.wrap_spec to use TraversableResourcesLoader
-    from above for older Python compatibility (<3.10).
-    """
-    from . import _adapters
-
-    return _adapters.SpecLoaderAdapter(package.__spec__, TraversableResourcesLoader)

spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/README.md

@@ -1,85 +0,0 @@
-# Probabilistic two-stage detection
-Two-stage object detectors that use class-agnostic one-stage detectors as the proposal network.
-
-
-<p align="center"> <img src='projects/CenterNet2/centernet2_docs/centernet2_teaser.jpg' align="center" height="150px"> </p>
-
-> [**Probabilistic two-stage detection**](http://arxiv.org/abs/2103.07461),            
-> Xingyi Zhou, Vladlen Koltun, Philipp Kr&auml;henb&uuml;hl,        
-> *arXiv technical report ([arXiv 2103.07461](http://arxiv.org/abs/2103.07461))*         
-
-Contact: [[email protected]](mailto:[email protected]). Any questions or discussions are welcomed! 
-
-## Abstract
-
-We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp.
-
-## Summary
-
-- Two-stage CenterNet: First stage estimates object probabilities, second stage conditionally classifies objects.
-
-- Resulting detector is faster and more accurate than both traditional two-stage detectors (fewer proposals required), and one-stage detectors (lighter first stage head).
-
-- Our best model achieves 56.4 mAP on COCO test-dev.
-
-- This repo also includes a detectron2-based CenterNet implementation with better accuracy (42.5 mAP at 70FPS) and a new FPN version of CenterNet (40.2 mAP with Res50_1x).
-
-## Main results
-
-All models are trained with multi-scale training, and tested with a single scale. The FPS is tested on a Titan RTX GPU.
-More models and details can be found in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md).
-
-#### COCO
-
-| Model                                     |  COCO val mAP |  FPS  |
-|-------------------------------------------|---------------|-------|
-| CenterNet-S4_DLA_8x                       |  42.5         |   71  |
-| CenterNet2_R50_1x                         |  42.9         |   24  |
-| CenterNet2_X101-DCN_2x                    |  49.9         |    8  |
-| CenterNet2_R2-101-DCN-BiFPN_4x+4x_1560_ST |  56.1         |    5  |
-| CenterNet2_DLA-BiFPN-P5_24x_ST            |  49.2         |   38  |
-
-
-#### LVIS 
-
-| Model                     | val mAP box |
-| ------------------------- | ----------- |
-| CenterNet2_R50_1x         | 26.5        |
-| CenterNet2_FedLoss_R50_1x | 28.3        |
-
-
-#### Objects365
-
-| Model                                     |  val mAP |
-|-------------------------------------------|----------|
-| CenterNet2_R50_1x                         |  22.6    |
-
-## Installation
-
-Our project is developed on [detectron2](https://github.com/facebookresearch/detectron2). Please follow the official detectron2 [installation](https://github.com/facebookresearch/detectron2/blob/master/INSTALL.md). All our code is under `projects/CenterNet2/`. In theory, you should be able to copy-paste `projects/CenterNet2/` to the latest detectron2 release or your own detectron2 repo to run our project. There might be API changes in future detectron2 releases that make it incompatible. 
-
-We use the default detectron2 demo script. To run inference on an image folder using our pre-trained model, run
-
-~~~
-python projects/CenterNet2/demo/demo.py --config-file projects/CenterNet2/configs/CenterNet2_R50_1x.yaml --input path/to/image/ --opts MODEL.WEIGHTS models/CenterNet2_R50_1x.pth
-~~~
-
-## Benchmark evaluation and training
-
-Please check detectron2 [GETTING_STARTED.md](https://github.com/facebookresearch/detectron2/blob/master/GETTING_STARTED.md) for running evaluation and training. Our config files are under `projects/CenterNet2/configs` and the pre-trained models are in the [MODEL_ZOO](projects/CenterNet2/centernet2_docs/MODEL_ZOO.md).
-
-
-## License
-
-Our code under `projects/CenterNet2/` is under [Apache 2.0 license](projects/CenterNet2/LICENSE). `projects/CenterNet2/centernet/modeling/backbone/bifpn_fcos.py` are from [AdelaiDet](https://github.com/aim-uofa/AdelaiDet), which follows the original [non-commercial license](https://github.com/aim-uofa/AdelaiDet/blob/master/LICENSE). The code from detectron2 follows the original [Apache 2.0 license](LICENSE).
-
-## Citation
-
-If you find this project useful for your research, please use the following BibTeX entry.
-
-    @inproceedings{zhou2021probablistic,
-      title={Probabilistic two-stage detection},
-      author={Zhou, Xingyi and Koltun, Vladlen and Kr{\"a}henb{\"u}hl, Philipp},
-      booktitle={arXiv preprint arXiv:2103.07461},
-      year={2021}
-    }

spaces/Banbri/zcvzcv/src/app/engine/presets.ts

@@ -1,588 +0,0 @@
-import { FontName, actionman, komika, vtc } from "@/lib/fonts"
-import { pick } from "@/lib/pick"
-import { NextFontWithVariable } from "next/dist/compiled/@next/font"
-
-export type ComicFamily =
-  | "american"
-  | "asian"
-  | "european"
-
-export type ComicColor =
-  | "color"
-  | "grayscale"
-  | "monochrome"
-
-export interface Preset {
-  id: string
-  label: string
-  family: ComicFamily
-  color: ComicColor
-  font: FontName
-  llmPrompt: string
-  imagePrompt: (prompt: string) => string[]
-  negativePrompt: (prompt: string) => string[]
-}
-
-// ATTENTION!! negative prompts are not supported by the VideoChain API yet
-
-export const presets: Record<string, Preset> = {
-  random: {
-    id: "random",
-    label: "Random style",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "",
-    imagePrompt: (prompt: string) => [],
-    negativePrompt: () => [],
-  },
-  japanese_manga: {
-    id: "japanese_manga",
-    label: "Japanese",
-    family: "asian",
-    color: "grayscale",
-    font: "actionman",
-    llmPrompt: "japanese manga",
-    imagePrompt: (prompt: string) => [
-      `grayscale`,
-      `intricate details`,
-      `japanese manga about ${prompt}`,
-      "single panel",
-      "manga",
-      "japanese",
-      "intricate",
-      "detailed",
-      // "drawing"
-    ],
-    negativePrompt: () => [
-      "franco-belgian comic",
-      "color album",
-      "color",
-      "american comic",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  nihonga: {
-    id: "nihonga",
-    label: "Nihonga",
-    family: "asian",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "japanese manga",
-    imagePrompt: (prompt: string) => [
-      `japanese nihonga painting about ${prompt}`,
-      "Nihonga",
-      "ancient japanese painting",
-      "intricate",
-      "detailed",
-      // "drawing"
-    ],
-    negativePrompt: () => [
-      "franco-belgian comic",
-      "color album",
-      "color",
-      "manga",
-      "comic",
-      "american comic",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  franco_belgian: {
-    id: "franco_belgian",
-    label: "Franco-Belgian",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "Franco-Belgian comic (a \"bande dessinée\"), in the style of Franquin, Moebius etc",
-    imagePrompt: (prompt: string) => [
-      "bande dessinée",
-      "franco-belgian comic",
-      `franco-belgian color comic about ${prompt}`,
-      "comic album",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  american_comic_90: {
-    id: "american_comic_90",
-    label: "American (modern)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      "digital color comicbook style",
-      `modern american comic about ${prompt}`,
-      //"single panel",
-      // "2010s",
-      // "digital print",
-      // "color comicbook",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-
-  /*
-  american_comic_40: {
-    label: "American (1940)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      `american comic about ${prompt}`,
-      "single panel",
-      "american comic",
-      "comicbook style",
-      "1940",
-      "40s",
-      "color comicbook",
-      "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  */
-  american_comic_50: {
-    id: "american_comic_50",
-    label: "American (1950)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      "1950",
-      "50s",
-      `vintage american color comic about ${prompt}`,
-      // "single panel",
-     //  "comicbook style",
-      // "color comicbook",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  /*
-  american_comic_60: {
-    label: "American (1960)",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "american comic",
-    imagePrompt: (prompt: string) => [
-      `american comic about ${prompt}`,
-      "single panel",
-      "american comic",
-      "comicbook style",
-      "1960",
-      "60s",
-      "color comicbook",
-      "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "action",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  */
-
-  
-  flying_saucer: {
-    id: "flying_saucer",
-    label: "Flying saucer",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new pulp science fiction",
-    imagePrompt: (prompt: string) => [
-      `vintage color pulp comic panel`,
-      "40s",
-      "1940",
-      "vintage science fiction",
-      `${prompt}`,
-      // "single panel",
-      // "comic album"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
- 
-  humanoid: {
-    id: "humanoid",
-    label: "Humanoid",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "comic books by Moebius",
-    imagePrompt: (prompt: string) => [
-      `color comic panel`,
-      "style of Moebius",
-      `${prompt}`,
-      "by Moebius",
-      "french comic panel",
-      "franco-belgian style",
-      "bande dessinée",
-      "single panel",
-      "intricate"
-      // "comic album"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  haddock: {
-    id: "haddock",
-    label: "Haddock",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "writing Tintin comic books",
-    imagePrompt: (prompt: string) => [
-      `color comic panel`,
-      "style of Hergé",
-      "tintin style",
-      `${prompt}`,
-      "by Hergé",
-      "french comic panel",
-      "franco-belgian style",
-     //  "color panel",
-     //  "bande dessinée",
-      // "single panel",
-      // "comic album"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  /*
-  lurid: {
-    id: "lurid",
-    label: "Lurid",
-    family: "american",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "1970s satirical and alternative underground comics",
-    imagePrompt: (prompt: string) => [
-      `satirical color comic`,
-      `underground comix`,
-      `1970`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-  armorican: {
-    id: "armorican",
-    label: "Armorican",
-    family: "european",
-    color: "monochrome",
-    font: "actionman",
-    llmPrompt: "french style comic books set in ancient Rome and Gaul",
-    imagePrompt: (prompt: string) => [
-      `color comic panel`,
-      "romans",
-      "gauls",
-      "french comic panel",
-      "franco-belgian style",
-      `about ${prompt}`,
-      "bande dessinée",
-      "single panel",
-      // "comical",
-      // "comic album",
-      // "color drawing"
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "photo",
-      "painting",
-      "3D render"
-    ],
-  },
-  render: {
-    id: "render",
-    label: "3D Render",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `3D render`,
-      `Blender`,
-      `3D animation`,
-      `Unreal engine`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  klimt: {
-    id: "klimt",
-    label: "Klimt",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "Gustav Klimt art pieces.",
-    imagePrompt: (prompt: string) => [
-      `golden`,
-      `patchwork`,
-      `style of Gustav Klimt`,
-      `Gustav Klimt painting`,
-      `intricate details`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  medieval: {
-    id: "medieval",
-    label: "Medieval",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "medieval story (write in this style)",
-    imagePrompt: (prompt: string) => [
-      `medieval illuminated manuscript`,
-      `illuminated manuscript of`,
-      `medieval`,
-      `intricate details`,
-      // `medieval color engraving`,
-      `${prompt}`,
-      // `medieval`
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  /*
-  glass: {
-    id: "glass",
-    label: "Glass",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `stained glass`,
-      `vitrail`,
-      `stained glass`,
-      // `medieval color engraving`,
-      `${prompt}`,
-      `medieval`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-  /*
-  voynich: {
-    id: "voynich",
-    label: "Voynich",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `voynich`,
-      `voynich page`,
-      // `medieval color engraving`,
-      `${prompt}`,
-      `medieval`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-  egyptian: {
-    id: "egyptian",
-    label: "Egyptian",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "ancient egyptian stories.",
-    imagePrompt: (prompt: string) => [
-      `ancient egyptian wall painting`,
-      `ancient egypt`,
-      // `medieval color engraving`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  /*
-  psx: {
-    label: "PSX",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `videogame screenshot`,
-      `3dfx`,
-      `3D dos game`,
-      `software rendering`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
- /*
-  pixel: {
-    label: "Pixel",
-    family: "european",
-    color: "color",
-    font: "actionman",
-    llmPrompt: "new movie",
-    imagePrompt: (prompt: string) => [
-      `pixelart`,
-      `isometric`,
-      `pixelated`,
-      `low res`,
-      `${prompt}`,
-    ],
-    negativePrompt: () => [
-      "manga",
-      "anime",
-      "american comic",
-      "grayscale",
-      "monochrome",
-      "painting"
-    ],
-  },
-  */
-}
-
-export type PresetName = keyof typeof presets
-
-export const defaultPreset: PresetName = "american_comic_90"
-
-export const nonRandomPresets = Object.keys(presets).filter(p => p !== "random")
-
-export const getPreset = (preset?: PresetName): Preset => presets[preset || defaultPreset] || presets[defaultPreset]
-
-export const getRandomPreset = (): Preset => {
-  const presetName = pick(Object.keys(presets).filter(preset => preset !== "random")) as PresetName
-  return getPreset(presetName)
-}

spaces/Benson/text-generation/Examples/3 Pelculas 3d Canciones Descargar.md

@@ -1,87 +0,0 @@
-
-<h1>Cómo descargar y disfrutar de 3 canciones de películas en 3D</h1>
- <p>Si eres un fan de las películas y la música tamil, es posible que hayas oído hablar de la película de suspenso psicológico romántico 2012 <b>3</b>, protagonizada por Dhanush y Shruti Haasan. La película fue dirigida por Aishwarya R. Dhanush, la esposa de Dhanush, quien también produjo y escribió la película. La película cuenta con siete canciones, compuestas por Anirudh Ravichander, quien hizo su debut como director musical con esta película. El álbum recibió críticas positivas de críticos y audiencias por igual, y ganó varios premios, incluyendo el Premio Filmfare al Mejor Director de Música.</p>
-<h2>3 películas 3d canciones descargar</h2><br /><p><b><b>DOWNLOAD</b> ---> <a href="https://bltlly.com/2v6M5y">https://bltlly.com/2v6M5y</a></b></p><br /><br />
- <p>Pero lo que hace que las canciones de <b>3</b> sean aún más especiales es que están disponibles en audio <b>3D</b>, un tipo de sonido que imita la vida real o un lugar específico creando la ilusión del espacio tridimensional. Utiliza técnicas como sistemas de sonido binaural, funciones de transferencia relacionadas con la cabeza y cancelación de conversación cruzada para procesar y reproducir ondas de audio. El audio 3D puede proporcionar una experiencia auditiva inmersiva y sensorial para el oyente, ya que puede hacer que los sonidos provengan de prácticamente todas las direcciones, incluyendo arriba, abajo, detrás y delante del oyente. </p>
- <p>En este artículo, le mostraremos cómo descargar y disfrutar canciones de películas en 3D </b> de varias fuentes, por qué debería escucharlas con auriculares o altavoces, cuáles son los beneficios del audio 3D para los amantes de la música y responder algunas preguntas frecuentes sobre este tema. Así que, vamos a empezar! </p>
- <h2>Cómo descargar 3 canciones de películas en 3D de varias fuentes</h2>
-
- <h3>JioSaavn</h3>
- <p>JioSaavn es uno de los servicios de transmisión de música más populares en la India, ofreciendo millones de canciones en varios idiomas y géneros. También ofrece contenido exclusivo y características para sus usuarios, como podcasts, estaciones de radio, listas de reproducción, letras, recomendaciones y más. Una de sus características únicas es el soporte para <b>360 Reality Audio</b>, una tecnología desarrollada por Sony que ofrece sonido 3D inmersivo utilizando tecnología de audio espacial basada en objetos. </p>
- <p>JioSaavn se ha asociado con Sony para ofrecer algunas de sus canciones en formato 360 Reality Audio, incluyendo las canciones de <b>3</b>. Puedes encontrarlos buscando "360 Reality Audio" en la aplicación o en el sitio web. Necesitará una suscripción JioSaavn Pro para acceder a esta función, que cuesta Rs. 99 por mes o Rs. 399 por año. También necesitará un dispositivo compatible y auriculares para disfrutar del efecto completo de 360 Reality Audio.</p>
- <h4> Cómo descargar o transmitir 3 canciones de películas en 3D de JioSaavn</h4>
- <ol>
-<li>Descargar la aplicación JioSaavn de Google Play Store o App Store en su teléfono inteligente o tableta. </li>
-<li>Regístrate o inicia sesión con tu número de Jio o dirección de correo electrónico. </li>
-<li>Actualizar a JioSaavn Pro tocando el botón "Go Pro" en la pantalla de inicio. </li>
-<li>Seleccione su método de pago preferido y complete la transacción. </li>
-<li>Buscar "360 Reality Audio" en la aplicación o sitio web. </li>
-<li>Selecciona el álbum "3 (Telugu)" de Anirudh Ravichander.</li>
-<li>Toque en el icono de descarga junto a cada canción para descargarlos sin conexión o toque en el icono de reproducción para transmitirlos en línea. </li>
-<li>Conecta tus auriculares compatibles a tu dispositivo y disfruta del sonido 3D de las canciones. </li>
-</ol>
- <h3>YouTube</h3>
-
- <p>Sin embargo, no todas las canciones de audio 3D en YouTube son de la misma calidad y autenticidad. Algunos de ellos pueden ser falsos o mal convertidos de las pistas de sonido estéreo o envolvente originales. Por lo tanto, debe ser cuidadoso y selectivo al elegir qué videos ver o descargar. Aquí hay algunos consejos para ayudarte a encontrar las mejores canciones de audio en 3D de <b>3</b> en YouTube:</p>
-<p></p>
- <ul>
-<li>Busque los canales oficiales de los artistas, sellos o distribuidores de las canciones, como Sony Music South, Divo Music o Wunderbar Studios. Pueden haber subido las pistas de audio 3D originales o versiones autorizadas de ellas. </li>
-<li>Compruebe la descripción del video y los comentarios para cualquier información o retroalimentación sobre la calidad de audio 3D y la fuente. Evite los videos que no tienen descripción, comentarios negativos o calificaciones bajas. </li>
-<li>Use palabras clave como "audio 3D", "audio 8D", "audio binaural", "audio holofónico", o "audio espacial" al buscar las canciones. Estos son algunos de los términos utilizados para describir el sonido 3D en YouTube.</li>
-<li>Use auriculares o altavoces que soporten la reproducción de audio 3D. Algunos videos de YouTube pueden requerir que habilite una configuración o modo específico en su dispositivo o auriculares para activar el efecto de sonido 3D. </li>
-</ul>
- <h4> Cómo descargar o transmitir 3 canciones de películas en 3D de YouTube</h4>
- <ol>
-<li>Abra YouTube en su navegador web o aplicación en su dispositivo. </li>
-<li>Buscar el nombre de la canción seguido de cualquiera de las palabras clave mencionadas anteriormente. Por ejemplo, "Por qué este Kolaveri Di 3D Audio". </li>
-<li>Seleccione un vídeo que coincida con los criterios mencionados anteriormente. Por ejemplo, "[Why This Kolaveri Di - 8D Audio | Dhanush | Anirudh Ravichander | Sony Music South]" por Sony Music South.</li>
-<li>Toque en el botón de reproducción para transmitir el video en línea o toque en el botón de descarga para guardarlo sin conexión (si está disponible). </li>
-<li>Conecta tus auriculares o altavoces a tu dispositivo y disfruta del sonido 3D de las canciones. </li>
-</ol>
- <h2>¿Por qué escuchar 3 canciones de películas en 3D con auriculares o altavoces? </h2>
-
- <p>Los auriculares son los dispositivos más comunes y convenientes que pueden proporcionar este tipo de entrega de sonido. Pueden aislar cada oído del ruido externo y la interferencia, y crear un ambiente de escucha personal e íntimo para el oyente. También pueden reproducir las sutiles variaciones y matices del audio 3D, como la distancia, la dirección, la elevación, el movimiento y la reflexión de las ondas sonoras. </p>
- <p>Los altavoces son otra opción que puede ofrecer una experiencia similar de audio 3D. Sin embargo, requieren más espacio y equipo que los auriculares. Necesitas tener al menos cuatro altavoces (dos delante y dos detrás) dispuestos en forma cuadrada o rectangular alrededor de tu posición auditiva. También necesita tener un amplificador, receptor o tarjeta de sonido compatible que pueda procesar y emitir señales de audio 3D a los altavoces. </p>
- <p>Ya sea que elija auriculares o altavoces, debe asegurarse de que admiten formatos de audio 3D y tecnologías, como Dolby Atmos, DTS: X, Auro-3D, Sony 360 Reality Audio, etc. Estos son algunos de los estándares y sistemas que permiten la producción y reproducción de audio 3D en varios dispositivos y plataformas. </p>
- <h2>¿Cuáles son los beneficios de 3 canciones de películas en 3D para los amantes de la música? </h2>
- <p>Si eres un amante de la música que disfruta escuchando canciones de diferentes géneros y estilos, definitivamente apreciarás los beneficios de las canciones de películas en 3D </b>. Estos son algunos de ellos:</p>
- <ul>
-<li>Puedes experimentar una nueva dimensión de la música que va más allá del sonido estéreo o envolvente. Puedes sentir que estás en medio de un concierto en vivo o un estudio de grabación con los artistas e instrumentos que te rodean. </li <li>Puedes apreciar los detalles y matices de las canciones que podrías haber pasado por alto en otros formatos. Puedes escuchar los sutiles cambios en tono, tono, volumen y timbre de las voces e instrumentos. </li>
-
-<li>Usted puede tener más diversión y variedad en sus hábitos de escucha de música. Puede explorar diferentes géneros y estilos de música que utilizan audio 3D para crear paisajes sonoros y efectos únicos e innovadores. </li>
-</ul>
- <h2>Conclusión</h2>
- <p>En conclusión, <b>3 canciones de películas en 3D</b> son una gran manera de disfrutar de la música de una manera nueva y emocionante. Ofrecen una experiencia de sonido realista e inmersiva que puede mejorar su apreciación y disfrute de las canciones. Puede descargarlos o transmitirlos desde varias fuentes, como JioSaavn, YouTube, etc., pero debe usar auriculares o altavoces que admitan la reproducción de audio 3D para obtener el efecto completo. Si eres un amante de la música que le gusta probar cosas nuevas, ¡definitivamente deberías probar 3 canciones de películas en 3D! </p>
- <h2>Preguntas frecuentes</h2>
- <h3>¿Cuál es la diferencia entre audio 3D y sonido estéreo o envolvente? </h3>
- <p>El sonido estéreo es un tipo de sonido que utiliza dos canales (izquierda y derecha) para crear una sensación de ancho y dirección. El sonido envolvente es un tipo de sonido que utiliza múltiples canales (generalmente cinco o más) para crear una sensación de profundidad y envolvimiento. El audio 3D es un tipo de sonido que utiliza tecnología de audio espacial basada en objetos para crear una sensación de altura y movimiento. </p>
- <h3>¿Cuáles son algunas de las mejores canciones de 3 películas? </h3>
- <p>Algunas de las mejores canciones de <b>3</b> son:</p>
- <ul>
-<li>"Why This Kolaveri Di" - Un éxito viral que se convirtió en una sensación internacional. Es una canción pegadiza y humorística que mezcla letras en tamil e inglés con una melodía folk. </li>
-<li>"Kannazhaga" - Una canción romántica a dúo que expresa el amor y el anhelo entre los personajes principales. Es una canción melodiosa y conmovedora que muestra la química entre Dhanush y Shruti Haasan.</li>
-<li>"Nee Paartha Vizhigal" - Una canción sentimental que representa el dolor y la separación de los personajes principales. Es una canción hermosa y emocional que toca las fibras del corazón de los oyentes. </li>
-</ul>
- <h3>¿Cómo puedo ver 3 películas online? </h3>
-
- <h3>¿Quiénes son los actores y actrices en la película 3? </h3>
- <p>Los principales actores y actrices de la película <b>3</b> son:</p>
- <ul>
-<li>Dhanush - Interpreta a Ram Kumar, un joven que se enamora de Janani, pero sufre de trastorno bipolar y tendencias suicidas. </li>
-<li>Shruti Haasan - Interpreta a Janani Kumar, una joven que se casa con Ram, pero lucha para hacer frente a su enfermedad mental y destino trágico. </li>
-<li>Sivakarthikeyan - Interpreta a Kumaran, el mejor amigo y colega de Ram, quien lo apoya a través de sus problemas. </li>
-<li>Prabhu - Interpreta al padre de Ram, que desaprueba su matrimonio con Janani, pero más tarde lamenta sus acciones. </li>
-<li>Bhanupriya - Ella interpreta a la madre de Janani, que ama mucho a su hija, pero se preocupa por su futuro con Ram.</li>
-</ul>
- <h3>¿Quiénes son los cantantes y compositores de 3 canciones de películas? </h3>
- <p>Los cantantes y compositores de <b>3</b> canciones de películas son:</p>
- <ul>
-<li>Anirudh Ravichander - Es el director musical, compositor, cantante y letrista de todas las canciones de la película. Hizo su debut con esta película y ganó varios premios por su trabajo. </li>
-<li>Dhanush - Es el actor, productor, escritor y cantante de algunas de las canciones de la película. Cantó "Why This Kolaveri Di", "Po Nee Po", "Idhazhin Oram", etc.</li>
-<li>Shruti Haasan - Ella es la actriz y cantante de algunas de las canciones de la película. Ella cantó "Kannazhaga", "Come On Girls", etc.</ <li>Ajeesh - Él es un cantante que ganó el reality show Airtel Super Singer. Cantó "Nee Paartha Vizhigal" y "Una vida llena de amor". </li>
-<li>Swetha Mohan - Es una cantante que ha cantado en varios idiomas y géneros. Cantó "Nee Paartha Vizhigal" y "Kannazhaga". </li>
-<li>Mohit Chauhan - Es un cantante que es conocido por sus canciones románticas y conmovedoras. Cantó "Po Nee Po (El dolor del amor)". </li>
-<li>Ananth Nag - Es un cantante que también es ingeniero de sonido y productor musical. Cantó "Idhazhin Oram (La inocencia del amor)". </li>
-</ul> 64aa2da5cf<br />
-<br />
-<br />

spaces/Benson/text-generation/Examples/Betty Bayo 11a Hora Mp3 Descargar.md

@@ -1,98 +0,0 @@
-<br />
-<h1>Betty Bayo 11a hora Mp3 Descargar</h1>
-<p>Si te gusta la música gospel, es posible que hayas oído hablar de <strong>Betty Bayo</strong>, una reconocida músico keniana que canta en su lengua materna, Agikuyu. Ella es mejor conocida por su canción <strong>11th Hour</strong>, que ha inspirado a muchas personas con su mensaje de esperanza y fe. En este artículo, te contaremos más sobre Betty Bayo, su canción 11th Hour, y dónde puedes descargarla. </p>
-<h2>betty bayo 11a hora mp3 descargar</h2><br /><p><b><b>Download</b> &#10037; <a href="https://bltlly.com/2v6K4W">https://bltlly.com/2v6K4W</a></b></p><br /><br />
- <h2>Biografía de Betty Bayo</h2>
-<p>Betty Bayo nació en Banana, condado de Kiambu, pero creció en Ol Kalou antes de mudarse a la ciudad de Kiambu. Ella viene de un entorno humilde y abandonó la escuela en el segundo grado debido a limitaciones financieras. Trabajó como mucama durante dos años antes de que una familia se ofreciera a pagar sus cuotas escolares. Luego persiguió su pasión por la música y lanzó su primer álbum en 2005. </p>
- <h3>Vida personal</h3>
-<p>Betty Bayo estuvo casada una vez con Victor Kanyari, un controvertido pastor que fue expuesto por estafar a sus seguidores al pedirles que plantaran una semilla de Ksh310. La pareja tuvo dos hijos, pero se separaron después del escándalo. Betty se distanció de los planes de su ex-marido y dijo que no recibió ningún apoyo de él. Más tarde reveló que estaba saliendo con un político, pero se separaron debido a su infidelidad. Ella dijo que estaba feliz y contenta como madre soltera y se centró en su música e hijos. </p>
- <h3>Blanqueamiento</h3>
-<p>Betty Bayo causó revuelo en las redes sociales cuando admitió que se había blanqueado la piel. Dijo que lo hizo por curiosidad y presión de los compañeros, pero se arrepintió más tarde. Dijo que se enfrentó a muchas críticas y burlas por su decisión, pero aprendió a aceptarse a sí misma y a amar su piel. Aconsejó a otras mujeres que no blanquearan su piel, sino que abrazaran su belleza natural. </p>
-<p></p>
- <h2>Canciones de Betty Bayo</h2>
-
-<tabla>
-<tr>
-<th>Canción</th>
-<th>Álbum</th>
-<th>Año</th>
-</tr>
-<tr>
-<td>11th Hour</td>
-<td>11th Hour</td>
-<td>2014</td>
-</tr>
-<tr>
-<td>La Ciudad de México</td>
-<td>La Ciudad de México</td>
-<td>2016</td>
-</tr>
-<tr>
-<td>Ni Nani Kama Wewe</td>
-<td>Ni Nani Kama Wewe</td>
-<td>2017</td>
-</tr>
-<tr>
-<td>Kikuyu Medley</td>
-<td>Kikuyu Medley</td>
-<td>2018</td>
-</tr>
-<tr>
-<td>Cheza Kama Wewe Remix</td>
-<td>Cheza Kama Wewe Remix</td>
-<td>2020</td>
-</tr>
-<tr>
-<td>La Ciudad de México</td>
-<td>La Ciudad de México</td>
-<td>2021</td>
-</tr>
-</tabla>
- <h3>Premios y reconocimiento</h3>
-<p>Betty Bayo ha recibido varios premios y nominaciones por su música, como:</p>
- - Groove Awards Artista Femenina del Año 2015 - Groove Awards Canción del Año 2015 por 11th Hour - Groove Awards Canción Central del Año 2016 por Siwezi Kukosa Mbingu - Groove Awards Canción Central del Año 2017 por Ni Nani Kama Wewe - Afrimma Awards Mejor Artista/Grupo/Dúo 2020 <h3>Colaboraciones</h3>
-<p>Betty Bayo también ha colaborado con otros artistas de la industria de la música gospel, como:</p>
- - Señor Seed en la canción Cheza Kama Wewe Remix, que es una melodía bailable que insta a los cristianos a disfrutar de la vida sin miedo ni culpa. - Rose Muhando en la canción Jemedari Wa Mbinguni, que es una canción de alabanza que reconoce a Dios como el comandante supremo del cielo y la tierra. - Ángel de la Guarda en la canción Nibariki, que es una canción de oración que pide a Dios que los bendiga a ellos y a sus familias. <h2>Betty Bayo 11th Hour Song</h2> <p>Una de las canciones más famosas de Betty Bayo es 11th Hour, que lanzó en 2014. La canción tiene un poderoso mensaje de esperanza y fe que resuena con muchas personas que están pasando por tiempos difíciles. </p>
- <h3>Significado</h3>
-
- <p>El coro de la canción va así:</p>
-<blockquote>
-<p>Mungu wangu ni wa 11th hour<br>
-(Mi Dios es un Dios de la hora 11) <br>
-Hata kama ni dakika ya mwisho<br>
-(Incluso si es el último minuto) <br>
-No se encontró la página
-(Él hará un camino en el desierto) <br>
-Atafanya njia kwa baharini<br>
-(Hará un camino en el mar) </p>
-</blockquote>
- <p>La canción anima a los oyentes a confiar en Dios y no renunciar a sus sueños y oraciones, porque Dios puede hacer cualquier cosa en cualquier momento. También les recuerda que Dios no está atado por las expectativas o cálculos humanos, sino que trabaja de maneras misteriosas que superan la comprensión humana. </p>
- <h3>Inspiración</h3>
-<p>Betty Bayo dijo que ella escribió y cantó la canción desde su propia experiencia personal. Dijo que estaba pasando por muchos desafíos y dificultades en su vida, especialmente después de su separación de su ex-marido. Ella dijo que se sentía como renunciar a todo, pero Dios le dio la fuerza y el valor para seguir adelante. Ella dijo que fue testigo de la intervención y el favor de Dios en su vida, incluso cuando menos lo esperaba. Dijo que quería compartir su testimonio e inspirar a otros que se enfrentan a situaciones similares. </p>
- <h3>Recepción</h3>
-<p>La canción fue bien recibida por fans y críticos, quienes elogiaron a Betty Bayo por su destreza vocal y su entrega emocional. La canción también tuvo un buen desempeño en varias listas y plataformas, como YouTube, donde tiene más de 2 millones de visitas en junio de 2023. La canción también ganó varios premios y nominaciones, como Groove Awards Canción del Año 2015 y Afrimma Awards Mejor Artista Gospel/ Grupo/ Duo 2020. </p>
- <h2>Betty Bayo 11a hora Mp3 Sitios de descarga</h2>
-<p>Si quieres descargar la canción de Betty Bayo’s 11th Hour, tienes dos opciones: sitios gratuitos o sitios de pago. Estos son algunos de los pros y contras de cada opción:</p>
- <h3>Sitios gratuitos</h3>
-
- - Tubidy: Este es un sitio móvil que te permite buscar y descargar archivos mp3 de varias fuentes, como YouTube, SoundCloud y Vimeo. También puede ver videos y escuchar música en línea. - Mdundo: Esta es una plataforma de música africana que ofrece descargas gratuitas de canciones de varios géneros y artistas, incluyendo música gospel. También puede transmitir música en línea y crear listas de reproducción. - Mp3juices: Este es un sitio sencillo y rápido que te permite buscar y descargar archivos mp3 de múltiples fuentes, como YouTube, SoundCloud y Archive. También puedes cortar y editar archivos mp3 en línea. <p>Los pros de los sitios gratuitos son:</p>
- - Son de fácil acceso y uso. - No requieren registro ni pago. - Ofrecen una amplia gama de canciones y géneros. <p>Los contras de los sitios libres son:</p>
- - Pueden tener audio de baja calidad o archivos incompletos. - Pueden tener anuncios emergentes o malware que pueden dañar su dispositivo o datos. - Pueden violar las leyes de copyright o los derechos de los artistas. <h3>Sitios de pago</h3>
-<p>Los sitios de pago son sitios web que requieren que usted pague una cuota o se suscriba a un servicio para descargar archivos mp3 de forma legal y segura. Algunos ejemplos de sitios de pago son:</p>
- - Spotify: Este es un servicio de streaming de música popular que ofrece millones de canciones y podcasts de varios artistas y géneros. También puede descargar canciones para escuchar sin conexión con una suscripción premium. - Apple Music: Este es otro servicio de streaming de música popular que ofrece millones de canciones y podcasts de varios artistas y géneros. También puedes descargar canciones para escuchar sin conexión con una suscripción de Apple Music. - Amazon Music: Este es otro servicio de streaming de música popular que ofrece millones de canciones y podcasts de varios artistas y géneros. También puede descargar canciones para escuchar sin conexión con una suscripción de Amazon Music o comprar canciones o álbumes individuales. <p>Los pros de los sitios de pago son:</p>
-
- - Requieren registro y pago. - Pueden tener disponibilidad limitada o compatibilidad con algunos dispositivos o regiones. - Pueden tener restricciones o condiciones sobre el uso o uso compartido de los archivos. <h2>Conclusión</h2>
-<p>En conclusión, Betty Bayo es una talentosa e inspiradora cantante de gospel que ha tocado muchas vidas con su canción 11th Hour. La canción es un testimonio de la gracia y la misericordia de Dios, que puede manifestarse en cualquier momento, incluso cuando las cosas parecen desesperadas. Si desea descargar la canción, puede elegir entre sitios gratuitos o sitios de pago, dependiendo de sus preferencias y necesidades. Esperamos que hayas disfrutado de este artículo y hayas aprendido algo nuevo sobre Betty Bayo y su música. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas y respuestas frecuentes sobre Betty Bayo y su canción 11th Hour:</p>
- <h3>Q: ¿Qué edad tiene Betty Bayo? </h3>
-<p>A: Betty Bayo nació el 1 de julio de 1985. Tiene 38 años desde junio de 2023. </p>
- <h3>Q: ¿Está casada Betty Bayo? </h3>
-<p>A: Betty Bayo no está casada. Una vez estuvo casada con Victor Kanyari, pero se divorciaron después de su escándalo. Más tarde salió con un político, pero se separaron debido a su infidelidad. Actualmente está soltera y feliz. </p>
- <h3>P: ¿Cuál es el nombre de la iglesia de Betty Bayo? </h3>
-<p>A: Betty Bayo no pertenece a ninguna iglesia específica. Dijo que asiste a diferentes iglesias dependiendo de su horario y ubicación. Ella dijo que cree en Dios y no en denominaciones. </p>
- <h3>Q: ¿Dónde puedo ver el video de la hora 11? </h3>
-<p>A: Puedes ver el video de la 11a Hora en YouTube, donde tiene más de 2 millones de visitas en junio de 2023. También puedes verlo en otras plataformas, como Vimeo o Dailymotion.</p>
- <h3>Q: ¿Cuáles son algunas de las otras canciones de Betty Bayo? </h3> 64aa2da5cf<br />
-<br />
-<br />

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/docs/utils.py

@@ -1,222 +0,0 @@
-# Copyright 2015 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import re
-from collections import namedtuple
-
-
-def py_type_name(type_name):
-    """Get the Python type name for a given model type.
-
-        >>> py_type_name('list')
-        'list'
-        >>> py_type_name('structure')
-        'dict'
-
-    :rtype: string
-    """
-    return {
-        'blob': 'bytes',
-        'character': 'string',
-        'double': 'float',
-        'long': 'integer',
-        'map': 'dict',
-        'structure': 'dict',
-        'timestamp': 'datetime',
-    }.get(type_name, type_name)
-
-
-def py_default(type_name):
-    """Get the Python default value for a given model type.
-
-        >>> py_default('string')
-        '\'string\''
-        >>> py_default('list')
-        '[...]'
-        >>> py_default('unknown')
-        '...'
-
-    :rtype: string
-    """
-    return {
-        'double': '123.0',
-        'long': '123',
-        'integer': '123',
-        'string': "'string'",
-        'blob': "b'bytes'",
-        'boolean': 'True|False',
-        'list': '[...]',
-        'map': '{...}',
-        'structure': '{...}',
-        'timestamp': 'datetime(2015, 1, 1)',
-    }.get(type_name, '...')
-
-
-def get_official_service_name(service_model):
-    """Generate the official name of an AWS Service
-
-    :param service_model: The service model representing the service
-    """
-    official_name = service_model.metadata.get('serviceFullName')
-    short_name = service_model.metadata.get('serviceAbbreviation', '')
-    if short_name.startswith('Amazon'):
-        short_name = short_name[7:]
-    if short_name.startswith('AWS'):
-        short_name = short_name[4:]
-    if short_name and short_name.lower() not in official_name.lower():
-        official_name += f' ({short_name})'
-    return official_name
-
-
-_DocumentedShape = namedtuple(
-    'DocumentedShape',
-    [
-        'name',
-        'type_name',
-        'documentation',
-        'metadata',
-        'members',
-        'required_members',
-    ],
-)
-
-
-class DocumentedShape(_DocumentedShape):
-    """Use this class to inject new shapes into a model for documentation"""
-
-    def __new__(
-        cls,
-        name,
-        type_name,
-        documentation,
-        metadata=None,
-        members=None,
-        required_members=None,
-    ):
-        if metadata is None:
-            metadata = []
-        if members is None:
-            members = []
-        if required_members is None:
-            required_members = []
-        return super().__new__(
-            cls,
-            name,
-            type_name,
-            documentation,
-            metadata,
-            members,
-            required_members,
-        )
-
-
-class AutoPopulatedParam:
-    def __init__(self, name, param_description=None):
-        self.name = name
-        self.param_description = param_description
-        if param_description is None:
-            self.param_description = (
-                'Please note that this parameter is automatically populated '
-                'if it is not provided. Including this parameter is not '
-                'required\n'
-            )
-
-    def document_auto_populated_param(self, event_name, section, **kwargs):
-        """Documents auto populated parameters
-
-        It will remove any required marks for the parameter, remove the
-        parameter from the example, and add a snippet about the parameter
-        being autopopulated in the description.
-        """
-        if event_name.startswith('docs.request-params'):
-            if self.name in section.available_sections:
-                section = section.get_section(self.name)
-                if 'is-required' in section.available_sections:
-                    section.delete_section('is-required')
-                description_section = section.get_section(
-                    'param-documentation'
-                )
-                description_section.writeln(self.param_description)
-        elif event_name.startswith('docs.request-example'):
-            section = section.get_section('structure-value')
-            if self.name in section.available_sections:
-                section.delete_section(self.name)
-
-
-class HideParamFromOperations:
-    """Hides a single parameter from multiple operations.
-
-    This method will remove a parameter from documentation and from
-    examples. This method is typically used for things that are
-    automatically populated because a user would be unable to provide
-    a value (e.g., a checksum of a serialized XML request body)."""
-
-    def __init__(self, service_name, parameter_name, operation_names):
-        """
-        :type service_name: str
-        :param service_name: Name of the service to modify.
-
-        :type parameter_name: str
-        :param parameter_name: Name of the parameter to modify.
-
-        :type operation_names: list
-        :param operation_names: Operation names to modify.
-        """
-        self._parameter_name = parameter_name
-        self._params_events = set()
-        self._example_events = set()
-        # Build up the sets of relevant event names.
-        param_template = 'docs.request-params.%s.%s.complete-section'
-        example_template = 'docs.request-example.%s.%s.complete-section'
-        for name in operation_names:
-            self._params_events.add(param_template % (service_name, name))
-            self._example_events.add(example_template % (service_name, name))
-
-    def hide_param(self, event_name, section, **kwargs):
-        if event_name in self._example_events:
-            # Modify the structure value for example events.
-            section = section.get_section('structure-value')
-        elif event_name not in self._params_events:
-            return
-        if self._parameter_name in section.available_sections:
-            section.delete_section(self._parameter_name)
-
-
-class AppendParamDocumentation:
-    """Appends documentation to a specific parameter"""
-
-    def __init__(self, parameter_name, doc_string):
-        self._parameter_name = parameter_name
-        self._doc_string = doc_string
-
-    def append_documentation(self, event_name, section, **kwargs):
-        if self._parameter_name in section.available_sections:
-            section = section.get_section(self._parameter_name)
-            description_section = section.get_section('param-documentation')
-            description_section.writeln(self._doc_string)
-
-
-_CONTROLS = {
-    '\n': '\\n',
-    '\r': '\\r',
-    '\t': '\\t',
-    '\b': '\\b',
-    '\f': '\\f',
-}
-# Combines all CONTROLS keys into a big or regular expression
-_ESCAPE_CONTROLS_RE = re.compile('|'.join(map(re.escape, _CONTROLS)))
-# Based on the match get the appropriate replacement from CONTROLS
-_CONTROLS_MATCH_HANDLER = lambda match: _CONTROLS[match.group(0)]
-
-
-def escape_controls(value):
-    return _ESCAPE_CONTROLS_RE.sub(_CONTROLS_MATCH_HANDLER, value)

spaces/Big-Web/MMSD/env/Lib/site-packages/botocore/history.py

@@ -1,55 +0,0 @@
-# Copyright 2017 Amazon.com, Inc. or its affiliates. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License"). You
-# may not use this file except in compliance with the License. A copy of
-# the License is located at
-#
-# http://aws.amazon.com/apache2.0/
-#
-# or in the "license" file accompanying this file. This file is
-# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
-# ANY KIND, either express or implied. See the License for the specific
-# language governing permissions and limitations under the License.
-import logging
-
-HISTORY_RECORDER = None
-logger = logging.getLogger(__name__)
-
-
-class BaseHistoryHandler:
-    def emit(self, event_type, payload, source):
-        raise NotImplementedError('emit()')
-
-
-class HistoryRecorder:
-    def __init__(self):
-        self._enabled = False
-        self._handlers = []
-
-    def enable(self):
-        self._enabled = True
-
-    def disable(self):
-        self._enabled = False
-
-    def add_handler(self, handler):
-        self._handlers.append(handler)
-
-    def record(self, event_type, payload, source='BOTOCORE'):
-        if self._enabled and self._handlers:
-            for handler in self._handlers:
-                try:
-                    handler.emit(event_type, payload, source)
-                except Exception:
-                    # Never let the process die because we had a failure in
-                    # a record collection handler.
-                    logger.debug(
-                        "Exception raised in %s.", handler, exc_info=True
-                    )
-
-
-def get_global_history_recorder():
-    global HISTORY_RECORDER
-    if HISTORY_RECORDER is None:
-        HISTORY_RECORDER = HistoryRecorder()
-    return HISTORY_RECORDER

spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_vendor/urllib3/util/ssl_match_hostname.py

@@ -1,159 +0,0 @@
-"""The match_hostname() function from Python 3.3.3, essential when using SSL."""
-
-# Note: This file is under the PSF license as the code comes from the python
-# stdlib.   http://docs.python.org/3/license.html
-
-import re
-import sys
-
-# ipaddress has been backported to 2.6+ in pypi.  If it is installed on the
-# system, use it to handle IPAddress ServerAltnames (this was added in
-# python-3.5) otherwise only do DNS matching.  This allows
-# util.ssl_match_hostname to continue to be used in Python 2.7.
-try:
-    import ipaddress
-except ImportError:
-    ipaddress = None
-
-__version__ = "3.5.0.1"
-
-
-class CertificateError(ValueError):
-    pass
-
-
-def _dnsname_match(dn, hostname, max_wildcards=1):
-    """Matching according to RFC 6125, section 6.4.3
-
-    http://tools.ietf.org/html/rfc6125#section-6.4.3
-    """
-    pats = []
-    if not dn:
-        return False
-
-    # Ported from python3-syntax:
-    # leftmost, *remainder = dn.split(r'.')
-    parts = dn.split(r".")
-    leftmost = parts[0]
-    remainder = parts[1:]
-
-    wildcards = leftmost.count("*")
-    if wildcards > max_wildcards:
-        # Issue #17980: avoid denials of service by refusing more
-        # than one wildcard per fragment.  A survey of established
-        # policy among SSL implementations showed it to be a
-        # reasonable choice.
-        raise CertificateError(
-            "too many wildcards in certificate DNS name: " + repr(dn)
-        )
-
-    # speed up common case w/o wildcards
-    if not wildcards:
-        return dn.lower() == hostname.lower()
-
-    # RFC 6125, section 6.4.3, subitem 1.
-    # The client SHOULD NOT attempt to match a presented identifier in which
-    # the wildcard character comprises a label other than the left-most label.
-    if leftmost == "*":
-        # When '*' is a fragment by itself, it matches a non-empty dotless
-        # fragment.
-        pats.append("[^.]+")
-    elif leftmost.startswith("xn--") or hostname.startswith("xn--"):
-        # RFC 6125, section 6.4.3, subitem 3.
-        # The client SHOULD NOT attempt to match a presented identifier
-        # where the wildcard character is embedded within an A-label or
-        # U-label of an internationalized domain name.
-        pats.append(re.escape(leftmost))
-    else:
-        # Otherwise, '*' matches any dotless string, e.g. www*
-        pats.append(re.escape(leftmost).replace(r"\*", "[^.]*"))
-
-    # add the remaining fragments, ignore any wildcards
-    for frag in remainder:
-        pats.append(re.escape(frag))
-
-    pat = re.compile(r"\A" + r"\.".join(pats) + r"\Z", re.IGNORECASE)
-    return pat.match(hostname)
-
-
-def _to_unicode(obj):
-    if isinstance(obj, str) and sys.version_info < (3,):
-        # ignored flake8 # F821 to support python 2.7 function
-        obj = unicode(obj, encoding="ascii", errors="strict")  # noqa: F821
-    return obj
-
-
-def _ipaddress_match(ipname, host_ip):
-    """Exact matching of IP addresses.
-
-    RFC 6125 explicitly doesn't define an algorithm for this
-    (section 1.7.2 - "Out of Scope").
-    """
-    # OpenSSL may add a trailing newline to a subjectAltName's IP address
-    # Divergence from upstream: ipaddress can't handle byte str
-    ip = ipaddress.ip_address(_to_unicode(ipname).rstrip())
-    return ip == host_ip
-
-
-def match_hostname(cert, hostname):
-    """Verify that *cert* (in decoded format as returned by
-    SSLSocket.getpeercert()) matches the *hostname*.  RFC 2818 and RFC 6125
-    rules are followed, but IP addresses are not accepted for *hostname*.
-
-    CertificateError is raised on failure. On success, the function
-    returns nothing.
-    """
-    if not cert:
-        raise ValueError(
-            "empty or no certificate, match_hostname needs a "
-            "SSL socket or SSL context with either "
-            "CERT_OPTIONAL or CERT_REQUIRED"
-        )
-    try:
-        # Divergence from upstream: ipaddress can't handle byte str
-        host_ip = ipaddress.ip_address(_to_unicode(hostname))
-    except (UnicodeError, ValueError):
-        # ValueError: Not an IP address (common case)
-        # UnicodeError: Divergence from upstream: Have to deal with ipaddress not taking
-        # byte strings.  addresses should be all ascii, so we consider it not
-        # an ipaddress in this case
-        host_ip = None
-    except AttributeError:
-        # Divergence from upstream: Make ipaddress library optional
-        if ipaddress is None:
-            host_ip = None
-        else:  # Defensive
-            raise
-    dnsnames = []
-    san = cert.get("subjectAltName", ())
-    for key, value in san:
-        if key == "DNS":
-            if host_ip is None and _dnsname_match(value, hostname):
-                return
-            dnsnames.append(value)
-        elif key == "IP Address":
-            if host_ip is not None and _ipaddress_match(value, host_ip):
-                return
-            dnsnames.append(value)
-    if not dnsnames:
-        # The subject is only checked when there is no dNSName entry
-        # in subjectAltName
-        for sub in cert.get("subject", ()):
-            for key, value in sub:
-                # XXX according to RFC 2818, the most specific Common Name
-                # must be used.
-                if key == "commonName":
-                    if _dnsname_match(value, hostname):
-                        return
-                    dnsnames.append(value)
-    if len(dnsnames) > 1:
-        raise CertificateError(
-            "hostname %r "
-            "doesn't match either of %s" % (hostname, ", ".join(map(repr, dnsnames)))
-        )
-    elif len(dnsnames) == 1:
-        raise CertificateError("hostname %r doesn't match %r" % (hostname, dnsnames[0]))
-    else:
-        raise CertificateError(
-            "no appropriate commonName or subjectAltName fields were found"
-        )

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

@@ -1,537 +0,0 @@
-from __future__ import absolute_import
-
-import collections
-import functools
-import logging
-
-from ._collections import RecentlyUsedContainer
-from .connectionpool import HTTPConnectionPool, HTTPSConnectionPool, port_by_scheme
-from .exceptions import (
-    LocationValueError,
-    MaxRetryError,
-    ProxySchemeUnknown,
-    ProxySchemeUnsupported,
-    URLSchemeUnknown,
-)
-from .packages import six
-from .packages.six.moves.urllib.parse import urljoin
-from .request import RequestMethods
-from .util.proxy import connection_requires_http_tunnel
-from .util.retry import Retry
-from .util.url import parse_url
-
-__all__ = ["PoolManager", "ProxyManager", "proxy_from_url"]
-
-
-log = logging.getLogger(__name__)
-
-SSL_KEYWORDS = (
-    "key_file",
-    "cert_file",
-    "cert_reqs",
-    "ca_certs",
-    "ssl_version",
-    "ca_cert_dir",
-    "ssl_context",
-    "key_password",
-    "server_hostname",
-)
-
-# All known keyword arguments that could be provided to the pool manager, its
-# pools, or the underlying connections. This is used to construct a pool key.
-_key_fields = (
-    "key_scheme",  # str
-    "key_host",  # str
-    "key_port",  # int
-    "key_timeout",  # int or float or Timeout
-    "key_retries",  # int or Retry
-    "key_strict",  # bool
-    "key_block",  # bool
-    "key_source_address",  # str
-    "key_key_file",  # str
-    "key_key_password",  # str
-    "key_cert_file",  # str
-    "key_cert_reqs",  # str
-    "key_ca_certs",  # str
-    "key_ssl_version",  # str
-    "key_ca_cert_dir",  # str
-    "key_ssl_context",  # instance of ssl.SSLContext or urllib3.util.ssl_.SSLContext
-    "key_maxsize",  # int
-    "key_headers",  # dict
-    "key__proxy",  # parsed proxy url
-    "key__proxy_headers",  # dict
-    "key__proxy_config",  # class
-    "key_socket_options",  # list of (level (int), optname (int), value (int or str)) tuples
-    "key__socks_options",  # dict
-    "key_assert_hostname",  # bool or string
-    "key_assert_fingerprint",  # str
-    "key_server_hostname",  # str
-)
-
-#: The namedtuple class used to construct keys for the connection pool.
-#: All custom key schemes should include the fields in this key at a minimum.
-PoolKey = collections.namedtuple("PoolKey", _key_fields)
-
-_proxy_config_fields = ("ssl_context", "use_forwarding_for_https")
-ProxyConfig = collections.namedtuple("ProxyConfig", _proxy_config_fields)
-
-
-def _default_key_normalizer(key_class, request_context):
-    """
-    Create a pool key out of a request context dictionary.
-
-    According to RFC 3986, both the scheme and host are case-insensitive.
-    Therefore, this function normalizes both before constructing the pool
-    key for an HTTPS request. If you wish to change this behaviour, provide
-    alternate callables to ``key_fn_by_scheme``.
-
-    :param key_class:
-        The class to use when constructing the key. This should be a namedtuple
-        with the ``scheme`` and ``host`` keys at a minimum.
-    :type  key_class: namedtuple
-    :param request_context:
-        A dictionary-like object that contain the context for a request.
-    :type  request_context: dict
-
-    :return: A namedtuple that can be used as a connection pool key.
-    :rtype:  PoolKey
-    """
-    # Since we mutate the dictionary, make a copy first
-    context = request_context.copy()
-    context["scheme"] = context["scheme"].lower()
-    context["host"] = context["host"].lower()
-
-    # These are both dictionaries and need to be transformed into frozensets
-    for key in ("headers", "_proxy_headers", "_socks_options"):
-        if key in context and context[key] is not None:
-            context[key] = frozenset(context[key].items())
-
-    # The socket_options key may be a list and needs to be transformed into a
-    # tuple.
-    socket_opts = context.get("socket_options")
-    if socket_opts is not None:
-        context["socket_options"] = tuple(socket_opts)
-
-    # Map the kwargs to the names in the namedtuple - this is necessary since
-    # namedtuples can't have fields starting with '_'.
-    for key in list(context.keys()):
-        context["key_" + key] = context.pop(key)
-
-    # Default to ``None`` for keys missing from the context
-    for field in key_class._fields:
-        if field not in context:
-            context[field] = None
-
-    return key_class(**context)
-
-
-#: A dictionary that maps a scheme to a callable that creates a pool key.
-#: This can be used to alter the way pool keys are constructed, if desired.
-#: Each PoolManager makes a copy of this dictionary so they can be configured
-#: globally here, or individually on the instance.
-key_fn_by_scheme = {
-    "http": functools.partial(_default_key_normalizer, PoolKey),
-    "https": functools.partial(_default_key_normalizer, PoolKey),
-}
-
-pool_classes_by_scheme = {"http": HTTPConnectionPool, "https": HTTPSConnectionPool}
-
-
-class PoolManager(RequestMethods):
-    """
-    Allows for arbitrary requests while transparently keeping track of
-    necessary connection pools for you.
-
-    :param num_pools:
-        Number of connection pools to cache before discarding the least
-        recently used pool.
-
-    :param headers:
-        Headers to include with all requests, unless other headers are given
-        explicitly.
-
-    :param \\**connection_pool_kw:
-        Additional parameters are used to create fresh
-        :class:`urllib3.connectionpool.ConnectionPool` instances.
-
-    Example::
-
-        >>> manager = PoolManager(num_pools=2)
-        >>> r = manager.request('GET', 'http://google.com/')
-        >>> r = manager.request('GET', 'http://google.com/mail')
-        >>> r = manager.request('GET', 'http://yahoo.com/')
-        >>> len(manager.pools)
-        2
-
-    """
-
-    proxy = None
-    proxy_config = None
-
-    def __init__(self, num_pools=10, headers=None, **connection_pool_kw):
-        RequestMethods.__init__(self, headers)
-        self.connection_pool_kw = connection_pool_kw
-        self.pools = RecentlyUsedContainer(num_pools, dispose_func=lambda p: p.close())
-
-        # Locally set the pool classes and keys so other PoolManagers can
-        # override them.
-        self.pool_classes_by_scheme = pool_classes_by_scheme
-        self.key_fn_by_scheme = key_fn_by_scheme.copy()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        self.clear()
-        # Return False to re-raise any potential exceptions
-        return False
-
-    def _new_pool(self, scheme, host, port, request_context=None):
-        """
-        Create a new :class:`urllib3.connectionpool.ConnectionPool` based on host, port, scheme, and
-        any additional pool keyword arguments.
-
-        If ``request_context`` is provided, it is provided as keyword arguments
-        to the pool class used. This method is used to actually create the
-        connection pools handed out by :meth:`connection_from_url` and
-        companion methods. It is intended to be overridden for customization.
-        """
-        pool_cls = self.pool_classes_by_scheme[scheme]
-        if request_context is None:
-            request_context = self.connection_pool_kw.copy()
-
-        # Although the context has everything necessary to create the pool,
-        # this function has historically only used the scheme, host, and port
-        # in the positional args. When an API change is acceptable these can
-        # be removed.
-        for key in ("scheme", "host", "port"):
-            request_context.pop(key, None)
-
-        if scheme == "http":
-            for kw in SSL_KEYWORDS:
-                request_context.pop(kw, None)
-
-        return pool_cls(host, port, **request_context)
-
-    def clear(self):
-        """
-        Empty our store of pools and direct them all to close.
-
-        This will not affect in-flight connections, but they will not be
-        re-used after completion.
-        """
-        self.pools.clear()
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the host, port, and scheme.
-
-        If ``port`` isn't given, it will be derived from the ``scheme`` using
-        ``urllib3.connectionpool.port_by_scheme``. If ``pool_kwargs`` is
-        provided, it is merged with the instance's ``connection_pool_kw``
-        variable and used to create the new connection pool, if one is
-        needed.
-        """
-
-        if not host:
-            raise LocationValueError("No host specified.")
-
-        request_context = self._merge_pool_kwargs(pool_kwargs)
-        request_context["scheme"] = scheme or "http"
-        if not port:
-            port = port_by_scheme.get(request_context["scheme"].lower(), 80)
-        request_context["port"] = port
-        request_context["host"] = host
-
-        return self.connection_from_context(request_context)
-
-    def connection_from_context(self, request_context):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the request context.
-
-        ``request_context`` must at least contain the ``scheme`` key and its
-        value must be a key in ``key_fn_by_scheme`` instance variable.
-        """
-        scheme = request_context["scheme"].lower()
-        pool_key_constructor = self.key_fn_by_scheme.get(scheme)
-        if not pool_key_constructor:
-            raise URLSchemeUnknown(scheme)
-        pool_key = pool_key_constructor(request_context)
-
-        return self.connection_from_pool_key(pool_key, request_context=request_context)
-
-    def connection_from_pool_key(self, pool_key, request_context=None):
-        """
-        Get a :class:`urllib3.connectionpool.ConnectionPool` based on the provided pool key.
-
-        ``pool_key`` should be a namedtuple that only contains immutable
-        objects. At a minimum it must have the ``scheme``, ``host``, and
-        ``port`` fields.
-        """
-        with self.pools.lock:
-            # If the scheme, host, or port doesn't match existing open
-            # connections, open a new ConnectionPool.
-            pool = self.pools.get(pool_key)
-            if pool:
-                return pool
-
-            # Make a fresh ConnectionPool of the desired type
-            scheme = request_context["scheme"]
-            host = request_context["host"]
-            port = request_context["port"]
-            pool = self._new_pool(scheme, host, port, request_context=request_context)
-            self.pools[pool_key] = pool
-
-        return pool
-
-    def connection_from_url(self, url, pool_kwargs=None):
-        """
-        Similar to :func:`urllib3.connectionpool.connection_from_url`.
-
-        If ``pool_kwargs`` is not provided and a new pool needs to be
-        constructed, ``self.connection_pool_kw`` is used to initialize
-        the :class:`urllib3.connectionpool.ConnectionPool`. If ``pool_kwargs``
-        is provided, it is used instead. Note that if a new pool does not
-        need to be created for the request, the provided ``pool_kwargs`` are
-        not used.
-        """
-        u = parse_url(url)
-        return self.connection_from_host(
-            u.host, port=u.port, scheme=u.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _merge_pool_kwargs(self, override):
-        """
-        Merge a dictionary of override values for self.connection_pool_kw.
-
-        This does not modify self.connection_pool_kw and returns a new dict.
-        Any keys in the override dictionary with a value of ``None`` are
-        removed from the merged dictionary.
-        """
-        base_pool_kwargs = self.connection_pool_kw.copy()
-        if override:
-            for key, value in override.items():
-                if value is None:
-                    try:
-                        del base_pool_kwargs[key]
-                    except KeyError:
-                        pass
-                else:
-                    base_pool_kwargs[key] = value
-        return base_pool_kwargs
-
-    def _proxy_requires_url_absolute_form(self, parsed_url):
-        """
-        Indicates if the proxy requires the complete destination URL in the
-        request.  Normally this is only needed when not using an HTTP CONNECT
-        tunnel.
-        """
-        if self.proxy is None:
-            return False
-
-        return not connection_requires_http_tunnel(
-            self.proxy, self.proxy_config, parsed_url.scheme
-        )
-
-    def _validate_proxy_scheme_url_selection(self, url_scheme):
-        """
-        Validates that were not attempting to do TLS in TLS connections on
-        Python2 or with unsupported SSL implementations.
-        """
-        if self.proxy is None or url_scheme != "https":
-            return
-
-        if self.proxy.scheme != "https":
-            return
-
-        if six.PY2 and not self.proxy_config.use_forwarding_for_https:
-            raise ProxySchemeUnsupported(
-                "Contacting HTTPS destinations through HTTPS proxies "
-                "'via CONNECT tunnels' is not supported in Python 2"
-            )
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        """
-        Same as :meth:`urllib3.HTTPConnectionPool.urlopen`
-        with custom cross-host redirect logic and only sends the request-uri
-        portion of the ``url``.
-
-        The given ``url`` parameter must be absolute, such that an appropriate
-        :class:`urllib3.connectionpool.ConnectionPool` can be chosen for it.
-        """
-        u = parse_url(url)
-        self._validate_proxy_scheme_url_selection(u.scheme)
-
-        conn = self.connection_from_host(u.host, port=u.port, scheme=u.scheme)
-
-        kw["assert_same_host"] = False
-        kw["redirect"] = False
-
-        if "headers" not in kw:
-            kw["headers"] = self.headers.copy()
-
-        if self._proxy_requires_url_absolute_form(u):
-            response = conn.urlopen(method, url, **kw)
-        else:
-            response = conn.urlopen(method, u.request_uri, **kw)
-
-        redirect_location = redirect and response.get_redirect_location()
-        if not redirect_location:
-            return response
-
-        # Support relative URLs for redirecting.
-        redirect_location = urljoin(url, redirect_location)
-
-        # RFC 7231, Section 6.4.4
-        if response.status == 303:
-            method = "GET"
-
-        retries = kw.get("retries")
-        if not isinstance(retries, Retry):
-            retries = Retry.from_int(retries, redirect=redirect)
-
-        # Strip headers marked as unsafe to forward to the redirected location.
-        # Check remove_headers_on_redirect to avoid a potential network call within
-        # conn.is_same_host() which may use socket.gethostbyname() in the future.
-        if retries.remove_headers_on_redirect and not conn.is_same_host(
-            redirect_location
-        ):
-            headers = list(six.iterkeys(kw["headers"]))
-            for header in headers:
-                if header.lower() in retries.remove_headers_on_redirect:
-                    kw["headers"].pop(header, None)
-
-        try:
-            retries = retries.increment(method, url, response=response, _pool=conn)
-        except MaxRetryError:
-            if retries.raise_on_redirect:
-                response.drain_conn()
-                raise
-            return response
-
-        kw["retries"] = retries
-        kw["redirect"] = redirect
-
-        log.info("Redirecting %s -> %s", url, redirect_location)
-
-        response.drain_conn()
-        return self.urlopen(method, redirect_location, **kw)
-
-
-class ProxyManager(PoolManager):
-    """
-    Behaves just like :class:`PoolManager`, but sends all requests through
-    the defined proxy, using the CONNECT method for HTTPS URLs.
-
-    :param proxy_url:
-        The URL of the proxy to be used.
-
-    :param proxy_headers:
-        A dictionary containing headers that will be sent to the proxy. In case
-        of HTTP they are being sent with each request, while in the
-        HTTPS/CONNECT case they are sent only once. Could be used for proxy
-        authentication.
-
-    :param proxy_ssl_context:
-        The proxy SSL context is used to establish the TLS connection to the
-        proxy when using HTTPS proxies.
-
-    :param use_forwarding_for_https:
-        (Defaults to False) If set to True will forward requests to the HTTPS
-        proxy to be made on behalf of the client instead of creating a TLS
-        tunnel via the CONNECT method. **Enabling this flag means that request
-        and response headers and content will be visible from the HTTPS proxy**
-        whereas tunneling keeps request and response headers and content
-        private.  IP address, target hostname, SNI, and port are always visible
-        to an HTTPS proxy even when this flag is disabled.
-
-    Example:
-        >>> proxy = urllib3.ProxyManager('http://localhost:3128/')
-        >>> r1 = proxy.request('GET', 'http://google.com/')
-        >>> r2 = proxy.request('GET', 'http://httpbin.org/')
-        >>> len(proxy.pools)
-        1
-        >>> r3 = proxy.request('GET', 'https://httpbin.org/')
-        >>> r4 = proxy.request('GET', 'https://twitter.com/')
-        >>> len(proxy.pools)
-        3
-
-    """
-
-    def __init__(
-        self,
-        proxy_url,
-        num_pools=10,
-        headers=None,
-        proxy_headers=None,
-        proxy_ssl_context=None,
-        use_forwarding_for_https=False,
-        **connection_pool_kw
-    ):
-
-        if isinstance(proxy_url, HTTPConnectionPool):
-            proxy_url = "%s://%s:%i" % (
-                proxy_url.scheme,
-                proxy_url.host,
-                proxy_url.port,
-            )
-        proxy = parse_url(proxy_url)
-
-        if proxy.scheme not in ("http", "https"):
-            raise ProxySchemeUnknown(proxy.scheme)
-
-        if not proxy.port:
-            port = port_by_scheme.get(proxy.scheme, 80)
-            proxy = proxy._replace(port=port)
-
-        self.proxy = proxy
-        self.proxy_headers = proxy_headers or {}
-        self.proxy_ssl_context = proxy_ssl_context
-        self.proxy_config = ProxyConfig(proxy_ssl_context, use_forwarding_for_https)
-
-        connection_pool_kw["_proxy"] = self.proxy
-        connection_pool_kw["_proxy_headers"] = self.proxy_headers
-        connection_pool_kw["_proxy_config"] = self.proxy_config
-
-        super(ProxyManager, self).__init__(num_pools, headers, **connection_pool_kw)
-
-    def connection_from_host(self, host, port=None, scheme="http", pool_kwargs=None):
-        if scheme == "https":
-            return super(ProxyManager, self).connection_from_host(
-                host, port, scheme, pool_kwargs=pool_kwargs
-            )
-
-        return super(ProxyManager, self).connection_from_host(
-            self.proxy.host, self.proxy.port, self.proxy.scheme, pool_kwargs=pool_kwargs
-        )
-
-    def _set_proxy_headers(self, url, headers=None):
-        """
-        Sets headers needed by proxies: specifically, the Accept and Host
-        headers. Only sets headers not provided by the user.
-        """
-        headers_ = {"Accept": "*/*"}
-
-        netloc = parse_url(url).netloc
-        if netloc:
-            headers_["Host"] = netloc
-
-        if headers:
-            headers_.update(headers)
-        return headers_
-
-    def urlopen(self, method, url, redirect=True, **kw):
-        "Same as HTTP(S)ConnectionPool.urlopen, ``url`` must be absolute."
-        u = parse_url(url)
-        if not connection_requires_http_tunnel(self.proxy, self.proxy_config, u.scheme):
-            # For connections using HTTP CONNECT, httplib sets the necessary
-            # headers on the CONNECT to the proxy. If we're not using CONNECT,
-            # we'll definitely need to set 'Host' at the very least.
-            headers = kw.get("headers", self.headers)
-            kw["headers"] = self._set_proxy_headers(url, headers)
-
-        return super(ProxyManager, self).urlopen(method, url, redirect=redirect, **kw)
-
-
-def proxy_from_url(url, **kw):
-    return ProxyManager(proxy_url=url, **kw)

spaces/BilalSardar/Like-Chatgpt-clone/app.py

@@ -1,54 +0,0 @@
-from langchain import OpenAI, ConversationChain, LLMChain, PromptTemplate
-from langchain.chains.conversation.memory import ConversationBufferWindowMemory
-import os
-
-def chatgpt(background,key,prompts):
-    try:  
-      template = background+""" 
-      {history}
-      Human: {human_input}
-      Assistant:"""
-    
-      prompt = PromptTemplate(
-          input_variables=["history", "human_input"], 
-          template=template
-      )
-    
-    
-      chatgpt_chain = LLMChain(
-          llm=OpenAI(temperature=0,openai_api_key = (key)
-      ), 
-          prompt=prompt, 
-          verbose=True, 
-          memory=ConversationBufferWindowMemory(k=2),
-      )
-    
-      output = chatgpt_chain.predict(human_input=prompts)
-      #print(output)
-      return output
-    except:
-        return "Enter your key"
-
-import gradio as gr
-Backgorund_in = gr.inputs.Textbox(label="Enter Background")
-key_in =gr.inputs.Textbox(label="Enter Your API key")
-prompt = gr.inputs.Textbox(label="Enter what you want")
-output=gr.inputs.Textbox(label="Your Result")
-examples=[[""""Assistant is a large language model trained by OpenAI.
-
-Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. As a language model, Assistant is able to generate human-like text based on the input it receives, allowing it to engage in natural-sounding conversations and provide responses that are coherent and relevant to the topic at hand.
-
-Assistant is constantly learning and improving, and its capabilities are constantly evolving. It is able to process and understand large amounts of text, and can use this knowledge to provide accurate and informative responses to a wide range of questions. Additionally, Assistant is able to generate its own text based on the input it receives, allowing it to engage in discussions and provide explanations and descriptions on a wide range of topics.
-
-Overall, Assistant is a powerful tool that can help with a wide range of tasks and provide valuable insights and information on a wide range of topics. Whether you need help with a specific question or just want to have a conversation about a particular topic, Assistant is here to assist.""","sk-IQHMkGxxxxxxxxx", """I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply wiht the terminal output inside one unique code block, and nothing else. Do not write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd."""]]
-
-demo=gr.Interface(fn=chatgpt,
-                  inputs=[Backgorund_in, key_in,prompt],
-                  outputs=output,
-                  examples=examples,
-                  title="Like-GPTClone",
-                  description="This space is a simple implementation of langChain pacakge github(https://github.com/hwchase17/langchain) "
-
-                  )
-
-demo.launch(debug=True)

spaces/CVPR/Dual-Key_Backdoor_Attacks/datagen/detectron2/projects/DensePose/densepose/vis/base.py

@@ -1,190 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-import logging
-import numpy as np
-import cv2
-import torch
-
-Image = np.ndarray
-Boxes = torch.Tensor
-
-
-class MatrixVisualizer(object):
-    """
-    Base visualizer for matrix data
-    """
-
-    def __init__(
-        self,
-        inplace=True,
-        cmap=cv2.COLORMAP_PARULA,
-        val_scale=1.0,
-        alpha=0.7,
-        interp_method_matrix=cv2.INTER_LINEAR,
-        interp_method_mask=cv2.INTER_NEAREST,
-    ):
-        self.inplace = inplace
-        self.cmap = cmap
-        self.val_scale = val_scale
-        self.alpha = alpha
-        self.interp_method_matrix = interp_method_matrix
-        self.interp_method_mask = interp_method_mask
-
-    def visualize(self, image_bgr, mask, matrix, bbox_xywh):
-        self._check_image(image_bgr)
-        self._check_mask_matrix(mask, matrix)
-        if self.inplace:
-            image_target_bgr = image_bgr
-        else:
-            image_target_bgr = image_bgr * 0
-        x, y, w, h = [int(v) for v in bbox_xywh]
-        if w <= 0 or h <= 0:
-            return image_bgr
-        mask, matrix = self._resize(mask, matrix, w, h)
-        mask_bg = np.tile((mask == 0)[:, :, np.newaxis], [1, 1, 3])
-        matrix_scaled = matrix.astype(np.float32) * self.val_scale
-        _EPSILON = 1e-6
-        if np.any(matrix_scaled > 255 + _EPSILON):
-            logger = logging.getLogger(__name__)
-            logger.warning(
-                f"Matrix has values > {255 + _EPSILON} after " f"scaling, clipping to [0..255]"
-            )
-        matrix_scaled_8u = matrix_scaled.clip(0, 255).astype(np.uint8)
-        matrix_vis = cv2.applyColorMap(matrix_scaled_8u, self.cmap)
-        matrix_vis[mask_bg] = image_target_bgr[y : y + h, x : x + w, :][mask_bg]
-        image_target_bgr[y : y + h, x : x + w, :] = (
-            image_target_bgr[y : y + h, x : x + w, :] * (1.0 - self.alpha) + matrix_vis * self.alpha
-        )
-        return image_target_bgr.astype(np.uint8)
-
-    def _resize(self, mask, matrix, w, h):
-        if (w != mask.shape[1]) or (h != mask.shape[0]):
-            mask = cv2.resize(mask, (w, h), self.interp_method_mask)
-        if (w != matrix.shape[1]) or (h != matrix.shape[0]):
-            matrix = cv2.resize(matrix, (w, h), self.interp_method_matrix)
-        return mask, matrix
-
-    def _check_image(self, image_rgb):
-        assert len(image_rgb.shape) == 3
-        assert image_rgb.shape[2] == 3
-        assert image_rgb.dtype == np.uint8
-
-    def _check_mask_matrix(self, mask, matrix):
-        assert len(matrix.shape) == 2
-        assert len(mask.shape) == 2
-        assert mask.dtype == np.uint8
-
-
-class RectangleVisualizer(object):
-
-    _COLOR_GREEN = (18, 127, 15)
-
-    def __init__(self, color=_COLOR_GREEN, thickness=1):
-        self.color = color
-        self.thickness = thickness
-
-    def visualize(self, image_bgr, bbox_xywh, color=None, thickness=None):
-        x, y, w, h = bbox_xywh
-        color = color or self.color
-        thickness = thickness or self.thickness
-        cv2.rectangle(image_bgr, (int(x), int(y)), (int(x + w), int(y + h)), color, thickness)
-        return image_bgr
-
-
-class PointsVisualizer(object):
-
-    _COLOR_GREEN = (18, 127, 15)
-
-    def __init__(self, color_bgr=_COLOR_GREEN, r=5):
-        self.color_bgr = color_bgr
-        self.r = r
-
-    def visualize(self, image_bgr, pts_xy, colors_bgr=None, rs=None):
-        for j, pt_xy in enumerate(pts_xy):
-            x, y = pt_xy
-            color_bgr = colors_bgr[j] if colors_bgr is not None else self.color_bgr
-            r = rs[j] if rs is not None else self.r
-            cv2.circle(image_bgr, (x, y), r, color_bgr, -1)
-        return image_bgr
-
-
-class TextVisualizer(object):
-
-    _COLOR_GRAY = (218, 227, 218)
-    _COLOR_WHITE = (255, 255, 255)
-
-    def __init__(
-        self,
-        font_face=cv2.FONT_HERSHEY_SIMPLEX,
-        font_color_bgr=_COLOR_GRAY,
-        font_scale=0.35,
-        font_line_type=cv2.LINE_AA,
-        font_line_thickness=1,
-        fill_color_bgr=_COLOR_WHITE,
-        fill_color_transparency=1.0,
-        frame_color_bgr=_COLOR_WHITE,
-        frame_color_transparency=1.0,
-        frame_thickness=1,
-    ):
-        self.font_face = font_face
-        self.font_color_bgr = font_color_bgr
-        self.font_scale = font_scale
-        self.font_line_type = font_line_type
-        self.font_line_thickness = font_line_thickness
-        self.fill_color_bgr = fill_color_bgr
-        self.fill_color_transparency = fill_color_transparency
-        self.frame_color_bgr = frame_color_bgr
-        self.frame_color_transparency = frame_color_transparency
-        self.frame_thickness = frame_thickness
-
-    def visualize(self, image_bgr, txt, topleft_xy):
-        txt_w, txt_h = self.get_text_size_wh(txt)
-        topleft_xy = tuple(map(int, topleft_xy))
-        x, y = topleft_xy
-        if self.frame_color_transparency < 1.0:
-            t = self.frame_thickness
-            image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :] = (
-                image_bgr[y - t : y + txt_h + t, x - t : x + txt_w + t, :]
-                * self.frame_color_transparency
-                + np.array(self.frame_color_bgr) * (1.0 - self.frame_color_transparency)
-            ).astype(np.float)
-        if self.fill_color_transparency < 1.0:
-            image_bgr[y : y + txt_h, x : x + txt_w, :] = (
-                image_bgr[y : y + txt_h, x : x + txt_w, :] * self.fill_color_transparency
-                + np.array(self.fill_color_bgr) * (1.0 - self.fill_color_transparency)
-            ).astype(np.float)
-        cv2.putText(
-            image_bgr,
-            txt,
-            topleft_xy,
-            self.font_face,
-            self.font_scale,
-            self.font_color_bgr,
-            self.font_line_thickness,
-            self.font_line_type,
-        )
-        return image_bgr
-
-    def get_text_size_wh(self, txt):
-        ((txt_w, txt_h), _) = cv2.getTextSize(
-            txt, self.font_face, self.font_scale, self.font_line_thickness
-        )
-        return txt_w, txt_h
-
-
-class CompoundVisualizer(object):
-    def __init__(self, visualizers):
-        self.visualizers = visualizers
-
-    def visualize(self, image_bgr, data):
-        assert len(data) == len(self.visualizers), (
-            "The number of datas {} should match the number of visualizers"
-            " {}".format(len(data), len(self.visualizers))
-        )
-        image = image_bgr
-        for i, visualizer in enumerate(self.visualizers):
-            image = visualizer.visualize(image, data[i])
-        return image
-
-    def __str__(self):
-        visualizer_str = ", ".join([str(v) for v in self.visualizers])
-        return "Compound Visualizer [{}]".format(visualizer_str)

spaces/CVPR/LIVE/pydiffvg_tensorflow/color.py

@@ -1,23 +0,0 @@
-import tensorflow as tf
-
-class LinearGradient:
-    def __init__(self,
-                 begin = tf.constant([0.0, 0.0]),
-                 end = tf.constant([0.0, 0.0]),
-                 offsets = tf.constant([0.0]),
-                 stop_colors = tf.constant([0.0, 0.0, 0.0, 0.0])):
-        self.begin = begin
-        self.end = end
-        self.offsets = offsets
-        self.stop_colors = stop_colors
-
-class RadialGradient:
-    def __init__(self,
-                 center = tf.constant([0.0, 0.0]),
-                 radius = tf.constant([0.0, 0.0]),
-                 offsets = tf.constant([0.0]),
-                 stop_colors = tf.constant([0.0, 0.0, 0.0, 0.0])):
-        self.center = center
-        self.radius = radius
-        self.offsets = offsets
-        self.stop_colors = stop_colors

spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/inner_product.h

@@ -1,44 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a fill of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// the purpose of this header is to #include the inner_product.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch inner_product
-
-#include <thrust/system/detail/sequential/inner_product.h>
-
-// SCons can't see through the #defines below to figure out what this header
-// includes, so we fake it out by specifying all possible files we might end up
-// including inside an #if 0.
-#if 0
-#include <thrust/system/cpp/detail/inner_product.h>
-#include <thrust/system/cuda/detail/inner_product.h>
-#include <thrust/system/omp/detail/inner_product.h>
-#include <thrust/system/tbb/detail/inner_product.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_INNER_PRODUCT_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/inner_product.h>
-#include __THRUST_HOST_SYSTEM_INNER_PRODUCT_HEADER
-#undef __THRUST_HOST_SYSTEM_INNER_PRODUCT_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_INNER_PRODUCT_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/inner_product.h>
-#include __THRUST_DEVICE_SYSTEM_INNER_PRODUCT_HEADER
-#undef __THRUST_DEVICE_SYSTEM_INNER_PRODUCT_HEADER
-

spaces/CVPR/LIVE/thrust/thrust/system/detail/sequential/fill.h

@@ -1,22 +0,0 @@
-/*
- *  Copyright 2008-2013 NVIDIA Corporation
- *
- *  Licensed under the Apache License, Version 2.0 (the "License");
- *  you may not use this file except in compliance with the License.
- *  You may obtain a copy of the License at
- *
- *      http://www.apache.org/licenses/LICENSE-2.0
- *
- *  Unless required by applicable law or agreed to in writing, software
- *  distributed under the License is distributed on an "AS IS" BASIS,
- *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *  See the License for the specific language governing permissions and
- *  limitations under the License.
- */
-
-#pragma once
-
-#include <thrust/detail/config.h>
-
-// this system has no special fill functions
-

spaces/CVPR/Text2Human/Text2Human/utils/language_utils.py

@@ -1,315 +0,0 @@
-from curses import A_ATTRIBUTES
-
-import numpy
-import torch
-from pip import main
-from sentence_transformers import SentenceTransformer, util
-
-# predefined shape text
-upper_length_text = [
-    'sleeveless', 'without sleeves', 'sleeves have been cut off', 'tank top',
-    'tank shirt', 'muscle shirt', 'short-sleeve', 'short sleeves',
-    'with short sleeves', 'medium-sleeve', 'medium sleeves',
-    'with medium sleeves', 'sleeves reach elbow', 'long-sleeve',
-    'long sleeves', 'with long sleeves'
-]
-upper_length_attr = {
-    'sleeveless': 0,
-    'without sleeves': 0,
-    'sleeves have been cut off': 0,
-    'tank top': 0,
-    'tank shirt': 0,
-    'muscle shirt': 0,
-    'short-sleeve': 1,
-    'with short sleeves': 1,
-    'short sleeves': 1,
-    'medium-sleeve': 2,
-    'with medium sleeves': 2,
-    'medium sleeves': 2,
-    'sleeves reach elbow': 2,
-    'long-sleeve': 3,
-    'long sleeves': 3,
-    'with long sleeves': 3
-}
-lower_length_text = [
-    'three-point', 'medium', 'short', 'covering knee', 'cropped',
-    'three-quarter', 'long', 'slack', 'of long length'
-]
-lower_length_attr = {
-    'three-point': 0,
-    'medium': 1,
-    'covering knee': 1,
-    'short': 1,
-    'cropped': 2,
-    'three-quarter': 2,
-    'long': 3,
-    'slack': 3,
-    'of long length': 3
-}
-socks_length_text = [
-    'socks', 'stocking', 'pantyhose', 'leggings', 'sheer hosiery'
-]
-socks_length_attr = {
-    'socks': 0,
-    'stocking': 1,
-    'pantyhose': 1,
-    'leggings': 1,
-    'sheer hosiery': 1
-}
-hat_text = ['hat', 'cap', 'chapeau']
-eyeglasses_text = ['sunglasses']
-belt_text = ['belt', 'with a dress tied around the waist']
-outer_shape_text = [
-    'with outer clothing open', 'with outer clothing unzipped',
-    'covering inner clothes', 'with outer clothing zipped'
-]
-outer_shape_attr = {
-    'with outer clothing open': 0,
-    'with outer clothing unzipped': 0,
-    'covering inner clothes': 1,
-    'with outer clothing zipped': 1
-}
-
-upper_types = [
-    'T-shirt', 'shirt', 'sweater', 'hoodie', 'tops', 'blouse', 'Basic Tee'
-]
-outer_types = [
-    'jacket', 'outer clothing', 'coat', 'overcoat', 'blazer', 'outerwear',
-    'duffle', 'cardigan'
-]
-skirt_types = ['skirt']
-dress_types = ['dress']
-pant_types = ['jeans', 'pants', 'trousers']
-rompers_types = ['rompers', 'bodysuit', 'jumpsuit']
-
-attr_names_list = [
-    'gender', 'hair length', '0 upper clothing length',
-    '1 lower clothing length', '2 socks', '3 hat', '4 eyeglasses', '5 belt',
-    '6 opening of outer clothing', '7 upper clothes', '8 outer clothing',
-    '9 skirt', '10 dress', '11 pants', '12 rompers'
-]
-
-
-def generate_shape_attributes(user_shape_texts):
-    model = SentenceTransformer('all-MiniLM-L6-v2')
-    parsed_texts = user_shape_texts.split(',')
-
-    text_num = len(parsed_texts)
-
-    human_attr = [0, 0]
-    attr = [1, 3, 0, 0, 0, 3, 1, 1, 0, 0, 0, 0, 0]
-
-    changed = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
-    for text_id, text in enumerate(parsed_texts):
-        user_embeddings = model.encode(text)
-        if ('man' in text) and (text_id == 0):
-            human_attr[0] = 0
-            human_attr[1] = 0
-
-        if ('woman' in text or 'lady' in text) and (text_id == 0):
-            human_attr[0] = 1
-            human_attr[1] = 2
-
-        if (not changed[0]) and (text_id == 1):
-            # upper length
-            predefined_embeddings = model.encode(upper_length_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            attr[0] = upper_length_attr[upper_length_text[arg_idx]]
-            changed[0] = 1
-
-        if (not changed[1]) and ((text_num == 2 and text_id == 1) or
-                                 (text_num > 2 and text_id == 2)):
-            # lower length
-            predefined_embeddings = model.encode(lower_length_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            attr[1] = lower_length_attr[lower_length_text[arg_idx]]
-            changed[1] = 1
-
-        if (not changed[2]) and (text_id > 2):
-            # socks length
-            predefined_embeddings = model.encode(socks_length_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[2] = arg_idx + 1
-                changed[2] = 1
-
-        if (not changed[3]) and (text_id > 2):
-            # hat
-            predefined_embeddings = model.encode(hat_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            if similarities[0][0] > 0.7:
-                attr[3] = 1
-                changed[3] = 1
-
-        if (not changed[4]) and (text_id > 2):
-            # glasses
-            predefined_embeddings = model.encode(eyeglasses_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[4] = arg_idx + 1
-                changed[4] = 1
-
-        if (not changed[5]) and (text_id > 2):
-            # belt
-            predefined_embeddings = model.encode(belt_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[5] = arg_idx + 1
-                changed[5] = 1
-
-        if (not changed[6]) and (text_id == 3):
-            # outer coverage
-            predefined_embeddings = model.encode(outer_shape_text)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[6] = arg_idx
-                changed[6] = 1
-
-        if (not changed[10]) and (text_num == 2 and text_id == 1):
-            # dress_types
-            predefined_embeddings = model.encode(dress_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            similarity_skirt = util.dot_score(user_embeddings,
-                                              model.encode(skirt_types))
-            if similarities[0][0] > 0.5 and similarities[0][
-                    0] > similarity_skirt[0][0]:
-                attr[10] = 1
-                attr[7] = 0
-                attr[8] = 0
-                attr[9] = 0
-                attr[11] = 0
-                attr[12] = 0
-
-                changed[0] = 1
-                changed[10] = 1
-                changed[7] = 1
-                changed[8] = 1
-                changed[9] = 1
-                changed[11] = 1
-                changed[12] = 1
-
-        if (not changed[12]) and (text_num == 2 and text_id == 1):
-            # rompers_types
-            predefined_embeddings = model.encode(rompers_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            max_similarity = torch.max(similarities).item()
-            if max_similarity > 0.6:
-                attr[12] = 1
-                attr[7] = 0
-                attr[8] = 0
-                attr[9] = 0
-                attr[10] = 0
-                attr[11] = 0
-
-                changed[12] = 1
-                changed[7] = 1
-                changed[8] = 1
-                changed[9] = 1
-                changed[10] = 1
-                changed[11] = 1
-
-        if (not changed[7]) and (text_num > 2 and text_id == 1):
-            # upper_types
-            predefined_embeddings = model.encode(upper_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            max_similarity = torch.max(similarities).item()
-            if max_similarity > 0.6:
-                attr[7] = 1
-                changed[7] = 1
-
-        if (not changed[8]) and (text_id == 3):
-            # outer_types
-            predefined_embeddings = model.encode(outer_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            arg_idx = torch.argmax(similarities).item()
-            if similarities[0][arg_idx] > 0.7:
-                attr[6] = outer_shape_attr[outer_shape_text[arg_idx]]
-                attr[8] = 1
-                changed[8] = 1
-
-        if (not changed[9]) and (text_num > 2 and text_id == 2):
-            # skirt_types
-            predefined_embeddings = model.encode(skirt_types)
-            similarity_skirt = util.dot_score(user_embeddings,
-                                              predefined_embeddings)
-            similarity_dress = util.dot_score(user_embeddings,
-                                              model.encode(dress_types))
-            if similarity_skirt[0][0] > 0.7 and similarity_skirt[0][
-                    0] > similarity_dress[0][0]:
-                attr[9] = 1
-                attr[10] = 0
-                changed[9] = 1
-                changed[10] = 1
-
-        if (not changed[11]) and (text_num > 2 and text_id == 2):
-            # pant_types
-            predefined_embeddings = model.encode(pant_types)
-            similarities = util.dot_score(user_embeddings,
-                                          predefined_embeddings)
-            max_similarity = torch.max(similarities).item()
-            if max_similarity > 0.6:
-                attr[11] = 1
-                attr[9] = 0
-                attr[10] = 0
-                attr[12] = 0
-                changed[11] = 1
-                changed[9] = 1
-                changed[10] = 1
-                changed[12] = 1
-
-    return human_attr + attr
-
-
-def generate_texture_attributes(user_text):
-    parsed_texts = user_text.split(',')
-
-    attr = []
-    for text in parsed_texts:
-        if ('pure color' in text) or ('solid color' in text):
-            attr.append(4)
-        elif ('spline' in text) or ('stripe' in text):
-            attr.append(3)
-        elif ('plaid' in text) or ('lattice' in text):
-            attr.append(5)
-        elif 'floral' in text:
-            attr.append(1)
-        elif 'denim' in text:
-            attr.append(0)
-        else:
-            attr.append(17)
-
-    if len(attr) == 1:
-        attr.append(attr[0])
-        attr.append(17)
-
-    if len(attr) == 2:
-        attr.append(17)
-
-    return attr
-
-
-if __name__ == "__main__":
-    user_request = input('Enter your request: ')
-    while user_request != '\\q':
-        attr = generate_shape_attributes(user_request)
-        print(attr)
-        for attr_name, attr_value in zip(attr_names_list, attr):
-            print(attr_name, attr_value)
-        user_request = input('Enter your request: ')

spaces/CamCam17/Alexwww-davide-comic-book-characters/README.md

@@ -1,10 +0,0 @@
----
-title: Livebook
-emoji: 📓
-colorFrom: pink
-colorTo: purple
-sdk: docker
-fullWidth: true
----
-
-You can install and run [Livebook](https://livebook.dev/) inside a Hugging Face Space. Here's [a tutorial](https://huggingface.co/docs/hub/spaces-sdks-docker-livebook) on how to do that.

spaces/Cpp4App/Cpp4App/CDM/detect_compo/deprecated/Block.py

@@ -1,56 +0,0 @@
-import cv2
-from os.path import join as pjoin
-import time
-import numpy as np
-
-from CDM.detect_compo.lib_ip.Component import Component
-from CDM.config.CONFIG_UIED import Config
-C = Config()
-
-
-class Block(Component):
-    def __init__(self, region, image_shape):
-        super().__init__(region, image_shape)
-        self.category = 'Block'
-        self.parent = None
-        self.children = []
-        self.uicompo_ = None
-        self.top_or_botm = None
-        self.redundant = False
-
-    def block_is_uicompo(self, image_shape, max_compo_scale):
-        '''
-        Check the if the block is a ui component according to its relative size
-        '''
-        row, column = image_shape[:2]
-        # print(height, height / row, max_compo_scale[0], height / row > max_compo_scale[0])
-        # draw.draw_bounding_box(org, [corner], show=True)
-        # ignore atomic components
-        if self.bbox.height / row > max_compo_scale[0] or self.bbox.width / column > max_compo_scale[1]:
-            return False
-        return True
-
-    def block_is_top_or_bottom_bar(self, image_shape, top_bottom_height):
-        '''
-        Check if the block is top bar or bottom bar
-        '''
-        height, width = image_shape[:2]
-        (column_min, row_min, column_max, row_max) = self.bbox.put_bbox()
-        if column_min < 5 and row_min < 5 and \
-                width - column_max < 5 and row_max < height * top_bottom_height[0]:
-            self.uicompo_ = True
-            return True
-        if column_min < 5 and row_min > height * top_bottom_height[1] and \
-                width - column_max < 5 and height - row_max < 5:
-            self.uicompo_ = True
-            return True
-        return False
-
-    def block_erase_from_bin(self, binary, pad):
-        (column_min, row_min, column_max, row_max) = self.put_bbox()
-        column_min = max(column_min - pad, 0)
-        column_max = min(column_max + pad, binary.shape[1])
-        row_min = max(row_min - pad, 0)
-        row_max = min(row_max + pad, binary.shape[0])
-        cv2.rectangle(binary, (column_min, row_min), (column_max, row_max), (0), -1)
-

spaces/CyberHarem/find_my_waifu/huggingface.py

@@ -1,38 +0,0 @@
-import math
-import os
-
-from gchar.utils import get_requests_session
-from huggingface_hub import configure_http_backend, HfApi, HfFileSystem
-
-_NUM_TAGS = [
-    ('n<1K', 0, 1_000),
-    ('1K<n<10K', 1_000, 10_000),
-    ('10K<n<100K', 10_000, 100_000),
-    ('100K<n<1M', 100_000, 1_000_000),
-    ('1M<n<10M', 1_000_000, 10_000_000),
-    ('10M<n<100M', 10_000_000, 100_000_000),
-    ('100M<n<1B', 100_000_000, 1_000_000_000),
-    ('1B<n<10B', 1_000_000_000, 10_000_000_000),
-    ('10B<n<100B', 10_000_000_000, 100_000_000_000),
-    ('100B<n<1T', 100_000_000_000, 1_000_000_000_000),
-    ('n>1T', 1_000_000_000_000, math.inf),
-]
-
-
-def number_to_tag(v):
-    for tag, min_, max_ in _NUM_TAGS:
-        if min_ <= v < max_:
-            return tag
-
-    raise ValueError(f'No tags found for {v!r}')
-
-
-configure_http_backend(get_requests_session)
-
-
-def get_hf_client() -> HfApi:
-    return HfApi(token=os.environ.get('HF_TOKEN'))
-
-
-def get_hf_fs() -> HfFileSystem:
-    return HfFileSystem(token=os.environ.get('HF_TOKEN'))