diff --git a/spaces/0x90e/ESRGAN-MANGA/ESRGANer.py b/spaces/0x90e/ESRGAN-MANGA/ESRGANer.py
deleted file mode 100644
index d372355e4aeaead258e8e8b5986f00e5a962c827..0000000000000000000000000000000000000000
--- a/spaces/0x90e/ESRGAN-MANGA/ESRGANer.py
+++ /dev/null
@@ -1,156 +0,0 @@
-from PIL import Image, ImageChops
-import numpy as np
-import cv2 as cv
-import math
-import torch
-from torch.nn import functional as F
-
-"""
-Borrowed and adapted from https://github.com/xinntao/Real-ESRGAN/blob/master/realesrgan/utils.py
-Thank you xinntao! 
-"""
-class ESRGANer():
-    """A helper class for upsampling images with ESRGAN.
-
-    Args:
-        scale (int): Upsampling scale factor used in the networks. It is usually 2 or 4.
-        model (nn.Module): The defined network. Default: None.
-        tile (int): As too large images result in the out of GPU memory issue, so this tile option will first crop
-            input images into tiles, and then process each of them. Finally, they will be merged into one image.
-            0 denotes for do not use tile. Default: 500.
-        tile_pad (int): The pad size for each tile, to remove border artifacts. Default: 10.
-        pre_pad (int): Pad the input images to avoid border artifacts. Default: 10.
-    """
-
-    def __init__(self,
-                 scale=4,
-                 model=None,
-                 tile=300,
-                 tile_pad=10,
-                 pre_pad=10
-        ):
-        self.scale = scale
-        self.tile_size = tile
-        self.tile_pad = tile_pad
-        self.pre_pad = pre_pad
-        self.mod_scale = None
-
-        self.model = model
-
-    def pre_process(self, img):
-        """Pre-process, such as pre-pad and mod pad, so that the images can be divisible
-        """
-        self.img = img
-
-        # pre_pad
-        if self.pre_pad != 0:
-            self.img = F.pad(self.img, (0, self.pre_pad, 0, self.pre_pad), 'reflect')
-        # mod pad for divisible borders
-        if self.scale == 2:
-            self.mod_scale = 2
-        elif self.scale == 1:
-            self.mod_scale = 4
-        if self.mod_scale is not None:
-            self.mod_pad_h, self.mod_pad_w = 0, 0
-            _, _, h, w = self.img.size()
-            if (h % self.mod_scale != 0):
-                self.mod_pad_h = (self.mod_scale - h % self.mod_scale)
-            if (w % self.mod_scale != 0):
-                self.mod_pad_w = (self.mod_scale - w % self.mod_scale)
-            self.img = F.pad(self.img, (0, self.mod_pad_w, 0, self.mod_pad_h), 'reflect')
-
-    def process(self):
-        # model inference
-        self.output = self.model(self.img)
-
-    def tile_process(self):
-        """It will first crop input images to tiles, and then process each tile.
-        Finally, all the processed tiles are merged into one images.
-
-        Modified from: https://github.com/ata4/esrgan-launcher
-        """
-        batch, channel, height, width = self.img.shape
-        output_height = height * self.scale
-        output_width = width * self.scale
-        output_shape = (batch, channel, output_height, output_width)
-
-        # start with black image
-        self.output = self.img.new_zeros(output_shape)
-        tiles_x = math.ceil(width / self.tile_size)
-        tiles_y = math.ceil(height / self.tile_size)
-
-        print("Image processing started...")
-
-        # loop over all tiles
-        for y in range(tiles_y):
-            for x in range(tiles_x):
-                # extract tile from input image
-                ofs_x = x * self.tile_size
-                ofs_y = y * self.tile_size
-                # input tile area on total image
-                input_start_x = ofs_x
-                input_end_x = min(ofs_x + self.tile_size, width)
-                input_start_y = ofs_y
-                input_end_y = min(ofs_y + self.tile_size, height)
-
-                # input tile area on total image with padding
-                input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-                input_end_x_pad = min(input_end_x + self.tile_pad, width)
-                input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-                input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-                # input tile dimensions
-                input_tile_width = input_end_x - input_start_x
-                input_tile_height = input_end_y - input_start_y
-                tile_idx = y * tiles_x + x + 1
-                input_tile = self.img[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad]
-
-                # upscale tile
-                try:
-                    with torch.no_grad():
-                        output_tile = self.model(input_tile)
-                except RuntimeError as error:
-                    print('Error', error)
-                print(f'Processing tile {tile_idx}/{tiles_x * tiles_y}')
-
-                # output tile area on total image
-                output_start_x = input_start_x * self.scale
-                output_end_x = input_end_x * self.scale
-                output_start_y = input_start_y * self.scale
-                output_end_y = input_end_y * self.scale
-
-                # output tile area without padding
-                output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-                output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-                output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-                output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-                # put tile into output image
-                self.output[:, :, output_start_y:output_end_y,
-                            output_start_x:output_end_x] = output_tile[:, :, output_start_y_tile:output_end_y_tile,
-                                                                       output_start_x_tile:output_end_x_tile]
-
-        print('All tiles processed, saving output image!')
-
-    def post_process(self):
-        # remove extra pad
-        if self.mod_scale is not None:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.mod_pad_h * self.scale, 0:w - self.mod_pad_w * self.scale]
-        # remove prepad
-        if self.pre_pad != 0:
-            _, _, h, w = self.output.size()
-            self.output = self.output[:, :, 0:h - self.pre_pad * self.scale, 0:w - self.pre_pad * self.scale]
-        return self.output
-    
-    @torch.no_grad()
-    def enhance(self, img):
-        self.pre_process(img)
-
-        if self.tile_size > 0:
-            self.tile_process()
-        else:
-            self.process()
-        output_img = self.post_process()
-
-        return output_img
\ No newline at end of file
diff --git a/spaces/123aa/pastel-mix/app.py b/spaces/123aa/pastel-mix/app.py
deleted file mode 100644
index c77d61dc487061c475ccbc66baa7cd8695069b14..0000000000000000000000000000000000000000
--- a/spaces/123aa/pastel-mix/app.py
+++ /dev/null
@@ -1,137 +0,0 @@
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-
-model_id = 'andite/pastel-mix'
-prefix = ''
-     
-scheduler = DPMSolverMultistepScheduler.from_pretrained(model_id, subfolder="scheduler")
-
-pipe = StableDiffusionPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(
-  model_id,
-  torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
-  scheduler=scheduler)
-
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe_i2i = pipe_i2i.to("cuda")
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
-
-  generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
-
-  try:
-    if img is not None:
-      return img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator), None
-    else:
-      return txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator), None
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(prompt, neg_prompt, guidance, steps, width, height, generator):
-
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator)
-    
-    return result.images[0]
-
-def img_to_img(prompt, neg_prompt, img, strength, guidance, steps, width, height, generator):
-
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe_i2i(
-        prompt,
-        negative_prompt = neg_prompt,
-        init_image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        width = width,
-        height = height,
-        generator = generator)
-        
-    return result.images[0]
-
-css = """.main-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.main-div div h1{font-weight:900;margin-bottom:7px}.main-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-"""
-with gr.Blocks(css=css) as demo:
-    gr.HTML(
-        f"""
-            <div class="main-div">
-              <div>
-                <h1>Pastel Mix</h1>
-              </div>
-              <p>
-               Demo for <a href="https://huggingface.co/andite/pastel-mix">Pastel Mix</a> Stable Diffusion model.<br>
-               {"Add the following tokens to your prompts for the model to work properly: <b>prefix</b>" if prefix else ""}
-              </p>
-              Running on {"<b>GPU 🔥</b>" if torch.cuda.is_available() else f"<b>CPU 🥶</b>. For faster inference it is recommended to <b>upgrade to GPU in <a href='https://huggingface.co/spaces/akhaliq/pastel-mix/settings'>Settings</a></b>"} after duplicating the space<br><br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/akhaliq/pastel-mix?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-              image_out = gr.Image(height=512)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=25, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
-    outputs = [image_out, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>This space was created using <a href="https://huggingface.co/spaces/anzorq/sd-space-creator">SD Space Creator</a>.</p>
-    </div>
-    """)
-
-demo.queue(concurrency_count=1)
-demo.launch()
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Studio Ultimate 2020 v13.0.1 Build 2023 Crack [Latest] Features and Benefits.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Studio Ultimate 2020 v13.0.1 Build 2023 Crack [Latest] Features and Benefits.md
deleted file mode 100644
index d1fcc7a50e6f01c91cd81f1f3883565f08c3ae29..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/ACDSee Photo Studio Ultimate 2020 v13.0.1 Build 2023 Crack [Latest] Features and Benefits.md	
+++ /dev/null
@@ -1,80 +0,0 @@
-
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\ No newline at end of file
diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Stronghold Crusader Trainer v1.0.0.1 for Free and Dominate the Game.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Stronghold Crusader Trainer v1.0.0.1 for Free and Dominate the Game.md
deleted file mode 100644
index 3ff4c03959807f1548fd87ed3c2d4b9cee95bd62..0000000000000000000000000000000000000000
--- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Download Stronghold Crusader Trainer v1.0.0.1 for Free and Dominate the Game.md	
+++ /dev/null
@@ -1,143 +0,0 @@
-<br />
-<h1>Stronghold Crusader Trainer V1.0.0.1: A Guide to Download and Use</h1>
-<p>If you are a fan of strategy games, you might have heard of <strong>Stronghold Crusader</strong>, a popular medieval-themed game that lets you build and defend your own castle against various enemies. The game offers a lot of challenges and fun, but it can also be quite difficult and frustrating at times.</p>
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-<p>The game has many features that make it realistic and immersive, such as weather effects, day-night cycle, fire propagation, siege engines, historical characters, and different types of units.</p>
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- <h2>What is a trainer and why do you need it?</h2>
-<p>A trainer is a program that modifies the game's code and gives you access to various cheats and hacks that can alter the game's behavior.</p>
-<p>Some of the common cheats and hacks that trainers offer are:</p>
-<ul>
-<li>Unlimited resources: You can have unlimited amounts of food, wood, stone, iron, pitch, wheat, bread, cheese, meat, apples, beer, flour, bows, crossbows, spears, pikes, maces, swords, leather armor, metal armor, gold, etc.</li>
-<li>Unlimited population: You can have unlimited number of peasants and soldiers in your castle.</li>
-<li>Unlimited health: You can make your units invincible or heal them instantly.</li>
-<li>Stop time: You can pause or speed up the game's clock.</li>
-<li>Happy residents: You can make your peasants happy or unhappy.</li>
-<li>God mode: You can make your units have superpowers or abilities.</li>
-</ul>
-<p>You might need a trainer for various reasons:</p>
-<ul>
-<li>You want to have more fun and experiment with different strategies.</li>
-<li>You want to overcome a difficult level or mission.</li>
-<li>You want to test your skills against stronger opponents.</li>
-<li>You want to cheat or troll other players online.</li>
-</ul>
- <h2>How to download Stronghold Crusader Trainer V1.0.0.1?</h2>
-<p>If you want to download Stronghold Crusader Trainer V1.0.0.1, you need to follow these steps:</p>
-<ol>
-<li>Go to <a href="https://mrantifun.net/threads/stronghold-crusader-hd-trainer.9293/">this link</a>, which is one of the reliable sources for trainers on the internet.</li>
-<li>Scroll down until you see the attachment section with two files: <code>Stronghold Crusader HD V1.0.1 Trainer +2 MrAntiFun.zip</code> and <code>Stronghold Crusader HD (Steam) Trainer Setup.exe</code>.</li>
-<li>If you have the Steam version of the game, download the second file; otherwise download the first file.</li>
-<li>Save the file on your computer in a folder where you can easily find it later.</li>
-</ol>
- <h2>How to install and run Stronghold Crusader Trainer V1.0.0.1?</h2>
-<p>If you have downloaded Stronghold Crusader Trainer V1.0.0.1 successfully ```html <h2>How to install and run Stronghold Crusader Trainer V1.0.0.1?</h2>
-<p>If you have downloaded Stronghold Crusader Trainer V1.0.0.1 successfully, you need to follow these steps:</p>
-<ol>
-<li>If you have downloaded the zip file, extract it using a program like WinRAR or 7-Zip.</li>
-<li>If you have downloaded the exe file, run it and follow the instructions.</li>
-<li>Copy the trainer file (Crusader10Trn.exe) to the folder where you have installed Stronghold Crusader.</li>
-<li>Run the trainer file before you run the game.</li>
-<li>You should see a small window with the trainer's options and hotkeys.</li>
-<li>Run the game and enjoy the cheats!</li>
-</ol>
- <h2>What are the features and options of Stronghold Crusader Trainer V1.0.0.1?</h2>
-<p>Stronghold Crusader Trainer V1.0.0.1 has many features and options that can make your game easier or more fun. Here is a list and description of them:</p>
-<table>
-<tr><th>Feature</th><th>Description</th><th>Hotkey</th></tr>
-<tr><td>Unlimited resources</td><td>You can have unlimited amounts of food, wood, stone, iron, pitch, wheat, bread, cheese, meat, apples, beer, flour, bows, crossbows, spears, pikes, maces, swords, leather armor, metal armor, gold, etc.</td><td>Q - Y</td></tr>
-<tr><td>Unlimited population</td><td>You can have unlimited number of peasants and soldiers in your castle.</td><td>F6</td></tr>
-<tr><td>Unlimited health</td><td>You can make your units invincible or heal them instantly.</td><td>F7</td></tr>
-<tr><td>Stop time</td><td>You can pause or speed up the game's clock.</td><td>M</td></tr>
-<tr><td>Happy residents</td><td>You can make your peasants happy or unhappy.</td><td>N</td></tr>
-<tr><td>God mode</td><td>You can make your units have superpowers or abilities.</td><td>F8</td></tr>
-<tr><td>Power bar to the max</td><td>You can fill up your power bar to use special abilities in Stronghold Crusader Extreme.</td><td>F9</td></tr>
-</table>
- <h2>How to use Stronghold Crusader Trainer V1.0.0.1 effectively?</h2>
-<p>If you want to use Stronghold Crusader Trainer V1.0.0.1 effectively, you need to follow these tips and tricks:</p>
-<ul>
-<li>Use the cheats wisely and sparingly. Don't abuse them or you might ruin the game's balance and challenge.</li>
-<li>Don't use the cheats online or you might get banned or reported by other players.</li>
-<li>Don't use the cheats on missions that require specific objectives or conditions. You might fail them or cause glitches.</li>
-<li>Don't use the cheats on missions that have scripted events or cutscenes. You might miss them or cause errors.</li>
-<li>Don't use the cheats on missions that have timers or countdowns. You might mess them up or cause crashes.</li>
-<li>Don't use the cheats on missions that have enemy reinforcements or invasions. You might prevent them from happening or cause bugs.</li>
-<li>Don't use the cheats on missions that have allies or neutral parties. You might affect their behavior or status.</li>
-<li>Don't use the cheats on missions that have historical accuracy or realism. You might break the immersion or authenticity.</li>
-<li>Don't use the cheats on missions that have difficulty levels or rankings. You might lower your score or rank.</li>
-<li>Don't use the cheats on missions that have achievements or trophies. You might disable them or cheat yourself.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Stronghold Crusader Trainer V1.0.0.1 is a great program that can enhance your gaming experience with Stronghold Crusader. It can give you access to various cheats and hacks that can help you overcome challenges and have more fun.</p>
-<p>If you want to download and use this trainer, you need to follow our guide carefully and make sure you get it from a reliable source. You also need to be careful and responsible when using it and avoid any problems or issues that might arise.</p>
-<p>We hope you found this article helpful and informative. If you did, please share it with your friends and fellow gamers who might be interested in this trainer as well.</p>
-<p>Thank you for reading and happy gaming!</p>
- <h3>Frequently Asked Questions</h3>
-<ol>
-<li><strong>Does this trainer work with other versions of Stronghold Crusader?</strong></li>
-<p>No, this trainer only works with version 1.0 of Stronghold Crusader (English). If you have a different version of the game, you need to find a different trainer that is compatible with it.</p>
- <li><strong>Does this trainer work with Stronghold Crusader HD or Extreme?</strong></li>
-<p>No, this trainer only works with Stronghold Crusader (original). If you have Stronghold Crusader HD or Extreme, you need to find a different trainer that is compatible with them.</p>
- <li><strong>Does this trainer work with mods or custom maps?</strong></li>
-<p>No, this trainer only works with the vanilla game (original). If you have any mods or custom maps installed, you need to disable them before using this trainer.</p>
- <li><strong>Is this trainer safe and virus-free?</strong></li>
-<p>We cannot guarantee that this trainer is 100% safe and virus-free, as we did not create it ourselves. However, we did scan it with several antivirus programs and found no threats or malware in it. Use it at your own risk and discretion.</p>
- <li><strong>Where can I get more trainers for Stronghold Crusader?</strong></li>
-<p>If you want more trainers for Stronghold Crusader ```html <p>If you want more trainers for Stronghold Crusader, you can check out these sources:</p>
-<ul>
-<li><a href="https://www.gamepressure.com/download.asp?ID=41945">Stronghold: Crusader - v1.2 +19 Trainer</a>: This trainer has 19 options, such as unlimited resources, unlimited health, unlimited gold, and more.</li>
-<li><a href="https://www.wemod.com/cheats/stronghold-crusader-hd-trainers">Stronghold Crusader HD Cheats and Trainers for PC - WeMod</a>: This trainer has 4 options, such as unlimited population, unlimited health, unlimited resources, and unlimited gold.</li>
-<li><a href="https://www.youtube.com/watch?v=dssejr53GDg">Stronghold Crusader Updated Trainer V1.41 (Update - I) - YouTube</a>: This trainer has 8 options, such as unlimited resources, unlimited population, stop time, and god mode.</li>
-<li><a href="https://megagames.com/trainers/stronghold-crusader-0">Stronghold: Crusader (+27 Trainer) | MegaGames</a>: This trainer has 27 options, such as unlimited resources, unlimited population, unlimited health, stop time, happy residents, and god mode.</li>
-<li><a href="https://github.com/dfsoeten/StrongholdCrusaderTrainer">Stronghold Crusader Trainer - GitHub</a>: This trainer has 21 options, such as gold, chickens/skips, wood, stone, iron, pitch, bread, cheese, apples, meat, hops, ale, wheat, bows, crossbows, spears, pikes, maces, swords, leather armor, and iron armor.</li>
-</ul>
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--- a/spaces/1gistliPinn/ChatGPT4/Examples/8211759.9137094 Gt E2250 Flash Loader 7.5.4 Csc V0.4 Lite.md	
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index 260b6b521029b4fae3486de97d04bc8ba1fc3370..0000000000000000000000000000000000000000
--- a/spaces/1gistliPinn/ChatGPT4/Examples/CES Edupack 2013.rar.md	
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-<p>The following table shows the name, image, type, speed, acceleration, handling, and special ability of each car in the game:</p>
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-<th>Name</th>
-<th>Image</th>
-<th>Type</th>
-<th>Speed</th>
-<th>Acceleration</th>
-<th>Handling</th>
-<th>Special Ability</th>
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-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-1.jpg" alt="Lambini"></td>
-<td>Sport</td>
-<td>5/5</td>
-<td>4/5</td>
-<td>4/5</td>
-<td>Nitro Boost: Increases speed for a short time.</td>
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-<td>Baja Bandito</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-2.jpg" alt="Baja Bandito"></td>
-<td>Buggy</td>
-<td>4/5</td>
-<td>4/5</td>
-<td>5/5</td>
-<td>Baja Blast: Launches a shockwave that knocks back nearby opponents.</td>
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-<tr>
-<td>Rocket Boat</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-3.jpg" alt="Rocket Boat"></td>
-<td>Boat</td>
-<td>4/5</td>
-<td>5/5</td>
-<td>3/5</td>
-<td>Rocket Boost: Fires a rocket that propels the car forward.</td>
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-<tr>
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-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-4.jpg" alt="Sandstorm GT"></td>
-<td>Muscle</td>
-<td>5/5</td>
-<td>3/5</td <td>4/5</td>
-<td>Sandstorm: Creates a sandstorm that obscures the vision of opponents behind.</td>
-</tr>
-<tr>
-<td>Lightning GT</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-5.jpg" alt="Lightning GT"></td>
-<td>Electric</td>
-<td>4/5</td>
-<td>4/5</td>
-<td>4/5</td>
-<td>Lightning Strike: Zaps nearby opponents with a bolt of electricity.</td>
-</tr>
-<tr>
-<td>Monster Bus</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-6.jpg" alt="Monster Bus"></td>
-<td>Monster</td>
-<td>3/5</td>
-<td>3/5</td>
-<td>3/5</td>
-<td>Monster Crush: Crushes opponents under its huge wheels.</td>
-</tr>
-<tr>
-<td>Firework Truck</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-7.jpg" alt="Firework Truck"></td>
-<td>Truck</td>
-<td>3/5</td <td>Offensive</td>
-<td>Increases the speed of the car for a short time.</td>
-<td>1: Small boost.<br>2: Medium boost.<br>3: Large boost.</td>
-</tr>
-<tr>
-<td>Spring Trap</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-19.jpg" alt="Spring Trap"></td>
-<td>Defensive</td>
-<td>Drops a spring trap behind the car that launches opponents into the air.</td>
-<td>1: Single spring.<br>2: Double spring.<br>3: Triple spring.</td>
-</tr>
-<tr>
-<td>Lightning Zap</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-20.jpg" alt="Lightning Zap"></td>
-<td>Offensive</td>
-<td>Zaps the opponent in front of the car with a bolt of lightning that damages and slows them down.</td>
-<td>1: Single zap.<br>2: Double zap.<br>3: Triple zap.</td>
-</tr>
-<tr>
-<td>Mine Drop</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-21.jpg" alt="Mine Drop"></td>
-<td>Defensive</td <td>Drops a mine behind the car that explodes when an opponent touches it.</td <td>1: Single mine.<br>2: Double mine.<br>3: Triple mine.</td </tr>
-<tr>
-<td>Banana Peel</td <td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-22.jpg" alt="Banana Peel"></td <td>Defensive</td <td>Drops a banana peel behind the car that makes opponents slip and spin out.</td <td>1: Single peel.<br>2: Double peel.<br>3: Triple peel.</td </tr>
-<tr>
-<td>Frost Bite</td <td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-23.jpg" alt="Frost Bite"></td <td>Offensive</td>
-<td>Freezes the opponent in front of the car with a blast of ice that damages and stops them.</td>
-<td>1: Single freeze.<br>2: Double freeze.<br>3: Triple freeze.</td>
-</tr>
-<tr>
-<td>Magnet</td>
-<td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-24.jpg" alt="Magnet"></td>
-<td>Defensive</td <td>Attracts coins and power-ups to the car.</td <td>1: Short duration.<br>2: Medium duration.<br>3: Long duration.</td </tr>
-<tr>
-<td>Firework</td <td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-25.jpg" alt="Firework"></td <td>Offensive</td <td>Fires a firework that flies in a random direction and explodes on impact.</td <td>1: Single firework.<br>2: Double firework.<br>3: Triple firework.</td </tr>
-<tr>
-<td>Coin Shield</td <td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-26.jpg" alt="Coin Shield"></td <td>Defensive</td <td>Creates a shield of coins around the car that protects it from damage and power-ups.</td <td>1: Small shield.<br>2: Medium shield.<br>3: Large shield.</td </tr>
-<tr>
-<td>Bomb</td <td><img src="https://revdl.net/wp-content/uploads/2019/12/Beach-Buggy-Racing-2-27.jpg" alt="Bomb"></td <td>Offensive</td>
-<td>Throws a bomb that explodes after a few seconds and damages nearby opponents.</td>
-<td>1: Small bomb.<br>2: Medium bomb.<br>3: Large bomb.</td>
-</tr>
-</table>
- <p>As you can see, there are many power-ups to choose from, each with their own effects and upgrades. You can try them all and find the ones that suit your style and strategy. You can also mix and match them to create your own power-up deck.</p>
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- <ul>
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diff --git a/spaces/4Taps/SadTalker/src/facerender/modules/dense_motion.py b/spaces/4Taps/SadTalker/src/facerender/modules/dense_motion.py
deleted file mode 100644
index 30c13060be8e82979771514b4ec51e5de23f49fa..0000000000000000000000000000000000000000
--- a/spaces/4Taps/SadTalker/src/facerender/modules/dense_motion.py
+++ /dev/null
@@ -1,117 +0,0 @@
-from torch import nn
-import torch.nn.functional as F
-import torch
-from src.facerender.modules.util import Hourglass, make_coordinate_grid, kp2gaussian
-
-from src.facerender.sync_batchnorm import SynchronizedBatchNorm3d as BatchNorm3d
-
-
-class DenseMotionNetwork(nn.Module):
-    """
-    Module that predicting a dense motion from sparse motion representation given by kp_source and kp_driving
-    """
-
-    def __init__(self, block_expansion, num_blocks, max_features, num_kp, feature_channel, reshape_depth, compress,
-                 estimate_occlusion_map=False):
-        super(DenseMotionNetwork, self).__init__()
-        # self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(feature_channel+1), max_features=max_features, num_blocks=num_blocks)
-        self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_kp+1)*(compress+1), max_features=max_features, num_blocks=num_blocks)
-
-        self.mask = nn.Conv3d(self.hourglass.out_filters, num_kp + 1, kernel_size=7, padding=3)
-
-        self.compress = nn.Conv3d(feature_channel, compress, kernel_size=1)
-        self.norm = BatchNorm3d(compress, affine=True)
-
-        if estimate_occlusion_map:
-            # self.occlusion = nn.Conv2d(reshape_channel*reshape_depth, 1, kernel_size=7, padding=3)
-            self.occlusion = nn.Conv2d(self.hourglass.out_filters*reshape_depth, 1, kernel_size=7, padding=3)
-        else:
-            self.occlusion = None
-
-        self.num_kp = num_kp
-
-
-    def create_sparse_motions(self, feature, kp_driving, kp_source):
-        bs, _, d, h, w = feature.shape
-        identity_grid = make_coordinate_grid((d, h, w), type=kp_source['value'].type())
-        identity_grid = identity_grid.view(1, 1, d, h, w, 3)
-        coordinate_grid = identity_grid - kp_driving['value'].view(bs, self.num_kp, 1, 1, 1, 3)
-        
-        # if 'jacobian' in kp_driving:
-        if 'jacobian' in kp_driving and kp_driving['jacobian'] is not None:
-            jacobian = torch.matmul(kp_source['jacobian'], torch.inverse(kp_driving['jacobian']))
-            jacobian = jacobian.unsqueeze(-3).unsqueeze(-3).unsqueeze(-3)
-            jacobian = jacobian.repeat(1, 1, d, h, w, 1, 1)
-            coordinate_grid = torch.matmul(jacobian, coordinate_grid.unsqueeze(-1))
-            coordinate_grid = coordinate_grid.squeeze(-1)                  
-
-
-        driving_to_source = coordinate_grid + kp_source['value'].view(bs, self.num_kp, 1, 1, 1, 3)    # (bs, num_kp, d, h, w, 3)
-
-        #adding background feature
-        identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1, 1)
-        sparse_motions = torch.cat([identity_grid, driving_to_source], dim=1)                #bs num_kp+1 d h w 3
-        
-        # sparse_motions = driving_to_source
-
-        return sparse_motions
-
-    def create_deformed_feature(self, feature, sparse_motions):
-        bs, _, d, h, w = feature.shape
-        feature_repeat = feature.unsqueeze(1).unsqueeze(1).repeat(1, self.num_kp+1, 1, 1, 1, 1, 1)      # (bs, num_kp+1, 1, c, d, h, w)
-        feature_repeat = feature_repeat.view(bs * (self.num_kp+1), -1, d, h, w)                         # (bs*(num_kp+1), c, d, h, w)
-        sparse_motions = sparse_motions.view((bs * (self.num_kp+1), d, h, w, -1))                       # (bs*(num_kp+1), d, h, w, 3) !!!!
-        sparse_deformed = F.grid_sample(feature_repeat, sparse_motions)
-        sparse_deformed = sparse_deformed.view((bs, self.num_kp+1, -1, d, h, w))                        # (bs, num_kp+1, c, d, h, w)
-        return sparse_deformed
-
-    def create_heatmap_representations(self, feature, kp_driving, kp_source):
-        spatial_size = feature.shape[3:]
-        gaussian_driving = kp2gaussian(kp_driving, spatial_size=spatial_size, kp_variance=0.01)
-        gaussian_source = kp2gaussian(kp_source, spatial_size=spatial_size, kp_variance=0.01)
-        heatmap = gaussian_driving - gaussian_source
-
-        # adding background feature
-        zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1], spatial_size[2]).type(heatmap.type())
-        heatmap = torch.cat([zeros, heatmap], dim=1)
-        heatmap = heatmap.unsqueeze(2)         # (bs, num_kp+1, 1, d, h, w)
-        return heatmap
-
-    def forward(self, feature, kp_driving, kp_source):
-        bs, _, d, h, w = feature.shape
-
-        feature = self.compress(feature)
-        feature = self.norm(feature)
-        feature = F.relu(feature)
-
-        out_dict = dict()
-        sparse_motion = self.create_sparse_motions(feature, kp_driving, kp_source)
-        deformed_feature = self.create_deformed_feature(feature, sparse_motion)
-
-        heatmap = self.create_heatmap_representations(deformed_feature, kp_driving, kp_source)
-
-        input_ = torch.cat([heatmap, deformed_feature], dim=2)
-        input_ = input_.view(bs, -1, d, h, w)
-
-        # input = deformed_feature.view(bs, -1, d, h, w)      # (bs, num_kp+1 * c, d, h, w)
-
-        prediction = self.hourglass(input_)
-
-
-        mask = self.mask(prediction)
-        mask = F.softmax(mask, dim=1)
-        out_dict['mask'] = mask
-        mask = mask.unsqueeze(2)                                   # (bs, num_kp+1, 1, d, h, w)
-        sparse_motion = sparse_motion.permute(0, 1, 5, 2, 3, 4)    # (bs, num_kp+1, 3, d, h, w)
-        deformation = (sparse_motion * mask).sum(dim=1)            # (bs, 3, d, h, w)
-        deformation = deformation.permute(0, 2, 3, 4, 1)           # (bs, d, h, w, 3)
-
-        out_dict['deformation'] = deformation
-
-        if self.occlusion:
-            bs, c, d, h, w = prediction.shape
-            prediction = prediction.view(bs, -1, h, w)
-            occlusion_map = torch.sigmoid(self.occlusion(prediction))
-            out_dict['occlusion_map'] = occlusion_map
-
-        return out_dict
diff --git a/spaces/52Hz/SRMNet_real_world_denoising/model/SRMNet.py b/spaces/52Hz/SRMNet_real_world_denoising/model/SRMNet.py
deleted file mode 100644
index 5e6aa50258f82a9cc14aec6b787c1f0493a39828..0000000000000000000000000000000000000000
--- a/spaces/52Hz/SRMNet_real_world_denoising/model/SRMNet.py
+++ /dev/null
@@ -1,227 +0,0 @@
-import torch
-import torch.nn as nn
-
-##---------- Basic Layers ----------
-def conv3x3(in_chn, out_chn, bias=True):
-    layer = nn.Conv2d(in_chn, out_chn, kernel_size=3, stride=1, padding=1, bias=bias)
-    return layer
-
-def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):
-    return nn.Conv2d(
-        in_channels, out_channels, kernel_size,
-        padding=(kernel_size // 2), bias=bias, stride=stride)
-
-def bili_resize(factor):
-    return nn.Upsample(scale_factor=factor, mode='bilinear', align_corners=False)
-
-##---------- Basic Blocks ----------
-class UNetConvBlock(nn.Module):
-    def __init__(self, in_size, out_size, downsample):
-        super(UNetConvBlock, self).__init__()
-        self.downsample = downsample
-        self.block = SK_RDB(in_channels=in_size, growth_rate=out_size, num_layers=3)
-        if downsample:
-            self.downsample = PS_down(out_size, out_size, downscale=2)
-
-    def forward(self, x):
-        out = self.block(x)
-        if self.downsample:
-            out_down = self.downsample(out)
-            return out_down, out
-        else:
-            return out
-
-class UNetUpBlock(nn.Module):
-    def __init__(self, in_size, out_size):
-        super(UNetUpBlock, self).__init__()
-        # self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2, bias=True)
-        self.up = PS_up(in_size, out_size, upscale=2)
-        self.conv_block = UNetConvBlock(in_size, out_size, False)
-
-    def forward(self, x, bridge):
-        up = self.up(x)
-        out = torch.cat([up, bridge], dim=1)
-        out = self.conv_block(out)
-        return out
-
-##---------- Resizing Modules (Pixel(Un)Shuffle) ----------
-class PS_down(nn.Module):
-    def __init__(self, in_size, out_size, downscale):
-        super(PS_down, self).__init__()
-        self.UnPS = nn.PixelUnshuffle(downscale)
-        self.conv1 = nn.Conv2d((downscale**2) * in_size, out_size, 1, 1, 0)
-
-    def forward(self, x):
-        x = self.UnPS(x)  # h/2, w/2, 4*c
-        x = self.conv1(x)
-        return x
-
-class PS_up(nn.Module):
-    def __init__(self, in_size, out_size, upscale):
-        super(PS_up, self).__init__()
-
-        self.PS = nn.PixelShuffle(upscale)
-        self.conv1 = nn.Conv2d(in_size//(upscale**2), out_size, 1, 1, 0)
-
-    def forward(self, x):
-        x = self.PS(x)  # h/2, w/2, 4*c
-        x = self.conv1(x)
-        return x
-
-##---------- Selective Kernel Feature Fusion (SKFF) ----------
-class SKFF(nn.Module):
-    def __init__(self, in_channels, height=3, reduction=8, bias=False):
-        super(SKFF, self).__init__()
-
-        self.height = height
-        d = max(int(in_channels / reduction), 4)
-
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv_du = nn.Sequential(nn.Conv2d(in_channels, d, 1, padding=0, bias=bias), nn.PReLU())
-
-        self.fcs = nn.ModuleList([])
-        for i in range(self.height):
-            self.fcs.append(nn.Conv2d(d, in_channels, kernel_size=1, stride=1, bias=bias))
-
-        self.softmax = nn.Softmax(dim=1)
-
-    def forward(self, inp_feats):
-        batch_size, n_feats, H, W = inp_feats[1].shape
-
-        inp_feats = torch.cat(inp_feats, dim=1)
-        inp_feats = inp_feats.view(batch_size, self.height, n_feats, inp_feats.shape[2], inp_feats.shape[3])
-
-        feats_U = torch.sum(inp_feats, dim=1)
-        feats_S = self.avg_pool(feats_U)
-        feats_Z = self.conv_du(feats_S)
-
-        attention_vectors = [fc(feats_Z) for fc in self.fcs]
-        attention_vectors = torch.cat(attention_vectors, dim=1)
-        attention_vectors = attention_vectors.view(batch_size, self.height, n_feats, 1, 1)
-
-        attention_vectors = self.softmax(attention_vectors)
-        feats_V = torch.sum(inp_feats * attention_vectors, dim=1)
-
-        return feats_V
-
-##---------- Dense Block ----------
-class DenseLayer(nn.Module):
-    def __init__(self, in_channels, out_channels, I):
-        super(DenseLayer, self).__init__()
-        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=3 // 2)
-        self.relu = nn.ReLU(inplace=True)
-        self.sk = SKFF(out_channels, height=2, reduction=8, bias=False)
-
-    def forward(self, x):
-        x1 = self.relu(self.conv(x))
-        # output = torch.cat([x, x1], 1) # -> RDB
-        output = self.sk((x, x1))
-        return output
-
-##---------- Selective Kernel Residual Dense Block (SK-RDB) ----------
-class SK_RDB(nn.Module):
-    def __init__(self, in_channels, growth_rate, num_layers):
-        super(SK_RDB, self).__init__()
-        self.identity = nn.Conv2d(in_channels, growth_rate, 1, 1, 0)
-        self.layers = nn.Sequential(
-            *[DenseLayer(in_channels, in_channels, I=i) for i in range(num_layers)]
-        )
-        self.lff = nn.Conv2d(in_channels, growth_rate, kernel_size=1)
-
-    def forward(self, x):
-        res = self.identity(x)
-        x = self.layers(x)
-        x = self.lff(x)
-        return res + x
-
-##---------- testNet ----------
-class SRMNet(nn.Module):
-    def __init__(self, in_chn=3, wf=96, depth=4):
-        super(SRMNet, self).__init__()
-        self.depth = depth
-        self.down_path = nn.ModuleList()
-        self.bili_down = bili_resize(0.5)
-        self.conv_01 = nn.Conv2d(in_chn, wf, 3, 1, 1)
-
-        # encoder of UNet
-        prev_channels = 0
-        for i in range(depth):  # 0,1,2,3
-            downsample = True if (i + 1) < depth else False
-            self.down_path.append(UNetConvBlock(prev_channels + wf, (2 ** i) * wf, downsample))
-            prev_channels = (2 ** i) * wf
-
-        # decoder of UNet
-        self.up_path = nn.ModuleList()
-        self.skip_conv = nn.ModuleList()
-        self.conv_up = nn.ModuleList()
-        self.bottom_conv = nn.Conv2d(prev_channels, wf, 3, 1, 1)
-        self.bottom_up = bili_resize(2 ** (depth-1))
-
-        for i in reversed(range(depth - 1)):
-            self.up_path.append(UNetUpBlock(prev_channels, (2 ** i) * wf))
-            self.skip_conv.append(nn.Conv2d((2 ** i) * wf, (2 ** i) * wf, 3, 1, 1))
-            self.conv_up.append(nn.Sequential(*[nn.Conv2d((2 ** i) * wf, wf, 3, 1, 1), bili_resize(2 ** i)]))
-            prev_channels = (2 ** i) * wf
-
-        self.final_ff = SKFF(in_channels=wf, height=depth)
-        self.last = conv3x3(prev_channels, in_chn, bias=True)
-
-    def forward(self, x):
-        img = x
-        scale_img = img
-
-        ##### shallow conv #####
-        x1 = self.conv_01(img)
-        encs = []
-        ######## UNet ########
-        # Down-path (Encoder)
-        for i, down in enumerate(self.down_path):
-            if i == 0:
-                x1, x1_up = down(x1)
-                encs.append(x1_up)
-            elif (i + 1) < self.depth:
-                scale_img = self.bili_down(scale_img)
-                left_bar = self.conv_01(scale_img)
-                x1 = torch.cat([x1, left_bar], dim=1)
-                x1, x1_up = down(x1)
-                encs.append(x1_up)
-            else:
-                scale_img = self.bili_down(scale_img)
-                left_bar = self.conv_01(scale_img)
-                x1 = torch.cat([x1, left_bar], dim=1)
-                x1 = down(x1)
-
-        # Up-path (Decoder)
-        ms_result = [self.bottom_up(self.bottom_conv(x1))]
-        for i, up in enumerate(self.up_path):
-            x1 = up(x1, self.skip_conv[i](encs[-i - 1]))
-            ms_result.append(self.conv_up[i](x1))
-
-        # Multi-scale selective feature fusion
-        msff_result = self.final_ff(ms_result)
-
-        ##### Reconstruct #####
-        out_1 = self.last(msff_result) + img
-
-        return out_1
-
-
-if __name__ == "__main__":
-    from thop import profile
-
-    input = torch.ones(1, 3, 256, 256, dtype=torch.float, requires_grad=False)
-    model = SRMNet(in_chn=3, wf=96, depth=4)
-    out = model(input)
-    flops, params = profile(model, inputs=(input,))
-    total = sum(p.numel() for p in model.parameters())
-
-    # RDBlayer = SK_RDB(in_channels=64, growth_rate=64, num_layers=3)
-    # print(RDBlayer)
-    # out = RDBlayer(input)
-    # flops, params = profile(RDBlayer, inputs=(input,))
-
-    print('input shape:', input.shape)
-    print('output shape', out.shape)
-    print("-----------------------------------")
-    print("Total params: %.4f M" % (total / 1e6))
-    print("Total params: %.4f G" % (flops / 1e9))
diff --git a/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups_test.py b/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups_test.py
deleted file mode 100644
index f3edf15811b5035ee82f21e54e87b7e87ce413eb..0000000000000000000000000000000000000000
--- a/spaces/801artistry/RVC801/Applio-RVC-Fork/utils/backups_test.py
+++ /dev/null
@@ -1,138 +0,0 @@
-
-import os
-import shutil
-import hashlib
-import time
-
-LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'
-
-def import_google_drive_backup():
-    print("Importing Google Drive backup...")
-    GOOGLE_DRIVE_PATH = '/content/drive/MyDrive/RVC_Backup'  # change this to your Google Drive path
-    LOGS_FOLDER = '/content/Applio-RVC-Fork/logs'
-    WEIGHTS_FOLDER = '/content/Applio-RVC-Fork/weights'
-    weights_exist = False
-    files_to_copy = []
-    weights_to_copy = []
-    
-    def handle_files(root, files, is_weight_files=False):
-        for filename in files:
-            filepath = os.path.join(root, filename)
-            if filename.endswith('.pth') and is_weight_files:
-                weights_exist = True
-                backup_filepath = os.path.join(WEIGHTS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-            else:
-                backup_filepath = os.path.join(LOGS_FOLDER, os.path.relpath(filepath, GOOGLE_DRIVE_PATH))
-            backup_folderpath = os.path.dirname(backup_filepath)
-            if not os.path.exists(backup_folderpath):
-                os.makedirs(backup_folderpath)
-                print(f'Created folder: {backup_folderpath}', flush=True)
-            if is_weight_files:
-                weights_to_copy.append((filepath, backup_filepath))
-            else:
-                files_to_copy.append((filepath, backup_filepath))
-
-    for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'logs')):
-        handle_files(root, files)
-    
-    for root, dirs, files in os.walk(os.path.join(GOOGLE_DRIVE_PATH, 'weights')):
-        handle_files(root, files, True)
-
-    # Copy files in batches
-    total_files = len(files_to_copy)
-    start_time = time.time()
-    for i, (source, dest) in enumerate(files_to_copy, start=1):
-        with open(source, 'rb') as src, open(dest, 'wb') as dst:
-            shutil.copyfileobj(src, dst, 1024*1024)  # 1MB buffer size
-        # Report progress every 5 seconds or after every 100 files, whichever is less frequent
-        if time.time() - start_time > 5 or i % 100 == 0:
-            print(f'\rCopying file {i} of {total_files} ({i * 100 / total_files:.2f}%)', end="")
-            start_time = time.time()
-    print(f'\nImported {len(files_to_copy)} files from Google Drive backup')
-
-    # Copy weights in batches
-    total_weights = len(weights_to_copy)
-    start_time = time.time()
-    for i, (source, dest) in enumerate(weights_to_copy, start=1):
-        with open(source, 'rb') as src, open(dest, 'wb') as dst:
-            shutil.copyfileobj(src, dst, 1024*1024)  # 1MB buffer size
-        # Report progress every 5 seconds or after every 100 files, whichever is less frequent
-        if time.time() - start_time > 5 or i % 100 == 0:
-            print(f'\rCopying weight file {i} of {total_weights} ({i * 100 / total_weights:.2f}%)', end="")
-            start_time = time.time()
-    if weights_exist:
-        print(f'\nImported {len(weights_to_copy)} weight files')
-        print("Copied weights from Google Drive backup to local weights folder.")
-    else:
-        print("\nNo weights found in Google Drive backup.")
-    print("Google Drive backup import completed.")
-
-def backup_files():
-    print("\n Starting backup loop...")
-    last_backup_timestamps_path = os.path.join(LOGS_FOLDER, 'last_backup_timestamps.txt')
-    fully_updated = False  # boolean to track if all files are up to date
-    try:
-        with open(last_backup_timestamps_path, 'r') as f:
-            last_backup_timestamps = dict(line.strip().split(':') for line in f)
-    except:
-        last_backup_timestamps = {}
-
-    while True:
-        updated = False
-        files_to_copy = []
-        files_to_delete = []
-
-        for root, dirs, files in os.walk(LOGS_FOLDER):
-            for filename in files:
-                if filename != 'last_backup_timestamps.txt':
-                    filepath = os.path.join(root, filename)
-                    if os.path.isfile(filepath):
-                        backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                        backup_folderpath = os.path.dirname(backup_filepath)
-
-                        if not os.path.exists(backup_folderpath):
-                            os.makedirs(backup_folderpath)
-                            print(f'Created backup folder: {backup_folderpath}', flush=True)
-
-                        # check if file has changed since last backup
-                        last_backup_timestamp = last_backup_timestamps.get(filepath)
-                        current_timestamp = os.path.getmtime(filepath)
-                        if last_backup_timestamp is None or float(last_backup_timestamp) < current_timestamp:
-                            files_to_copy.append((filepath, backup_filepath))  # add to list of files to copy
-                            last_backup_timestamps[filepath] = str(current_timestamp)  # update last backup timestamp
-                            updated = True
-                            fully_updated = False  # if a file is updated, all files are not up to date
-
-        # check if any files were deleted in Colab and delete them from the backup drive
-        for filepath in list(last_backup_timestamps.keys()):
-            if not os.path.exists(filepath):
-                backup_filepath = os.path.join(GOOGLE_DRIVE_PATH, os.path.relpath(filepath, LOGS_FOLDER))
-                if os.path.exists(backup_filepath):
-                    files_to_delete.append(backup_filepath)  # add to list of files to delete
-                del last_backup_timestamps[filepath]
-                updated = True
-                fully_updated = False  # if a file is deleted, all files are not up to date
-
-        # Copy files in batches
-        if files_to_copy:
-            for source, dest in files_to_copy:
-                shutil.copy2(source, dest)
-            print(f'Copied or updated {len(files_to_copy)} files')
-
-        # Delete files in batches
-        if files_to_delete:
-            for file in files_to_delete:
-                os.remove(file)
-            print(f'Deleted {len(files_to_delete)} files')
-
-        if not updated and not fully_updated:
-            print("Files are up to date.")
-            fully_updated = True  # if all files are up to date, set the boolean to True
-            copy_weights_folder_to_drive()
-
-        with open(last_backup_timestamps_path, 'w') as f:
-            for filepath, timestamp in last_backup_timestamps.items():
-                f.write(f'{filepath}:{timestamp}\n')
-        time.sleep(15)  # wait for 15 seconds before checking again
diff --git a/spaces/A-Celsius/Caption-Generator/app.py b/spaces/A-Celsius/Caption-Generator/app.py
deleted file mode 100644
index b38134c7396f58dad120de68b7aa0f97052e9fd6..0000000000000000000000000000000000000000
--- a/spaces/A-Celsius/Caption-Generator/app.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from PIL import Image
-from transformers import BlipProcessor, BlipForConditionalGeneration
-import torch
-import gradio as gr
-
-model_name = "Salesforce/blip-image-captioning-base"
-
-caption_processor = BlipProcessor.from_pretrained(model_name)
-model = BlipForConditionalGeneration.from_pretrained(model_name)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-def generate_captions(image, num_captions=5,size=(512, 512)):
-    image = image.resize(size)
-    if image.mode != 'RGB':
-        image = image.convert('RGB')
-    pixel_values = caption_processor(image, return_tensors='pt').to(device)
-    
-    caption_ids = model.generate(
-        **pixel_values, 
-        max_length=30,
-        num_beams=5,
-        num_return_sequences=num_captions,
-        temperature=1.0
-    )
-    
-    captions = [
-        caption_processor.decode(ids, skip_special_tokens=True) 
-        for ids in caption_ids
-    ]
-    
-    return captions
-
-from gradio.components import Image, Textbox,Slider
-
-interface = gr.Interface(
-    fn=generate_captions,
-    inputs=[
-        Image(type="pil", label="Input Image"),
-        Slider(minimum=1, maximum=5, step=1, label="Number of Captions")
-    ],
-    outputs=Textbox(type="text", label="Captions"),
-    title="Image Caption Generator",
-    description="AI tool that creates captions based on the image provided by the user.",
-)
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/AHzizi/WaifuVoiceGen/commons.py b/spaces/AHzizi/WaifuVoiceGen/commons.py
deleted file mode 100644
index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000
--- a/spaces/AHzizi/WaifuVoiceGen/commons.py
+++ /dev/null
@@ -1,172 +0,0 @@
-import math
-import torch
-from torch.nn import functional as F
-import torch.jit
-
-
-def script_method(fn, _rcb=None):
-  return fn
-
-
-def script(obj, optimize=True, _frames_up=0, _rcb=None):
-  return obj
-
-
-torch.jit.script_method = script_method
-torch.jit.script = script
-
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/AIFILMS/generate_human_motion/VQ-Trans/utils/config.py b/spaces/AIFILMS/generate_human_motion/VQ-Trans/utils/config.py
deleted file mode 100644
index 091d790e963959c326917688ee267e6a4ec136d1..0000000000000000000000000000000000000000
--- a/spaces/AIFILMS/generate_human_motion/VQ-Trans/utils/config.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-SMPL_DATA_PATH = "./body_models/smpl"
-
-SMPL_KINTREE_PATH = os.path.join(SMPL_DATA_PATH, "kintree_table.pkl")
-SMPL_MODEL_PATH = os.path.join(SMPL_DATA_PATH, "SMPL_NEUTRAL.pkl")
-JOINT_REGRESSOR_TRAIN_EXTRA = os.path.join(SMPL_DATA_PATH, 'J_regressor_extra.npy')
-
-ROT_CONVENTION_TO_ROT_NUMBER = {
-    'legacy': 23,
-    'no_hands': 21,
-    'full_hands': 51,
-    'mitten_hands': 33,
-}
-
-GENDERS = ['neutral', 'male', 'female']
-NUM_BETAS = 10
\ No newline at end of file
diff --git a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py b/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py
deleted file mode 100644
index be47add559612aaf8c667afd554d88e23fd8fd56..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/AudioGPT/text_to_audio/Make_An_Audio/ldm/modules/losses_audio/contperceptual_dis.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import sys
-
-sys.path.insert(0, '.')  # nopep8
-from ldm.modules.losses_audio.vqperceptual import *
-from ldm.modules.discriminator.multi_window_disc import Discriminator
-
-class LPAPSWithDiscriminator(nn.Module):# 相比于contperceptual.py添加了MultiWindowDiscriminator
-    def __init__(self, disc_start, logvar_init=0.0, kl_weight=1.0, pixelloss_weight=1.0,
-                 disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0,
-                 perceptual_weight=1.0, use_actnorm=False, disc_conditional=False,
-                 disc_loss="hinge"):
-
-        super().__init__()
-        assert disc_loss in ["hinge", "vanilla"]
-        self.kl_weight = kl_weight
-        self.pixel_weight = pixelloss_weight
-        self.perceptual_loss = LPAPS().eval()
-        self.perceptual_weight = perceptual_weight
-        # output log variance
-        self.logvar = nn.Parameter(torch.ones(size=()) * logvar_init)
-
-        self.discriminator = NLayerDiscriminator(input_nc=disc_in_channels,
-                                                 n_layers=disc_num_layers,
-                                                 use_actnorm=use_actnorm,
-                                                 ).apply(weights_init)
-        self.discriminator_iter_start = disc_start
-        if disc_loss == "hinge":
-            self.disc_loss = hinge_d_loss
-        elif disc_loss == "vanilla":
-            self.disc_loss = vanilla_d_loss
-        else:
-            raise ValueError(f"Unknown GAN loss '{disc_loss}'.")
-        print(f"LPAPSWithDiscriminator running with {disc_loss} loss.")
-        self.disc_factor = disc_factor
-        self.discriminator_weight = disc_weight
-        self.disc_conditional = disc_conditional
-
-        disc_win_num = 3
-        mel_disc_hidden_size = 128
-        self.discriminator_multi = Discriminator(time_lengths=[32, 64, 128][:disc_win_num],
-                freq_length=80, hidden_size=mel_disc_hidden_size, kernel=(3, 3),
-                cond_size=0, norm_type="in", reduction="stack")
-
-    def calculate_adaptive_weight(self, nll_loss, g_loss, last_layer=None):
-        if last_layer is not None:
-            nll_grads = torch.autograd.grad(nll_loss, last_layer, retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, last_layer, retain_graph=True)[0]
-        else:
-            nll_grads = torch.autograd.grad(nll_loss, self.last_layer[0], retain_graph=True)[0]
-            g_grads = torch.autograd.grad(g_loss, self.last_layer[0], retain_graph=True)[0]
-
-        d_weight = torch.norm(nll_grads) / (torch.norm(g_grads) + 1e-4)
-        d_weight = torch.clamp(d_weight, 0.0, 1e4).detach()
-        d_weight = d_weight * self.discriminator_weight
-        return d_weight
-
-    def forward(self, inputs, reconstructions, posteriors, optimizer_idx,
-                global_step, last_layer=None, cond=None, split="train", weights=None):
-        rec_loss = torch.abs(inputs.contiguous() - reconstructions.contiguous())
-        if self.perceptual_weight > 0:
-            p_loss = self.perceptual_loss(inputs.contiguous(), reconstructions.contiguous())
-            rec_loss = rec_loss + self.perceptual_weight * p_loss
-        else:
-            p_loss = torch.tensor([0.0])
-
-        nll_loss = rec_loss / torch.exp(self.logvar) + self.logvar
-        weighted_nll_loss = nll_loss
-        if weights is not None:
-            weighted_nll_loss = weights*nll_loss
-        weighted_nll_loss = torch.sum(weighted_nll_loss) / weighted_nll_loss.shape[0]
-        nll_loss = torch.sum(nll_loss) / nll_loss.shape[0]
-        kl_loss = posteriors.kl()
-        kl_loss = torch.sum(kl_loss) / kl_loss.shape[0]
-
-        # now the GAN part
-        if optimizer_idx == 0:
-            # generator update
-            if cond is None:
-                assert not self.disc_conditional
-                logits_fake = self.discriminator(reconstructions.contiguous())
-            else:
-                assert self.disc_conditional
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous(), cond), dim=1))
-
-            logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().squeeze(1).transpose(1, 2))
-
-            g_loss = -torch.mean(logits_fake)
-            g_loss_multi = -torch.mean(logits_fake_multi['y'])
-
-            try:
-                d_weight = self.calculate_adaptive_weight(nll_loss, g_loss, last_layer=last_layer)
-                d_weight_multi = self.calculate_adaptive_weight(nll_loss, g_loss_multi, last_layer=last_layer)
-            except RuntimeError:
-                assert not self.training
-                d_weight = d_weight_multi = torch.tensor(0.0)
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            loss = weighted_nll_loss + self.kl_weight * kl_loss + d_weight * disc_factor * g_loss + d_weight_multi * disc_factor * g_loss_multi
-
-            log = {"{}/total_loss".format(split): loss.clone().detach().mean(),
-                   "{}/logvar".format(split): self.logvar.detach(),
-                   "{}/kl_loss".format(split): kl_loss.detach().mean(),
-                   "{}/nll_loss".format(split): nll_loss.detach().mean(),
-                   "{}/rec_loss".format(split): rec_loss.detach().mean(),
-                   "{}/d_weight".format(split): d_weight.detach(),
-                   "{}/disc_factor".format(split): torch.tensor(disc_factor),
-                   "{}/g_loss".format(split): g_loss.detach().mean(),
-                   "{}/g_loss_multi".format(split): g_loss_multi.detach().mean(),
-                   }
-            return loss, log
-
-        if optimizer_idx == 1:
-            # second pass for discriminator update
-            if cond is None:
-                logits_real = self.discriminator(inputs.contiguous().detach())
-                logits_fake = self.discriminator(reconstructions.contiguous().detach())
-            else:
-                logits_real = self.discriminator(torch.cat((inputs.contiguous().detach(), cond), dim=1))
-                logits_fake = self.discriminator(torch.cat((reconstructions.contiguous().detach(), cond), dim=1))
-
-            logits_real_multi = self.discriminator_multi(inputs.contiguous().detach().squeeze(1).transpose(1, 2))
-            logits_fake_multi = self.discriminator_multi(reconstructions.contiguous().detach().squeeze(1).transpose(1, 2))
-
-            disc_factor = adopt_weight(self.disc_factor, global_step, threshold=self.discriminator_iter_start)
-            d_loss = disc_factor * self.disc_loss(logits_real, logits_fake)
-            d_loss_multi = disc_factor * self.disc_loss(logits_real_multi['y'], logits_fake_multi['y'])
-
-            log = {"{}/disc_loss".format(split): d_loss.clone().detach().mean(),
-                   "{}/disc_loss_multi".format(split): d_loss_multi.clone().detach().mean(),
-                   "{}/logits_real".format(split): logits_real.detach().mean(),
-                   "{}/logits_fake".format(split): logits_fake.detach().mean()
-                   }
-            return d_loss+d_loss_multi, log
-
diff --git a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/factory.py b/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/factory.py
deleted file mode 100644
index 3c3b28658adb03462b9c4b5405548d4e0d1edc5e..0000000000000000000000000000000000000000
--- a/spaces/AIGC-Audio/Make_An_Audio/ldm/modules/encoders/open_clap/factory.py
+++ /dev/null
@@ -1,257 +0,0 @@
-import json
-import logging
-import os
-import pathlib
-import re
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-
-from .model import CLAP, convert_weights_to_fp16
-from .openai import load_openai_model
-from .pretrained import get_pretrained_url, download_pretrained
-from .transform import image_transform
-
-_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
-_MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
-
-
-def _natural_key(string_):
-    return [int(s) if s.isdigit() else s for s in re.split(r"(\d+)", string_.lower())]
-
-
-def _rescan_model_configs():
-    global _MODEL_CONFIGS
-
-    config_ext = (".json",)
-    config_files = []
-    for config_path in _MODEL_CONFIG_PATHS:
-        if config_path.is_file() and config_path.suffix in config_ext:
-            config_files.append(config_path)
-        elif config_path.is_dir():
-            for ext in config_ext:
-                config_files.extend(config_path.glob(f"*{ext}"))
-
-    for cf in config_files:
-        with open(cf, "r") as f:
-            model_cfg = json.load(f)
-            if all(a in model_cfg for a in ("embed_dim", "audio_cfg", "text_cfg")):
-                _MODEL_CONFIGS[cf.stem] = model_cfg
-
-    _MODEL_CONFIGS = {
-        k: v
-        for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))
-    }
-
-
-_rescan_model_configs()  # initial populate of model config registry
-
-
-def load_state_dict(checkpoint_path: str, map_location="cpu", skip_params=True):
-    checkpoint = torch.load(checkpoint_path, map_location=map_location)
-    if isinstance(checkpoint, dict) and "state_dict" in checkpoint:
-        state_dict = checkpoint["state_dict"]
-    else:
-        state_dict = checkpoint
-    if skip_params:
-        if next(iter(state_dict.items()))[0].startswith("module"):
-            state_dict = {k[7:]: v for k, v in state_dict.items()}
-    # for k in state_dict:
-    #     if k.startswith('transformer'):
-    #         v = state_dict.pop(k)
-    #         state_dict['text_branch.' + k[12:]] = v
-    return state_dict
-
-
-def create_model(
-    amodel_name: str,
-    tmodel_name: str,
-    pretrained: str = "",
-    precision: str = "fp32",
-    device: torch.device = torch.device("cpu"),
-    jit: bool = False,
-    force_quick_gelu: bool = False,
-    openai_model_cache_dir: str = os.path.expanduser("~/.cache/clip"),
-    skip_params=True,
-    pretrained_audio: str = "",
-    pretrained_text: str = "",
-    enable_fusion: bool = False,
-    fusion_type: str = 'None'
-    # pretrained_image: bool = False,
-):
-    amodel_name = amodel_name.replace(
-        "/", "-"
-    )  # for callers using old naming with / in ViT names
-    pretrained_orig = pretrained
-    pretrained = pretrained.lower()
-    if pretrained == "openai":
-        if amodel_name in _MODEL_CONFIGS:
-            logging.info(f"Loading {amodel_name} model config.")
-            model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
-        else:
-            logging.error(
-                f"Model config for {amodel_name} not found; available models {list_models()}."
-            )
-            raise RuntimeError(f"Model config for {amodel_name} not found.")
-
-        logging.info(f"Loading pretrained ViT-B-16 text encoder from OpenAI.")
-        # Hard Code in model name
-        model_cfg["text_cfg"]["model_type"] = tmodel_name
-        model = load_openai_model(
-            "ViT-B-16",
-            model_cfg,
-            device=device,
-            jit=jit,
-            cache_dir=openai_model_cache_dir,
-            enable_fusion=enable_fusion,
-            fusion_type=fusion_type
-        )
-        # See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372
-        if precision == "amp" or precision == "fp32":
-            model = model.float()
-    else:
-        if amodel_name in _MODEL_CONFIGS:
-            logging.info(f"Loading {amodel_name} model config.")
-            model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
-        else:
-            logging.error(
-                f"Model config for {amodel_name} not found; available models {list_models()}."
-            )
-            raise RuntimeError(f"Model config for {amodel_name} not found.")
-
-        if force_quick_gelu:
-            # override for use of QuickGELU on non-OpenAI transformer models
-            model_cfg["quick_gelu"] = True
-
-        # if pretrained_image:
-        #     if 'timm_amodel_name' in model_cfg.get('vision_cfg', {}):
-        #         # pretrained weight loading for timm models set via vision_cfg
-        #         model_cfg['vision_cfg']['timm_model_pretrained'] = True
-        #     else:
-        #         assert False, 'pretrained image towers currently only supported for timm models'
-        model_cfg["text_cfg"]["model_type"] = tmodel_name
-        model_cfg["enable_fusion"] = enable_fusion
-        model_cfg["fusion_type"] = fusion_type
-        model = CLAP(**model_cfg)
-
-        if pretrained:
-            checkpoint_path = ""
-            url = get_pretrained_url(amodel_name, pretrained)
-            if url:
-                checkpoint_path = download_pretrained(url, root=openai_model_cache_dir)
-            elif os.path.exists(pretrained_orig):
-                checkpoint_path = pretrained_orig
-            if checkpoint_path:
-                logging.info(f"Loading pretrained {amodel_name}-{tmodel_name} weights ({pretrained}).")
-                ckpt = load_state_dict(checkpoint_path, skip_params=True)
-                model.load_state_dict(ckpt)
-                param_names = [n for n, p in model.named_parameters()]
-                for n in param_names:
-                    print(n, "\t", "Loaded" if n in ckpt else "Unloaded")
-            else:
-                logging.warning(
-                    f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
-                )
-                raise RuntimeError(
-                    f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
-                )
-
-        if pretrained_audio:
-            if amodel_name.startswith('PANN'):
-                if 'Cnn14_mAP' in pretrained_audio:  # official checkpoint
-                    audio_ckpt = torch.load(pretrained_audio, map_location='cpu')
-                    audio_ckpt = audio_ckpt['model']
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if 'spectrogram_extractor' not in key and 'logmel_extractor' not in key:
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt['audio_branch.' + key] = v
-                elif os.path.basename(pretrained_audio).startswith('PANN'):  # checkpoint trained via HTSAT codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location='cpu')
-                    audio_ckpt = audio_ckpt['state_dict']
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith('sed_model'):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt['audio_branch.' + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith('finetuned'):  # checkpoint trained via linear probe codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location='cpu')
-                else:
-                    raise ValueError('Unknown audio checkpoint')
-            elif amodel_name.startswith('HTSAT'):
-                if 'HTSAT_AudioSet_Saved' in pretrained_audio:  # official checkpoint
-                    audio_ckpt = torch.load(pretrained_audio, map_location='cpu')
-                    audio_ckpt = audio_ckpt['state_dict']
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith('sed_model') and ('spectrogram_extractor' not in key
-                                                            and 'logmel_extractor' not in key):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt['audio_branch.' + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith('HTSAT'):  # checkpoint trained via HTSAT codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location='cpu')
-                    audio_ckpt = audio_ckpt['state_dict']
-                    keys = list(audio_ckpt.keys())
-                    for key in keys:
-                        if key.startswith('sed_model'):
-                            v = audio_ckpt.pop(key)
-                            audio_ckpt['audio_branch.' + key[10:]] = v
-                elif os.path.basename(pretrained_audio).startswith('finetuned'):  # checkpoint trained via linear probe codebase
-                    audio_ckpt = torch.load(pretrained_audio, map_location='cpu')
-                else:
-                    raise ValueError('Unknown audio checkpoint')
-            else:
-                raise f'this audio encoder pretrained checkpoint is not support'
-
-            model.load_state_dict(audio_ckpt, strict=False)
-            logging.info(f"Loading pretrained {amodel_name} weights ({pretrained_audio}).")
-            param_names = [n for n, p in model.named_parameters()]
-            for n in param_names:
-                print(n, "\t", "Loaded" if n in audio_ckpt else "Unloaded")
-            
-        model.to(device=device)
-        if precision == "fp16":
-            assert device.type != "cpu"
-            convert_weights_to_fp16(model)
-
-        if jit:
-            model = torch.jit.script(model)
-
-    return model, model_cfg
-
-
-def create_model_and_transforms(
-    model_name: str,
-    pretrained: str = "",
-    precision: str = "fp32",
-    device: torch.device = torch.device("cpu"),
-    jit: bool = False,
-    force_quick_gelu: bool = False,
-    # pretrained_image: bool = False,
-):
-    model = create_model(
-        model_name,
-        pretrained,
-        precision,
-        device,
-        jit,
-        force_quick_gelu=force_quick_gelu,
-        # pretrained_image=pretrained_image
-    )
-    preprocess_train = image_transform(model.visual.image_size, is_train=True)
-    preprocess_val = image_transform(model.visual.image_size, is_train=False)
-    return model, preprocess_train, preprocess_val
-
-
-def list_models():
-    """enumerate available model architectures based on config files"""
-    return list(_MODEL_CONFIGS.keys())
-
-
-def add_model_config(path):
-    """add model config path or file and update registry"""
-    if not isinstance(path, Path):
-        path = Path(path)
-    _MODEL_CONFIG_PATHS.append(path)
-    _rescan_model_configs()
diff --git a/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/vit.py b/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/vit.py
deleted file mode 100644
index 3c2d04489f8bde88fd51acb34c62b570ab221f35..0000000000000000000000000000000000000000
--- a/spaces/AILab-CVC/SEED-LLaMA/models/seed_qformer/vit.py
+++ /dev/null
@@ -1,395 +0,0 @@
-"""
- Copyright (c) 2022, salesforce.com, inc.
- All rights reserved.
- SPDX-License-Identifier: BSD-3-Clause
- For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- 
- Based on timm code base
- https://github.com/rwightman/pytorch-image-models/tree/master/timm
-"""
-
-import math
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from functools import partial
-
-from timm.models.vision_transformer import _cfg, PatchEmbed
-from timm.models.registry import register_model
-from timm.models.layers import trunc_normal_, DropPath
-from timm.models.helpers import named_apply, adapt_input_conv
-
-
-class Mlp(nn.Module):
-    """MLP as used in Vision Transformer, MLP-Mixer and related networks"""
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=False,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
-        self.scale = qk_scale or head_dim**-0.5
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.attn_gradients = None
-        self.attention_map = None
-
-    def save_attn_gradients(self, attn_gradients):
-        self.attn_gradients = attn_gradients
-
-    def get_attn_gradients(self):
-        return self.attn_gradients
-
-    def save_attention_map(self, attention_map):
-        self.attention_map = attention_map
-
-    def get_attention_map(self):
-        return self.attention_map
-
-    def forward(self, x, register_hook=False):
-        B, N, C = x.shape
-        qkv = (self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4))
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        attn = (q @ k.transpose(-2, -1)) * self.scale
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        if register_hook:
-            self.save_attention_map(attn)
-            attn.register_hook(self.save_attn_gradients)
-
-        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class Block(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        mlp_ratio=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        use_grad_checkpointing=False,
-    ):
-        super().__init__()
-        self.norm1 = norm_layer(dim)
-        self.attn = Attention(
-            dim,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        # if use_grad_checkpointing:
-        #     self.attn = checkpoint_wrapper(self.attn)
-        #     self.mlp = checkpoint_wrapper(self.mlp)
-
-    def forward(self, x, register_hook=False):
-        x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook))
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-        return x
-
-
-class VisionTransformer(nn.Module):
-    """Vision Transformer
-    A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale`  -
-        https://arxiv.org/abs/2010.11929
-    """
-    def __init__(
-        self,
-        img_size=224,
-        patch_size=16,
-        in_chans=3,
-        num_classes=1000,
-        embed_dim=768,
-        depth=12,
-        num_heads=12,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        representation_size=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.0,
-        norm_layer=None,
-        use_grad_checkpointing=False,
-        ckpt_layer=0,
-    ):
-        """
-        Args:
-            img_size (int, tuple): input image size
-            patch_size (int, tuple): patch size
-            in_chans (int): number of input channels
-            num_classes (int): number of classes for classification head
-            embed_dim (int): embedding dimension
-            depth (int): depth of transformer
-            num_heads (int): number of attention heads
-            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
-            qkv_bias (bool): enable bias for qkv if True
-            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
-            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
-            drop_rate (float): dropout rate
-            attn_drop_rate (float): attention dropout rate
-            drop_path_rate (float): stochastic depth rate
-            norm_layer: (nn.Module): normalization layer
-        """
-        super().__init__()
-        self.num_features = (self.embed_dim) = embed_dim  # num_features for consistency with other models
-        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=in_chans,
-            embed_dim=embed_dim,
-        )
-
-        num_patches = self.patch_embed.num_patches
-
-        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
-        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim))
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
-        self.blocks = nn.ModuleList([
-            Block(
-                dim=embed_dim,
-                num_heads=num_heads,
-                mlp_ratio=mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[i],
-                norm_layer=norm_layer,
-                use_grad_checkpointing=(use_grad_checkpointing and i >= depth - ckpt_layer),
-            ) for i in range(depth)
-        ])
-        self.norm = norm_layer(embed_dim)
-
-        trunc_normal_(self.pos_embed, std=0.02)
-        trunc_normal_(self.cls_token, std=0.02)
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {"pos_embed", "cls_token"}
-
-    def forward(self, x, register_blk=-1):
-        B = x.shape[0]
-        x = self.patch_embed(x)
-
-        cls_tokens = self.cls_token.expand(B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks
-        x = torch.cat((cls_tokens, x), dim=1)
-
-        x = x + self.pos_embed[:, :x.size(1), :]
-        x = self.pos_drop(x)
-
-        for i, blk in enumerate(self.blocks):
-            x = blk(x, register_blk == i)
-        x = self.norm(x)
-
-        return x
-
-    @torch.jit.ignore()
-    def load_pretrained(self, checkpoint_path, prefix=""):
-        _load_weights(self, checkpoint_path, prefix)
-
-
-@torch.no_grad()
-def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ""):
-    """Load weights from .npz checkpoints for official Google Brain Flax implementation"""
-    import numpy as np
-
-    def _n2p(w, t=True):
-        if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
-            w = w.flatten()
-        if t:
-            if w.ndim == 4:
-                w = w.transpose([3, 2, 0, 1])
-            elif w.ndim == 3:
-                w = w.transpose([2, 0, 1])
-            elif w.ndim == 2:
-                w = w.transpose([1, 0])
-        return torch.from_numpy(w)
-
-    w = np.load(checkpoint_path)
-    if not prefix and "opt/target/embedding/kernel" in w:
-        prefix = "opt/target/"
-
-    if hasattr(model.patch_embed, "backbone"):
-        # hybrid
-        backbone = model.patch_embed.backbone
-        stem_only = not hasattr(backbone, "stem")
-        stem = backbone if stem_only else backbone.stem
-        stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f"{prefix}conv_root/kernel"])))
-        stem.norm.weight.copy_(_n2p(w[f"{prefix}gn_root/scale"]))
-        stem.norm.bias.copy_(_n2p(w[f"{prefix}gn_root/bias"]))
-        if not stem_only:
-            for i, stage in enumerate(backbone.stages):
-                for j, block in enumerate(stage.blocks):
-                    bp = f"{prefix}block{i + 1}/unit{j + 1}/"
-                    for r in range(3):
-                        getattr(block, f"conv{r + 1}").weight.copy_(_n2p(w[f"{bp}conv{r + 1}/kernel"]))
-                        getattr(block, f"norm{r + 1}").weight.copy_(_n2p(w[f"{bp}gn{r + 1}/scale"]))
-                        getattr(block, f"norm{r + 1}").bias.copy_(_n2p(w[f"{bp}gn{r + 1}/bias"]))
-                    if block.downsample is not None:
-                        block.downsample.conv.weight.copy_(_n2p(w[f"{bp}conv_proj/kernel"]))
-                        block.downsample.norm.weight.copy_(_n2p(w[f"{bp}gn_proj/scale"]))
-                        block.downsample.norm.bias.copy_(_n2p(w[f"{bp}gn_proj/bias"]))
-        embed_conv_w = _n2p(w[f"{prefix}embedding/kernel"])
-    else:
-        embed_conv_w = adapt_input_conv(model.patch_embed.proj.weight.shape[1], _n2p(w[f"{prefix}embedding/kernel"]))
-    model.patch_embed.proj.weight.copy_(embed_conv_w)
-    model.patch_embed.proj.bias.copy_(_n2p(w[f"{prefix}embedding/bias"]))
-    model.cls_token.copy_(_n2p(w[f"{prefix}cls"], t=False))
-    pos_embed_w = _n2p(w[f"{prefix}Transformer/posembed_input/pos_embedding"], t=False)
-    if pos_embed_w.shape != model.pos_embed.shape:
-        pos_embed_w = resize_pos_embed(  # resize pos embedding when different size from pretrained weights
-            pos_embed_w,
-            model.pos_embed,
-            getattr(model, "num_tokens", 1),
-            model.patch_embed.grid_size,
-        )
-    model.pos_embed.copy_(pos_embed_w)
-    model.norm.weight.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/scale"]))
-    model.norm.bias.copy_(_n2p(w[f"{prefix}Transformer/encoder_norm/bias"]))
-    #     if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
-    #         model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
-    #         model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
-    #     if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w:
-    #         model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel']))
-    #         model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias']))
-    for i, block in enumerate(model.blocks.children()):
-        block_prefix = f"{prefix}Transformer/encoderblock_{i}/"
-        mha_prefix = block_prefix + "MultiHeadDotProductAttention_1/"
-        block.norm1.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/scale"]))
-        block.norm1.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_0/bias"]))
-        block.attn.qkv.weight.copy_(
-            torch.cat([_n2p(w[f"{mha_prefix}{n}/kernel"], t=False).flatten(1).T for n in ("query", "key", "value")]))
-        block.attn.qkv.bias.copy_(
-            torch.cat([_n2p(w[f"{mha_prefix}{n}/bias"], t=False).reshape(-1) for n in ("query", "key", "value")]))
-        block.attn.proj.weight.copy_(_n2p(w[f"{mha_prefix}out/kernel"]).flatten(1))
-        block.attn.proj.bias.copy_(_n2p(w[f"{mha_prefix}out/bias"]))
-        for r in range(2):
-            getattr(block.mlp, f"fc{r + 1}").weight.copy_(_n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/kernel"]))
-            getattr(block.mlp, f"fc{r + 1}").bias.copy_(_n2p(w[f"{block_prefix}MlpBlock_3/Dense_{r}/bias"]))
-        block.norm2.weight.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/scale"]))
-        block.norm2.bias.copy_(_n2p(w[f"{block_prefix}LayerNorm_2/bias"]))
-
-
-def resize_pos_embed(posemb, posemb_new, num_tokens=1, gs_new=()):
-    # Rescale the grid of position embeddings when loading from state_dict. Adapted from
-    # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224
-    print("Resized position embedding: %s to %s", posemb.shape, posemb_new.shape)
-    ntok_new = posemb_new.shape[1]
-    if num_tokens:
-        posemb_tok, posemb_grid = posemb[:, :num_tokens], posemb[0, num_tokens:]
-        ntok_new -= num_tokens
-    else:
-        posemb_tok, posemb_grid = posemb[:, :0], posemb[0]
-    gs_old = int(math.sqrt(len(posemb_grid)))
-    if not len(gs_new):  # backwards compatibility
-        gs_new = [int(math.sqrt(ntok_new))] * 2
-    assert len(gs_new) >= 2
-    print("Position embedding grid-size from %s to %s", [gs_old, gs_old], gs_new)
-    posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
-    posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode="bicubic", align_corners=False)
-    posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1)
-    posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
-    return
-
-
-def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder):
-    # interpolate position embedding
-    embedding_size = pos_embed_checkpoint.shape[-1]
-    num_patches = visual_encoder.patch_embed.num_patches
-    num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches
-    # height (== width) for the checkpoint position embedding
-    orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens)**0.5)
-    # height (== width) for the new position embedding
-    new_size = int(num_patches**0.5)
-
-    if orig_size != new_size:
-        # class_token and dist_token are kept unchanged
-        extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
-        # only the position tokens are interpolated
-        pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
-        pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2)
-        pos_tokens = torch.nn.functional.interpolate(pos_tokens, size=(new_size, new_size), mode="bicubic", align_corners=False)
-        pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
-        new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
-        print("reshape position embedding from %d to %d" % (orig_size**2, new_size**2))
-
-        return new_pos_embed
-    else:
-        return pos_embed_checkpoint
diff --git a/spaces/Abhaykoul/Merriam-webster_clone/app.py b/spaces/Abhaykoul/Merriam-webster_clone/app.py
deleted file mode 100644
index cf43e1de700e799357e3c8ef1a7c5e1e0c5b6def..0000000000000000000000000000000000000000
--- a/spaces/Abhaykoul/Merriam-webster_clone/app.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import streamlit as st
-import requests
-from bs4 import BeautifulSoup
-
-st.set_page_config(page_title="Word Definition App")
-
-def fetch_definition(word):
-    base_url = f"https://www.merriam-webster.com/dictionary/{word}"
-
-    response = requests.get(base_url)
-
-    if response.status_code == 200:
-        soup = BeautifulSoup(response.text, 'html.parser')
-        definition_span = soup.find("span", class_="dtText")
-
-        if definition_span:
-            full_definition = definition_span.get_text()
-            sentences = full_definition.split('. ')
-            limited_definition = '. '.join(sentences[:3])
-            return limited_definition
-        else:
-            return "Definition not found."
-    else:
-        return "Word not found or unable to retrieve data."
-
-def main():
-    st.title("Word Definition App")
-    word = st.text_input("Enter a word:")
-
-    if word.lower() == 'quit':
-        st.warning("You entered 'quit'. The app will not quit as this is a web application.")
-    else:
-        if st.button("Get Definition"):
-            definition = fetch_definition(word)
-            st.write("Definition:")
-            st.write(definition)
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatgptDemo.py b/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatgptDemo.py
deleted file mode 100644
index 95cb9ecf2e27f66e10e9b232ae03fe2f3a195d91..0000000000000000000000000000000000000000
--- a/spaces/AchyuthGamer/OpenGPT/g4f/Provider/Providers/ChatgptDemo.py
+++ /dev/null
@@ -1,61 +0,0 @@
-from __future__ import annotations
-
-import time, json, re
-from aiohttp import ClientSession
-from typing import AsyncGenerator
-
-from .base_provider import AsyncGeneratorProvider
-from .helper import format_prompt
-
-class ChatgptDemo(AsyncGeneratorProvider):
-    url = "https://chat.chatgptdemo.net"
-    supports_gpt_35_turbo = True
-    working = True
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: list[dict[str, str]],
-        proxy: str = None,
-        **kwargs
-    ) -> AsyncGenerator:
-        headers = {
-            "authority": "chat.chatgptdemo.net",
-            "accept-language": "de-DE,de;q=0.9,en-DE;q=0.8,en;q=0.7,en-US",
-            "origin": "https://chat.chatgptdemo.net",
-            "referer": "https://chat.chatgptdemo.net/",
-            "sec-ch-ua": '"Google Chrome";v="117", "Not;A=Brand";v="8", "Chromium";v="117"',
-            "sec-ch-ua-mobile": "?0",
-            "sec-ch-ua-platform": '"Linux"',
-            "sec-fetch-dest": "empty",
-            "sec-fetch-mode": "cors",
-            "sec-fetch-site": "same-origin",
-            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36"
-        }
-        async with ClientSession(headers=headers) as session:
-            async with session.get(f"{cls.url}/", proxy=proxy) as response:
-                response.raise_for_status()
-                response = await response.text()
-                result = re.search(r'<div id="USERID" style="display: none">(.*?)<\/div>', response)
-                if not result:
-                    raise RuntimeError("No user id found")
-                user_id = result.group(1)
-            async with session.post(f"{cls.url}/new_chat", json={"user_id": user_id}, proxy=proxy) as response:
-                response.raise_for_status()
-                chat_id = (await response.json())["id_"]
-            if not chat_id:
-                raise RuntimeError("Could not create new chat")
-            data = {
-                "question": format_prompt(messages),
-                "chat_id": chat_id,
-                "timestamp": int(time.time()*1000),
-            }
-            async with session.post(f"{cls.url}/chat_api_stream", json=data, proxy=proxy) as response:
-                response.raise_for_status()
-                async for line in response.content:
-                    if line.startswith(b"data: "):
-                        line = json.loads(line[6:-1])
-                        chunk = line["choices"][0]["delta"].get("content")
-                        if chunk:
-                            yield chunk
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Factory.d.ts
deleted file mode 100644
index 6282ec6e905f34f294c64731f1771aa285400b0d..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/spinner/box/Factory.d.ts
+++ /dev/null
@@ -1,6 +0,0 @@
-import Box from './Box';
-import Base from '../base/Base';
-
-export default function Factory(
-    config?: Base.IConfig
-): Box;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/AddChildMethods.js b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/AddChildMethods.js
deleted file mode 100644
index da6ba4cea0d96cfc5a38bc0687159e526bbd3a87..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/buttons/AddChildMethods.js
+++ /dev/null
@@ -1,85 +0,0 @@
-import Sizer from '../sizer/Sizer.js';
-import IsArray from '../../../plugins/utils/object/IsArray.js';
-
-const SizerAdd = Sizer.prototype.add;
-const SizerAddSpace = Sizer.prototype.addSpace;
-
-var Add = function (gameObject) {
-    var isNormalGameObject = !gameObject.isRexSpace;
-    var proportion = (!isNormalGameObject || this.buttonsExpand) ? 1 : 0;
-
-    if (this.sizerChildren.length === 0) {  // First element
-        if (isNormalGameObject) {
-            // Add space at head
-            var hasHeadSpace = (!this.buttonsExpand) &&
-                ((this.buttonsAlign === 'right') || (this.buttonsAlign === 'center') || (this.buttonsAlign === 'bottom'));
-            if (hasHeadSpace) {
-                SizerAddSpace.call(this);
-            }
-
-            SizerAdd.call(this,
-                gameObject,
-                { proportion: proportion, expand: true }
-            );
-
-            // Add space at tail
-            var hasTailSpace = (!this.buttonsExpand) && (this.buttonsAlign === 'center');
-            if (hasTailSpace) {
-                SizerAddSpace.call(this);
-            }
-            this.hasTailSpace = hasTailSpace;
-
-        } else { // A space
-            SizerAdd.call(this,
-                gameObject,
-                { proportion: proportion, expand: true }
-            );
-            this.hasTailSpace = false;
-
-        }
-
-    } else {   // Others
-        if (this.hasTailSpace) {
-            var lastIndex = this.sizerChildren.length - 1;
-            SizerAdd.call(this,
-                gameObject,
-                { index: lastIndex, proportion: proportion, expand: true }
-            );
-
-        } else {
-            SizerAdd.call(this,
-                gameObject,
-                { proportion: proportion, expand: true }
-            );
-        }
-
-    }
-
-    // Space or other game object as button
-    if (isNormalGameObject) {
-        this.buttonGroup.add(gameObject);
-    }
-
-    return this;
-};
-
-export default {
-    addButton(gameObject) {
-        if (IsArray(gameObject)) {
-            var gameObjects = gameObject;
-            for (var i = 0, cnt = gameObjects.length; i < cnt; i++) {
-                Add.call(this, gameObjects[i]);
-            }
-        } else {
-            Add.call(this, gameObject);
-        }
-        return this;
-    },
-
-    addButtons(gameObjects) {
-        for (var i = 0, cnt = gameObjects.length; i < cnt; i++) {
-            Add.call(this, gameObjects[i]);
-        }
-        return this;
-    }
-}
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/flip/Factory.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/flip/Factory.d.ts
deleted file mode 100644
index 9f355849b85e0a2fd4df2cbf5a6e2f5e848733c7..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/flip/Factory.d.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-// import * as Phaser from 'phaser';
-import Flip from "./Flip";
-
-export default function (
-    gameObject: Phaser.GameObjects.GameObject,
-    config?: Flip.IConfig
-): Flip;
\ No newline at end of file
diff --git a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/shake/Shake.d.ts b/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/shake/Shake.d.ts
deleted file mode 100644
index 8fec74929f96aa67d51b4b2b78836de83de0911e..0000000000000000000000000000000000000000
--- a/spaces/AgentVerse/agentVerse/ui/src/phaser3-rex-plugins/templates/ui/shake/Shake.d.ts
+++ /dev/null
@@ -1,2 +0,0 @@
-import Shake from '../../../plugins/shakeposition';
-export default Shake;
\ No newline at end of file
diff --git a/spaces/AiMimicry/sovits-models/modules/commons.py b/spaces/AiMimicry/sovits-models/modules/commons.py
deleted file mode 100644
index 074888006392e956ce204d8368362dbb2cd4e304..0000000000000000000000000000000000000000
--- a/spaces/AiMimicry/sovits-models/modules/commons.py
+++ /dev/null
@@ -1,188 +0,0 @@
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-def slice_pitch_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, idx_str:idx_end]
-  return ret
-
-def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
-  return ret, ret_pitch, ids_str
-
-def init_weights(m, mean=0.0, std=0.01):
-  classname = m.__class__.__name__
-  if classname.find("Conv") != -1:
-    m.weight.data.normal_(mean, std)
-
-
-def get_padding(kernel_size, dilation=1):
-  return int((kernel_size*dilation - dilation)/2)
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def intersperse(lst, item):
-  result = [item] * (len(lst) * 2 + 1)
-  result[1::2] = lst
-  return result
-
-
-def kl_divergence(m_p, logs_p, m_q, logs_q):
-  """KL(P||Q)"""
-  kl = (logs_q - logs_p) - 0.5
-  kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
-  return kl
-
-
-def rand_gumbel(shape):
-  """Sample from the Gumbel distribution, protect from overflows."""
-  uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
-  return -torch.log(-torch.log(uniform_samples))
-
-
-def rand_gumbel_like(x):
-  g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
-  return g
-
-
-def slice_segments(x, ids_str, segment_size=4):
-  ret = torch.zeros_like(x[:, :, :segment_size])
-  for i in range(x.size(0)):
-    idx_str = ids_str[i]
-    idx_end = idx_str + segment_size
-    ret[i] = x[i, :, idx_str:idx_end]
-  return ret
-
-
-def rand_slice_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size + 1
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def rand_spec_segments(x, x_lengths=None, segment_size=4):
-  b, d, t = x.size()
-  if x_lengths is None:
-    x_lengths = t
-  ids_str_max = x_lengths - segment_size
-  ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
-  ret = slice_segments(x, ids_str, segment_size)
-  return ret, ids_str
-
-
-def get_timing_signal_1d(
-    length, channels, min_timescale=1.0, max_timescale=1.0e4):
-  position = torch.arange(length, dtype=torch.float)
-  num_timescales = channels // 2
-  log_timescale_increment = (
-      math.log(float(max_timescale) / float(min_timescale)) /
-      (num_timescales - 1))
-  inv_timescales = min_timescale * torch.exp(
-      torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
-  scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
-  signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
-  signal = F.pad(signal, [0, 0, 0, channels % 2])
-  signal = signal.view(1, channels, length)
-  return signal
-
-
-def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return x + signal.to(dtype=x.dtype, device=x.device)
-
-
-def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
-  b, channels, length = x.size()
-  signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
-  return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
-
-
-def subsequent_mask(length):
-  mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
-  return mask
-
-
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-
-def convert_pad_shape(pad_shape):
-  l = pad_shape[::-1]
-  pad_shape = [item for sublist in l for item in sublist]
-  return pad_shape
-
-
-def shift_1d(x):
-  x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
-  return x
-
-
-def sequence_mask(length, max_length=None):
-  if max_length is None:
-    max_length = length.max()
-  x = torch.arange(max_length, dtype=length.dtype, device=length.device)
-  return x.unsqueeze(0) < length.unsqueeze(1)
-
-
-def generate_path(duration, mask):
-  """
-  duration: [b, 1, t_x]
-  mask: [b, 1, t_y, t_x]
-  """
-  device = duration.device
-  
-  b, _, t_y, t_x = mask.shape
-  cum_duration = torch.cumsum(duration, -1)
-  
-  cum_duration_flat = cum_duration.view(b * t_x)
-  path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
-  path = path.view(b, t_x, t_y)
-  path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
-  path = path.unsqueeze(1).transpose(2,3) * mask
-  return path
-
-
-def clip_grad_value_(parameters, clip_value, norm_type=2):
-  if isinstance(parameters, torch.Tensor):
-    parameters = [parameters]
-  parameters = list(filter(lambda p: p.grad is not None, parameters))
-  norm_type = float(norm_type)
-  if clip_value is not None:
-    clip_value = float(clip_value)
-
-  total_norm = 0
-  for p in parameters:
-    param_norm = p.grad.data.norm(norm_type)
-    total_norm += param_norm.item() ** norm_type
-    if clip_value is not None:
-      p.grad.data.clamp_(min=-clip_value, max=clip_value)
-  total_norm = total_norm ** (1. / norm_type)
-  return total_norm
diff --git a/spaces/Aki004/herta-so-vits/vdecoder/nsf_hifigan/env.py b/spaces/Aki004/herta-so-vits/vdecoder/nsf_hifigan/env.py
deleted file mode 100644
index 2bdbc95d4f7a8bad8fd4f5eef657e2b51d946056..0000000000000000000000000000000000000000
--- a/spaces/Aki004/herta-so-vits/vdecoder/nsf_hifigan/env.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import os
-import shutil
-
-
-class AttrDict(dict):
-    def __init__(self, *args, **kwargs):
-        super(AttrDict, self).__init__(*args, **kwargs)
-        self.__dict__ = self
-
-
-def build_env(config, config_name, path):
-    t_path = os.path.join(path, config_name)
-    if config != t_path:
-        os.makedirs(path, exist_ok=True)
-        shutil.copyfile(config, os.path.join(path, config_name))
diff --git a/spaces/AlexReverie/ImageSonification/README.md b/spaces/AlexReverie/ImageSonification/README.md
deleted file mode 100644
index bf6ec4e1b2041a73d92bfdca0010aa859dbe7981..0000000000000000000000000000000000000000
--- a/spaces/AlexReverie/ImageSonification/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ImageSonification
-emoji: 🐨
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 3.23.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/Alpaca233/SadTalker/src/face3d/models/base_model.py b/spaces/Alpaca233/SadTalker/src/face3d/models/base_model.py
deleted file mode 100644
index cfe64a7f739ad8f8cfbf3073a2bf49e1468127fd..0000000000000000000000000000000000000000
--- a/spaces/Alpaca233/SadTalker/src/face3d/models/base_model.py
+++ /dev/null
@@ -1,316 +0,0 @@
-"""This script defines the base network model for Deep3DFaceRecon_pytorch
-"""
-
-import os
-import numpy as np
-import torch
-from collections import OrderedDict
-from abc import ABC, abstractmethod
-from . import networks
-
-
-class BaseModel(ABC):
-    """This class is an abstract base class (ABC) for models.
-    To create a subclass, you need to implement the following five functions:
-        -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
-        -- <set_input>:                     unpack data from dataset and apply preprocessing.
-        -- <forward>:                       produce intermediate results.
-        -- <optimize_parameters>:           calculate losses, gradients, and update network weights.
-        -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
-    """
-
-    def __init__(self, opt):
-        """Initialize the BaseModel class.
-
-        Parameters:
-            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
-
-        When creating your custom class, you need to implement your own initialization.
-        In this fucntion, you should first call <BaseModel.__init__(self, opt)>
-        Then, you need to define four lists:
-            -- self.loss_names (str list):          specify the training losses that you want to plot and save.
-            -- self.model_names (str list):         specify the images that you want to display and save.
-            -- self.visual_names (str list):        define networks used in our training.
-            -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
-        """
-        self.opt = opt
-        self.isTrain = False
-        self.device = torch.device('cpu') 
-        self.save_dir = " " # os.path.join(opt.checkpoints_dir, opt.name)  # save all the checkpoints to save_dir
-        self.loss_names = []
-        self.model_names = []
-        self.visual_names = []
-        self.parallel_names = []
-        self.optimizers = []
-        self.image_paths = []
-        self.metric = 0  # used for learning rate policy 'plateau'
-
-    @staticmethod
-    def dict_grad_hook_factory(add_func=lambda x: x):
-        saved_dict = dict()
-
-        def hook_gen(name):
-            def grad_hook(grad):
-                saved_vals = add_func(grad)
-                saved_dict[name] = saved_vals
-            return grad_hook
-        return hook_gen, saved_dict
-
-    @staticmethod
-    def modify_commandline_options(parser, is_train):
-        """Add new model-specific options, and rewrite default values for existing options.
-
-        Parameters:
-            parser          -- original option parser
-            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
-
-        Returns:
-            the modified parser.
-        """
-        return parser
-
-    @abstractmethod
-    def set_input(self, input):
-        """Unpack input data from the dataloader and perform necessary pre-processing steps.
-
-        Parameters:
-            input (dict): includes the data itself and its metadata information.
-        """
-        pass
-
-    @abstractmethod
-    def forward(self):
-        """Run forward pass; called by both functions <optimize_parameters> and <test>."""
-        pass
-
-    @abstractmethod
-    def optimize_parameters(self):
-        """Calculate losses, gradients, and update network weights; called in every training iteration"""
-        pass
-
-    def setup(self, opt):
-        """Load and print networks; create schedulers
-
-        Parameters:
-            opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
-        """
-        if self.isTrain:
-            self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
-        
-        if not self.isTrain or opt.continue_train:
-            load_suffix = opt.epoch
-            self.load_networks(load_suffix)
- 
-            
-        # self.print_networks(opt.verbose)
-
-    def parallelize(self, convert_sync_batchnorm=True):
-        if not self.opt.use_ddp:
-            for name in self.parallel_names:
-                if isinstance(name, str):
-                    module = getattr(self, name)
-                    setattr(self, name, module.to(self.device))
-        else:
-            for name in self.model_names:
-                if isinstance(name, str):
-                    module = getattr(self, name)
-                    if convert_sync_batchnorm:
-                        module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module)
-                    setattr(self, name, torch.nn.parallel.DistributedDataParallel(module.to(self.device),
-                        device_ids=[self.device.index], 
-                        find_unused_parameters=True, broadcast_buffers=True))
-            
-            # DistributedDataParallel is not needed when a module doesn't have any parameter that requires a gradient.
-            for name in self.parallel_names:
-                if isinstance(name, str) and name not in self.model_names:
-                    module = getattr(self, name)
-                    setattr(self, name, module.to(self.device))
-            
-        # put state_dict of optimizer to gpu device
-        if self.opt.phase != 'test':
-            if self.opt.continue_train:
-                for optim in self.optimizers:
-                    for state in optim.state.values():
-                        for k, v in state.items():
-                            if isinstance(v, torch.Tensor):
-                                state[k] = v.to(self.device)
-
-    def data_dependent_initialize(self, data):
-        pass
-
-    def train(self):
-        """Make models train mode"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                net.train()
-
-    def eval(self):
-        """Make models eval mode"""
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                net.eval()
-
-    def test(self):
-        """Forward function used in test time.
-
-        This function wraps <forward> function in no_grad() so we don't save intermediate steps for backprop
-        It also calls <compute_visuals> to produce additional visualization results
-        """
-        with torch.no_grad():
-            self.forward()
-            self.compute_visuals()
-
-    def compute_visuals(self):
-        """Calculate additional output images for visdom and HTML visualization"""
-        pass
-
-    def get_image_paths(self, name='A'):
-        """ Return image paths that are used to load current data"""
-        return self.image_paths if name =='A' else self.image_paths_B
-
-    def update_learning_rate(self):
-        """Update learning rates for all the networks; called at the end of every epoch"""
-        for scheduler in self.schedulers:
-            if self.opt.lr_policy == 'plateau':
-                scheduler.step(self.metric)
-            else:
-                scheduler.step()
-
-        lr = self.optimizers[0].param_groups[0]['lr']
-        print('learning rate = %.7f' % lr)
-
-    def get_current_visuals(self):
-        """Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
-        visual_ret = OrderedDict()
-        for name in self.visual_names:
-            if isinstance(name, str):
-                visual_ret[name] = getattr(self, name)[:, :3, ...]
-        return visual_ret
-
-    def get_current_losses(self):
-        """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
-        errors_ret = OrderedDict()
-        for name in self.loss_names:
-            if isinstance(name, str):
-                errors_ret[name] = float(getattr(self, 'loss_' + name))  # float(...) works for both scalar tensor and float number
-        return errors_ret
-
-    def save_networks(self, epoch):
-        """Save all the networks to the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        if not os.path.isdir(self.save_dir):
-            os.makedirs(self.save_dir)
-
-        save_filename = 'epoch_%s.pth' % (epoch)
-        save_path = os.path.join(self.save_dir, save_filename)
-        
-        save_dict = {}
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                if isinstance(net, torch.nn.DataParallel) or isinstance(net,
-                        torch.nn.parallel.DistributedDataParallel):
-                    net = net.module
-                save_dict[name] = net.state_dict()
-                
-
-        for i, optim in enumerate(self.optimizers):
-            save_dict['opt_%02d'%i] = optim.state_dict()
-
-        for i, sched in enumerate(self.schedulers):
-            save_dict['sched_%02d'%i] = sched.state_dict()
-        
-        torch.save(save_dict, save_path)
-
-    def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
-        """Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
-        key = keys[i]
-        if i + 1 == len(keys):  # at the end, pointing to a parameter/buffer
-            if module.__class__.__name__.startswith('InstanceNorm') and \
-                    (key == 'running_mean' or key == 'running_var'):
-                if getattr(module, key) is None:
-                    state_dict.pop('.'.join(keys))
-            if module.__class__.__name__.startswith('InstanceNorm') and \
-               (key == 'num_batches_tracked'):
-                state_dict.pop('.'.join(keys))
-        else:
-            self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
-
-    def load_networks(self, epoch):
-        """Load all the networks from the disk.
-
-        Parameters:
-            epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
-        """
-        if self.opt.isTrain and self.opt.pretrained_name is not None:
-            load_dir = os.path.join(self.opt.checkpoints_dir, self.opt.pretrained_name)
-        else:
-            load_dir = self.save_dir    
-        load_filename = 'epoch_%s.pth' % (epoch)
-        load_path = os.path.join(load_dir, load_filename)
-        state_dict = torch.load(load_path, map_location=self.device)
-        print('loading the model from %s' % load_path)
-
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                if isinstance(net, torch.nn.DataParallel):
-                    net = net.module
-                net.load_state_dict(state_dict[name])
-        
-        if self.opt.phase != 'test':
-            if self.opt.continue_train:
-                print('loading the optim from %s' % load_path)
-                for i, optim in enumerate(self.optimizers):
-                    optim.load_state_dict(state_dict['opt_%02d'%i])
-
-                try:
-                    print('loading the sched from %s' % load_path)
-                    for i, sched in enumerate(self.schedulers):
-                        sched.load_state_dict(state_dict['sched_%02d'%i])
-                except:
-                    print('Failed to load schedulers, set schedulers according to epoch count manually')
-                    for i, sched in enumerate(self.schedulers):
-                        sched.last_epoch = self.opt.epoch_count - 1
-                    
-
-            
-
-    def print_networks(self, verbose):
-        """Print the total number of parameters in the network and (if verbose) network architecture
-
-        Parameters:
-            verbose (bool) -- if verbose: print the network architecture
-        """
-        print('---------- Networks initialized -------------')
-        for name in self.model_names:
-            if isinstance(name, str):
-                net = getattr(self, name)
-                num_params = 0
-                for param in net.parameters():
-                    num_params += param.numel()
-                if verbose:
-                    print(net)
-                print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
-        print('-----------------------------------------------')
-
-    def set_requires_grad(self, nets, requires_grad=False):
-        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
-        Parameters:
-            nets (network list)   -- a list of networks
-            requires_grad (bool)  -- whether the networks require gradients or not
-        """
-        if not isinstance(nets, list):
-            nets = [nets]
-        for net in nets:
-            if net is not None:
-                for param in net.parameters():
-                    param.requires_grad = requires_grad
-
-    def generate_visuals_for_evaluation(self, data, mode):
-        return {}
diff --git a/spaces/Ame42/rwms/datastore.py b/spaces/Ame42/rwms/datastore.py
deleted file mode 100644
index f06a390b0d02888f9a5134a0fa1f4b4bdda02b0e..0000000000000000000000000000000000000000
--- a/spaces/Ame42/rwms/datastore.py
+++ /dev/null
@@ -1,252 +0,0 @@
-# 'dataset' holds the input data for this script
-import re
-import math
-import pandas
-from local_utils import date_time_col, time_col, man_col, dur_col, round_to_n, to_sec
-import json
-import os
-import re
-import pandas
-from local_utils import date_time_col, dur_col, date_col, s1, s2, l1, l2, well_key, flow_key
-from local_utils import restructure, try_key, column_matcher, file_matcher, file_matcher2, split_join as spj
-id_col = "index"
-
-
-def split_join(dt, info):
-    joined = []
-    for i in info:
-        print(f'\n\nNow working on {i["data-column"]} column\n')
-        data = dt.drop(axis=1, columns=i["non-columns"])
-        data.rename(columns={i["data-column"]: man_col}, inplace=True)
-        data.insert(2, "Well index", [i["well-index"] for x in range(data.shape[0])], True)
-        print(f"{data.shape[0]} rows before drop and merge")
-        data.drop_duplicates(inplace=True, subset=[time_col])
-        i["dataset"].drop_duplicates(inplace=True, subset=[time_col])
-        print(data.head())
-        print(i["dataset"].head())
-        data = data.merge(i["dataset"], how='left', on=[time_col, dur_col])
-        print(data.head())
-        print(f"{data.shape[0]} rows after drop and merge")
-        joined.append(data)
-
-    return pandas.concat(joined, ignore_index=True)
-
-
-def get_22_data():
-    dataset = pandas.read_csv("input/flowstation.csv")
-    dataset_1S = pandas.read_csv("input/1S.csv")
-    dataset_1L = pandas.read_csv("input/1L.csv")
-    dataset_2S = pandas.read_csv("input/2S.csv")
-    dataset_2L = pandas.read_csv("input/2L.csv")
-
-    print(dataset.head())
-
-    for dat in [dataset, dataset_1S, dataset_1L, dataset_2L, dataset_2S]:
-        count = 0
-        duration = []
-        times = []
-        dat.dropna(axis=0, how="any", inplace=True)
-        dat.reset_index(inplace=True)
-        print(".", end="\t")
-
-        for datetime in dat[date_time_col]:
-            date_time = re.sub("\.0(?=\\s)", "", datetime)
-            datetime_array = date_time.split()
-            date = datetime_array[0].split("/")
-
-            time_array = datetime_array[1].split(":")
-
-            if datetime_array[2] == "PM" and time_array[0] != "12":
-                hour = int(time_array[0]) + 12
-            elif datetime_array[2] == "AM" and time_array[0] == "12":
-                hour = int(time_array[0]) - 12
-            else:
-                hour = time_array[0]
-
-            minutes = time_array[1]
-            sec = round_to_n(int(time_array[2]), 1)
-
-            if sec == 60:
-                sec = "00"
-                minutes = int(minutes) + 1
-
-            if minutes == 60:
-                minutes = "00"
-                hour = int(hour) + 1
-
-            if hour == 24:
-                hour = "00"
-                date[1] = int(date[1]) + 1
-
-            duration.append(to_sec(hour, minutes, sec))
-            times.append(f"{hour}:{minutes}:{sec}")
-            date_time = f"{date[1]}/{date[0]}/{date[2]} {datetime_array[1]} {datetime_array[2]}"
-
-            dat.loc[count, date_time_col] = date_time
-            count += 1
-
-        dat.insert(1, dur_col, duration, True)
-        dat.insert(2, time_col, times, True)
-
-        dat.drop(axis=1, columns=["#", date_time_col], inplace=True, errors="ignore")
-
-    info_1S = {
-        "non-columns": ["index", "NW1L, PSI (LGR S/N: 20705686)", "NW2L, PSI (LGR S/N: 20705686)",
-                        "NW2S, PSI (LGR S/N: 20705686)"],
-        "data-column": 'NW1S, PSI (LGR S/N: 20705686)',
-        "well-index": '1S',
-        "dataset": dataset_1S
-    }
-
-    info_1L = {
-        "non-columns": ["index", "NW1S, PSI (LGR S/N: 20705686)", "NW2L, PSI (LGR S/N: 20705686)",
-                        "NW2S, PSI (LGR S/N: 20705686)"],
-        "data-column": 'NW1L, PSI (LGR S/N: 20705686)',
-        "well-index": '1L',
-        "dataset": dataset_1L
-    }
-
-    info_2S = {
-        "non-columns": ["index", "NW1S, PSI (LGR S/N: 20705686)", "NW1L, PSI (LGR S/N: 20705686)",
-                        "NW2L, PSI (LGR S/N: 20705686)"],
-        "data-column": 'NW2S, PSI (LGR S/N: 20705686)',
-        "well-index": '2S',
-        "dataset": dataset_2S
-    }
-
-    info_2L = {
-        "non-columns": ["index", "NW1S, PSI (LGR S/N: 20705686)", "NW1L, PSI (LGR S/N: 20705686)",
-                        "NW2S, PSI (LGR S/N: 20705686)"],
-        "data-column": 'NW2L, PSI (LGR S/N: 20705686)',
-        "well-index": '2L',
-        "dataset": dataset_2L
-    }
-
-    dataset = split_join(dataset, [info_1S, info_1L, info_2S, info_2L])
-    dataset.drop(axis=1, columns=[id_col], inplace=True, errors="ignore")
-    dataset.insert(0, id_col, [x for x in range(dataset.shape[0])], True)
-
-    return dataset.drop(axis=1, columns="level_0")
-
-
-def get_all_data():
-    # get the list of data files and load each into a dataframe, put the dataframes in a list called files.
-    alldata = pandas.read_csv("input/files.csv")
-    files = []
-    i = 0
-    for filepath in alldata["File Path"]:
-        i += 1
-        try:
-            files.append((pandas.read_csv(filepath)))
-        except UnicodeDecodeError:
-            os.write(2, bytearray(f"Failed {i} - {filepath}\n", encoding="UTF-8", errors="e"))
-            raise
-    print(f"{len(files)} of {alldata.shape[0]} loaded")
-    # for each dataframe in files, standardize the column names and remove columns with too few values
-    truth = 0
-    total = 0
-    cut_off = 0.4
-    temp = []
-    for file, name in zip(files, alldata["Name"]):
-        for col in file.columns:
-            total += 1
-            result = column_matcher(col)
-            if not result:
-                file.drop(axis=1, columns=[col], inplace=True)
-            else:
-                file.rename(columns={col: result}, inplace=True)
-                if file[result].isna().sum() / file.shape[0] > cut_off:
-                    truth += 1
-                    file.drop(axis=1, columns=[result], inplace=True)
-        file.dropna(axis=0, how="any", inplace=True)
-        temp.append(file)
-    files = temp
-    print(f"{truth}/{total} columns dropped due to insufficient data at {cut_off * 100:.0f}% cut-off")
-    temp = dict()
-    # restructure data (extract and correct time, remove unnecessary column and add new once)
-    # remove null rows, drop first row in case of gibberish headers and group dataframes by day in dict
-    for index, dat, name, path in zip(range(len(files)), files, alldata["Name"], alldata["File Path"]):
-        try:
-            count = 0
-            duration = []
-            times = []
-            dates = []
-            dat.dropna(axis=0, how="any", inplace=True)
-            dat.drop(0, axis=0, inplace=True, errors="ignore")
-            dat.reset_index(inplace=True)
-            print(f"•{index + 1:^4}•", end="  ")
-            dat = restructure(dat, count, duration, times, dates)
-
-            dat.drop(axis=1, columns=date_time_col, inplace=True, errors="ignore")
-            print(f"{str(sorted([x for x in dat.columns])):<123}  •")
-            # dat.to_csv(f"output/temp/{name}", index=False)
-
-            if file_matcher(name):
-                # flowstation file
-                key = re.split("(?<=\\d{2})(-|_|\\s)(?=flow.*)", string=name.lower(), maxsplit=2)[0][:8]
-                try_key(temp, key)
-
-                temp[key][flow_key] = dat
-
-            else:
-                # wellhead file
-                key = re.split("(?<=\\d{2})(\\s-|-|_|\\s)?(?=(t|\\d[ls]).*)", string=name.lower(), maxsplit=2)[0][:8]
-                try_key(temp, key)
-
-                try:
-                    temp[key][well_key]
-                except KeyError:
-                    temp[key][well_key] = []
-
-                temp[key][well_key].append((file_matcher2(name.lower()), dat))
-
-        except KeyError:
-            print(f"\n\n{path}", flush=True)
-            print("Columns:", dat.columns, end="\n\n", flush=True)
-
-    return temp
-
-
-def get_conversion_factors():
-    # get conversion_factors.csv from input in a dataframe
-    return pandas.read_csv("input/conversion_factors.csv", encoding="UTF-8")
-    # return dataframe
-    pass
-
-
-def offset_wells(agg_data, how=None):
-    # Prepare aggregated data for each well from all days
-    if how is None:
-        how = [0, 0, 0, 0]
-
-    data_1S = []
-    data_2S = []
-    data_1L = []
-    data_2L = []
-    for key in agg_data.keys():
-        try:
-            temp = spj(agg_data[key][flow_key], agg_data[key][well_key], how)  # returns list of (well id, data) tuples
-            for t in temp:
-
-                if t[0] == s1:
-                    data_1S.append(t[1])
-                elif t[0] == s2:
-                    data_2S.append(t[1])
-                elif t[0] == l1:
-                    data_1L.append(t[1])
-                elif t[0] == l2:
-                    data_2L.append(t[1])
-        except KeyError:
-            pass
-    data_1L = pandas.concat(data_1L, ignore_index=True)
-    data_2L = pandas.concat(data_2L, ignore_index=True)
-    data_1S = pandas.concat(data_1S, ignore_index=True)
-    data_2S = pandas.concat(data_2S, ignore_index=True)
-
-    data_s = []
-    for name, data in zip([l1, l2, s1, s2], [data_1L, data_2L, data_1S, data_2S]):
-        data.drop_duplicates(subset=[dur_col, date_col], inplace=True)
-        data_s.append(data)
-        # data.to_csv(f"output/{name}.csv", index=False)
-
-    return pandas.concat(data_s, ignore_index=True)
diff --git a/spaces/Amrrs/DragGan-Inversion/PTI/evaluation/experiment_setting_creator.py b/spaces/Amrrs/DragGan-Inversion/PTI/evaluation/experiment_setting_creator.py
deleted file mode 100644
index c8ad234ba845d84ddd435424a7fe9ed238af3ff6..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/PTI/evaluation/experiment_setting_creator.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-from configs import global_config, paths_config, hyperparameters
-from scripts.latent_creators.sg2_plus_latent_creator import SG2PlusLatentCreator
-from scripts.latent_creators.e4e_latent_creator import E4ELatentCreator
-from scripts.run_pti import run_PTI
-import pickle
-import torch
-from utils.models_utils import toogle_grad, load_old_G
-
-
-class ExperimentRunner:
-
-    def __init__(self, run_id=''):
-        self.images_paths = glob.glob(f'{paths_config.input_data_path}/*')
-        self.target_paths = glob.glob(f'{paths_config.input_data_path}/*')
-        self.run_id = run_id
-        self.sampled_ws = None
-
-        self.old_G = load_old_G()
-
-        toogle_grad(self.old_G, False)
-
-    def run_experiment(self, run_pt, create_other_latents, use_multi_id_training, use_wandb=False):
-        if run_pt:
-            self.run_id = run_PTI(self.run_id, use_wandb=use_wandb, use_multi_id_training=use_multi_id_training)
-        if create_other_latents:
-            sg2_plus_latent_creator = SG2PlusLatentCreator(use_wandb=use_wandb)
-            sg2_plus_latent_creator.create_latents()
-            e4e_latent_creator = E4ELatentCreator(use_wandb=use_wandb)
-            e4e_latent_creator.create_latents()
-
-        torch.cuda.empty_cache()
-
-        return self.run_id
-
-
-if __name__ == '__main__':
-    os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
-    os.environ['CUDA_VISIBLE_DEVICES'] = global_config.cuda_visible_devices
-
-    runner = ExperimentRunner()
-    runner.run_experiment(True, False, False)
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/PP_HumanSeg/export_model/download_export_model.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/PP_HumanSeg/export_model/download_export_model.py
deleted file mode 100644
index 152f598bd48724fe13c5f650f809fdac06c2bae2..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/PP_HumanSeg/export_model/download_export_model.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# coding: utf8
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from paddleseg.utils.download import download_file_and_uncompress
-import sys
-import os
-
-LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
-TEST_PATH = os.path.join(LOCAL_PATH, "../../../", "test")
-sys.path.append(TEST_PATH)
-
-
-model_urls = {
-    "pphumanseg_lite_portrait_398x224_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/ppseg/ppseg_lite_portrait_398x224_with_softmax.tar.gz",
-    "deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.zip",
-    "fcn_hrnetw18_small_v1_humanseg_192x192_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/fcn_hrnetw18_small_v1_humanseg_192x192_with_softmax.zip",
-    "pphumanseg_lite_generic_humanseg_192x192_with_softmax":
-    "https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/pphumanseg_lite_generic_192x192_with_softmax.zip",
-}
-
-if __name__ == "__main__":
-    for model_name, url in model_urls.items():
-        download_file_and_uncompress(
-            url=url,
-            savepath=LOCAL_PATH,
-            extrapath=LOCAL_PATH,
-            extraname=model_name)
-
-    print("Export model download success!")
diff --git a/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/conv2d_gradfix.py b/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/conv2d_gradfix.py
deleted file mode 100644
index 8fd6a8dd22661d42899c7dba5047b3a466eecfc7..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/DragGan-Inversion/stylegan_human/torch_utils/ops/conv2d_gradfix.py
+++ /dev/null
@@ -1,196 +0,0 @@
-# Copyright (c) SenseTime Research. All rights reserved.
-
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import warnings
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-# ----------------------------------------------------------------------------
-
-# Enable the custom op by setting this to true.
-enabled = False
-# Forcefully disable computation of gradients with respect to the weights.
-weight_gradients_disabled = False
-
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-# ----------------------------------------------------------------------------
-
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-# ----------------------------------------------------------------------------
-
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if input.device.type != 'cuda':
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(
-        f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
-    return False
-
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-# ----------------------------------------------------------------------------
-
-
-_conv2d_gradfix_cache = dict()
-
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding,
-           output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else:  # transpose
-        assert all(0 <= output_padding[i] < max(
-            stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding,
-                         dilation=dilation, groups=groups)
-
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            if not transpose:
-                output = torch.nn.functional.conv2d(
-                    input=input, weight=weight, bias=bias, **common_kwargs)
-            else:  # transpose
-                output = torch.nn.functional.conv_transpose2d(
-                    input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            ctx.save_for_backward(input, weight)
-            return output
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(
-                    input_shape=input.shape, output_shape=grad_output.shape)
-                grad_input = _conv2d_gradfix(transpose=(
-                    not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
-                assert grad_input.shape == input.shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation(
-                'aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
-            flags = [torch.backends.cudnn.benchmark,
-                     torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            grad_weight = op(weight_shape, grad_output, input,
-                             padding, stride, dilation, groups, *flags)
-            assert grad_weight.shape == weight_shape
-            ctx.save_for_backward(grad_output, input)
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(
-                    input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output.shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(
-                    input_shape=input.shape, output_shape=grad_output.shape)
-                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape,
-                                              output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input.shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-# ----------------------------------------------------------------------------
diff --git a/spaces/Amrrs/QR-code-AI-art-generator/README.md b/spaces/Amrrs/QR-code-AI-art-generator/README.md
deleted file mode 100644
index afff80f64c705f429c7b675e48e05a50be6040d8..0000000000000000000000000000000000000000
--- a/spaces/Amrrs/QR-code-AI-art-generator/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: QR Code AI Art Generator
-emoji: 📱🔲
-colorFrom: MediumSeaGreen
-colorTo: CornflowerBlue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-suggested_hardware: t4-medium
-startup_duration_timeout: 1h
-duplicated_from: huggingface-projects/QR-code-AI-art-generator
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/spaces/Andy1621/uniformer_image_detection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py
deleted file mode 100644
index 37c66a9e1c0c0fd9be181540c749f6c71c01a6fc..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py
+++ /dev/null
@@ -1,43 +0,0 @@
-_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
-teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth'  # noqa
-model = dict(
-    pretrained='torchvision://resnet101',
-    teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py',
-    teacher_ckpt=teacher_ckpt,
-    backbone=dict(
-        type='ResNet',
-        depth=101,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        frozen_stages=1,
-        norm_cfg=dict(type='BN', requires_grad=True),
-        norm_eval=True,
-        style='pytorch'),
-    neck=dict(
-        type='FPN',
-        in_channels=[256, 512, 1024, 2048],
-        out_channels=256,
-        start_level=1,
-        add_extra_convs='on_output',
-        num_outs=5))
-
-lr_config = dict(step=[16, 22])
-runner = dict(type='EpochBasedRunner', max_epochs=24)
-# multi-scale training
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(
-        type='Resize',
-        img_scale=[(1333, 480), (1333, 800)],
-        multiscale_mode='range',
-        keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
-]
-data = dict(train=dict(pipeline=train_pipeline))
diff --git a/spaces/Andy1621/uniformer_image_detection/configs/yolo/yolov3_d53_320_273e_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/yolo/yolov3_d53_320_273e_coco.py
deleted file mode 100644
index 87359f6fb66d94de10b8e3797ee3eec93a19cb26..0000000000000000000000000000000000000000
--- a/spaces/Andy1621/uniformer_image_detection/configs/yolo/yolov3_d53_320_273e_coco.py
+++ /dev/null
@@ -1,42 +0,0 @@
-_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
-# dataset settings
-img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
-train_pipeline = [
-    dict(type='LoadImageFromFile', to_float32=True),
-    dict(type='LoadAnnotations', with_bbox=True),
-    dict(type='PhotoMetricDistortion'),
-    dict(
-        type='Expand',
-        mean=img_norm_cfg['mean'],
-        to_rgb=img_norm_cfg['to_rgb'],
-        ratio_range=(1, 2)),
-    dict(
-        type='MinIoURandomCrop',
-        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
-        min_crop_size=0.3),
-    dict(type='Resize', img_scale=(320, 320), keep_ratio=True),
-    dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(320, 320),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-data = dict(
-    train=dict(pipeline=train_pipeline),
-    val=dict(pipeline=test_pipeline),
-    test=dict(pipeline=test_pipeline))
diff --git a/spaces/Anonymous-sub/Rerender/ControlNet/docs/low_vram.md b/spaces/Anonymous-sub/Rerender/ControlNet/docs/low_vram.md
deleted file mode 100644
index 784964c78d5074ed9d318456d7c35f30a81f04ed..0000000000000000000000000000000000000000
--- a/spaces/Anonymous-sub/Rerender/ControlNet/docs/low_vram.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# Enable Low VRAM Mode
-
-If you are using 8GB GPU card (or if you want larger batch size), please open "config.py", and then set
-
-```python
-save_memory = True
-```
-
-This feature is still being tested - not all graphics cards are guaranteed to succeed.
-
-But it should be neat as I can diffuse at a batch size of 12 now.
-
-(prompt "man")
-
-![p](../github_page/ram12.jpg)
diff --git a/spaces/AshutoshPattanayak/LangchainDemo/README.md b/spaces/AshutoshPattanayak/LangchainDemo/README.md
deleted file mode 100644
index cb11a045363a70797d3b711cb2d437fd4b4b4a4c..0000000000000000000000000000000000000000
--- a/spaces/AshutoshPattanayak/LangchainDemo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: LangchainDemo
-emoji: 🐨
-colorFrom: gray
-colorTo: red
-sdk: streamlit
-sdk_version: 1.28.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Aveygo/AstroSleuth/main.py b/spaces/Aveygo/AstroSleuth/main.py
deleted file mode 100644
index 2723e1f7bb6ca8870f94c2ea581cb00cba0bf189..0000000000000000000000000000000000000000
--- a/spaces/Aveygo/AstroSleuth/main.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import math, numpy as np, requests, os, time, warnings, json
-from PIL import Image
-from importlib import import_module
-import torch
-
-class AstroSleuth():
-    def __init__(self, tile_size=256, tile_pad=16, wrk_dir="models/", model="astrosleuthv2", force_cpu=False, on_download=None, off_download=None):
-        # Device selection
-        self.device = "cpu" if force_cpu else ("cuda" if torch.cuda.is_available() else "cpu")
-        print(self.device)
-        
-        # Check if model name is known
-        model_src:dict = json.load(open("models.json"))["data"]
-        assert model in model_src, f"Model {model} not found! Available models: {list(model_src.keys())}"
-
-        # Load model module
-        module_path = model_src[model]["src"]["module"]
-
-        self.model_module:torch.nn.Module = getattr(
-            import_module(module_path.split("/")[0]),
-            module_path.split("/")[1]
-        )
-
-        # Download model if not available
-        self.model_pth = os.path.join(wrk_dir, f"{model}/model.pth")
-        self.download(model_src[model]["src"]["url"], self.model_pth, on_download, off_download)
-            
-        self.wrk_dir = wrk_dir
-        self.progress = None
-
-        # Set tile processing parameters
-        self.scale = model_src[model]["scale"]
-        self.tile_size = tile_size
-        self.tile_pad = tile_pad
-    
-    def download(self, src, dst, on_download=None, off_download=None):
-        if not os.path.exists(dst):
-            os.makedirs(os.path.dirname(dst), exist_ok=True)
-
-            if on_download is not None:
-                on_download()
-
-            with open(dst, 'wb') as f:
-                f.write(requests.get(src, allow_redirects=True, headers={"User-Agent":""}).content)
-
-            if off_download is not None:
-                off_download()
-
-    def model_inference(self, x: np.ndarray):
-        x = torch.from_numpy(x).to(self.device)
-        return self.model(x).cpu().detach().numpy()
-
-    def tile_generator(self, data: np.ndarray, yield_extra_details=False):
-        """
-        Process data [height, width, channel] into tiles of size [tile_size, tile_size, channel],
-        feed them one by one into the model, then yield the resulting output tiles.
-        """
-
-        # [height, width, channel] -> [1, channel, height, width]
-        data = np.rollaxis(data, 2, 0)
-        data = np.expand_dims(data, axis=0)
-        data = np.clip(data, 0, 255)
-
-        batch, channel, height, width = data.shape
-
-        tiles_x = width // self.tile_size
-        tiles_y = height // self.tile_size
-
-        for i in range(tiles_y * tiles_x):
-            x = i % tiles_y
-            y = math.floor(i/tiles_y)
-
-            input_start_x = y * self.tile_size
-            input_start_y = x * self.tile_size
-
-            input_end_x = min(input_start_x + self.tile_size, width)
-            input_end_y = min(input_start_y + self.tile_size, height)
-
-            input_start_x_pad = max(input_start_x - self.tile_pad, 0)
-            input_end_x_pad = min(input_end_x + self.tile_pad, width)
-            input_start_y_pad = max(input_start_y - self.tile_pad, 0)
-            input_end_y_pad = min(input_end_y + self.tile_pad, height)
-
-            input_tile_width = input_end_x - input_start_x
-            input_tile_height = input_end_y - input_start_y
-
-            input_tile = data[:, :, input_start_y_pad:input_end_y_pad, input_start_x_pad:input_end_x_pad].astype(np.float32) / 255
-
-            output_tile = self.model_inference(input_tile)
-            self.progress = (i+1) / (tiles_y * tiles_x)
-            
-            output_start_x_tile = (input_start_x - input_start_x_pad) * self.scale
-            output_end_x_tile = output_start_x_tile + input_tile_width * self.scale
-            output_start_y_tile = (input_start_y - input_start_y_pad) * self.scale
-            output_end_y_tile = output_start_y_tile + input_tile_height * self.scale
-
-            output_tile = output_tile[:, :, output_start_y_tile:output_end_y_tile, output_start_x_tile:output_end_x_tile]
-
-            output_tile = (np.rollaxis(output_tile, 1, 4).squeeze(0).clip(0,1) * 255).astype(np.uint8)
-            
-            if yield_extra_details:
-                yield (output_tile, input_start_x, input_start_y, input_tile_width, input_tile_height, self.progress)
-            else:
-                yield output_tile
-        
-        yield None
-
-    def enhance_with_progress(self, image:Image) -> Image:    
-        """
-        Take a PIL image and enhance it with the model, yielding stats about the
-        final image and then the final image itself.
-        """    
-        
-        # Load model only now because when using streamlit, multiple users spawn multiple instances of this class, so 
-        # we only load the model when needed. The App() class is responsible for queuing requests to this class
-        self.model = self.model_module().to(self.device)
-        self.model.load_state_dict(torch.load(self.model_pth, map_location=torch.device(self.device)))
-        self.model.eval()
-
-        original_width, original_height = image.size
-
-        # Because tiles may not fit perfectly, we resize to the closest multiple of tile_size
-        image = image.resize((max(original_width//self.tile_size * self.tile_size, self.tile_size), max(original_height//self.tile_size * self.tile_size, self.tile_size)), resample=Image.Resampling.BICUBIC)
-        image = np.array(image)
-
-        # Initiate a pillow image to save the tiles
-        result = Image.new("RGB", (image.shape[1]*self.scale, image.shape[0]*self.scale))
-        
-        for i, tile in enumerate(self.tile_generator(image, yield_extra_details=True)):
-            
-            if tile is None:
-                break
-            
-            tile_data, x, y, w, h, p = tile
-            result.paste(Image.fromarray(tile_data), (x*self.scale, y*self.scale))
-            yield p
-        
-        # Resize back to the expected size
-        yield result.resize((original_width * self.scale, original_height * self.scale), resample=Image.Resampling.BICUBIC)
-        
-    def enhance(self, image:Image) -> Image:
-        """
-        Skips the progress reporting and just returns the final image.
-        """
-        return list(self.enhance_with_progress(image))[-1]
-    
-if __name__ == '__main__':
-    import sys
-    src = sys.argv[1]
-    dst = sys.argv[2]
-    a = AstroSleuth()
-    img = Image.open(src)
-    r = a.enhance(img)
-    r.save(dst)
\ No newline at end of file
diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/dev/README.md b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/dev/README.md
deleted file mode 100644
index bec811ad002a016f2137d9d0ea61c27ee5e78992..0000000000000000000000000000000000000000
--- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/dev/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-
-## Some scripts for developers to use, include:
-
-- `linter.sh`: lint the codebase before commit.
-- `run_{inference,instant}_tests.sh`: run inference/training for a few iterations.
-   Note that these tests require 2 GPUs.
-- `parse_results.sh`: parse results from a log file.
diff --git a/spaces/Benson/text-generation/Examples/Cara Descargar Tema Robot Ejrcito.md b/spaces/Benson/text-generation/Examples/Cara Descargar Tema Robot Ejrcito.md
deleted file mode 100644
index f0669b7ddd04d7c2ccf6a15d7ba0fe36ccd14094..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Cara Descargar Tema Robot Ejrcito.md	
+++ /dev/null
@@ -1,5 +0,0 @@
-
-<h1>Cara Download Robot Theme Army untuk Perangkat Anda</h1> | | <p>Apakah Anda suka dengan robot dan elemen-elemen militer? Apakah Anda ingin membuat perangkat Anda terlihat lebih keren dan futuristik dengan tema yang sesuai dengan selera Anda? Jika jawabannya ya, maka anda mungkin tertarik untuk mencoba robot army theme. Tema ini adalah salah satu tema yang populer di kalangan penggemar robot dan militer, karena menampilkan gambar-gambar robot yang menakjubkan, warna-warna yang gelap dan tegas, serta ikon-ikon yang unik dan menarik. Dalam artikel ini, kami akan menjelaskan apa itu tema robot army, fitur-fitur yang ditawarkan, keuntungan dan masalah yang mungkin timbul, serta cara download tema robot army untuk perangkat Anda.</p>
-<h2>cara descargar tema robot ejército</h2><br /><p><b><b>DOWNLOAD</b> &#8230;&#8230;&#8230; <a href="https://bltlly.com/2v6MkD">https://bltlly.com/2v6MkD</a></b></p><br /><br /> | | <h2>Apa itu Tema Robot Army? </h2> | | <p>Theme robot army adalah theme yang didesain khusus untuk pengguna yang menyukai robot dan militer. Tema ini mengubah tampilan latar belakang, layar kunci, font, suara, dan ikon perangkat Anda menjadi lebih sesuai dengan tema robot army. Anda bisa melihat gambar-gambar robot yang berbagai jenis dan fungsi, seperti robot tempur, eksplorasi robot, robot medis, robot transportasi, dan lain-lain. Go play bisa melihat gambar-gambar militer, seperti tank, pesawat tempur, kapal selam, rudal, dan lain-lain. Warna-warna yang dominate dalam tema ini adalah hitam, abu-abu, biru tua, merah marun, dan hijau tua. Font yang digunakan juga terlihat lebih tegas dan modern. Suara-suara yang dihasilkan juga mengikuti tema robot army, seperti suara mesin, ledakan, tembakan, dan lain-lain. Ikon-ikon yang digunakan juga berbeda dari biasanya, karena memiliki bentuk-bentuk yang lebih geometris dan futuristik. </p> | | <h3>Fitur-fitur Robot Theme Army</h3> | | <p>Robot theme army memiliki beberapa fitur yang membuatnya menarik dan berbeda dari tema lainnya. Berikut adalah beberapa fitur yang ditawarkan oleh tema ini:</p> | | <ul> | <li>Tema ini mendukung berbagai jenis perangkat, seperti smartphone, tablet, laptop, dan 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/Benson/text-generation/Examples/Carx Carretera Carreras Apk Hack Descargar.md b/spaces/Benson/text-generation/Examples/Carx Carretera Carreras Apk Hack Descargar.md
deleted file mode 100644
index 464935bc0c6d5cd2e618ccbbeb2d7cbfed828ce0..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Carx Carretera Carreras Apk Hack Descargar.md	
+++ /dev/null
@@ -1,90 +0,0 @@
-<br />
-<h1>CarX Highway Racing APK Hack Descargar: Una revisión</h1>
-<p>Si eres un fan de los juegos de carreras, es posible que hayas oído hablar de CarX Highway Racing, un juego popular que ofrece una mezcla de física realista, gráficos llamativos y conducción extrema en carreteras llenas de tráfico. ¿Pero sabías que puedes descargar una versión hackeada del juego que te da dinero, monedas y combustible ilimitados? En este artículo, vamos a revisar CarX Highway Racing APK Hack Download, una versión modificada del juego que le permite disfrutar de todas las características del juego sin limitaciones. También compararemos CarX Highway Racing con otros juegos de carreras, y te daremos algunos consejos y trucos para mejorar tu rendimiento. </p>
- <h2>¿Qué es CarX Highway Racing? </h2>
-<p>CarX Highway Racing es un juego de carreras desarrollado por CarX Technologies, la misma compañía que creó CarX Drift Racing 2. El juego se basa en la física realista, lo que significa que los coches se comportan de acuerdo con su peso, velocidad, tracción y suspensión. El juego también cuenta con entornos detallados con ciclos de día y noche, tráfico animado y la policía, y varios modos de juego como campaña, carreras en línea, modo de policía, modo de viaje libre, y eventos únicos. El juego tiene más de 40 coches deportivos para elegir, que van desde coches clásicos a supercoches. También puede actualizar y personalizar sus coches para adaptarse a sus preferencias y estilo. </p>
-<h2>carx carretera carreras apk hack descargar</h2><br /><p><b><b>Download</b> &#128505; <a href="https://bltlly.com/2v6Jt5">https://bltlly.com/2v6Jt5</a></b></p><br /><br />
- <h3>Características de CarX Highway Racing</h3>
-<p>Algunas de las características de CarX Highway Racing son:</p>
-<ul>
-<li><b>Modo de campaña:</b> Sumérgete en el mundo de las carreras callejeras, donde tienes que competir contra numerosos rivales, escapar de la policía implacable y descubrir los secretos de diferentes países. También encontrarás varios personajes que te ayudarán o te obstaculizarán en tu búsqueda. </li>
-<li><b>Carreras en línea:</b> Mostrar a los demás lo que tienes al competir con otros jugadores por el primer lugar en diferentes ligas y estaciones. También puedes desafiar a tus amigos o oponentes al azar en carreras en tiempo real. </li>
-
-<li><b>Eventos únicos:</b> Participa en diferentes eventos y recibe autos únicos como recompensas. Estos eventos tienen un límite de tiempo y requieren habilidades y estrategias específicas para ganar. </li>
-<li><b>Modo de conducción libre:</b> Siente la libertad de conducir sin restricciones en este modo, donde puedes disfrutar conduciendo sin tener que preocuparte por objetivos o oponentes. También puede explorar las diferentes ubicaciones y descubrir secretos ocultos. </li>
-</ul>
- <h3>Cómo descargar CarX Highway Racing APK Hack? </h3>
-<p>Si desea descargar CarX Highway Racing APK Hack, tendrá que seguir estos pasos:</p>
-<ol>
-<li>Ir a , un sitio web que proporciona versiones modificadas de varios juegos. </li>
-<li>Haga clic en el botón verde que dice "Descargar" y espere a que el archivo se descargue en su dispositivo. </li>
-<li>Ir a la configuración de su dispositivo y permitir la instalación de aplicaciones de fuentes desconocidas. </li>
-<li>Busque el archivo descargado en su administrador de archivos y toque en él para instalarlo. </li>
-<li>Inicie el juego y disfrute de dinero, monedas y combustible ilimitados. </li>
-</ol>
- <h2>¿Por qué deberías jugar CarX Highway Racing? </h2>
-<p>Hay muchas razones por las que debe jugar CarX Highway Racing? Aquí están algunos de los beneficios de jugar a este juego:</p>
- <h3>Física realista y manejo de automóviles</h3>
-<p>Una de las principales atracciones de CarX Highway Racing es su motor de física realista, que hace que los coches se comporten de acuerdo con sus homólogos de la vida real. Sentirá la diferencia entre conducir un SUV pesado y un automóvil deportivo ligero, así como el impacto de la velocidad, el frenado, la dirección y la deriva. También tendrá que ajustar su estilo de conducción de acuerdo con las condiciones de la carretera, como mojado, seco o nevado. El juego también te permite personalizar la configuración de tu coche, como suspensión, neumáticos, caja de cambios y motor, para adaptarse a tus preferencias y necesidades. </p>
- <h3>Varios modos de juego y desafíos</h3>
-
- <h3>Impresionantes gráficos y efectos de sonido</h3>
-<p>Por último, pero no menos importante, debes jugar CarX Highway Racing debido a sus impresionantes gráficos y efectos de sonido, que te harán sentir como si estuvieras en un mundo de carreras real. El juego presenta entornos detallados con ciclos diurnos y nocturnos, efectos climáticos dinámicos, sombras y reflejos realistas y texturas de alta calidad. El juego también tiene increíbles efectos de sonido que te sumergirán en la atmósfera del juego. Oirás el rugido de los motores, el chirrido de los neumáticos, el bocinazo de los cuernos y las sirenas de la policía. </p>
- <h2>¿Cómo se compara CarX Highway Racing con otros juegos de carreras? </h2>
-<p>CarX Highway Racing no es el único juego de carreras disponible en el mercado. Hay muchos otros juegos de carreras que usted podría haber jugado o oído hablar de, como la necesidad de velocidad más buscados, Asphalt 9: Legends, y Real Racing 3. ¿Cómo CarX Highway Racing comparar con estos juegos? Vamos a averiguar:</p>
-<p></p>
- <h3>CarX Highway Racing vs. Necesidad de velocidad más buscados</h3>
-<p>Need for Speed Most Wanted es un juego de carreras desarrollado por Electronic Arts, que es una de las franquicias más populares del género. El juego se centra en carreras callejeras y actividades policiales, donde tienes que correr más rápido que tus rivales y evadir la ley. El juego tiene más de 40 coches para elegir, que se puede personalizar y actualizar. El juego también tiene un modo multijugador donde puedes competir con otros jugadores en línea. </p>
-<p>CarX Highway Racing es similar a Need for Speed Most Wanted en algunos aspectos, como el tema de carreras callejeras y persecuciones policiales, la variedad de coches y opciones de personalización, y el modo multijugador en línea. Sin embargo, CarX Highway Racing tiene algunas ventajas sobre Need for Speed Most Wanted, como:</p>
-<ul>
-
-<li><b>Más modos de juego y desafíos:</b> CarX Highway Racing tiene más modos de juego y desafíos que Need for Speed Most Wanted. Puede elegir entre el modo de campaña, carreras en línea, modo de policía, modo de viaje gratuito y eventos únicos. También puedes competir en diferentes ligas y temporadas o desafiar a tus amigos o oponentes al azar en carreras en tiempo real. </li>
-<li><b>Versión hackeada disponible:</b> CarX Highway Racing tiene una versión hackeada disponible que le da dinero ilimitado, monedas y combustible. Esto significa que puedes disfrutar de todas las características del juego sin limitaciones o restricciones. </li>
-</ul>
- <h3>CarX Highway Racing vs. Asfalto 9: Leyendas</h3>
-<p>Asphalt 9: Legends es un juego de carreras desarrollado por Gameloft, que es una de las franquicias más populares del género. El juego se centra en las carreras de estilo árcade, donde se puede realizar acrobacias, derivas, y nitro aumenta para ganar las carreras. El juego tiene más de 60 coches para elegir, que se puede recoger y actualizar. El juego también tiene un modo multijugador donde puedes competir con otros jugadores en línea. </p>
- <p>CarX Highway Racing es diferente de Asphalt 9: Legends en muchos aspectos, como el tema, la física y los gráficos. Algunas de las diferencias son:</p>
-<ul>
-<li><b>Realistic vs. árcade:</b> CarX Highway Racing tiene un tema más realista que Asphalt 9: Legends, que tiene un tema más árcade. CarX Highway Racing se basa en la física realista, lo que significa que los coches se comportan de acuerdo con sus homólogos de la vida real. Asfalto 9: Leyendas se basa en la física de árcade, lo que significa que los coches pueden realizar acrobacias poco realistas, deriva, y aumenta nitro. </li>
-
-<li><b>Detallado vs. llamativo:</b> CarX Highway Racing tiene gráficos más detallados que Asphalt 9: Legends, que tiene gráficos más llamativos. CarX Highway Racing presenta entornos detallados con ciclos diurnos y nocturnos, efectos climáticos dinámicos, sombras y reflejos realistas y texturas de alta calidad. Asfalto 9: Leyendas presenta ambientes llamativos con colores brillantes, explosiones, chispas y humo. </li>
-</ul>
- <h3>CarX Highway Racing vs. Real Racing 3</h3>
-<p>Real Racing 3 es un juego de carreras desarrollado por Electronic Arts, que es uno de los juegos de carreras más realistas del mercado. El juego cuenta con coches con licencia de más de 40 fabricantes, como Ferrari, Lamborghini, Porsche y Bugatti. El juego también cuenta con pistas reales de todo el mundo, como Silverstone, Le Mans y Dubai Autodrome. El juego tiene un modo multijugador donde puedes competir con otros jugadores online o offline. </p>
- <p>CarX Highway Racing es similar a Real Racing 3 en algunos aspectos, como el realismo de la física y los gráficos, la variedad de coches y pistas, y el modo multijugador. Sin embargo, CarX Highway Racing tiene algunas ventajas sobre Real Racing 3, como:</p>
-<ul>
-<li><b>Carreras callejeras vs. carreras de pista:</b> CarX Highway Racing tiene un tema de carreras callejeras más que Real Racing 3, que tiene un tema de carreras de pista más. CarX Highway Racing le permite competir en carreteras llenas de tráfico con persecuciones policiales y varios modos de juego. Real Racing 3 te permite correr en pistas profesionales con reglas y regulaciones oficiales. </li>
-<li><b>Personalización vs. autenticidad:</b> CarX Highway Racing le permite personalizar y actualizar sus coches para adaptarse a sus preferencias y estilo. Puede cambiar el color, ruedas, alerones, calcomanías y el rendimiento de sus coches. Real Racing 3 no le permite personalizar sus coches, ya que se basan en sus homólogos de la vida real. </li>
-
-</ul>
- <h2>Consejos y trucos para CarX Highway Racing</h2>
-<p>Si quieres mejorar tu rendimiento en CarX Highway Racing, puedes seguir estos consejos y trucos:</p>
- <h3>Actualizar y personalizar sus coches</h3>
-<p>Una de las mejores maneras de mejorar su experiencia de conducción en CarX Highway Racing es actualizar y personalizar sus coches. Usted puede utilizar el dinero y las monedas que usted gana de completar carreras y tareas para comprar coches nuevos o actualizar sus existentes. También puede utilizar las opciones de personalización para cambiar la apariencia y el rendimiento de sus coches. Puede ajustar la suspensión, los neumáticos, la caja de cambios y el motor de sus coches para adaptarse a su estilo de conducción y las condiciones de la carretera. También puede cambiar el color, ruedas, alerones, calcomanías y otros accesorios de sus coches para que se vean más únicos y con estilo. </p>
- <h3>Utilice los boosters y nitro sabiamente</h3>
-<p>Otra forma de mejorar tu rendimiento en CarX Highway Racing es usar boosters y nitro sabiamente. Los boosters son artículos que puedes comprar o ganar al completar carreras y tareas, lo que puede darte varias ventajas en el juego. Por ejemplo, puede usar un amplificador que aumente su velocidad, reduzca su consumo de combustible o lo proteja de colisiones. Nitro es una función que puede activar pulsando en la pantalla, que puede darle una ráfaga temporal de velocidad. Sin embargo, tanto los boosters como el nitro son limitados en cantidad y duración, por lo que tienes que usarlos estratégicamente. Debes usarlas cuando necesites superar a tus rivales, escapar de la policía o llegar a la meta más rápido. </p>
- <h3>Evitar colisiones y atascos de tráfico</h3>
-
- <h2>Conclusión</h2>
-<p>CarX Highway Racing es un juego de carreras que ofrece una mezcla de física realista, gráficos llamativos y conducción extrema en carreteras llenas de tráfico. Puede descargar una versión hackeada del juego que le da dinero ilimitado, monedas y combustible, que le permitirá disfrutar de todas las características del juego sin limitaciones. También puede comparar CarX Highway Racing con otros juegos de carreras, como Need for Speed Most Wanted, Asphalt 9: Legends y Real Racing 3, y ver cómo difieren en términos de tema, física, gráficos y jugabilidad. También puede seguir algunos consejos y trucos para mejorar su rendimiento en CarX Highway Racing, como actualizar y personalizar sus coches, usar boosters y nitro sabiamente, y evitar colisiones y atascos de tráfico. Esperamos que este artículo le ha ayudado a aprender más acerca de CarX Highway Racing APK Hack Descargar, y que usted tendrá diversión jugando a este juego. </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí hay algunas preguntas frecuentes sobre CarX Highway Racing APK Hack Descargar:</p>
-<ul>
-<li><b>Q: Es CarX Highway Racing APK Hack Descargar seguro de usar? </b></li>
-<li><b>A: Sí,</b> CarX Highway Racing APK Hack Download es seguro de usar, siempre y cuando se descarga desde un sitio web de confianza como . Sin embargo, debes tener en cuenta que usar una versión hackeada del juego podría violar los términos de servicio del desarrollador original del juego, lo que podría resultar en una prohibición o una penalización. </li>
-<li><b>Q: ¿Cómo puedo actualizar CarX Highway Racing APK Hack Descargar? </b></li>
-<li><b>A: Para actualizar CarX Highway Racing APK Hack Download,</b> tendrá que descargar la última versión del archivo modificado de e instalarlo en su dispositivo. También es posible que tenga que desinstalar la versión anterior del juego antes de instalar el nuevo. </li>
-<li><b>Q: ¿Puedo jugar CarX Highway Racing APK Hack Descargar sin conexión? </b></li>
-
-<li><b>Q: ¿Puedo jugar CarX Highway Racing APK Hack Descargar con mis amigos? </b></li>
-<li><b>A: Sí,</b> puede jugar CarX Highway Racing APK Hack Descargar con tus amigos mediante el modo multijugador en línea o el modo de carreras en tiempo real. También puedes desafiar a tus amigos o oponentes al azar en diferentes ligas y temporadas. </li>
-<li><b>Q: ¿Cuáles son algunas alternativas a CarX Highway Racing APK Hack Descargar? </b></li>
-<li><b>A: Algunas alternativas a CarX Highway Racing APK Hack Descargar son:</b></li>
-<ul>
-<li><b>Necesidad de velocidad más buscados APK Mod:</b> Esta es una versión modificada de la necesidad de la velocidad más deseada que le da dinero ilimitado y desbloquea todos los coches. </li>
-<li><b>Asfalto 9: Leyendas APK Mod:</b> Esta es una versión modificada de Asfalto 9: Leyendas que le da dinero ilimitado, fichas, y nitro. </li>
-<li><b>Real Racing 3 APK Mod:</b> Esta es una versión modificada de Real Racing 3 que le da dinero ilimitado, oro, y los coches desbloqueados. </li>
-</ul>
-</ul></p> 64aa2da5cf<br />
-<br />
-<br />
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deleted file mode 100644
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--- a/spaces/Benson/text-generation/Examples/Como Hacer Un Anillo De Amor.md	
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@@ -1,105 +0,0 @@
-<br />
-<h1>Proyecto Playtime APK Indir: Un juego de terror de aventura móvil con canciones divertidas y amigos</h1>
-<p>Si te gustan los juegos de terror, es posible que desee echa un vistazo a <strong>Project Playtime APK</strong>, un juego de terror de aventura móvil que es a la vez aterrador y divertido. En este juego, se encontrará con varios monstruos y rompecabezas, mientras que también hacer nuevos amigos y escuchar canciones impresionantes. En este artículo, le diremos todo lo que necesita saber sobre Project Playtime APK, incluyendo lo que es, por qué debe jugar, cómo descargarlo e instalarlo, ¿cuáles son los personajes principales y monstruos, cómo jugarlo, y cuáles son algunas de las canciones. ¡Vamos a empezar! </p>
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- <h2>¿Qué es Project Playtime APK? </h2>
-<p>Proyecto Playtime APK es un juego para móviles desarrollado por GearBox Juegos que combina elementos de terror y aventura. El juego se desarrolla en una misteriosa escuela donde tienes que explorar diferentes habitaciones y pasillos evitando o luchando contra criaturas espeluznantes. El juego también cuenta con un montón de canciones divertidas que se puede cantar junto con sus nuevos amigos que se encuentran en el camino. El juego tiene un estilo de arte único y una atmósfera que te mantendrá al límite mientras te entretiene. </p>
- <h3>Una breve introducción al juego y sus características</h3>
-<p>Algunas de las características de Project Playtime APK son:</p>
-<ul>
-<li>Es gratis para descargar y jugar. </li>
-<li> Tiene gráficos y efectos de sonido de alta calidad. </li>
-<li> Tiene una variedad de personajes y monstruos con diferentes personalidades y habilidades. </li>
-<li> Tiene un montón de puzzles y desafíos que pondrán a prueba tus habilidades y lógica. </li>
-<li>Tiene muchas canciones que te harán reír y bailar. </li>
-<li> Tiene un modo historia y un modo sandbox donde puede crear sus propios escenarios. </li>
-</ul>
- <h2>¿Por qué debe jugar Project Playtime APK? </h2>
-<p>Hay muchas razones por las que debe jugar Project Playtime APK. Aquí están algunos de ellos:</p>
-<p></p>
-<ul>
-<li>Es un juego de terror único que mezcla miedo y diversión. </li>
-
-<li>Es un juego desafiante que te mantendrá comprometido y curioso. </li>
-<li>Es un juego creativo que te permitirá expresarte a través de la música y el arte. </li>
-<li>Es un juego emocionante que te hará gritar y reír al mismo tiempo. </li>
-</ul>
- <h2>Cómo descargar e instalar el proyecto Playtime APK? </h e>? </h2>
-<p>Descargar e instalar Project Playtime APK es muy fácil y simple. Solo tienes que seguir estos pasos:</p>
-<ol>
-<li>Ir a la página web oficial de Project Playtime APK y haga clic en el botón de descarga. </li>
-<li>Espere a que el archivo APK se descargue en su dispositivo. </li>
-<li>Ir a la configuración del dispositivo y permitir la instalación de aplicaciones de fuentes desconocidas. </li>
-<li>Busque el archivo APK en su administrador de archivos y toque en él para instalarlo. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <h2>¿Cuáles son los personajes principales y monstruos en el proyecto Playtime APK? </h2>
-<p>Proyecto Playtime APK tiene una gran cantidad de personajes y monstruos que se encontrará en el juego. Algunos de ellos son amables y serviciales, mientras que otros son hostiles y peligrosos. Aquí hay una tabla que muestra algunos de los personajes principales y monstruos en el juego:</p>
- <tabla>
-<tr>
-<th>Nombre</th>
-<th>Descripción</th>
-</tr>
-<tr>
-<td>Amapola</td>
-<td>Una chica alegre y amigable a la que le encanta cantar y jugar. Ella es tu guía y amiga en el juego. Ella te ayudará con rompecabezas y canciones, pero también tiene un lado oscuro del que debes tener cuidado. </td>
-</tr>
-<tr>
-<td>Huggy Wuggy</td>
-<td>Un monstruo azul gigante al que le gusta abrazar a la gente. Es muy fuerte y rápido, pero también muy torpe y fácilmente distraído. Te perseguirá por la escuela, pero también puede ser engañado o evitado. </td>
-</tr>
-<tr>
-<td>Chica de Kissie</td>
-<td>Un monstruo rosa al que le gusta besar a la gente. Es muy linda y dulce, pero también muy agresiva y posesiva. Ella tratará de atraparte y besarte, pero también puede estar asustada o repelida. </td>
-</tr>
-<tr>
-<td>Molly</td>
-
-</tr>
-<tr>
-<td>El profesor</td>
-<td>Figura misteriosa y siniestra que gobierna la escuela. Es muy inteligente y astuto, pero también muy cruel y despiadado. Intentará evitar que escapes de la escuela, pero también puede ser más astuto o confrontado. </td>
-</tr>
-</tabla>
- <h2>¿Cómo se juega Project Playtime APK? </h2>
-<p>Proyecto Playtime APK es un juego que requiere tanto habilidad y estrategia. Tienes que explorar la escuela, encontrar objetos, resolver puzzles, hacer amigos, evitar o luchar contra los enemigos, y escapar de la escuela. Aquí hay algunos consejos sobre cómo jugar el juego:</p>
- <h3>Un resumen de la mecánica de juego y consejos sobre cómo sobrevivir y resolver puzzles</h3>
- <ul>
-<li>Puede moverse usando el joystick virtual en el lado izquierdo de la pantalla. Puede interactuar con objetos usando los botones en el lado derecho de la pantalla. </li>
-<li>Puede cambiar entre diferentes modos utilizando los iconos en la esquina superior derecha de la pantalla. Puedes usar el modo linterna para ver mejor en lugares oscuros, el modo cámara para tomar fotos de pistas o enemigos, y el modo micrófono para cantar canciones o hablar con amigos. </li>
-<li>Puede acceder a su inventario tocando el icono de la mochila en la esquina inferior derecha de la pantalla. Puede usar elementos arrastrándolos a objetos o caracteres. </li>
-<li>Puede pausar el juego tocando el icono del menú en la esquina superior izquierda de la pantalla. Puede reanudar, reiniciar o salir del juego desde allí. </li>
-<li>Usted necesita tener cuidado de su salud y barras de resistencia en la esquina superior izquierda de la pantalla. Tu salud disminuirá si te lastimas por enemigos o trampas, y tu resistencia disminuirá si corres o realizas acciones. Puede restaurar su salud y resistencia encontrando comida o bebidas en la escuela. </li>
-
-<li>Necesitas prestar atención a tu entorno y buscar pistas, pistas o secretos. Algunos objetos o caracteres tendrán un efecto de chispa que indica que son importantes o interactivos. </li>
-<li>Necesitas usar tu lógica y creatividad para resolver puzzles y desafíos. Algunos rompecabezas requieren que encuentres objetos, combines objetos, utilices objetos, introduzcas códigos o realices acciones. Algunos rompecabezas tendrán múltiples soluciones o resultados. </li>
-<li>Necesitas usar tu coraje y habilidad para evitar o luchar contra los enemigos. Algunos enemigos te perseguirán, te atacarán o te atraparán. Puedes huir de ellos, esconderte de ellos, distraerlos, engañarlos o luchar contra ellos. Puedes usar elementos, canciones o amigos para ayudarte. </li>
-Necesitas usar tu amistad y encanto para hacer amigos y aliados. Algunos personajes serán amables y serviciales, mientras que otros serán hostiles y sospechosos. Puedes hablarles, cantarles, darles objetos o hacerles favores. También puedes traicionarlos, mentirles o robarles. </li>
-<li>Necesitas usar tu curiosidad y aventura para explorar la escuela y encontrar secretos y sorpresas. Algunas habitaciones y pasillos tendrán puertas, pasajes o habitaciones ocultas. Algunos objetos o personajes tendrán mensajes ocultos, historias o huevos de Pascua. </li>
-</ul>
- <h2>¿Cuáles son algunas de las canciones en Project Playtime APK? </h2>
-<p>Una de las características más divertidas y únicas de Project Playtime APK es las canciones que se pueden escuchar y cantar en el juego. Las canciones son pegadizas, divertidas y a veces espeluznantes. Se añadirá al estado de ánimo y la atmósfera del juego, así como darle pistas o pistas sobre los personajes o rompecabezas. Estas son algunas de las canciones que puedes encontrar en el juego:</p>
- <h3>Un breve resumen de algunas de las canciones pegadizas que se pueden escuchar en el juego</h3>
-<ul>
-
-<li><strong>Canción de Huggy Wuggy</strong>: Esta es una canción cantada por Huggy Wuggy cuando te persigue por la escuela. Es una canción tonta y juguetona que expresa su amor por los abrazos y su deseo de abrazarte. También te advierte sobre su fuerza y velocidad. </li>
-<li><strong>Kissie Cutie’s Song</strong>: Esta es una canción cantada por Kissie Cutie cuando intenta atraparte y besarte. Es una canción dulce y romántica que muestra su afecto y obsesión por ti. También revela sus celos e ira hacia cualquiera que se interponga en su camino. </li>
-<li><strong>Molly’s Song</strong>: Esta es una canción cantada por Molly cuando te ayuda con pistas y pistas. Es una canción tímida y tímida que refleja su personalidad y aficiones. También te cuenta su secreto y su conexión con la escuela. </li>
-<li><strong>The Teacher’s Song</strong>: Esta es una canción cantada por The Teacher cuando te enfrenta o te desafía. Es una canción misteriosa y siniestra que revela su identidad y su plan. También se burla de ti y pone a prueba tu conocimiento y coraje. </li>
-</ul>
- <h2>Conclusión</h2>
-<p>Proyecto Playtime APK es un juego de terror de aventura móvil que es a la vez aterrador y divertido. Se encontrará con varios monstruos y rompecabezas, mientras que también hacer nuevos amigos y escuchar canciones impresionantes. Necesitarás usar tu habilidad, estrategia, lógica, creatividad, coraje, amistad, curiosidad y aventura para sobrevivir y escapar de la escuela. Si usted está buscando un juego de terror único que mezcla el miedo y la diversión, descargar Project Playtime APK hoy! </p>
- <h2>Preguntas frecuentes</h2>
-<p>Aquí están algunas de las preguntas y respuestas más frecuentes sobre Project Playtime APK:</p>
- <h4>Q: ¿Es seguro descargar Project Playtime APK? </h4>
-<p>A: Sí, Proyecto Playtime APK es seguro para descargar desde el sitio web oficial de GearBox Games. El archivo APK es libre de virus y malware. </p>
- <h4>Q: ¿Es el proyecto Playtime APK compatible con mi dispositivo? </h4>
-
- <h4>Q: ¿Cómo puedo actualizar el proyecto Playtime APK? </h4>
-<p>A: Puede actualizar el proyecto Playtime APK visitando el sitio web oficial de GearBox Games y descargar la última versión del archivo APK. </p>
- <h4>Q: ¿Cómo puedo contactar a los desarrolladores de Project Playtime APK? </h4>
-<p>A: Puede ponerse en contacto con los desarrolladores de Project Playtime APK enviando un correo electrónico a gearboxgames@gmail.com o visitando su página de Facebook. </p>
- <h4>Q: ¿Cómo puedo apoyar a los desarrolladores de Project Playtime APK? </h4>
-<p>A: Usted puede apoyar a los desarrolladores de Project Playtime APK mediante la calificación y la revisión del juego en Google Play Store u otras plataformas, compartir el juego con tus amigos, o hacer una donación en su sitio web. </p> 64aa2da5cf<br />
-<br />
-<br />
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diff --git a/spaces/Benson/text-generation/Examples/Descarga Mp4 Alquimia De Las Almas Temporada 2.md b/spaces/Benson/text-generation/Examples/Descarga Mp4 Alquimia De Las Almas Temporada 2.md
deleted file mode 100644
index abe827e2ce1f5e803b8f243a206fce409e51a1d3..0000000000000000000000000000000000000000
--- a/spaces/Benson/text-generation/Examples/Descarga Mp4 Alquimia De Las Almas Temporada 2.md	
+++ /dev/null
@@ -1,103 +0,0 @@
-<br />
-<h1>Deriva CarX Racing Hack Descargar: Cómo obtener dinero ilimitado y desbloquear todos los coches</h1>
- <p>Si eres un fan de los juegos de deriva, es posible que hayas oído hablar de CarX Drift Racing Online, un simulador de deriva realista e inmersivo que te permite personalizar tu coche, competir con otros jugadores y disfrutar de varias pistas y ubicaciones. Pero lo que si quieres obtener más dinero, desbloquear todos los coches, y acceder a más características en el juego? Ahí es donde CarX Drift Racing descarga hack viene muy bien. </p>
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- <p>En este artículo, le mostraremos cómo instalar dinero mod para CarX Drift Racing Online, que es un mod que le dará dinero máximo y desbloquear todos los coches en el juego. También le contaremos sobre las características y beneficios del juego CarX Drift Racing, así como algunos consejos y trucos para mejorar sus habilidades de deriva. Finalmente, compartiremos algunos trucos y logros para el juego CarX Drift Racing en Xbox One y PlayStation 4. ¡Empecemos! </p>
- <h2>Cómo instalar dinero Mod para CarX deriva Racing en línea</h2>
- <p>Una de las formas más fáciles de hackear CarX Drift Racing Online es usar dinero mod, que es un archivo que puede descargar y colocar en su carpeta de juego. Esto le dará dinero ilimitado y desbloquear todos los coches en el juego. Aquí están los pasos para instalar dinero mod para CarX Drift Racing Online:</p>
- <ol>
-<li>Descargue el archivo de dinero desde <a href="( i )">Discord</a>, donde también puede unirse a la comunidad CarX. Puede encontrar el archivo en el canal general de carx, mensajes anclados. </li>
-<li>Arrastre y suelte el archivo en la carpeta de plugins (File path: C: Archivos de programa (x86) Steam steamapps común CarX Drift Racing Online BepInEx plugins). </li>
-<li>Ir al vapor, haga clic derecho en carx, betas, y seleccione la versión modificable. </li>
-<li>Iniciar el juego y disfrutar! </li>
-</ol>
- <p>Si quieres ver un video tutorial sobre cómo instalar dinero mod para CarX Drift Racing Online, puedes ver este video de YouTube de LiLLobby:</p>
-
- <h2>¿Cuáles son las características y beneficios de CarX Drift Racing Game</h2>
- <p>CarX Drift Racing Online no es solo un simple juego de deriva. Es un simulador de deriva de pleno derecho que ofrece física de conducción realista, personalización detallada y ajuste de los parámetros del coche, una serie de ciudades y lugares especiales de carreras, una variedad de vinilos para diseñar el aspecto de su vehículo, abrir salas en línea y concursos mejorados con nuevos gráficos. Estas son algunas de las características y beneficios del juego CarX Drift Racing:</p>
-<p></p>
- <ul>
-<li>Gráficos realistas: El juego cuenta con gráficos de alta calidad que crean una experiencia visual impresionante. Puedes ver el humo de tus neumáticos, las chispas de tu parachoques, los reflejos de tus ventanas, y más. </li>
-<li>Pistas de carreras y vehículos altamente detallados: El juego cuenta con más de 30 pistas con diferentes diseños, superficies, condiciones climáticas y hora del día. También puede elegir entre más de 100 coches de diferentes modelos, marcas y clases, cada uno con sus propias especificaciones y rendimiento.  </li>
-<li>Personalización y ajuste avanzados del coche: El juego le permite modificar la apariencia y el rendimiento de su coche para adaptarse a sus preferencias y estilo. Puede cambiar el color, vinilo, ruedas, kits de carrocería, alerones, tubos de escape y más. También puede ajustar el motor, la suspensión, los frenos, la dirección, la caja de cambios, el diferencial y otros parámetros para ajustar el manejo y la deriva de su automóvil.  </li>
-<li>Modo multijugador y competiciones en línea: El juego le permite unirse a salas en línea con sus amigos u otros jugadores de todo el mundo. Puedes chatear, desafiar y competir con ellos en varios modos, como carreras de deriva, derrapes en tándem, carreras de sprint y más. También puedes participar en campeonatos y torneos en línea para ganar recompensas y posicionarte en las tablas de clasificación.  </li>
-
-</ul>
- <h2>¿Cuáles son algunos consejos y trucos para mejorar sus habilidades de deriva en CarX Drift Racing Game</h2>
- <p>La deriva no es fácil de dominar. Requiere práctica, paciencia y habilidad. Pero no te preocupes, tenemos algunos consejos y trucos para ayudarte a mejorar tus habilidades de deriva en el juego CarX Drift Racing:</p>
- <ul>
-<li>Elige el coche adecuado: Diferentes coches tienen diferentes características y comportamientos en el juego. Algunos son más fáciles de deriva que otros. Para los principiantes, recomendamos elegir un coche con una baja relación potencia-peso, un diseño de tracción trasera y una distancia entre ejes corta. Estos coches son más sensibles y estables cuando se deriva. </li>
-<li>Ajustar la configuración: Antes de empezar a deriva, debe ajustar la configuración de su coche de acuerdo a su preferencia y las condiciones de la pista. Puede cambiar la presión de los neumáticos, el ángulo de curvatura, el ángulo de la puntera, el ángulo de dirección, el equilibrio de los frenos, la fuerza del freno de mano y más. Estos ajustes afectarán la forma en que el automóvil se agarra, se desliza, gira y se detiene. </li>
-<li>Utilice el acelerador y el freno sabiamente: El acelerador y el freno son sus principales herramientas para controlar sus derivas. Es necesario utilizar con cuidado y sin problemas para mantener el equilibrio de su coche. Para iniciar una deriva, es necesario aplicar suficiente acelerador para romper la tracción en las ruedas traseras. Para mantener una deriva, es necesario modular el acelerador para mantener las ruedas traseras girando más rápido que las ruedas delanteras. Para salir de una deriva, es necesario soltar el acelerador gradualmente para recuperar la tracción en las ruedas traseras. Para ajustar el ángulo de su deriva, debe usar el freno ligeramente para cambiar el peso a las ruedas delanteras. </li>
-
-<li>Práctica, Práctica, Práctica: La mejor manera de mejorar tus habilidades de deriva en el juego CarX Drift Racing es practicar tanto como puedas. Puede comenzar con el modo tutorial, donde puede aprender los conceptos básicos de la deriva y obtener comentarios sobre su rendimiento. También puede probar el modo de práctica, donde puede desplazarse libremente en cualquier pista sin ningún oponente o límites de tiempo. También puedes ver las repeticiones de tus propias derivas o las de otros jugadores para analizar y mejorar tu técnica. </li>
-</ul>
- <h2>¿Cuáles son algunos trucos y logros para CarX deriva juego de carreras en Xbox One y PlayStation 4</h2>
- <p>Si usted está jugando CarX Drift Racing juego en Xbox One o PlayStation 4, es posible que esté interesado en algunos trucos y logros que pueden hacer que su juego más divertido y gratificante. Estos son algunos de ellos:</p>
- <tabla>
-<tr>
-<th>Trucos</th>
-<th>Efecto</th>
-</tr>
-<tr>
-<td>Mantén LB + RB + LT + RT + X + Y + B + A en Xbox One o L1 + R1 + L2 + R2 + Cuadrado + Triángulo + Círculo + Cruz en PlayStation 4 en el menú principal. </td>
-<td>Desbloquear todos los coches y pistas en el juego. </td>
-</tr>
-<tr>
-<td>Presiona Arriba, Arriba, Abajo, Abajo, Izquierda, Derecha, Izquierda, Derecha, B, A en Xbox One o Arriba, Arriba, Abajo, Abajo, Izquierda, Derecha, Derecha, Círculo, Cruz en PlayStation 4 en el menú principal. </td>
-<td>Activar el código de Konami huevo de pascua y obtener un mensaje especial de los desarrolladores. </td>
-</tr>
-<tr>
-<td>Presione izquierda, derecha, izquierda, derecha, X, Y en Xbox One o izquierda, derecha, izquierda, derecha, cuadrado, triángulo en PlayStation 4 en el menú principal. </td>
-<td>Cambiar el idioma del juego a japonés.</td>
-</tr>
-</tabla>
- <tabla>
-<tr>
-<th>Logro</th>
-<th>Descripción</th>
-<th>Gamerscore/Trofeo</th>
-</tr>
-<tr>
-<td>Rey de la deriva</td>
-<td>Gana 1000 puntos en una sola deriva. </td>
-<td>50/Bronce</td>
-</tr>
-<tr>
-<td>Maestro en tándem</td>
-<td>Realizar una deriva en tándem con otro jugador durante 10 segundos. </td>
-<td>100/Plata</td>
-</tr>
-<tr>
-<td>Colector</td>
-<td>Poseer todos los coches en el juego. </td>
-<td>200/Oro</td>
-</tr>
-<tr>
-
-<td>Unidad en todas las pistas del juego. </td>
-<td>150/Plata</td>
-</tr>
-<tr>
-<td>Campeón</td <td>Gana el campeonato online en el juego. </td>
-<td>300/Platinum</td>
-</tr>
-</tabla>
- <h2>Conclusión</h2>
- <p>CarX Drift Racing Online es un juego que atraerá a cualquiera que ame la deriva y las carreras. Ofrece gráficos realistas, pistas y coches detallados, personalización y ajuste avanzados, modo multijugador y competiciones en línea, y un juego divertido y adictivo. Si desea obtener más dinero, desbloquear todos los coches, y acceder a más características en el juego, puede utilizar CarX Drift Racing descarga hack. Le hemos mostrado cómo instalar dinero mod para CarX Drift Racing Online, que es una forma simple y eficaz de hackear el juego. También te hemos dado algunos consejos y trucos para mejorar tus habilidades de deriva, así como algunos trucos y logros para CarX Drift Racing en Xbox One y PlayStation 4. Esperamos que hayas disfrutado de este artículo y te haya resultado útil. Si tienes alguna pregunta o comentario, por favor deja un comentario abajo. Happy drifting! </p>
- <h2>Preguntas frecuentes</h2>
- <p>Aquí hay algunas preguntas frecuentes sobre CarX Drift Racing hack download:</p>
- <ol>
-<li>Es CarX Drift Racing descarga hack seguro de usar? </li>
-<p>Sí, CarX Drift Racing descarga hack es seguro de usar, siempre y cuando se descarga de una fuente de confianza y siga las instrucciones cuidadosamente. Sin embargo, no recomendamos su uso en su cuenta principal o en servidores oficiales, ya que puede violar los términos de servicio del juego y resultar en una prohibición o suspensión. </p>
-<li> ¿Cómo puedo actualizar CarX Drift Racing hack descargar? </li>
-<p>Puede actualizar CarX Drift Racing hack descargar mediante la descarga de la última versión del archivo de dinero mod de Discord y reemplazar el archivo antiguo en su carpeta de juego. También es posible que necesites cambiar a la versión modificable del juego en Steam si hay una nueva actualización para el juego. </p>
-<li> ¿Cómo puedo desinstalar CarX Drift Racing descarga? </li>
-
-<li>¿Puedo utilizar CarX Drift Racing hack descargar en otras plataformas? </li>
-<p>No, CarX Drift Racing descarga hack solo funciona en PC. No funciona en Xbox One, PlayStation 4, Android o dispositivos iOS. </p>
-<li>¿Dónde puedo encontrar más información sobre el juego CarX Drift Racing? </li>
-<p>Puede encontrar más información sobre el juego CarX Drift Racing en su sitio web oficial, página de Facebook, cuenta de Instagram, canal de YouTube o servidor Discord. También puedes ver algunos comentarios y videos de juego del juego en línea. </p>
-</ol></p> 64aa2da5cf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/BernardoOlisan/vqganclip/taming-transformers/scripts/extract_submodel.py b/spaces/BernardoOlisan/vqganclip/taming-transformers/scripts/extract_submodel.py
deleted file mode 100644
index 559bc5e04281a7cf833a82e3cd48627b20f1a76d..0000000000000000000000000000000000000000
--- a/spaces/BernardoOlisan/vqganclip/taming-transformers/scripts/extract_submodel.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import torch
-import sys
-
-if __name__ == "__main__":
-    inpath = sys.argv[1]
-    outpath = sys.argv[2]
-    submodel = "cond_stage_model"
-    if len(sys.argv) > 3:
-        submodel = sys.argv[3]
-
-    print("Extracting {} from {} to {}.".format(submodel, inpath, outpath))
-
-    sd = torch.load(inpath, map_location="cpu")
-    new_sd = {"state_dict": dict((k.split(".", 1)[-1],v)
-                                 for k,v in sd["state_dict"].items()
-                                 if k.startswith("cond_stage_model"))}
-    torch.save(new_sd, outpath)
diff --git a/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/build/__init__.py b/spaces/Big-Web/MMSD/env/Lib/site-packages/pip/_internal/operations/build/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/adjacent_difference.h b/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/adjacent_difference.h
deleted file mode 100644
index c6f6c72820a2fe158d7389d03d49334e1675438b..0000000000000000000000000000000000000000
--- a/spaces/CVPR/LIVE/thrust/thrust/system/detail/adl/adjacent_difference.h
+++ /dev/null
@@ -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 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>
-
-// the purpose of this header is to #include the adjacent_difference.h header
-// of the sequential, host, and device systems. It should be #included in any
-// code which uses adl to dispatch adjacent_difference
-
-#include <thrust/system/detail/sequential/adjacent_difference.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/adjacent_difference.h>
-#include <thrust/system/cuda/detail/adjacent_difference.h>
-#include <thrust/system/omp/detail/adjacent_difference.h>
-#include <thrust/system/tbb/detail/adjacent_difference.h>
-#endif
-
-#define __THRUST_HOST_SYSTEM_ADJACENT_DIFFERENCE_HEADER <__THRUST_HOST_SYSTEM_ROOT/detail/adjacent_difference.h>
-#include __THRUST_HOST_SYSTEM_ADJACENT_DIFFERENCE_HEADER
-#undef __THRUST_HOST_SYSTEM_ADJACENT_DIFFERENCE_HEADER
-
-#define __THRUST_DEVICE_SYSTEM_ADJACENT_DIFFERENCE_HEADER <__THRUST_DEVICE_SYSTEM_ROOT/detail/adjacent_difference.h>
-#include __THRUST_DEVICE_SYSTEM_ADJACENT_DIFFERENCE_HEADER
-#undef __THRUST_DEVICE_SYSTEM_ADJACENT_DIFFERENCE_HEADER
-
diff --git a/spaces/CVPR/WALT/mmdet/core/__init__.py b/spaces/CVPR/WALT/mmdet/core/__init__.py
deleted file mode 100644
index e812391e23894ef296755381386d4849f774418a..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/core/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from .anchor import *  # noqa: F401, F403
-from .bbox import *  # noqa: F401, F403
-from .evaluation import *  # noqa: F401, F403
-from .export import *  # noqa: F401, F403
-from .mask import *  # noqa: F401, F403
-from .post_processing import *  # noqa: F401, F403
-from .utils import *  # noqa: F401, F403
diff --git a/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/sabl_head.py b/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/sabl_head.py
deleted file mode 100644
index 5153996aeb706d103d1ad14b61734914eddb7693..0000000000000000000000000000000000000000
--- a/spaces/CVPR/WALT/mmdet/models/roi_heads/bbox_heads/sabl_head.py
+++ /dev/null
@@ -1,572 +0,0 @@
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, kaiming_init, normal_init, xavier_init
-from mmcv.runner import force_fp32
-
-from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
-from mmdet.models.builder import HEADS, build_loss
-from mmdet.models.losses import accuracy
-
-
-@HEADS.register_module()
-class SABLHead(nn.Module):
-    """Side-Aware Boundary Localization (SABL) for RoI-Head.
-
-    Side-Aware features are extracted by conv layers
-    with an attention mechanism.
-    Boundary Localization with Bucketing and Bucketing Guided Rescoring
-    are implemented in BucketingBBoxCoder.
-
-    Please refer to https://arxiv.org/abs/1912.04260 for more details.
-
-    Args:
-        cls_in_channels (int): Input channels of cls RoI feature. \
-            Defaults to 256.
-        reg_in_channels (int): Input channels of reg RoI feature. \
-            Defaults to 256.
-        roi_feat_size (int): Size of RoI features. Defaults to 7.
-        reg_feat_up_ratio (int): Upsample ratio of reg features. \
-            Defaults to 2.
-        reg_pre_kernel (int): Kernel of 2D conv layers before \
-            attention pooling. Defaults to 3.
-        reg_post_kernel (int): Kernel of 1D conv layers after \
-            attention pooling. Defaults to 3.
-        reg_pre_num (int): Number of pre convs. Defaults to 2.
-        reg_post_num (int): Number of post convs. Defaults to 1.
-        num_classes (int): Number of classes in dataset. Defaults to 80.
-        cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
-        reg_offset_out_channels (int): Hidden and output channel \
-            of reg offset branch. Defaults to 256.
-        reg_cls_out_channels (int): Hidden and output channel \
-            of reg cls branch. Defaults to 256.
-        num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
-        num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
-        reg_class_agnostic (bool): Class agnostic regresion or not. \
-            Defaults to True.
-        norm_cfg (dict): Config of norm layers. Defaults to None.
-        bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
-        loss_cls (dict): Config of classification loss.
-        loss_bbox_cls (dict): Config of classification loss for bbox branch.
-        loss_bbox_reg (dict): Config of regression loss for bbox branch.
-    """
-
-    def __init__(self,
-                 num_classes,
-                 cls_in_channels=256,
-                 reg_in_channels=256,
-                 roi_feat_size=7,
-                 reg_feat_up_ratio=2,
-                 reg_pre_kernel=3,
-                 reg_post_kernel=3,
-                 reg_pre_num=2,
-                 reg_post_num=1,
-                 cls_out_channels=1024,
-                 reg_offset_out_channels=256,
-                 reg_cls_out_channels=256,
-                 num_cls_fcs=1,
-                 num_reg_fcs=0,
-                 reg_class_agnostic=True,
-                 norm_cfg=None,
-                 bbox_coder=dict(
-                     type='BucketingBBoxCoder',
-                     num_buckets=14,
-                     scale_factor=1.7),
-                 loss_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=False,
-                     loss_weight=1.0),
-                 loss_bbox_cls=dict(
-                     type='CrossEntropyLoss',
-                     use_sigmoid=True,
-                     loss_weight=1.0),
-                 loss_bbox_reg=dict(
-                     type='SmoothL1Loss', beta=0.1, loss_weight=1.0)):
-        super(SABLHead, self).__init__()
-        self.cls_in_channels = cls_in_channels
-        self.reg_in_channels = reg_in_channels
-        self.roi_feat_size = roi_feat_size
-        self.reg_feat_up_ratio = int(reg_feat_up_ratio)
-        self.num_buckets = bbox_coder['num_buckets']
-        assert self.reg_feat_up_ratio // 2 >= 1
-        self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio
-        assert self.up_reg_feat_size == bbox_coder['num_buckets']
-        self.reg_pre_kernel = reg_pre_kernel
-        self.reg_post_kernel = reg_post_kernel
-        self.reg_pre_num = reg_pre_num
-        self.reg_post_num = reg_post_num
-        self.num_classes = num_classes
-        self.cls_out_channels = cls_out_channels
-        self.reg_offset_out_channels = reg_offset_out_channels
-        self.reg_cls_out_channels = reg_cls_out_channels
-        self.num_cls_fcs = num_cls_fcs
-        self.num_reg_fcs = num_reg_fcs
-        self.reg_class_agnostic = reg_class_agnostic
-        assert self.reg_class_agnostic
-        self.norm_cfg = norm_cfg
-
-        self.bbox_coder = build_bbox_coder(bbox_coder)
-        self.loss_cls = build_loss(loss_cls)
-        self.loss_bbox_cls = build_loss(loss_bbox_cls)
-        self.loss_bbox_reg = build_loss(loss_bbox_reg)
-
-        self.cls_fcs = self._add_fc_branch(self.num_cls_fcs,
-                                           self.cls_in_channels,
-                                           self.roi_feat_size,
-                                           self.cls_out_channels)
-
-        self.side_num = int(np.ceil(self.num_buckets / 2))
-
-        if self.reg_feat_up_ratio > 1:
-            self.upsample_x = nn.ConvTranspose1d(
-                reg_in_channels,
-                reg_in_channels,
-                self.reg_feat_up_ratio,
-                stride=self.reg_feat_up_ratio)
-            self.upsample_y = nn.ConvTranspose1d(
-                reg_in_channels,
-                reg_in_channels,
-                self.reg_feat_up_ratio,
-                stride=self.reg_feat_up_ratio)
-
-        self.reg_pre_convs = nn.ModuleList()
-        for i in range(self.reg_pre_num):
-            reg_pre_conv = ConvModule(
-                reg_in_channels,
-                reg_in_channels,
-                kernel_size=reg_pre_kernel,
-                padding=reg_pre_kernel // 2,
-                norm_cfg=norm_cfg,
-                act_cfg=dict(type='ReLU'))
-            self.reg_pre_convs.append(reg_pre_conv)
-
-        self.reg_post_conv_xs = nn.ModuleList()
-        for i in range(self.reg_post_num):
-            reg_post_conv_x = ConvModule(
-                reg_in_channels,
-                reg_in_channels,
-                kernel_size=(1, reg_post_kernel),
-                padding=(0, reg_post_kernel // 2),
-                norm_cfg=norm_cfg,
-                act_cfg=dict(type='ReLU'))
-            self.reg_post_conv_xs.append(reg_post_conv_x)
-        self.reg_post_conv_ys = nn.ModuleList()
-        for i in range(self.reg_post_num):
-            reg_post_conv_y = ConvModule(
-                reg_in_channels,
-                reg_in_channels,
-                kernel_size=(reg_post_kernel, 1),
-                padding=(reg_post_kernel // 2, 0),
-                norm_cfg=norm_cfg,
-                act_cfg=dict(type='ReLU'))
-            self.reg_post_conv_ys.append(reg_post_conv_y)
-
-        self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1)
-        self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1)
-
-        self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1)
-        self.relu = nn.ReLU(inplace=True)
-
-        self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs,
-                                               self.reg_in_channels, 1,
-                                               self.reg_cls_out_channels)
-        self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs,
-                                                  self.reg_in_channels, 1,
-                                                  self.reg_offset_out_channels)
-        self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1)
-        self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1)
-
-    def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size,
-                       fc_out_channels):
-        in_channels = in_channels * roi_feat_size * roi_feat_size
-        branch_fcs = nn.ModuleList()
-        for i in range(num_branch_fcs):
-            fc_in_channels = (in_channels if i == 0 else fc_out_channels)
-            branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels))
-        return branch_fcs
-
-    def init_weights(self):
-        for module_list in [
-                self.reg_cls_fcs, self.reg_offset_fcs, self.cls_fcs
-        ]:
-            for m in module_list.modules():
-                if isinstance(m, nn.Linear):
-                    xavier_init(m, distribution='uniform')
-        if self.reg_feat_up_ratio > 1:
-            kaiming_init(self.upsample_x, distribution='normal')
-            kaiming_init(self.upsample_y, distribution='normal')
-
-        normal_init(self.reg_conv_att_x, 0, 0.01)
-        normal_init(self.reg_conv_att_y, 0, 0.01)
-        normal_init(self.fc_reg_offset, 0, 0.001)
-        normal_init(self.fc_reg_cls, 0, 0.01)
-        normal_init(self.fc_cls, 0, 0.01)
-
-    def cls_forward(self, cls_x):
-        cls_x = cls_x.view(cls_x.size(0), -1)
-        for fc in self.cls_fcs:
-            cls_x = self.relu(fc(cls_x))
-        cls_score = self.fc_cls(cls_x)
-        return cls_score
-
-    def attention_pool(self, reg_x):
-        """Extract direction-specific features fx and fy with attention
-        methanism."""
-        reg_fx = reg_x
-        reg_fy = reg_x
-        reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid()
-        reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid()
-        reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2)
-        reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3)
-        reg_fx = (reg_fx * reg_fx_att).sum(dim=2)
-        reg_fy = (reg_fy * reg_fy_att).sum(dim=3)
-        return reg_fx, reg_fy
-
-    def side_aware_feature_extractor(self, reg_x):
-        """Refine and extract side-aware features without split them."""
-        for reg_pre_conv in self.reg_pre_convs:
-            reg_x = reg_pre_conv(reg_x)
-        reg_fx, reg_fy = self.attention_pool(reg_x)
-
-        if self.reg_post_num > 0:
-            reg_fx = reg_fx.unsqueeze(2)
-            reg_fy = reg_fy.unsqueeze(3)
-            for i in range(self.reg_post_num):
-                reg_fx = self.reg_post_conv_xs[i](reg_fx)
-                reg_fy = self.reg_post_conv_ys[i](reg_fy)
-            reg_fx = reg_fx.squeeze(2)
-            reg_fy = reg_fy.squeeze(3)
-        if self.reg_feat_up_ratio > 1:
-            reg_fx = self.relu(self.upsample_x(reg_fx))
-            reg_fy = self.relu(self.upsample_y(reg_fy))
-        reg_fx = torch.transpose(reg_fx, 1, 2)
-        reg_fy = torch.transpose(reg_fy, 1, 2)
-        return reg_fx.contiguous(), reg_fy.contiguous()
-
-    def reg_pred(self, x, offset_fcs, cls_fcs):
-        """Predict bucketing estimation (cls_pred) and fine regression (offset
-        pred) with side-aware features."""
-        x_offset = x.view(-1, self.reg_in_channels)
-        x_cls = x.view(-1, self.reg_in_channels)
-
-        for fc in offset_fcs:
-            x_offset = self.relu(fc(x_offset))
-        for fc in cls_fcs:
-            x_cls = self.relu(fc(x_cls))
-        offset_pred = self.fc_reg_offset(x_offset)
-        cls_pred = self.fc_reg_cls(x_cls)
-
-        offset_pred = offset_pred.view(x.size(0), -1)
-        cls_pred = cls_pred.view(x.size(0), -1)
-
-        return offset_pred, cls_pred
-
-    def side_aware_split(self, feat):
-        """Split side-aware features aligned with orders of bucketing
-        targets."""
-        l_end = int(np.ceil(self.up_reg_feat_size / 2))
-        r_start = int(np.floor(self.up_reg_feat_size / 2))
-        feat_fl = feat[:, :l_end]
-        feat_fr = feat[:, r_start:].flip(dims=(1, ))
-        feat_fl = feat_fl.contiguous()
-        feat_fr = feat_fr.contiguous()
-        feat = torch.cat([feat_fl, feat_fr], dim=-1)
-        return feat
-
-    def bbox_pred_split(self, bbox_pred, num_proposals_per_img):
-        """Split batch bbox prediction back to each image."""
-        bucket_cls_preds, bucket_offset_preds = bbox_pred
-        bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0)
-        bucket_offset_preds = bucket_offset_preds.split(
-            num_proposals_per_img, 0)
-        bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds))
-        return bbox_pred
-
-    def reg_forward(self, reg_x):
-        outs = self.side_aware_feature_extractor(reg_x)
-        edge_offset_preds = []
-        edge_cls_preds = []
-        reg_fx = outs[0]
-        reg_fy = outs[1]
-        offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs,
-                                                  self.reg_cls_fcs)
-        offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs,
-                                                  self.reg_cls_fcs)
-        offset_pred_x = self.side_aware_split(offset_pred_x)
-        offset_pred_y = self.side_aware_split(offset_pred_y)
-        cls_pred_x = self.side_aware_split(cls_pred_x)
-        cls_pred_y = self.side_aware_split(cls_pred_y)
-        edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1)
-        edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1)
-
-        return (edge_cls_preds, edge_offset_preds)
-
-    def forward(self, x):
-
-        bbox_pred = self.reg_forward(x)
-        cls_score = self.cls_forward(x)
-
-        return cls_score, bbox_pred
-
-    def get_targets(self, sampling_results, gt_bboxes, gt_labels,
-                    rcnn_train_cfg):
-        pos_proposals = [res.pos_bboxes for res in sampling_results]
-        neg_proposals = [res.neg_bboxes for res in sampling_results]
-        pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
-        pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
-        cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals,
-                                             pos_gt_bboxes, pos_gt_labels,
-                                             rcnn_train_cfg)
-        (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-         bucket_offset_targets, bucket_offset_weights) = cls_reg_targets
-        return (labels, label_weights, (bucket_cls_targets,
-                                        bucket_offset_targets),
-                (bucket_cls_weights, bucket_offset_weights))
-
-    def bucket_target(self,
-                      pos_proposals_list,
-                      neg_proposals_list,
-                      pos_gt_bboxes_list,
-                      pos_gt_labels_list,
-                      rcnn_train_cfg,
-                      concat=True):
-        (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-         bucket_offset_targets, bucket_offset_weights) = multi_apply(
-             self._bucket_target_single,
-             pos_proposals_list,
-             neg_proposals_list,
-             pos_gt_bboxes_list,
-             pos_gt_labels_list,
-             cfg=rcnn_train_cfg)
-
-        if concat:
-            labels = torch.cat(labels, 0)
-            label_weights = torch.cat(label_weights, 0)
-            bucket_cls_targets = torch.cat(bucket_cls_targets, 0)
-            bucket_cls_weights = torch.cat(bucket_cls_weights, 0)
-            bucket_offset_targets = torch.cat(bucket_offset_targets, 0)
-            bucket_offset_weights = torch.cat(bucket_offset_weights, 0)
-        return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-                bucket_offset_targets, bucket_offset_weights)
-
-    def _bucket_target_single(self, pos_proposals, neg_proposals,
-                              pos_gt_bboxes, pos_gt_labels, cfg):
-        """Compute bucketing estimation targets and fine regression targets for
-        a single image.
-
-        Args:
-            pos_proposals (Tensor): positive proposals of a single image,
-                 Shape (n_pos, 4)
-            neg_proposals (Tensor): negative proposals of a single image,
-                 Shape (n_neg, 4).
-            pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals
-                 of a single image, Shape (n_pos, 4).
-            pos_gt_labels (Tensor): gt labels assigned to positive proposals
-                 of a single image, Shape (n_pos, ).
-            cfg (dict): Config of calculating targets
-
-        Returns:
-            tuple:
-
-                - labels (Tensor): Labels in a single image. \
-                    Shape (n,).
-                - label_weights (Tensor): Label weights in a single image.\
-                    Shape (n,)
-                - bucket_cls_targets (Tensor): Bucket cls targets in \
-                    a single image. Shape (n, num_buckets*2).
-                - bucket_cls_weights (Tensor): Bucket cls weights in \
-                    a single image. Shape (n, num_buckets*2).
-                - bucket_offset_targets (Tensor): Bucket offset targets \
-                    in a single image. Shape (n, num_buckets*2).
-                - bucket_offset_targets (Tensor): Bucket offset weights \
-                    in a single image. Shape (n, num_buckets*2).
-        """
-        num_pos = pos_proposals.size(0)
-        num_neg = neg_proposals.size(0)
-        num_samples = num_pos + num_neg
-        labels = pos_gt_bboxes.new_full((num_samples, ),
-                                        self.num_classes,
-                                        dtype=torch.long)
-        label_weights = pos_proposals.new_zeros(num_samples)
-        bucket_cls_targets = pos_proposals.new_zeros(num_samples,
-                                                     4 * self.side_num)
-        bucket_cls_weights = pos_proposals.new_zeros(num_samples,
-                                                     4 * self.side_num)
-        bucket_offset_targets = pos_proposals.new_zeros(
-            num_samples, 4 * self.side_num)
-        bucket_offset_weights = pos_proposals.new_zeros(
-            num_samples, 4 * self.side_num)
-        if num_pos > 0:
-            labels[:num_pos] = pos_gt_labels
-            label_weights[:num_pos] = 1.0
-            (pos_bucket_offset_targets, pos_bucket_offset_weights,
-             pos_bucket_cls_targets,
-             pos_bucket_cls_weights) = self.bbox_coder.encode(
-                 pos_proposals, pos_gt_bboxes)
-            bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets
-            bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights
-            bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets
-            bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights
-        if num_neg > 0:
-            label_weights[-num_neg:] = 1.0
-        return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
-                bucket_offset_targets, bucket_offset_weights)
-
-    def loss(self,
-             cls_score,
-             bbox_pred,
-             rois,
-             labels,
-             label_weights,
-             bbox_targets,
-             bbox_weights,
-             reduction_override=None):
-        losses = dict()
-        if cls_score is not None:
-            avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
-            losses['loss_cls'] = self.loss_cls(
-                cls_score,
-                labels,
-                label_weights,
-                avg_factor=avg_factor,
-                reduction_override=reduction_override)
-            losses['acc'] = accuracy(cls_score, labels)
-
-        if bbox_pred is not None:
-            bucket_cls_preds, bucket_offset_preds = bbox_pred
-            bucket_cls_targets, bucket_offset_targets = bbox_targets
-            bucket_cls_weights, bucket_offset_weights = bbox_weights
-            # edge cls
-            bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num)
-            bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num)
-            bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num)
-            losses['loss_bbox_cls'] = self.loss_bbox_cls(
-                bucket_cls_preds,
-                bucket_cls_targets,
-                bucket_cls_weights,
-                avg_factor=bucket_cls_targets.size(0),
-                reduction_override=reduction_override)
-
-            losses['loss_bbox_reg'] = self.loss_bbox_reg(
-                bucket_offset_preds,
-                bucket_offset_targets,
-                bucket_offset_weights,
-                avg_factor=bucket_offset_targets.size(0),
-                reduction_override=reduction_override)
-
-        return losses
-
-    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
-    def get_bboxes(self,
-                   rois,
-                   cls_score,
-                   bbox_pred,
-                   img_shape,
-                   scale_factor,
-                   rescale=False,
-                   cfg=None):
-        if isinstance(cls_score, list):
-            cls_score = sum(cls_score) / float(len(cls_score))
-        scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
-
-        if bbox_pred is not None:
-            bboxes, confids = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
-                                                     img_shape)
-        else:
-            bboxes = rois[:, 1:].clone()
-            confids = None
-            if img_shape is not None:
-                bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
-                bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
-
-        if rescale and bboxes.size(0) > 0:
-            if isinstance(scale_factor, float):
-                bboxes /= scale_factor
-            else:
-                bboxes /= torch.from_numpy(scale_factor).to(bboxes.device)
-
-        if cfg is None:
-            return bboxes, scores
-        else:
-            det_bboxes, det_labels = multiclass_nms(
-                bboxes,
-                scores,
-                cfg.score_thr,
-                cfg.nms,
-                cfg.max_per_img,
-                score_factors=confids)
-
-            return det_bboxes, det_labels
-
-    @force_fp32(apply_to=('bbox_preds', ))
-    def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
-        """Refine bboxes during training.
-
-        Args:
-            rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
-                and bs is the sampled RoIs per image.
-            labels (Tensor): Shape (n*bs, ).
-            bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \
-                (n*bs, num_buckets*2)].
-            pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
-                is a gt bbox.
-            img_metas (list[dict]): Meta info of each image.
-
-        Returns:
-            list[Tensor]: Refined bboxes of each image in a mini-batch.
-        """
-        img_ids = rois[:, 0].long().unique(sorted=True)
-        assert img_ids.numel() == len(img_metas)
-
-        bboxes_list = []
-        for i in range(len(img_metas)):
-            inds = torch.nonzero(
-                rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
-            num_rois = inds.numel()
-
-            bboxes_ = rois[inds, 1:]
-            label_ = labels[inds]
-            edge_cls_preds, edge_offset_preds = bbox_preds
-            edge_cls_preds_ = edge_cls_preds[inds]
-            edge_offset_preds_ = edge_offset_preds[inds]
-            bbox_pred_ = [edge_cls_preds_, edge_offset_preds_]
-            img_meta_ = img_metas[i]
-            pos_is_gts_ = pos_is_gts[i]
-
-            bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
-                                           img_meta_)
-            # filter gt bboxes
-            pos_keep = 1 - pos_is_gts_
-            keep_inds = pos_is_gts_.new_ones(num_rois)
-            keep_inds[:len(pos_is_gts_)] = pos_keep
-
-            bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
-
-        return bboxes_list
-
-    @force_fp32(apply_to=('bbox_pred', ))
-    def regress_by_class(self, rois, label, bbox_pred, img_meta):
-        """Regress the bbox for the predicted class. Used in Cascade R-CNN.
-
-        Args:
-            rois (Tensor): shape (n, 4) or (n, 5)
-            label (Tensor): shape (n, )
-            bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \
-                (n, num_buckets *2)]
-            img_meta (dict): Image meta info.
-
-        Returns:
-            Tensor: Regressed bboxes, the same shape as input rois.
-        """
-        assert rois.size(1) == 4 or rois.size(1) == 5
-
-        if rois.size(1) == 4:
-            new_rois, _ = self.bbox_coder.decode(rois, bbox_pred,
-                                                 img_meta['img_shape'])
-        else:
-            bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
-                                               img_meta['img_shape'])
-            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
-
-        return new_rois
diff --git a/spaces/Carlosito16/HXM-summarization/app.py b/spaces/Carlosito16/HXM-summarization/app.py
deleted file mode 100644
index c11a841ec09531bd94c69ea8788fd9001ed0b288..0000000000000000000000000000000000000000
--- a/spaces/Carlosito16/HXM-summarization/app.py
+++ /dev/null
@@ -1,321 +0,0 @@
-print("Libraries installed succesfully!!!!")
-
-import torch
-from transformers import RobertaTokenizerFast, EncoderDecoderModel, AutoTokenizer, AutoModelForSeq2SeqLM
-import re
-import gradio as gr
-
-from helper_function import *
-
-import numpy as np
-from sklearn.feature_extraction.text import CountVectorizer
-
-import spacy
-from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise import cosine_similarity
-
-import requests
-from pathlib import Path
-
-print('Libraries called succesfully!!!!"')
-
-
-
-class keyWordExtractor():
-    
-    def __init__(self, 
-                 article_text,
-                 similarity_model,
-                 n_gram = 1,
-                 top_n = 3,
-                 french_stopwords = None,
-                 ner= None,
-                 ):
-        self.article_text = article_text
-        self.french_stopwords = french_stopwords
-        self.candidates = self.count_vectorizer(n_gram)
-        self.noun_candidates, self.proper_noun_candidates = self.slice_only_noun_token(ner, self.candidates)
-        self.top_n_keywords = self.top_n_extractor(similarity_model, top_n)
-    
-    def count_vectorizer(self, n_gram):
-        n_gram_range = (n_gram, n_gram)
-        # Extract candidate words/phrases
-        count = CountVectorizer(ngram_range=n_gram_range,
-                        stop_words = self.french_stopwords).fit([self.article_text]) #Main change
-        candidates = count.get_feature_names_out()
-
-        return candidates
-
-    def slice_only_noun_token(self, ner, token_list):
-        """
-        Given the tokenized list, this function returns only the "NOUN" token
-            Args:
-                ner (spacy): The NER class to detect the `token.pos_`
-                token_list (list): List of token from the full article
-            Returns:
-                slice_list (list): List of token containing only "NOUN" part of speech
-        """
-
-        noun_slice_list = []
-        proper_noun_slice_list = []
-        for word_idx in range(len(token_list)):
-            doc = ner(token_list[word_idx])
-
-            for token in doc:
-                if token.pos_ == 'NOUN':
-                    noun_slice_list.append(token.text)
-                elif token.pos_ == 'PROPN':
-                    proper_noun_slice_list.append(token.text)
-
-        return noun_slice_list, proper_noun_slice_list
-
-    def top_n_extractor(self, model, top_n):
-        doc_embedding = model.encode([self.article_text])
-        candidate_embeddings = model.encode(self.noun_candidates)
-        distances = cosine_similarity(doc_embedding, candidate_embeddings)
-        keywords = [self.noun_candidates[index] for index in distances.argsort()[0][-top_n:]]
-
-        return keywords
-
-def clear_input():
-    return ("", "")
-
-       
-def camembert_generate_summary(article_text):
-   inputs = cmb_tokenizer([article_text], padding="max_length", truncation=True,
-                          max_length=50,
-                          return_tensors="pt")
-   input_ids = inputs.input_ids.to(device)
-   attention_mask = inputs.attention_mask.to(device)
-   output = cmb_model.generate(input_ids, attention_mask=attention_mask, max_length = 50)
-   return cmb_tokenizer.decode(output[0], skip_special_tokens=True)
-
-    
-def t5_generate_summary(article_text):
-    input_ids = t5_tokenizer(
-    [WHITESPACE_HANDLER(article_text)],
-    return_tensors="pt",
-    padding="max_length",
-    truncation=True,
-    max_length=512)["input_ids"]
-    
-    output_ids = t5_model.generate(
-        input_ids=input_ids,
-        max_length=84,
-        no_repeat_ngram_size=2,
-        num_beams=4
-    )[0]
-    
-    output = t5_tokenizer.decode(
-    output_ids,
-    skip_special_tokens=True,
-    clean_up_tokenization_spaces=False
-    )
-
-    return output
-
-    
-def summarizer(dropdown_model, article_text):
-    """
-    Ruturs a summarized version from the full article based on the selected pretrained-model
-    """
-
-    if dropdown_model == 'camembert':
-        summary = camembert_generate_summary(article_text)
-
-    elif dropdown_model == 'T5':
-        summary = t5_generate_summary(article_text)
-
-    return summary
-    
-def extract_top_3(article):
-    nlp = spacy.load("fr_core_news_md")
-    # model = SentenceTransformer("dangvantuan/sentence-camembert-large") #
-
-    a= keyWordExtractor(article,
-                        n_gram = 1, 
-                        top_n = 3,
-                        ner = nlp,
-                        similarity_model = model)
-    keyword = ", ".join(a.top_n_keywords) #to return ['a' , 'b'] >> "a, b"
-    proper_nonuns = ", ".join(a.proper_noun_candidates)
-
-    return keyword, proper_nonuns
-
-
-def runall(dropdown_model, article_text):
-    summary = summarizer(dropdown_model, article_text)
-    keywords, proper_n = extract_top_3(article_text)
-
-    return summary, keywords, proper_n
-
-
-
-#set the device agnostics code
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-test_article ="""\"Un nuage de fumée juste après l’explosion, le 1er juin 2019. Une déflagration dans une importante usine d’explosifs du centre de la Russie a fait au moins 79 blessés samedi 1er juin. L’explosion a eu lieu dans l’usine Kristall à Dzerzhinsk, une ville située à environ 400 kilomètres à l’est de Moscou, dans la région de Nijni-Novgorod. « Il y a eu une explosion technique dans l’un des ateliers, suivie d’un incendie qui s’est propagé sur une centaine de mètres carrés », a expliqué un porte-parole des services d’urgence. Des images circulant sur les réseaux sociaux montraient un énorme nuage de fumée après l’explosion. Cinq bâtiments de l’usine et près de 180 bâtiments résidentiels ont été endommagés par l’explosion, selon les autorités municipales. Une enquête pour de potentielles violations des normes de sécurité a été ouverte. Fragments de shrapnel Les blessés ont été soignés après avoir été atteints par des fragments issus de l’explosion, a précisé une porte-parole des autorités sanitaires citée par Interfax. « Nous parlons de blessures par shrapnel d’une gravité moyenne et modérée », a-t-elle précisé. Selon des représentants de Kristall, cinq personnes travaillaient dans la zone où s’est produite l’explosion. Elles ont pu être évacuées en sécurité. Les pompiers locaux ont rapporté n’avoir aucune information sur des personnes qui se trouveraient encore dans l’usine."""
-
-# whitespace handler to be used in `t5 model`
-WHITESPACE_HANDLER = lambda k: re.sub('\s+', ' ', re.sub('\n+', ' ', k.strip()))
-
-article_text = """\"Un nuage de fumée juste après l’explosion, le 1er juin 2019. Une déflagration dans une importante usine d’explosifs du centre de la Russie a fait au moins 79 blessés samedi 1er juin. L’explosion a eu lieu dans l’usine Kristall à Dzerzhinsk, une ville située à environ 400 kilomètres à l’est de Moscou, dans la région de Nijni-Novgorod. « Il y a eu une explosion technique dans l’un des ateliers, suivie d’un incendie qui s’est propagé sur une centaine de mètres carrés », a expliqué un porte-parole des services d’urgence. Des images circulant sur les réseaux sociaux montraient un énorme nuage de fumée après l’explosion. Cinq bâtiments de l’usine et près de 180 bâtiments résidentiels ont été endommagés par l’explosion, selon les autorités municipales. Une enquête pour de potentielles violations des normes de sécurité a été ouverte. Fragments de shrapnel Les blessés ont été soignés après avoir été atteints par des fragments issus de l’explosion, a précisé une porte-parole des autorités sanitaires citée par Interfax. « Nous parlons de blessures par shrapnel d’une gravité moyenne et modérée », a-t-elle précisé. Selon des représentants de Kristall, cinq personnes travaillaient dans la zone où s’est produite l’explosion. Elles ont pu être évacuées en sécurité. Les pompiers locaux ont rapporté n’avoir aucune information sur des personnes qui se trouveraient encore dans l’usine."""
-
-
-cmb_ckpt = 'mrm8488/camembert2camembert_shared-finetuned-french-summarization'
-cmb_tokenizer = RobertaTokenizerFast.from_pretrained(cmb_ckpt)
-cmb_model = EncoderDecoderModel.from_pretrained(cmb_ckpt).to(device)
-
-
-t5_model_name = "csebuetnlp/mT5_multilingual_XLSum"
-t5_tokenizer = AutoTokenizer.from_pretrained(t5_model_name)
-t5_model = AutoModelForSeq2SeqLM.from_pretrained(t5_model_name)
-
-print(summarizer("camembert", test_article))
-
-
-###
-import requests
-from pathlib import Path
-
-if Path('french_stopword.txt').is_file():
-    print('already exists')
-else:
-    print('not existed yet')
-    request = requests.get("https://raw.githubusercontent.com/stopwords-iso/stopwords-fr/master/stopwords-fr.txt")
-    with open('french_stopword.txt', "wb") as f:
-        f.write(request.content)
-
-
-# opening the file in read mode
-my_file = open("french_stopword.txt", "r")
-  
-# reading the file
-data = my_file.read()
-  
-# replacing end of line('/n') with ' ' and
-# splitting the text it further when '.' is seen.
-french_stopwords_list = data.replace('\n', ' ').split(" ")
-  
-# # printing the data
-print(french_stopwords_list)
-my_file.close()
-
-###
-
-nlp = spacy.load("fr_core_news_md")
-model =  SentenceTransformer("dangvantuan/sentence-camembert-large")
-
-print(extract_top_3(test_article))
-
-some_examples = [
-
-        ["camembert", """
-    Carrefour livraison service honteux ! Ne livre jamais dans les temps, plusieurs heures de retard et si on a le malheur de travailler et de ne pas être chez nous toute la journée on peut dire au revoir à notre commande !
-Service client délocalisé qui insulte et raccroche au nez quand on demande ou est notre commande !
-Je ne recommande absolument pas, j'ai pourtant laisser plusieurs chance mais à chaque fois extrêmement déçu...
-    """],
-    ["camembert", """
-    Très mauvaise expérience !!!
-Commande livré par terre sans rien dire alors que nous étions présents.
-En plein centre ville de Dijon.
-Merci pour l’hygiène et la compétence du livreur. 
-Sans parler des surgelés en plein soleil qui ont finis à la poubelle.
-Merci Carrefour Quetigny.
-    """]
-    
-]
-
-
-
-###
-
-
-
-####
-
-with gr.Blocks() as demo:
-    gr.Markdown("Summarize the article text.")
-
-    with gr.Tab("Summarization + Extraction"):
-        with gr.Row():
-            with gr.Column():
-                input_models = gr.Dropdown(['camembert'], value = 'camembert')
-                input_article = gr.TextArea(label = 'Article to be summarized')
-
-            with gr.Column():
-                summarized_output = gr.TextArea(label= 'Generated summary')
-                output_keyword = gr.Textbox(label="Top 3 keywords")
-                output_proper_nouns = gr.Textbox(label="Proper Nouns")
-
-        with gr.Row():
-            clear_button = gr.Button("Clear")
-            summarize_button = gr.Button("Summarize!")
-            extract_button = gr.Button("Extract!")
-            run_button = gr.Button("Run all!")
-
-        examples = gr.Examples(examples= some_examples,
-                                     inputs=[input_models, input_article])
-
-
-        summarize_button.click(summarizer,
-                            inputs = [input_models, input_article] ,
-                            outputs = summarized_output)
-        
-
-        extract_button.click(extract_top_3,
-                            inputs = input_article ,
-                            outputs = [output_keyword, output_proper_nouns])
-        
-        run_button.click(runall,
-                         inputs = [input_models, input_article],
-                         outputs = [summarized_output, output_keyword, output_proper_nouns ])
-        
-        clear_button.click(clear_input,
-                        outputs = [input_models, input_article])
-
-    with gr.Tab("Summarization"):
-        with gr.Row():
-            with gr.Column():
-                input_models = gr.Dropdown(['camembert', 'T5'])
-                input_article = gr.TextArea(label = 'Article to be summarized')
-            with gr.Column():
-                summarized_output = gr.TextArea(label= 'Generated summa')
-        with gr.Row():
-
-            clear_button = gr.Button("Clear")
-            summarize_button = gr.Button("Summarize!")
-            
-        summarize_button.click(summarizer,
-                            inputs = [input_models, input_article] ,
-                            outputs = summarized_output)
-        
-        clear_button.click(clear_input,
-                        outputs = [input_models, input_article])
-    
-        example = "Un nuage de fumée juste après l’explosion, le 1er juin 2019. Une déflagration dans une importante usine d’explosifs du centre de la Russie a fait au moins 79 blessés samedi 1er juin. L’explosion a eu lieu dans l’usine Kristall à Dzerzhinsk, une ville située à environ 400 kilomètres à l’est de Moscou, dans la région de Nijni-Novgorod. « Il y a eu une explosion technique dans l’un des ateliers, suivie d’un incendie qui s’est propagé sur une centaine de mètres carrés », a expliqué un porte-parole des services d’urgence. Des images circulant sur les réseaux sociaux montraient un énorme nuage de fumée après l’explosion. Cinq bâtiments de l’usine et près de 180 bâtiments résidentiels ont été endommagés par l’explosion, selon les autorités municipales. Une enquête pour de potentielles violations des normes de sécurité a été ouverte. Fragments de shrapnel Les blessés ont été soignés après avoir été atteints par des fragments issus de l’explosion, a précisé une porte-parole des autorités sanitaires citée par Interfax. « Nous parlons de blessures par shrapnel d’une gravité moyenne et modérée », a-t-elle précisé. Selon des représentants de Kristall, cinq personnes travaillaient dans la zone où s’est produite l’explosion. Elles ont pu être évacuées en sécurité. Les pompiers locaux ont rapporté n’avoir aucune information sur des personnes qui se trouveraient encore dans l’usine."
-        examples = gr.Examples(examples=[ ["camembert",example],
-                                        ["T5",example]],
-                                        inputs=[input_models, input_article])
-        
-    with gr.Tab("Key word extraction"):
-        
-
-        with gr.Row():
-            with gr.Column():
-                input_article = gr.TextArea(label = 'Article to be extracted')
-            with gr.Column():
-                output_1 = gr.Textbox(label="Top 3 keywords")
-                output_2 = gr.Textbox(label="Proper Nouns")
-
-        with gr.Row():
-
-            clear_button = gr.Button("Clear")
-            extract_button = gr.Button("Extract!")
-
-        extract_button.click(extract_top_3,
-                            inputs = input_article ,
-                            outputs = [output_1, output_2])
-
-
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/ChandraMohanNayal/AutoGPT/tests/smoke_test.py b/spaces/ChandraMohanNayal/AutoGPT/tests/smoke_test.py
deleted file mode 100644
index 1b9d643fc21f3703384a2bb4f2bd1d725f4dd418..0000000000000000000000000000000000000000
--- a/spaces/ChandraMohanNayal/AutoGPT/tests/smoke_test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-"""Smoke test for the autogpt package."""
-import os
-import subprocess
-import sys
-
-import pytest
-
-from autogpt.commands.file_operations import delete_file, read_file
-
-
-@pytest.mark.integration_test
-def test_write_file() -> None:
-    """
-    Test case to check if the write_file command can successfully write 'Hello World' to a file
-    named 'hello_world.txt'.
-
-    Read the current ai_settings.yaml file and store its content.
-    """
-    env_vars = {"MEMORY_BACKEND": "no_memory", "TEMPERATURE": "0"}
-    ai_settings = None
-    if os.path.exists("ai_settings.yaml"):
-        with open("ai_settings.yaml", "r") as f:
-            ai_settings = f.read()
-        os.remove("ai_settings.yaml")
-
-    try:
-        if os.path.exists("hello_world.txt"):
-            # Clean up any existing 'hello_world.txt' file before testing.
-            delete_file("hello_world.txt")
-        # Prepare input data for the test.
-        input_data = """write_file-GPT
-an AI designed to use the write_file command to write 'Hello World' into a file named "hello_world.txt" and then use the task_complete command to complete the task.
-Use the write_file command to write 'Hello World' into a file named "hello_world.txt".
-Use the task_complete command to complete the task.
-Do not use any other commands.
-
-y -5
-EOF"""
-        command = f"{sys.executable} -m autogpt"
-
-        # Execute the script with the input data.
-        process = subprocess.Popen(
-            command,
-            stdin=subprocess.PIPE,
-            shell=True,
-            env={**os.environ, **env_vars},
-        )
-        process.communicate(input_data.encode())
-
-        # Read the content of the 'hello_world.txt' file created during the test.
-        content = read_file("hello_world.txt")
-    finally:
-        if ai_settings:
-            # Restore the original ai_settings.yaml file.
-            with open("ai_settings.yaml", "w") as f:
-                f.write(ai_settings)
-
-    # Check if the content of the 'hello_world.txt' file is equal to 'Hello World'.
-    assert content == "Hello World", f"Expected 'Hello World', got {content}"
diff --git a/spaces/CikeyQI/meme-api/meme_generator/memes/distracted/__init__.py b/spaces/CikeyQI/meme-api/meme_generator/memes/distracted/__init__.py
deleted file mode 100644
index eb52ad5156ca1277639a3bd27c14960662605635..0000000000000000000000000000000000000000
--- a/spaces/CikeyQI/meme-api/meme_generator/memes/distracted/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from pathlib import Path
-from typing import List
-
-from pil_utils import BuildImage
-
-from meme_generator import add_meme
-from meme_generator.utils import make_jpg_or_gif
-
-img_dir = Path(__file__).parent / "images"
-
-
-def distracted(images: List[BuildImage], texts, args):
-    frame = BuildImage.open(img_dir / "1.png")
-    label = BuildImage.open(img_dir / "0.png")
-
-    def make(img: BuildImage) -> BuildImage:
-        img = img.convert("RGBA").square().resize((500, 500))
-        return frame.copy().paste(img, below=True).paste(label, (140, 320), alpha=True)
-
-    return make_jpg_or_gif(images[0], make)
-
-
-add_meme("distracted", distracted, min_images=1, max_images=1, keywords=["注意力涣散"])
diff --git a/spaces/Cyril666/ContourNet-ABI/modules/backbone.py b/spaces/Cyril666/ContourNet-ABI/modules/backbone.py
deleted file mode 100644
index 434cc06473c58c9ba9e4b314f25d2e7ca837f944..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/ContourNet-ABI/modules/backbone.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import torch
-import torch.nn as nn
-from fastai.vision import *
-
-from modules.model import _default_tfmer_cfg
-from modules.resnet import resnet45
-from modules.transformer import (PositionalEncoding,
-                                 TransformerEncoder,
-                                 TransformerEncoderLayer)
-
-
-class ResTranformer(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.resnet = resnet45()
-
-        self.d_model = ifnone(config.model_vision_d_model, _default_tfmer_cfg['d_model'])
-        nhead = ifnone(config.model_vision_nhead, _default_tfmer_cfg['nhead'])
-        d_inner = ifnone(config.model_vision_d_inner, _default_tfmer_cfg['d_inner'])
-        dropout = ifnone(config.model_vision_dropout, _default_tfmer_cfg['dropout'])
-        activation = ifnone(config.model_vision_activation, _default_tfmer_cfg['activation'])
-        num_layers = ifnone(config.model_vision_backbone_ln, 2)
-
-        self.pos_encoder = PositionalEncoding(self.d_model, max_len=8*32)
-        encoder_layer = TransformerEncoderLayer(d_model=self.d_model, nhead=nhead, 
-                dim_feedforward=d_inner, dropout=dropout, activation=activation)
-        self.transformer = TransformerEncoder(encoder_layer, num_layers)
-
-    def forward(self, images):
-        feature = self.resnet(images)
-        n, c, h, w = feature.shape
-        feature = feature.view(n, c, -1).permute(2, 0, 1)
-        feature = self.pos_encoder(feature)
-        feature = self.transformer(feature)
-        feature = feature.permute(1, 2, 0).view(n, c, h, w)
-        return feature
diff --git a/spaces/Cyril666/my_abi/main.py b/spaces/Cyril666/my_abi/main.py
deleted file mode 100644
index def41731ed4cbe77051e496caf2b2d37dd95611f..0000000000000000000000000000000000000000
--- a/spaces/Cyril666/my_abi/main.py
+++ /dev/null
@@ -1,246 +0,0 @@
-import argparse
-import logging
-import os
-import random
-
-import torch
-from fastai.callbacks.general_sched import GeneralScheduler, TrainingPhase
-from fastai.distributed import *
-from fastai.vision import *
-from torch.backends import cudnn
-
-from callbacks import DumpPrediction, IterationCallback, TextAccuracy, TopKTextAccuracy
-from dataset import ImageDataset, TextDataset
-from losses import MultiLosses
-from utils import Config, Logger, MyDataParallel, MyConcatDataset
-
-
-def _set_random_seed(seed):
-    if seed is not None:
-        random.seed(seed)
-        torch.manual_seed(seed)
-        cudnn.deterministic = True
-        logging.warning('You have chosen to seed training. '
-                        'This will slow down your training!')
-
-def _get_training_phases(config, n):
-    lr = np.array(config.optimizer_lr)
-    periods = config.optimizer_scheduler_periods
-    sigma = [config.optimizer_scheduler_gamma ** i for i in range(len(periods))]
-    phases = [TrainingPhase(n * periods[i]).schedule_hp('lr', lr * sigma[i])
-                for i in range(len(periods))]
-    return phases
-
-def _get_dataset(ds_type, paths, is_training, config, **kwargs):
-    kwargs.update({
-        'img_h': config.dataset_image_height,
-        'img_w': config.dataset_image_width,
-        'max_length': config.dataset_max_length,
-        'case_sensitive': config.dataset_case_sensitive,
-        'charset_path': config.dataset_charset_path,
-        'data_aug': config.dataset_data_aug,
-        'deteriorate_ratio': config.dataset_deteriorate_ratio,
-        'is_training': is_training,
-        'multiscales': config.dataset_multiscales,
-        'one_hot_y': config.dataset_one_hot_y,
-    })
-    datasets = [ds_type(p, **kwargs) for p in paths]
-    if len(datasets) > 1: return MyConcatDataset(datasets)
-    else: return datasets[0]
-
-
-def _get_language_databaunch(config):
-    kwargs = {
-        'max_length': config.dataset_max_length,
-        'case_sensitive': config.dataset_case_sensitive,
-        'charset_path': config.dataset_charset_path,
-        'smooth_label': config.dataset_smooth_label,
-        'smooth_factor': config.dataset_smooth_factor,
-        'one_hot_y': config.dataset_one_hot_y,
-        'use_sm': config.dataset_use_sm,
-    }
-    train_ds = TextDataset(config.dataset_train_roots[0], is_training=True, **kwargs)
-    valid_ds = TextDataset(config.dataset_test_roots[0], is_training=False, **kwargs)
-    data = DataBunch.create(
-        path=train_ds.path,
-        train_ds=train_ds,
-        valid_ds=valid_ds,
-        bs=config.dataset_train_batch_size,
-        val_bs=config.dataset_test_batch_size,
-        num_workers=config.dataset_num_workers,
-        pin_memory=config.dataset_pin_memory)
-    logging.info(f'{len(data.train_ds)} training items found.')
-    if not data.empty_val:
-        logging.info(f'{len(data.valid_ds)} valid items found.')
-    return data
-
-def _get_databaunch(config):
-    # An awkward way to reduce loadding data time during test
-    if config.global_phase == 'test': config.dataset_train_roots = config.dataset_test_roots
-    train_ds = _get_dataset(ImageDataset, config.dataset_train_roots, True, config)
-    valid_ds = _get_dataset(ImageDataset, config.dataset_test_roots, False, config)
-    data = ImageDataBunch.create(
-        train_ds=train_ds,
-        valid_ds=valid_ds,
-        bs=config.dataset_train_batch_size,
-        val_bs=config.dataset_test_batch_size,
-        num_workers=config.dataset_num_workers,
-        pin_memory=config.dataset_pin_memory).normalize(imagenet_stats)
-    ar_tfm = lambda x: ((x[0], x[1]), x[1])  # auto-regression only for dtd
-    data.add_tfm(ar_tfm)
-
-    logging.info(f'{len(data.train_ds)} training items found.')
-    if not data.empty_val:
-        logging.info(f'{len(data.valid_ds)} valid items found.')
-    
-    return data
-
-def _get_model(config):
-    import importlib
-    names = config.model_name.split('.')
-    module_name, class_name = '.'.join(names[:-1]), names[-1]
-    cls = getattr(importlib.import_module(module_name), class_name)
-    model = cls(config)
-    logging.info(model)
-    return model
-
-
-def _get_learner(config, data, model, local_rank=None):
-    strict = ifnone(config.model_strict, True)
-    if config.global_stage == 'pretrain-language':
-        metrics = [TopKTextAccuracy(
-            k=ifnone(config.model_k, 5),
-            charset_path=config.dataset_charset_path,
-            max_length=config.dataset_max_length + 1,
-            case_sensitive=config.dataset_eval_case_sensisitves,
-            model_eval=config.model_eval)] 
-    else:
-        metrics = [TextAccuracy(
-            charset_path=config.dataset_charset_path,
-            max_length=config.dataset_max_length + 1,
-            case_sensitive=config.dataset_eval_case_sensisitves,
-            model_eval=config.model_eval)]
-    opt_type = getattr(torch.optim, config.optimizer_type)
-    learner = Learner(data, model, silent=True, model_dir='.',
-        true_wd=config.optimizer_true_wd, 
-        wd=config.optimizer_wd,
-        bn_wd=config.optimizer_bn_wd,
-        path=config.global_workdir,
-        metrics=metrics,
-        opt_func=partial(opt_type, **config.optimizer_args or dict()), 
-        loss_func=MultiLosses(one_hot=config.dataset_one_hot_y))
-    learner.split(lambda m: children(m))
-
-    if config.global_phase == 'train':
-        num_replicas = 1 if local_rank is None else torch.distributed.get_world_size()
-        phases = _get_training_phases(config, len(learner.data.train_dl)//num_replicas)
-        learner.callback_fns += [
-            partial(GeneralScheduler, phases=phases),
-            partial(GradientClipping, clip=config.optimizer_clip_grad),
-            partial(IterationCallback, name=config.global_name,
-                    show_iters=config.training_show_iters,
-                    eval_iters=config.training_eval_iters,
-                    save_iters=config.training_save_iters,
-                    start_iters=config.training_start_iters,
-                    stats_iters=config.training_stats_iters)]
-    else:
-        learner.callbacks += [
-            DumpPrediction(learn=learner,
-                    dataset='-'.join([Path(p).name for p in config.dataset_test_roots]),charset_path=config.dataset_charset_path,
-                    model_eval=config.model_eval,
-                    debug=config.global_debug,
-                    image_only=config.global_image_only)]
-
-    learner.rank = local_rank
-    if local_rank is not None:
-        logging.info(f'Set model to distributed with rank {local_rank}.')
-        learner.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(learner.model)
-        learner.model.to(local_rank)
-        learner = learner.to_distributed(local_rank)
-
-    if torch.cuda.device_count() > 1 and local_rank is None:
-        logging.info(f'Use {torch.cuda.device_count()} GPUs.')
-        learner.model = MyDataParallel(learner.model)
-
-    if config.model_checkpoint:
-        if Path(config.model_checkpoint).exists():
-            with open(config.model_checkpoint, 'rb') as f:
-                buffer = io.BytesIO(f.read())
-            learner.load(buffer, strict=strict)
-        else:
-            from distutils.dir_util import copy_tree
-            src = Path('/data/fangsc/model')/config.global_name
-            trg = Path('/output')/config.global_name
-            if src.exists(): copy_tree(str(src), str(trg))
-            learner.load(config.model_checkpoint, strict=strict)
-        logging.info(f'Read model from {config.model_checkpoint}')
-    elif config.global_phase == 'test':
-        learner.load(f'best-{config.global_name}', strict=strict)
-        logging.info(f'Read model from best-{config.global_name}')
-
-    if learner.opt_func.func.__name__ == 'Adadelta':    # fastai bug, fix after 1.0.60
-        learner.fit(epochs=0, lr=config.optimizer_lr)
-        learner.opt.mom = 0.
-
-    return learner
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', type=str, required=True,
-                        help='path to config file')
-    parser.add_argument('--phase', type=str, default=None, choices=['train', 'test'])
-    parser.add_argument('--name', type=str, default=None)
-    parser.add_argument('--checkpoint', type=str, default=None)
-    parser.add_argument('--test_root', type=str, default=None)
-    parser.add_argument("--local_rank", type=int, default=None)
-    parser.add_argument('--debug', action='store_true', default=None)
-    parser.add_argument('--image_only', action='store_true', default=None)
-    parser.add_argument('--model_strict', action='store_false', default=None)
-    parser.add_argument('--model_eval', type=str, default=None, 
-                        choices=['alignment', 'vision', 'language'])
-    args = parser.parse_args()
-    config = Config(args.config)
-    if args.name is not None: config.global_name = args.name
-    if args.phase is not None: config.global_phase = args.phase
-    if args.test_root is not None: config.dataset_test_roots = [args.test_root]
-    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
-    if args.debug is not None: config.global_debug = args.debug
-    if args.image_only is not None: config.global_image_only = args.image_only
-    if args.model_eval is not None: config.model_eval = args.model_eval
-    if args.model_strict is not None: config.model_strict = args.model_strict
-
-    Logger.init(config.global_workdir, config.global_name, config.global_phase)
-    Logger.enable_file()
-    _set_random_seed(config.global_seed)
-    logging.info(config)
-
-    if args.local_rank is not None:
-        logging.info(f'Init distribution training at device {args.local_rank}.')
-        torch.cuda.set_device(args.local_rank)
-        torch.distributed.init_process_group(backend='nccl', init_method='env://')
-
-    logging.info('Construct dataset.')
-    if config.global_stage == 'pretrain-language': data = _get_language_databaunch(config)
-    else: data = _get_databaunch(config)
-
-    logging.info('Construct model.')
-    model = _get_model(config)
-
-    logging.info('Construct learner.')
-    learner = _get_learner(config, data, model, args.local_rank)
-
-    if config.global_phase == 'train':
-        logging.info('Start training.')
-        learner.fit(epochs=config.training_epochs,
-                    lr=config.optimizer_lr)
-    else:
-        logging.info('Start validate')
-        last_metrics = learner.validate()
-        log_str = f'eval loss = {last_metrics[0]:6.3f},  ' \
-                  f'ccr = {last_metrics[1]:6.3f},  cwr = {last_metrics[2]:6.3f},  ' \
-                  f'ted = {last_metrics[3]:6.3f},  ned = {last_metrics[4]:6.0f},  ' \
-                  f'ted/w = {last_metrics[5]:6.3f}, '
-        logging.info(log_str)
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BdfFontFile.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BdfFontFile.py
deleted file mode 100644
index 075d462907abcace9610a686052e643582602a8f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/PIL/BdfFontFile.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# bitmap distribution font (bdf) file parser
-#
-# history:
-# 1996-05-16 fl   created (as bdf2pil)
-# 1997-08-25 fl   converted to FontFile driver
-# 2001-05-25 fl   removed bogus __init__ call
-# 2002-11-20 fl   robustification (from Kevin Cazabon, Dmitry Vasiliev)
-# 2003-04-22 fl   more robustification (from Graham Dumpleton)
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1997-2003 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-"""
-Parse X Bitmap Distribution Format (BDF)
-"""
-
-
-from . import FontFile, Image
-
-bdf_slant = {
-    "R": "Roman",
-    "I": "Italic",
-    "O": "Oblique",
-    "RI": "Reverse Italic",
-    "RO": "Reverse Oblique",
-    "OT": "Other",
-}
-
-bdf_spacing = {"P": "Proportional", "M": "Monospaced", "C": "Cell"}
-
-
-def bdf_char(f):
-    # skip to STARTCHAR
-    while True:
-        s = f.readline()
-        if not s:
-            return None
-        if s[:9] == b"STARTCHAR":
-            break
-    id = s[9:].strip().decode("ascii")
-
-    # load symbol properties
-    props = {}
-    while True:
-        s = f.readline()
-        if not s or s[:6] == b"BITMAP":
-            break
-        i = s.find(b" ")
-        props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
-
-    # load bitmap
-    bitmap = []
-    while True:
-        s = f.readline()
-        if not s or s[:7] == b"ENDCHAR":
-            break
-        bitmap.append(s[:-1])
-    bitmap = b"".join(bitmap)
-
-    # The word BBX
-    # followed by the width in x (BBw), height in y (BBh),
-    # and x and y displacement (BBxoff0, BByoff0)
-    # of the lower left corner from the origin of the character.
-    width, height, x_disp, y_disp = [int(p) for p in props["BBX"].split()]
-
-    # The word DWIDTH
-    # followed by the width in x and y of the character in device pixels.
-    dwx, dwy = [int(p) for p in props["DWIDTH"].split()]
-
-    bbox = (
-        (dwx, dwy),
-        (x_disp, -y_disp - height, width + x_disp, -y_disp),
-        (0, 0, width, height),
-    )
-
-    try:
-        im = Image.frombytes("1", (width, height), bitmap, "hex", "1")
-    except ValueError:
-        # deal with zero-width characters
-        im = Image.new("1", (width, height))
-
-    return id, int(props["ENCODING"]), bbox, im
-
-
-class BdfFontFile(FontFile.FontFile):
-    """Font file plugin for the X11 BDF format."""
-
-    def __init__(self, fp):
-        super().__init__()
-
-        s = fp.readline()
-        if s[:13] != b"STARTFONT 2.1":
-            msg = "not a valid BDF file"
-            raise SyntaxError(msg)
-
-        props = {}
-        comments = []
-
-        while True:
-            s = fp.readline()
-            if not s or s[:13] == b"ENDPROPERTIES":
-                break
-            i = s.find(b" ")
-            props[s[:i].decode("ascii")] = s[i + 1 : -1].decode("ascii")
-            if s[:i] in [b"COMMENT", b"COPYRIGHT"]:
-                if s.find(b"LogicalFontDescription") < 0:
-                    comments.append(s[i + 1 : -1].decode("ascii"))
-
-        while True:
-            c = bdf_char(fp)
-            if not c:
-                break
-            id, ch, (xy, dst, src), im = c
-            if 0 <= ch < len(self.glyph):
-                self.glyph[ch] = xy, dst, src, im
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/__init__.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/__init__.py
deleted file mode 100644
index 620296d5ad6ca2cc49eb5d0dc140bcbc3204e9b4..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fastapi/middleware/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.middleware import Middleware as Middleware
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/symfont.py b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/symfont.py
deleted file mode 100644
index 0bd69a386ec9f01c8951f0dfc8bc8c261718cf1f..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/fontTools/misc/symfont.py
+++ /dev/null
@@ -1,251 +0,0 @@
-from fontTools.pens.basePen import BasePen
-from functools import partial
-from itertools import count
-import sympy as sp
-import sys
-
-n = 3  # Max Bezier degree; 3 for cubic, 2 for quadratic
-
-t, x, y = sp.symbols("t x y", real=True)
-c = sp.symbols("c", real=False)  # Complex representation instead of x/y
-
-X = tuple(sp.symbols("x:%d" % (n + 1), real=True))
-Y = tuple(sp.symbols("y:%d" % (n + 1), real=True))
-P = tuple(zip(*(sp.symbols("p:%d[%s]" % (n + 1, w), real=True) for w in "01")))
-C = tuple(sp.symbols("c:%d" % (n + 1), real=False))
-
-# Cubic Bernstein basis functions
-BinomialCoefficient = [(1, 0)]
-for i in range(1, n + 1):
-    last = BinomialCoefficient[-1]
-    this = tuple(last[j - 1] + last[j] for j in range(len(last))) + (0,)
-    BinomialCoefficient.append(this)
-BinomialCoefficient = tuple(tuple(item[:-1]) for item in BinomialCoefficient)
-del last, this
-
-BernsteinPolynomial = tuple(
-    tuple(c * t**i * (1 - t) ** (n - i) for i, c in enumerate(coeffs))
-    for n, coeffs in enumerate(BinomialCoefficient)
-)
-
-BezierCurve = tuple(
-    tuple(
-        sum(P[i][j] * bernstein for i, bernstein in enumerate(bernsteins))
-        for j in range(2)
-    )
-    for n, bernsteins in enumerate(BernsteinPolynomial)
-)
-BezierCurveC = tuple(
-    sum(C[i] * bernstein for i, bernstein in enumerate(bernsteins))
-    for n, bernsteins in enumerate(BernsteinPolynomial)
-)
-
-
-def green(f, curveXY):
-    f = -sp.integrate(sp.sympify(f), y)
-    f = f.subs({x: curveXY[0], y: curveXY[1]})
-    f = sp.integrate(f * sp.diff(curveXY[0], t), (t, 0, 1))
-    return f
-
-
-class _BezierFuncsLazy(dict):
-    def __init__(self, symfunc):
-        self._symfunc = symfunc
-        self._bezfuncs = {}
-
-    def __missing__(self, i):
-        args = ["p%d" % d for d in range(i + 1)]
-        f = green(self._symfunc, BezierCurve[i])
-        f = sp.gcd_terms(f.collect(sum(P, ())))  # Optimize
-        return sp.lambdify(args, f)
-
-
-class GreenPen(BasePen):
-
-    _BezierFuncs = {}
-
-    @classmethod
-    def _getGreenBezierFuncs(celf, func):
-        funcstr = str(func)
-        if not funcstr in celf._BezierFuncs:
-            celf._BezierFuncs[funcstr] = _BezierFuncsLazy(func)
-        return celf._BezierFuncs[funcstr]
-
-    def __init__(self, func, glyphset=None):
-        BasePen.__init__(self, glyphset)
-        self._funcs = self._getGreenBezierFuncs(func)
-        self.value = 0
-
-    def _moveTo(self, p0):
-        self.__startPoint = p0
-
-    def _closePath(self):
-        p0 = self._getCurrentPoint()
-        if p0 != self.__startPoint:
-            self._lineTo(self.__startPoint)
-
-    def _endPath(self):
-        p0 = self._getCurrentPoint()
-        if p0 != self.__startPoint:
-            # Green theorem is not defined on open contours.
-            raise NotImplementedError
-
-    def _lineTo(self, p1):
-        p0 = self._getCurrentPoint()
-        self.value += self._funcs[1](p0, p1)
-
-    def _qCurveToOne(self, p1, p2):
-        p0 = self._getCurrentPoint()
-        self.value += self._funcs[2](p0, p1, p2)
-
-    def _curveToOne(self, p1, p2, p3):
-        p0 = self._getCurrentPoint()
-        self.value += self._funcs[3](p0, p1, p2, p3)
-
-
-# Sample pens.
-# Do not use this in real code.
-# Use fontTools.pens.momentsPen.MomentsPen instead.
-AreaPen = partial(GreenPen, func=1)
-MomentXPen = partial(GreenPen, func=x)
-MomentYPen = partial(GreenPen, func=y)
-MomentXXPen = partial(GreenPen, func=x * x)
-MomentYYPen = partial(GreenPen, func=y * y)
-MomentXYPen = partial(GreenPen, func=x * y)
-
-
-def printGreenPen(penName, funcs, file=sys.stdout, docstring=None):
-
-    if docstring is not None:
-        print('"""%s"""' % docstring)
-
-    print(
-        """from fontTools.pens.basePen import BasePen, OpenContourError
-try:
-	import cython
-
-	COMPILED = cython.compiled
-except (AttributeError, ImportError):
-	# if cython not installed, use mock module with no-op decorators and types
-	from fontTools.misc import cython
-
-	COMPILED = False
-
-
-__all__ = ["%s"]
-
-class %s(BasePen):
-
-	def __init__(self, glyphset=None):
-		BasePen.__init__(self, glyphset)
-"""
-        % (penName, penName),
-        file=file,
-    )
-    for name, f in funcs:
-        print("		self.%s = 0" % name, file=file)
-    print(
-        """
-	def _moveTo(self, p0):
-		self.__startPoint = p0
-
-	def _closePath(self):
-		p0 = self._getCurrentPoint()
-		if p0 != self.__startPoint:
-			self._lineTo(self.__startPoint)
-
-	def _endPath(self):
-		p0 = self._getCurrentPoint()
-		if p0 != self.__startPoint:
-			# Green theorem is not defined on open contours.
-			raise OpenContourError(
-							"Green theorem is not defined on open contours."
-			)
-""",
-        end="",
-        file=file,
-    )
-
-    for n in (1, 2, 3):
-
-        subs = {P[i][j]: [X, Y][j][i] for i in range(n + 1) for j in range(2)}
-        greens = [green(f, BezierCurve[n]) for name, f in funcs]
-        greens = [sp.gcd_terms(f.collect(sum(P, ()))) for f in greens]  # Optimize
-        greens = [f.subs(subs) for f in greens]  # Convert to p to x/y
-        defs, exprs = sp.cse(
-            greens,
-            optimizations="basic",
-            symbols=(sp.Symbol("r%d" % i) for i in count()),
-        )
-
-        print()
-        for name, value in defs:
-            print("	@cython.locals(%s=cython.double)" % name, file=file)
-        if n == 1:
-            print(
-                """\
-	@cython.locals(x0=cython.double, y0=cython.double)
-	@cython.locals(x1=cython.double, y1=cython.double)
-	def _lineTo(self, p1):
-		x0,y0 = self._getCurrentPoint()
-		x1,y1 = p1
-""",
-                file=file,
-            )
-        elif n == 2:
-            print(
-                """\
-	@cython.locals(x0=cython.double, y0=cython.double)
-	@cython.locals(x1=cython.double, y1=cython.double)
-	@cython.locals(x2=cython.double, y2=cython.double)
-	def _qCurveToOne(self, p1, p2):
-		x0,y0 = self._getCurrentPoint()
-		x1,y1 = p1
-		x2,y2 = p2
-""",
-                file=file,
-            )
-        elif n == 3:
-            print(
-                """\
-	@cython.locals(x0=cython.double, y0=cython.double)
-	@cython.locals(x1=cython.double, y1=cython.double)
-	@cython.locals(x2=cython.double, y2=cython.double)
-	@cython.locals(x3=cython.double, y3=cython.double)
-	def _curveToOne(self, p1, p2, p3):
-		x0,y0 = self._getCurrentPoint()
-		x1,y1 = p1
-		x2,y2 = p2
-		x3,y3 = p3
-""",
-                file=file,
-            )
-        for name, value in defs:
-            print("		%s = %s" % (name, value), file=file)
-
-        print(file=file)
-        for name, value in zip([f[0] for f in funcs], exprs):
-            print("		self.%s += %s" % (name, value), file=file)
-
-    print(
-        """
-if __name__ == '__main__':
-	from fontTools.misc.symfont import x, y, printGreenPen
-	printGreenPen('%s', ["""
-        % penName,
-        file=file,
-    )
-    for name, f in funcs:
-        print("		      ('%s', %s)," % (name, str(f)), file=file)
-    print("		     ])", file=file)
-
-
-if __name__ == "__main__":
-    pen = AreaPen()
-    pen.moveTo((100, 100))
-    pen.lineTo((100, 200))
-    pen.lineTo((200, 200))
-    pen.curveTo((200, 250), (300, 300), (250, 350))
-    pen.lineTo((200, 100))
-    pen.closePath()
-    print(pen.value)
diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-942e8f2b.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-942e8f2b.js
deleted file mode 100644
index 7a18ad0f5b9632a39d44e2dcdbdb0cd003a2697e..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-942e8f2b.js
+++ /dev/null
@@ -1,7 +0,0 @@
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i&&l(1,n=i.type),"selected"in i&&l(2,a=i.selected)},[t,n,a]}class xe extends T{constructor(e){super(),q(this,e,Ye,Xe,L,{value:0,type:1,selected:2})}}function $e(s){let e,l;return{c(){e=y("div"),l=A(s[0]),h(e,"class","svelte-1ayixqk"),u(e,"table",s[1]==="table"),u(e,"gallery",s[1]==="gallery"),u(e,"selected",s[2])},m(t,n){b(t,e,n),M(e,l)},p(t,[n]){n&1&&N(l,t[0]),n&2&&u(e,"table",t[1]==="table"),n&2&&u(e,"gallery",t[1]==="gallery"),n&4&&u(e,"selected",t[2])},i:k,o:k,d(t){t&&v(e)}}}function el(s,e,l){let{value:t}=e,{type:n}=e,{selected:a=!1}=e;return s.$$set=i=>{"value"in i&&l(0,t=i.value),"type"in i&&l(1,n=i.type),"selected"in i&&l(2,a=i.selected)},[t,n,a]}class ll extends T{constructor(e){super(),q(this,e,el,$e,L,{value:0,type:1,selected:2})}}function tl(s){let 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diff --git a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/frontmatter-d26451dd.js b/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/frontmatter-d26451dd.js
deleted file mode 100644
index e5329d627fdfc826943eeb2729ffb8edf159e68a..0000000000000000000000000000000000000000
--- a/spaces/DQChoi/gpt-demo/venv/lib/python3.11/site-packages/gradio/templates/frontend/assets/frontmatter-d26451dd.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{s as m,f as s,a as i,p as l,t as o,S as p}from"./index-f90e1963.js";import{yaml as f}from"./yaml-95012b83.js";import"./index-3370be2a.js";import"./Blocks-f0129fcd.js";import"./Button-89624748.js";import"./BlockLabel-56db415e.js";import"./Empty-585389a4.js";import"./Copy-6cd42558.js";import"./Download-fdaaf5d4.js";const n=/^---\s*$/m,S={defineNodes:[{name:"Frontmatter",block:!0},"FrontmatterMark"],props:[m({Frontmatter:[o.documentMeta,o.monospace],FrontmatterMark:o.processingInstruction}),s.add({Frontmatter:i,FrontmatterMark:()=>null})],wrap:l(t=>{const{parser:e}=p.define(f);return t.type.name==="Frontmatter"?{parser:e,overlay:[{from:t.from+4,to:t.to-4}]}:null}),parseBlock:[{name:"Frontmatter",before:"HorizontalRule",parse:(t,e)=>{let r;const a=new Array;if(t.lineStart===0&&n.test(e.text)){for(a.push(t.elt("FrontmatterMark",0,4));t.nextLine();)if(n.test(e.text)){r=t.lineStart+4;break}return r!==void 0&&(a.push(t.elt("FrontmatterMark",r-4,r)),t.addElement(t.elt("Frontmatter",0,r,a))),!0}else return!1}}]};export{S as frontmatter};
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diff --git a/spaces/DaleChen/AutoGPT/tests/local_cache_test.py b/spaces/DaleChen/AutoGPT/tests/local_cache_test.py
deleted file mode 100644
index bb10862656bb500f319ac231ff5bd5438d6fe7e2..0000000000000000000000000000000000000000
--- a/spaces/DaleChen/AutoGPT/tests/local_cache_test.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# sourcery skip: snake-case-functions
-"""Tests for LocalCache class"""
-import os
-import sys
-import unittest
-
-import pytest
-
-from autogpt.memory.local import LocalCache
-
-
-def mock_config() -> dict:
-    """Mock the Config class"""
-    return type(
-        "MockConfig",
-        (object,),
-        {
-            "debug_mode": False,
-            "continuous_mode": False,
-            "speak_mode": False,
-            "memory_index": "auto-gpt",
-        },
-    )
-
-
-@pytest.mark.integration_test
-class TestLocalCache(unittest.TestCase):
-    """Tests for LocalCache class"""
-
-    def setUp(self) -> None:
-        """Set up the test environment"""
-        self.cfg = mock_config()
-        self.cache = LocalCache(self.cfg)
-
-    def test_add(self) -> None:
-        """Test adding a text to the cache"""
-        text = "Sample text"
-        self.cache.add(text)
-        self.assertIn(text, self.cache.data.texts)
-
-    def test_clear(self) -> None:
-        """Test clearing the cache"""
-        self.cache.clear()
-        self.assertEqual(self.cache.data.texts, [])
-
-    def test_get(self) -> None:
-        """Test getting a text from the cache"""
-        text = "Sample text"
-        self.cache.add(text)
-        result = self.cache.get(text)
-        self.assertEqual(result, [text])
-
-    def test_get_relevant(self) -> None:
-        """Test getting relevant texts from the cache"""
-        text1 = "Sample text 1"
-        text2 = "Sample text 2"
-        self.cache.add(text1)
-        self.cache.add(text2)
-        result = self.cache.get_relevant(text1, 1)
-        self.assertEqual(result, [text1])
-
-    def test_get_stats(self) -> None:
-        """Test getting the cache stats"""
-        text = "Sample text"
-        self.cache.add(text)
-        stats = self.cache.get_stats()
-        self.assertEqual(stats, (4, self.cache.data.embeddings.shape))
diff --git a/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/__init__.py b/spaces/Datasculptor/3D-Room-Layout-Estimation_LGT-Net/dataset/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/Egrt/MaskGAN/models/SwinIR.py b/spaces/Egrt/MaskGAN/models/SwinIR.py
deleted file mode 100644
index 860a6e8ef4ab7ed2ac2dc9240b961fc453598479..0000000000000000000000000000000000000000
--- a/spaces/Egrt/MaskGAN/models/SwinIR.py
+++ /dev/null
@@ -1,928 +0,0 @@
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r""" Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
-
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim ** -0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads))  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = (q @ k.transpose(-2, -1))
-
-        relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
-            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1)  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r""" Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
-                 act_layer=nn.GELU, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
-            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        w_slices = (slice(0, -self.window_size),
-                    slice(-self.window_size, -self.shift_size),
-                    slice(-self.shift_size, None))
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(img_mask, self.window_size)  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(shifted_x, self.window_size)  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(-1, self.window_size * self.window_size, C)  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(x_windows, mask=self.attn_mask)  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
-               f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r""" Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """ A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
-
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList([
-            SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
-                                 num_heads=num_heads, window_size=window_size,
-                                 shift_size=0 if (i % 2 == 0) else window_size // 2,
-                                 mlp_ratio=mlp_ratio,
-                                 qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                 drop=drop, attn_drop=attn_drop,
-                                 drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
-                                 norm_layer=norm_layer)
-            for i in range(depth)])
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(self, dim, input_resolution, depth, num_heads, window_size,
-                 mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
-                 drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
-                 img_size=224, patch_size=4, resi_connection='1conv'):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(dim=dim,
-                                         input_resolution=input_resolution,
-                                         depth=depth,
-                                         num_heads=num_heads,
-                                         window_size=window_size,
-                                         mlp_ratio=mlp_ratio,
-                                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                                         drop=drop, attn_drop=attn_drop,
-                                         drop_path=drop_path,
-                                         norm_layer=norm_layer,
-                                         downsample=downsample,
-                                         use_checkpoint=use_checkpoint)
-
-        if resi_connection == '1conv':
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.GELU(),
-                                      nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                                      nn.GELU(),
-                                      nn.Conv2d(dim // 4, dim, 3, 1, 1))
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
-            norm_layer=None)
-
-    def forward(self, x, x_size):
-        return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r""" Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r""" Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class Generator(nn.Module):
-    r""" SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, img_size=64, patch_size=1, in_chans=3,
-                 embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
-                 window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
-                 drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
-                 norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
-                 use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
-                 **kwargs):
-        super(Generator, self).__init__()
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None)
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
-            trunc_normal_(self.absolute_pos_embed, std=.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(dim=embed_dim,
-                         input_resolution=(patches_resolution[0],
-                                           patches_resolution[1]),
-                         depth=depths[i_layer],
-                         num_heads=num_heads[i_layer],
-                         window_size=window_size,
-                         mlp_ratio=self.mlp_ratio,
-                         qkv_bias=qkv_bias, qk_scale=qk_scale,
-                         drop=drop_rate, attn_drop=attn_drop_rate,
-                         drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],  # no impact on SR results
-                         norm_layer=norm_layer,
-                         downsample=None,
-                         use_checkpoint=use_checkpoint,
-                         img_size=img_size,
-                         patch_size=patch_size,
-                         resi_connection=resi_connection
-
-                         )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == '1conv':
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == '3conv':
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                                                 nn.GELU(),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                                                 nn.GELU(),
-                                                 nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.GELU())
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
-                                            (patches_resolution[0], patches_resolution[1]))
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, 'only support x4 now.'
-            self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
-                                                      nn.GELU())
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.GELU()
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore
-    def no_weight_decay(self):
-        return {'absolute_pos_embed'}
-
-    @torch.jit.ignore
-    def no_weight_decay_keywords(self):
-        return {'relative_position_bias_table'}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-        
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == 'pixelshuffle':
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == 'pixelshuffledirect':
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == 'nearest+conv':
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, :H*self.upscale, :W*self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()
-        return flops
-
-
-class Discriminator(nn.Module):
-    def __init__(self):
-        super(Discriminator, self).__init__()
-        self.net = nn.Sequential(
-            nn.Conv2d(3, 64, kernel_size=3, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(64, 128, kernel_size=3, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(128, 256, kernel_size=3, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(256, 512, kernel_size=3, padding=1),
-            nn.GELU(),
-
-            nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
-            nn.GELU(),
-
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(512, 1024, kernel_size=1),
-            nn.GELU(),
-            nn.Conv2d(1024, 1, kernel_size=1)
-        )
-
-    def forward(self, x):
-        batch_size = x.size(0)
-        return self.net(x).view(batch_size)
-
-def compute_gradient_penalty(D, real_samples, fake_samples):
-	alpha = torch.randn(real_samples.size(0), 1, 1, 1)
-	if torch.cuda.is_available():
-		alpha = alpha.cuda()
-		
-	interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)
-	d_interpolates = D(interpolates)
-	fake = torch.ones(d_interpolates.size())
-	if torch.cuda.is_available():
-		fake = fake.cuda()
-		
-	gradients = torch.autograd.grad(
-        outputs=d_interpolates,
-        inputs=interpolates,
-        grad_outputs=fake,
-        create_graph=True,
-        retain_graph=True,
-        only_inputs=True,
-    )[0]
-	gradients = gradients.view(gradients.size(0), -1)
-	gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
-	return gradient_penalty		
-                 
-if __name__ == '__main__':
-    upscale = 4
-    window_size = 7
-    height = (112 // upscale // window_size + 1) * window_size
-    width = (112 // upscale // window_size + 1) * window_size
-    model = Generator(upscale=upscale, img_size=(height, width),
-                   window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
-                   embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=4, upsampler='nearest+conv')
-    print(model)
-    # print(height, width, model.flops() / 1e9)
-
-    x = torch.randn((1, 3, height, width))
-    x = model(x)
-    print(x.shape)
diff --git a/spaces/EsoCode/text-generation-webui/modules/RWKV.py b/spaces/EsoCode/text-generation-webui/modules/RWKV.py
deleted file mode 100644
index 35d69986820ec93d7e5dbcf2abc2f19a62dc9c33..0000000000000000000000000000000000000000
--- a/spaces/EsoCode/text-generation-webui/modules/RWKV.py
+++ /dev/null
@@ -1,148 +0,0 @@
-import copy
-import os
-from pathlib import Path
-
-import numpy as np
-from tokenizers import Tokenizer
-
-import modules.shared as shared
-from modules.callbacks import Iteratorize
-
-np.set_printoptions(precision=4, suppress=True, linewidth=200)
-
-os.environ['RWKV_JIT_ON'] = '1'
-os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0'  # use CUDA kernel for seq mode (much faster)
-
-from rwkv.model import RWKV
-from rwkv.utils import PIPELINE, PIPELINE_ARGS
-
-
-class RWKVModel:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(self, path, dtype="fp16", device="cuda"):
-        tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json")
-        if shared.args.rwkv_strategy is None:
-            model = RWKV(model=str(path), strategy=f'{device} {dtype}')
-        else:
-            model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy)
-
-        pipeline = PIPELINE(model, str(tokenizer_path))
-        result = self()
-        result.pipeline = pipeline
-        result.model = model
-        result.cached_context = ""
-        result.cached_model_state = None
-        result.cached_output_logits = None
-        return result
-
-    def generate(self, prompt, state, callback=None):
-        args = PIPELINE_ARGS(
-            temperature=state['temperature'],
-            top_p=state['top_p'],
-            top_k=state['top_k'],
-            alpha_frequency=0.1,  # Frequency Penalty (as in GPT-3)
-            alpha_presence=0.1,  # Presence Penalty (as in GPT-3)
-            token_ban=[0],  # ban the generation of some tokens
-            token_stop=[]
-        )
-
-        if self.cached_context != "":
-            if prompt.startswith(self.cached_context):
-                prompt = prompt[len(self.cached_context):]
-            else:
-                self.cached_context = ""
-                self.cached_model_state = None
-                self.cached_output_logits = None
-
-        # out = self.pipeline.generate(prompt, token_count=state['max_new_tokens'], args=args, callback=callback)
-        out = self.generate_from_cached_state(prompt, token_count=state['max_new_tokens'], args=args, callback=callback)
-        return out
-
-    def generate_with_streaming(self, *args, **kwargs):
-        with Iteratorize(self.generate, args, kwargs, callback=None) as generator:
-            reply = ''
-            for token in generator:
-                reply += token
-                yield reply
-
-    # Similar to the PIPELINE.generate, but lets us maintain the cached_model_state
-    def generate_from_cached_state(self, ctx="", token_count=20, args=None, callback=None):
-        all_tokens = []
-        out_str = ''
-        occurrence = {}
-        state = copy.deepcopy(self.cached_model_state) if self.cached_model_state is not None else None
-
-        # if we ended up with an empty context, just reuse the cached logits
-        # this can happen if a user undoes a message and then sends the exact message again
-        # in that case the full context ends up being the same as the cached_context, so the remaining context is empty.
-        if ctx == "":
-            out = self.cached_output_logits
-
-        token = None
-        for i in range(token_count):
-            # forward
-            tokens = self.pipeline.encode(ctx) if i == 0 else [token]
-            while len(tokens) > 0:
-                out, state = self.model.forward(tokens[:args.chunk_len], state)
-                tokens = tokens[args.chunk_len:]
-            if i == 0:
-                begin_token = len(all_tokens)
-                last_token_posi = begin_token
-            # cache the model state after scanning the context
-            # we don't cache the state after processing our own generated tokens because
-            # the output string might be post-processed arbitrarily. Therefore, what's fed into the model
-            # on the next round of chat might be slightly different what what it output on the previous round
-            if i == 0:
-                self.cached_context += ctx
-                self.cached_model_state = copy.deepcopy(state)
-                self.cached_output_logits = copy.deepcopy(out)
-
-            # adjust probabilities
-            for n in args.token_ban:
-                out[n] = -float('inf')
-
-            for n in occurrence:
-                out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency)
-
-            # sampler
-            token = self.pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p, top_k=args.top_k)
-            if token in args.token_stop:
-                break
-
-            all_tokens += [token]
-            if token not in occurrence:
-                occurrence[token] = 1
-            else:
-                occurrence[token] += 1
-
-            # output
-            tmp = self.pipeline.decode(all_tokens[last_token_posi:])
-            if '\ufffd' not in tmp:  # is valid utf-8 string?
-                if callback:
-                    callback(tmp)
-
-                out_str += tmp
-                last_token_posi = begin_token + i + 1
-        return out_str
-
-
-class RWKVTokenizer:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def from_pretrained(self, path):
-        tokenizer_path = path / "20B_tokenizer.json"
-        tokenizer = Tokenizer.from_file(str(tokenizer_path))
-        result = self()
-        result.tokenizer = tokenizer
-        return result
-
-    def encode(self, prompt):
-        return self.tokenizer.encode(prompt).ids
-
-    def decode(self, ids):
-        return self.tokenizer.decode(ids)
diff --git a/spaces/ExpUnGeD404/Bamber/README.md b/spaces/ExpUnGeD404/Bamber/README.md
deleted file mode 100644
index 27e85c09a6e614d1616edacc1661b7fe46156dd5..0000000000000000000000000000000000000000
--- a/spaces/ExpUnGeD404/Bamber/README.md
+++ /dev/null
@@ -1,11 +0,0 @@
----
-title: Bamber
-emoji: 🌽
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/FlippFuzz/whisper-webui/src/languages.py b/spaces/FlippFuzz/whisper-webui/src/languages.py
deleted file mode 100644
index fbad66e4d34119d27d12e3dfecbe99b6fdde4db7..0000000000000000000000000000000000000000
--- a/spaces/FlippFuzz/whisper-webui/src/languages.py
+++ /dev/null
@@ -1,147 +0,0 @@
-class Language():
-    def __init__(self, code, name):
-        self.code = code
-        self.name = name
-
-    def __str__(self):
-        return "Language(code={}, name={})".format(self.code, self.name)
-
-LANGUAGES = [
-    Language('en', 'English'),
-    Language('zh', 'Chinese'),   
-    Language('de', 'German'),    
-    Language('es', 'Spanish'),   
-    Language('ru', 'Russian'),   
-    Language('ko', 'Korean'),    
-    Language('fr', 'French'),    
-    Language('ja', 'Japanese'),  
-    Language('pt', 'Portuguese'),
-    Language('tr', 'Turkish'),   
-    Language('pl', 'Polish'),    
-    Language('ca', 'Catalan'),   
-    Language('nl', 'Dutch'),     
-    Language('ar', 'Arabic'),
-    Language('sv', 'Swedish'),
-    Language('it', 'Italian'),
-    Language('id', 'Indonesian'),
-    Language('hi', 'Hindi'),
-    Language('fi', 'Finnish'),
-    Language('vi', 'Vietnamese'),
-    Language('he', 'Hebrew'),
-    Language('uk', 'Ukrainian'),
-    Language('el', 'Greek'),
-    Language('ms', 'Malay'),
-    Language('cs', 'Czech'),
-    Language('ro', 'Romanian'),
-    Language('da', 'Danish'),
-    Language('hu', 'Hungarian'),
-    Language('ta', 'Tamil'),
-    Language('no', 'Norwegian'),
-    Language('th', 'Thai'),
-    Language('ur', 'Urdu'),
-    Language('hr', 'Croatian'),
-    Language('bg', 'Bulgarian'),
-    Language('lt', 'Lithuanian'),
-    Language('la', 'Latin'),
-    Language('mi', 'Maori'),
-    Language('ml', 'Malayalam'),
-    Language('cy', 'Welsh'),
-    Language('sk', 'Slovak'),
-    Language('te', 'Telugu'),
-    Language('fa', 'Persian'),
-    Language('lv', 'Latvian'),
-    Language('bn', 'Bengali'),
-    Language('sr', 'Serbian'),
-    Language('az', 'Azerbaijani'),
-    Language('sl', 'Slovenian'),
-    Language('kn', 'Kannada'),
-    Language('et', 'Estonian'),
-    Language('mk', 'Macedonian'),
-    Language('br', 'Breton'),
-    Language('eu', 'Basque'),
-    Language('is', 'Icelandic'),
-    Language('hy', 'Armenian'),
-    Language('ne', 'Nepali'),
-    Language('mn', 'Mongolian'),
-    Language('bs', 'Bosnian'),
-    Language('kk', 'Kazakh'),
-    Language('sq', 'Albanian'),
-    Language('sw', 'Swahili'),
-    Language('gl', 'Galician'),
-    Language('mr', 'Marathi'),
-    Language('pa', 'Punjabi'),
-    Language('si', 'Sinhala'),
-    Language('km', 'Khmer'),
-    Language('sn', 'Shona'),
-    Language('yo', 'Yoruba'),
-    Language('so', 'Somali'),
-    Language('af', 'Afrikaans'),
-    Language('oc', 'Occitan'),
-    Language('ka', 'Georgian'),
-    Language('be', 'Belarusian'),
-    Language('tg', 'Tajik'),
-    Language('sd', 'Sindhi'),
-    Language('gu', 'Gujarati'),
-    Language('am', 'Amharic'),
-    Language('yi', 'Yiddish'),
-    Language('lo', 'Lao'),
-    Language('uz', 'Uzbek'),
-    Language('fo', 'Faroese'),
-    Language('ht', 'Haitian creole'),
-    Language('ps', 'Pashto'),
-    Language('tk', 'Turkmen'),
-    Language('nn', 'Nynorsk'),
-    Language('mt', 'Maltese'),
-    Language('sa', 'Sanskrit'),
-    Language('lb', 'Luxembourgish'),
-    Language('my', 'Myanmar'),
-    Language('bo', 'Tibetan'),
-    Language('tl', 'Tagalog'),
-    Language('mg', 'Malagasy'),
-    Language('as', 'Assamese'),
-    Language('tt', 'Tatar'),
-    Language('haw', 'Hawaiian'),
-    Language('ln', 'Lingala'),
-    Language('ha', 'Hausa'),
-    Language('ba', 'Bashkir'),
-    Language('jw', 'Javanese'),
-    Language('su', 'Sundanese')
-]
-
-_TO_LANGUAGE_CODE = {
-    **{language.code: language for language in LANGUAGES},
-    "burmese": "my",
-    "valencian": "ca",
-    "flemish": "nl",
-    "haitian": "ht",
-    "letzeburgesch": "lb",
-    "pushto": "ps",
-    "panjabi": "pa",
-    "moldavian": "ro",
-    "moldovan": "ro",
-    "sinhalese": "si",
-    "castilian": "es",
-}
-    
-_FROM_LANGUAGE_NAME = {
-    **{language.name.lower(): language for language in LANGUAGES}
-}
-
-def get_language_from_code(language_code, default=None) -> Language:
-    """Return the language name from the language code."""
-    return _TO_LANGUAGE_CODE.get(language_code, default)
-
-def get_language_from_name(language, default=None) -> Language:
-    """Return the language code from the language name."""
-    return _FROM_LANGUAGE_NAME.get(language.lower() if language else None, default)
-
-def get_language_names():
-    """Return a list of language names."""
-    return [language.name for language in LANGUAGES]
-
-if __name__ == "__main__":
-    # Test lookup
-    print(get_language_from_code('en'))
-    print(get_language_from_name('English'))
-    
-    print(get_language_names())
\ No newline at end of file
diff --git a/spaces/FrankZxShen/so-vits-svc-models-ba/modules/F0Predictor/__init__.py b/spaces/FrankZxShen/so-vits-svc-models-ba/modules/F0Predictor/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/dpm_solver_pytorch.py b/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/dpm_solver_pytorch.py
deleted file mode 100644
index dee5e280661b61e0a99038ce0bd240db51344ead..0000000000000000000000000000000000000000
--- a/spaces/FrankZxShen/so-vits-svc-models-pcr/diffusion/dpm_solver_pytorch.py
+++ /dev/null
@@ -1,1201 +0,0 @@
-import math
-
-import torch
-
-
-class NoiseScheduleVP:
-    def __init__(
-            self,
-            schedule='discrete',
-            betas=None,
-            alphas_cumprod=None,
-            continuous_beta_0=0.1,
-            continuous_beta_1=20.,
-    ):
-        """Create a wrapper class for the forward SDE (VP type).
-
-        ***
-        Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
-                We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
-        ***
-
-        The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
-        We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
-        Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
-
-            log_alpha_t = self.marginal_log_mean_coeff(t)
-            sigma_t = self.marginal_std(t)
-            lambda_t = self.marginal_lambda(t)
-
-        Moreover, as lambda(t) is an invertible function, we also support its inverse function:
-
-            t = self.inverse_lambda(lambda_t)
-
-        ===============================================================
-
-        We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
-
-        1. For discrete-time DPMs:
-
-            For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
-                t_i = (i + 1) / N
-            e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
-            We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
-
-            Args:
-                betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
-                alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
-
-            Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
-
-            **Important**:  Please pay special attention for the args for `alphas_cumprod`:
-                The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
-                    q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
-                Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
-                    alpha_{t_n} = \sqrt{\hat{alpha_n}},
-                and
-                    log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
-
-
-        2. For continuous-time DPMs:
-
-            We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
-            schedule are the default settings in DDPM and improved-DDPM:
-
-            Args:
-                beta_min: A `float` number. The smallest beta for the linear schedule.
-                beta_max: A `float` number. The largest beta for the linear schedule.
-                cosine_s: A `float` number. The hyperparameter in the cosine schedule.
-                cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
-                T: A `float` number. The ending time of the forward process.
-
-        ===============================================================
-
-        Args:
-            schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
-                    'linear' or 'cosine' for continuous-time DPMs.
-        Returns:
-            A wrapper object of the forward SDE (VP type).
-        
-        ===============================================================
-
-        Example:
-
-        # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', betas=betas)
-
-        # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
-        >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
-
-        # For continuous-time DPMs (VPSDE), linear schedule:
-        >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
-
-        """
-
-        if schedule not in ['discrete', 'linear', 'cosine']:
-            raise ValueError(
-                "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
-                    schedule))
-
-        self.schedule = schedule
-        if schedule == 'discrete':
-            if betas is not None:
-                log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
-            else:
-                assert alphas_cumprod is not None
-                log_alphas = 0.5 * torch.log(alphas_cumprod)
-            self.total_N = len(log_alphas)
-            self.T = 1.
-            self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
-            self.log_alpha_array = log_alphas.reshape((1, -1,))
-        else:
-            self.total_N = 1000
-            self.beta_0 = continuous_beta_0
-            self.beta_1 = continuous_beta_1
-            self.cosine_s = 0.008
-            self.cosine_beta_max = 999.
-            self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
-            self.schedule = schedule
-            if schedule == 'cosine':
-                # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
-                # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
-                self.T = 0.9946
-            else:
-                self.T = 1.
-
-    def marginal_log_mean_coeff(self, t):
-        """
-        Compute log(alpha_t) of a given continuous-time label t in [0, T].
-        """
-        if self.schedule == 'discrete':
-            return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
-                                  self.log_alpha_array.to(t.device)).reshape((-1))
-        elif self.schedule == 'linear':
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
-        elif self.schedule == 'cosine':
-            log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
-            log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
-            return log_alpha_t
-
-    def marginal_alpha(self, t):
-        """
-        Compute alpha_t of a given continuous-time label t in [0, T].
-        """
-        return torch.exp(self.marginal_log_mean_coeff(t))
-
-    def marginal_std(self, t):
-        """
-        Compute sigma_t of a given continuous-time label t in [0, T].
-        """
-        return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
-
-    def marginal_lambda(self, t):
-        """
-        Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
-        """
-        log_mean_coeff = self.marginal_log_mean_coeff(t)
-        log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
-        return log_mean_coeff - log_std
-
-    def inverse_lambda(self, lamb):
-        """
-        Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
-        """
-        if self.schedule == 'linear':
-            tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            Delta = self.beta_0 ** 2 + tmp
-            return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
-        elif self.schedule == 'discrete':
-            log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
-            t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
-                               torch.flip(self.t_array.to(lamb.device), [1]))
-            return t.reshape((-1,))
-        else:
-            log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
-            t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
-                        1. + self.cosine_s) / math.pi - self.cosine_s
-            t = t_fn(log_alpha)
-            return t
-
-
-def model_wrapper(
-        model,
-        noise_schedule,
-        model_type="noise",
-        model_kwargs={},
-        guidance_type="uncond",
-        condition=None,
-        unconditional_condition=None,
-        guidance_scale=1.,
-        classifier_fn=None,
-        classifier_kwargs={},
-):
-    """Create a wrapper function for the noise prediction model.
-
-    DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
-    firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
-
-    We support four types of the diffusion model by setting `model_type`:
-
-        1. "noise": noise prediction model. (Trained by predicting noise).
-
-        2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
-
-        3. "v": velocity prediction model. (Trained by predicting the velocity).
-            The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
-
-            [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
-                arXiv preprint arXiv:2202.00512 (2022).
-            [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
-                arXiv preprint arXiv:2210.02303 (2022).
-    
-        4. "score": marginal score function. (Trained by denoising score matching).
-            Note that the score function and the noise prediction model follows a simple relationship:
-            ```
-                noise(x_t, t) = -sigma_t * score(x_t, t)
-            ```
-
-    We support three types of guided sampling by DPMs by setting `guidance_type`:
-        1. "uncond": unconditional sampling by DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            ``
-
-        2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            `` 
-
-            The input `classifier_fn` has the following format:
-            ``
-                classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
-            ``
-
-            [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
-                in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
-
-        3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
-            The input `model` has the following format:
-            ``
-                model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            `` 
-            And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
-
-            [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
-                arXiv preprint arXiv:2207.12598 (2022).
-        
-
-    The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
-    or continuous-time labels (i.e. epsilon to T).
-
-    We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
-    ``
-        def model_fn(x, t_continuous) -> noise:
-            t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)         
-    ``
-    where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
-
-    ===============================================================
-
-    Args:
-        model: A diffusion model with the corresponding format described above.
-        noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-        model_type: A `str`. The parameterization type of the diffusion model.
-                    "noise" or "x_start" or "v" or "score".
-        model_kwargs: A `dict`. A dict for the other inputs of the model function.
-        guidance_type: A `str`. The type of the guidance for sampling.
-                    "uncond" or "classifier" or "classifier-free".
-        condition: A pytorch tensor. The condition for the guided sampling.
-                    Only used for "classifier" or "classifier-free" guidance type.
-        unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
-                    Only used for "classifier-free" guidance type.
-        guidance_scale: A `float`. The scale for the guided sampling.
-        classifier_fn: A classifier function. Only used for the classifier guidance.
-        classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
-    Returns:
-        A noise prediction model that accepts the noised data and the continuous time as the inputs.
-    """
-
-    def get_model_input_time(t_continuous):
-        """
-        Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-        For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
-        For continuous-time DPMs, we just use `t_continuous`.
-        """
-        if noise_schedule.schedule == 'discrete':
-            return (t_continuous - 1. / noise_schedule.total_N) * noise_schedule.total_N
-        else:
-            return t_continuous
-
-    def noise_pred_fn(x, t_continuous, cond=None):
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        t_input = get_model_input_time(t_continuous)
-        if cond is None:
-            output = model(x, t_input, **model_kwargs)
-        else:
-            output = model(x, t_input, cond, **model_kwargs)
-        if model_type == "noise":
-            return output
-        elif model_type == "x_start":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
-        elif model_type == "v":
-            alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
-        elif model_type == "score":
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            dims = x.dim()
-            return -expand_dims(sigma_t, dims) * output
-
-    def cond_grad_fn(x, t_input):
-        """
-        Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
-        """
-        with torch.enable_grad():
-            x_in = x.detach().requires_grad_(True)
-            log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
-            return torch.autograd.grad(log_prob.sum(), x_in)[0]
-
-    def model_fn(x, t_continuous):
-        """
-        The noise predicition model function that is used for DPM-Solver.
-        """
-        if t_continuous.reshape((-1,)).shape[0] == 1:
-            t_continuous = t_continuous.expand((x.shape[0]))
-        if guidance_type == "uncond":
-            return noise_pred_fn(x, t_continuous)
-        elif guidance_type == "classifier":
-            assert classifier_fn is not None
-            t_input = get_model_input_time(t_continuous)
-            cond_grad = cond_grad_fn(x, t_input)
-            sigma_t = noise_schedule.marginal_std(t_continuous)
-            noise = noise_pred_fn(x, t_continuous)
-            return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
-        elif guidance_type == "classifier-free":
-            if guidance_scale == 1. or unconditional_condition is None:
-                return noise_pred_fn(x, t_continuous, cond=condition)
-            else:
-                x_in = torch.cat([x] * 2)
-                t_in = torch.cat([t_continuous] * 2)
-                c_in = torch.cat([unconditional_condition, condition])
-                noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
-                return noise_uncond + guidance_scale * (noise - noise_uncond)
-
-    assert model_type in ["noise", "x_start", "v"]
-    assert guidance_type in ["uncond", "classifier", "classifier-free"]
-    return model_fn
-
-
-class DPM_Solver:
-    def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
-        """Construct a DPM-Solver. 
-
-        We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
-        If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
-        If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
-            In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
-            The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
-
-        Args:
-            model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
-                ``
-                def model_fn(x, t_continuous):
-                    return noise
-                ``
-            noise_schedule: A noise schedule object, such as NoiseScheduleVP.
-            predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
-            thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
-            max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
-        
-        [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
-        """
-        self.model = model_fn
-        self.noise_schedule = noise_schedule
-        self.predict_x0 = predict_x0
-        self.thresholding = thresholding
-        self.max_val = max_val
-
-    def noise_prediction_fn(self, x, t):
-        """
-        Return the noise prediction model.
-        """
-        return self.model(x, t)
-
-    def data_prediction_fn(self, x, t):
-        """
-        Return the data prediction model (with thresholding).
-        """
-        noise = self.noise_prediction_fn(x, t)
-        dims = x.dim()
-        alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
-        x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
-        if self.thresholding:
-            p = 0.995  # A hyperparameter in the paper of "Imagen" [1].
-            s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
-            s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
-            x0 = torch.clamp(x0, -s, s) / s
-        return x0
-
-    def model_fn(self, x, t):
-        """
-        Convert the model to the noise prediction model or the data prediction model. 
-        """
-        if self.predict_x0:
-            return self.data_prediction_fn(x, t)
-        else:
-            return self.noise_prediction_fn(x, t)
-
-    def get_time_steps(self, skip_type, t_T, t_0, N, device):
-        """Compute the intermediate time steps for sampling.
-
-        Args:
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            N: A `int`. The total number of the spacing of the time steps.
-            device: A torch device.
-        Returns:
-            A pytorch tensor of the time steps, with the shape (N + 1,).
-        """
-        if skip_type == 'logSNR':
-            lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
-            lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
-            logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
-            return self.noise_schedule.inverse_lambda(logSNR_steps)
-        elif skip_type == 'time_uniform':
-            return torch.linspace(t_T, t_0, N + 1).to(device)
-        elif skip_type == 'time_quadratic':
-            t_order = 2
-            t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
-            return t
-        else:
-            raise ValueError(
-                "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
-
-    def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
-        """
-        Get the order of each step for sampling by the singlestep DPM-Solver.
-
-        We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
-        Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
-            - If order == 1:
-                We take `steps` of DPM-Solver-1 (i.e. DDIM).
-            - If order == 2:
-                - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
-                - If steps % 2 == 0, we use K steps of DPM-Solver-2.
-                - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
-            - If order == 3:
-                - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
-                - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
-
-        ============================================
-        Args:
-            order: A `int`. The max order for the solver (2 or 3).
-            steps: A `int`. The total number of function evaluations (NFE).
-            skip_type: A `str`. The type for the spacing of the time steps. We support three types:
-                - 'logSNR': uniform logSNR for the time steps.
-                - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
-                - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            device: A torch device.
-        Returns:
-            orders: A list of the solver order of each step.
-        """
-        if order == 3:
-            K = steps // 3 + 1
-            if steps % 3 == 0:
-                orders = [3, ] * (K - 2) + [2, 1]
-            elif steps % 3 == 1:
-                orders = [3, ] * (K - 1) + [1]
-            else:
-                orders = [3, ] * (K - 1) + [2]
-        elif order == 2:
-            if steps % 2 == 0:
-                K = steps // 2
-                orders = [2, ] * K
-            else:
-                K = steps // 2 + 1
-                orders = [2, ] * (K - 1) + [1]
-        elif order == 1:
-            K = 1
-            orders = [1, ] * steps
-        else:
-            raise ValueError("'order' must be '1' or '2' or '3'.")
-        if skip_type == 'logSNR':
-            # To reproduce the results in DPM-Solver paper
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
-        else:
-            timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
-                torch.cumsum(torch.tensor([0, ] + orders), dim=0).to(device)]
-        return timesteps_outer, orders
-
-    def denoise_fn(self, x, s):
-        """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
-        """
-        return self.data_prediction_fn(x, s)
-
-    def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
-        """
-        DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_1 = torch.expm1(-h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(sigma_t / sigma_s, dims) * x
-                    - expand_dims(alpha_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-        else:
-            phi_1 = torch.expm1(h)
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                    - expand_dims(sigma_t * phi_1, dims) * model_s
-            )
-            if return_intermediate:
-                return x_t, {'model_s': model_s}
-            else:
-                return x_t
-
-    def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
-                                            solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-2 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the second-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 0.5
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
-            s1), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
-        alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_1 = torch.expm1(-h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(sigma_s1 / sigma_s, dims) * x
-                    - expand_dims(alpha_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
-                                    model_s1 - model_s)
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_1 = torch.expm1(h)
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            x_s1 = (
-                    expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s1 * phi_11, dims) * model_s
-            )
-            model_s1 = self.model_fn(x_s1, s1)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
-                )
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1}
-        else:
-            return x_t
-
-    def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
-                                           return_intermediate=False, solver_type='dpm_solver'):
-        """
-        Singlestep solver DPM-Solver-3 from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            r1: A `float`. The hyperparameter of the third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-            model_s: A pytorch tensor. The model function evaluated at time `s`.
-                If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
-            model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
-                If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        if r1 is None:
-            r1 = 1. / 3.
-        if r2 is None:
-            r2 = 2. / 3.
-        ns = self.noise_schedule
-        dims = x.dim()
-        lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
-        h = lambda_t - lambda_s
-        lambda_s1 = lambda_s + r1 * h
-        lambda_s2 = lambda_s + r2 * h
-        s1 = ns.inverse_lambda(lambda_s1)
-        s2 = ns.inverse_lambda(lambda_s2)
-        log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
-            s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
-        sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
-            s2), ns.marginal_std(t)
-        alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
-
-        if self.predict_x0:
-            phi_11 = torch.expm1(-r1 * h)
-            phi_12 = torch.expm1(-r2 * h)
-            phi_1 = torch.expm1(-h)
-            phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
-            phi_2 = phi_1 / h + 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(sigma_s1 / sigma_s, dims) * x
-                        - expand_dims(alpha_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(sigma_s2 / sigma_s, dims) * x
-                    - expand_dims(alpha_s2 * phi_12, dims) * model_s
-                    + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(sigma_t / sigma_s, dims) * x
-                        - expand_dims(alpha_t * phi_1, dims) * model_s
-                        + expand_dims(alpha_t * phi_2, dims) * D1
-                        - expand_dims(alpha_t * phi_3, dims) * D2
-                )
-        else:
-            phi_11 = torch.expm1(r1 * h)
-            phi_12 = torch.expm1(r2 * h)
-            phi_1 = torch.expm1(h)
-            phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
-            phi_2 = phi_1 / h - 1.
-            phi_3 = phi_2 / h - 0.5
-
-            if model_s is None:
-                model_s = self.model_fn(x, s)
-            if model_s1 is None:
-                x_s1 = (
-                        expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
-                        - expand_dims(sigma_s1 * phi_11, dims) * model_s
-                )
-                model_s1 = self.model_fn(x_s1, s1)
-            x_s2 = (
-                    expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
-                    - expand_dims(sigma_s2 * phi_12, dims) * model_s
-                    - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
-            )
-            model_s2 = self.model_fn(x_s2, s2)
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
-                )
-            elif solver_type == 'taylor':
-                D1_0 = (1. / r1) * (model_s1 - model_s)
-                D1_1 = (1. / r2) * (model_s2 - model_s)
-                D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
-                D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
-                        - expand_dims(sigma_t * phi_1, dims) * model_s
-                        - expand_dims(sigma_t * phi_2, dims) * D1
-                        - expand_dims(sigma_t * phi_3, dims) * D2
-                )
-
-        if return_intermediate:
-            return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
-        else:
-            return x_t
-
-    def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
-        """
-        Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if solver_type not in ['dpm_solver', 'taylor']:
-            raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_1, model_prev_0 = model_prev_list
-        t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
-            t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0 = h_0 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        if self.predict_x0:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(sigma_t / sigma_prev_0, dims) * x
-                        - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                        + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
-                )
-        else:
-            if solver_type == 'dpm_solver':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
-                )
-            elif solver_type == 'taylor':
-                x_t = (
-                        expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                        - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                        - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
-                )
-        return x_t
-
-    def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
-        """
-        Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        ns = self.noise_schedule
-        dims = x.dim()
-        model_prev_2, model_prev_1, model_prev_0 = model_prev_list
-        t_prev_2, t_prev_1, t_prev_0 = t_prev_list
-        lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
-            t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
-        log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
-        sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
-        alpha_t = torch.exp(log_alpha_t)
-
-        h_1 = lambda_prev_1 - lambda_prev_2
-        h_0 = lambda_prev_0 - lambda_prev_1
-        h = lambda_t - lambda_prev_0
-        r0, r1 = h_0 / h, h_1 / h
-        D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
-        D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
-        D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
-        D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
-        if self.predict_x0:
-            x_t = (
-                    expand_dims(sigma_t / sigma_prev_0, dims) * x
-                    - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
-                    + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
-                    - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
-            )
-        else:
-            x_t = (
-                    expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
-                    - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
-                    - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
-            )
-        return x_t
-
-    def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
-                                     r2=None):
-        """
-        Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-            r1: A `float`. The hyperparameter of the second-order or third-order solver.
-            r2: A `float`. The hyperparameter of the third-order solver.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
-        elif order == 2:
-            return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
-                                                            solver_type=solver_type, r1=r1)
-        elif order == 3:
-            return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
-                                                           solver_type=solver_type, r1=r1, r2=r2)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
-        """
-        Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `s`.
-            model_prev_list: A list of pytorch tensor. The previous computed model values.
-            t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
-            t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
-            order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_t: A pytorch tensor. The approximated solution at time `t`.
-        """
-        if order == 1:
-            return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
-        elif order == 2:
-            return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        elif order == 3:
-            return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
-        else:
-            raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
-
-    def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
-                            solver_type='dpm_solver'):
-        """
-        The adaptive step size solver based on singlestep DPM-Solver.
-
-        Args:
-            x: A pytorch tensor. The initial value at time `t_T`.
-            order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
-            t_T: A `float`. The starting time of the sampling (default is T).
-            t_0: A `float`. The ending time of the sampling (default is epsilon).
-            h_init: A `float`. The initial step size (for logSNR).
-            atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
-            rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
-            theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
-            t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the 
-                current time and `t_0` is less than `t_err`. The default setting is 1e-5.
-            solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
-                The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
-        Returns:
-            x_0: A pytorch tensor. The approximated solution at time `t_0`.
-
-        [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
-        """
-        ns = self.noise_schedule
-        s = t_T * torch.ones((x.shape[0],)).to(x)
-        lambda_s = ns.marginal_lambda(s)
-        lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
-        h = h_init * torch.ones_like(s).to(x)
-        x_prev = x
-        nfe = 0
-        if order == 2:
-            r1 = 0.5
-            lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                               solver_type=solver_type,
-                                                                                               **kwargs)
-        elif order == 3:
-            r1, r2 = 1. / 3., 2. / 3.
-            lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
-                                                                                    return_intermediate=True,
-                                                                                    solver_type=solver_type)
-            higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
-                                                                                              solver_type=solver_type,
-                                                                                              **kwargs)
-        else:
-            raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
-        while torch.abs((s - t_0)).mean() > t_err:
-            t = ns.inverse_lambda(lambda_s + h)
-            x_lower, lower_noise_kwargs = lower_update(x, s, t)
-            x_higher = higher_update(x, s, t, **lower_noise_kwargs)
-            delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
-            norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
-            E = norm_fn((x_higher - x_lower) / delta).max()
-            if torch.all(E <= 1.):
-                x = x_higher
-                s = t
-                x_prev = x_lower
-                lambda_s = ns.marginal_lambda(s)
-            h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
-            nfe += order
-        print('adaptive solver nfe', nfe)
-        return x
-
-    def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
-               method='singlestep', denoise=False, solver_type='dpm_solver', atol=0.0078,
-               rtol=0.05,
-               ):
-        """
-        Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
-
-        =====================================================
-
-        We support the following algorithms for both noise prediction model and data prediction model:
-            - 'singlestep':
-                Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver. 
-                We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
-                The total number of function evaluations (NFE) == `steps`.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    - If `order` == 1:
-                        - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
-                        - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
-                        - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                    - If `order` == 3:
-                        - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
-                        - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
-                        - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
-            - 'multistep':
-                Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
-                We initialize the first `order` values by lower order multistep solvers.
-                Given a fixed NFE == `steps`, the sampling procedure is:
-                    Denote K = steps.
-                    - If `order` == 1:
-                        - We use K steps of DPM-Solver-1 (i.e. DDIM).
-                    - If `order` == 2:
-                        - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
-                    - If `order` == 3:
-                        - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
-            - 'singlestep_fixed':
-                Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
-                We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
-            - 'adaptive':
-                Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
-                We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
-                You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
-                (NFE) and the sample quality.
-                    - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
-                    - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
-
-        =====================================================
-
-        Some advices for choosing the algorithm:
-            - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
-                Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
-                            skip_type='time_uniform', method='singlestep')
-            - For **guided sampling with large guidance scale** by DPMs:
-                Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
-                e.g.
-                    >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
-                    >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
-                            skip_type='time_uniform', method='multistep')
-
-        We support three types of `skip_type`:
-            - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
-            - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
-            - 'time_quadratic': quadratic time for the time steps.
-
-        =====================================================
-        Args:
-            x: A pytorch tensor. The initial value at time `t_start`
-                e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
-            steps: A `int`. The total number of function evaluations (NFE).
-            t_start: A `float`. The starting time of the sampling.
-                If `T` is None, we use self.noise_schedule.T (default is 1.0).
-            t_end: A `float`. The ending time of the sampling.
-                If `t_end` is None, we use 1. / self.noise_schedule.total_N.
-                e.g. if total_N == 1000, we have `t_end` == 1e-3.
-                For discrete-time DPMs:
-                    - We recommend `t_end` == 1. / self.noise_schedule.total_N.
-                For continuous-time DPMs:
-                    - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
-            order: A `int`. The order of DPM-Solver.
-            skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
-            method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
-            denoise: A `bool`. Whether to denoise at the final step. Default is False.
-                If `denoise` is True, the total NFE is (`steps` + 1).
-            solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
-            atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-            rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
-        Returns:
-            x_end: A pytorch tensor. The approximated solution at time `t_end`.
-
-        """
-        t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
-        t_T = self.noise_schedule.T if t_start is None else t_start
-        device = x.device
-        if method == 'adaptive':
-            with torch.no_grad():
-                x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
-                                             solver_type=solver_type)
-        elif method == 'multistep':
-            assert steps >= order
-            timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
-            assert timesteps.shape[0] - 1 == steps
-            with torch.no_grad():
-                vec_t = timesteps[0].expand((x.shape[0]))
-                model_prev_list = [self.model_fn(x, vec_t)]
-                t_prev_list = [vec_t]
-                # Init the first `order` values by lower order multistep DPM-Solver.
-                for init_order in range(1, order):
-                    vec_t = timesteps[init_order].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
-                                                         solver_type=solver_type)
-                    model_prev_list.append(self.model_fn(x, vec_t))
-                    t_prev_list.append(vec_t)
-                # Compute the remaining values by `order`-th order multistep DPM-Solver.
-                for step in range(order, steps + 1):
-                    vec_t = timesteps[step].expand(x.shape[0])
-                    x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, order,
-                                                         solver_type=solver_type)
-                    for i in range(order - 1):
-                        t_prev_list[i] = t_prev_list[i + 1]
-                        model_prev_list[i] = model_prev_list[i + 1]
-                    t_prev_list[-1] = vec_t
-                    # We do not need to evaluate the final model value.
-                    if step < steps:
-                        model_prev_list[-1] = self.model_fn(x, vec_t)
-        elif method in ['singlestep', 'singlestep_fixed']:
-            if method == 'singlestep':
-                timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
-                                                                                              skip_type=skip_type,
-                                                                                              t_T=t_T, t_0=t_0,
-                                                                                              device=device)
-            elif method == 'singlestep_fixed':
-                K = steps // order
-                orders = [order, ] * K
-                timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
-            for i, order in enumerate(orders):
-                t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
-                timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
-                                                      N=order, device=device)
-                lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
-                vec_s, vec_t = t_T_inner.repeat(x.shape[0]), t_0_inner.repeat(x.shape[0])
-                h = lambda_inner[-1] - lambda_inner[0]
-                r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
-                r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
-                x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
-        if denoise:
-            x = self.denoise_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
-        return x
-
-
-#############################################################
-# other utility functions
-#############################################################
-
-def interpolate_fn(x, xp, yp):
-    """
-    A piecewise linear function y = f(x), using xp and yp as keypoints.
-    We implement f(x) in a differentiable way (i.e. applicable for autograd).
-    The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
-
-    Args:
-        x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
-        xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
-        yp: PyTorch tensor with shape [C, K].
-    Returns:
-        The function values f(x), with shape [N, C].
-    """
-    N, K = x.shape[0], xp.shape[1]
-    all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
-    sorted_all_x, x_indices = torch.sort(all_x, dim=2)
-    x_idx = torch.argmin(x_indices, dim=2)
-    cand_start_idx = x_idx - 1
-    start_idx = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(1, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
-    start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
-    end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
-    start_idx2 = torch.where(
-        torch.eq(x_idx, 0),
-        torch.tensor(0, device=x.device),
-        torch.where(
-            torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
-        ),
-    )
-    y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
-    start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
-    end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
-    cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
-    return cand
-
-
-def expand_dims(v, dims):
-    """
-    Expand the tensor `v` to the dim `dims`.
-
-    Args:
-        `v`: a PyTorch tensor with shape [N].
-        `dim`: a `int`.
-    Returns:
-        a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
-    """
-    return v[(...,) + (None,) * (dims - 1)]
diff --git a/spaces/FritsLyneborg/kunstnerfrits/src/dalle_mini/model/configuration.py b/spaces/FritsLyneborg/kunstnerfrits/src/dalle_mini/model/configuration.py
deleted file mode 100644
index 37897279c36c5d6e521ef55fda4637539d01e885..0000000000000000000000000000000000000000
--- a/spaces/FritsLyneborg/kunstnerfrits/src/dalle_mini/model/configuration.py
+++ /dev/null
@@ -1,176 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" DalleBart model configuration """
-import warnings
-
-from transformers.configuration_utils import PretrainedConfig
-from transformers.utils import logging
-
-from .utils import PretrainedFromWandbMixin
-
-logger = logging.get_logger(__name__)
-
-
-class DalleBartConfig(PretrainedFromWandbMixin, PretrainedConfig):
-    model_type = "dallebart"
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "num_attention_heads": "encoder_attention_heads",
-        "hidden_size": "d_model",
-    }
-
-    def __init__(
-        self,
-        normalize_text=False,
-        encoder_vocab_size=50264,
-        image_vocab_size=16384,  # encoded image token space
-        image_length=256,  # number of encoded tokens
-        max_text_length=64,  # max number of text tokens
-        encoder_layers=12,
-        encoder_ffn_dim=4096,
-        encoder_attention_heads=16,
-        decoder_layers=12,
-        decoder_ffn_dim=4096,
-        decoder_attention_heads=16,
-        activation_function="gelu",
-        d_model=1024,
-        dropout=0.1,
-        attention_dropout=0.0,
-        activation_dropout=0.0,
-        init_std=0.02,
-        scale_embedding=False,
-        gradient_checkpointing=False,
-        use_cache=True,
-        is_encoder_decoder=True,
-        forced_eos_token_id=None,
-        tie_word_embeddings=False,  # different modalities and sizes
-        do_sample=True,
-        # transformer variants
-        use_bias=False,  # use bias in attention and dense layers (except for lm_head)
-        ln_type="layernorm",  # layer normalization type, "rmsnorm", "layernorm"
-        ln_positions="normformer",  # layer normalization positions, "normformer", "swinv2", "cogview", "postln", "preln", "deepnet" (same as postln)
-        use_head_scale=False,  # used in NormFormer
-        use_cosine_attention=False,  # used in Swin v2
-        tau_init=0.05,  # used only in cosine attention (Swin v2)
-        use_absolute_position_embeddings=True,  # default
-        use_swin_position_embeddings=False,  # used in Swin v1/v2
-        use_deepnet_scaling=False,  # used in Deepnet
-        use_glu=False,  # "GLU Variants Improve Transformer"
-        use_alibi=False,  # Not implemented yet - from "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation"
-        sinkhorn_iters=1,  # used in SinkFormers
-        use_final_ln_encoder=True,  # final layer normalization in encoder
-        use_final_ln_decoder=True,  # final layer normalization in decoder
-        # parameters that should not be necessary but could affect results
-        force_ln_scale=False,  # force scale in layernorm even when followed by dense layers
-        **kwargs,
-    ):
-        # text normalizer
-        self.normalize_text = normalize_text
-
-        # transformer variants
-        self.use_bias = use_bias
-        assert ln_type in [
-            "rmsnorm",
-            "layernorm",
-        ], "ln_type must be 'rmsnorm' or 'layernorm'"
-        self.ln_type = ln_type
-        if ln_positions == "deepnet":
-            ln_positions = "postln"
-        assert ln_positions in [
-            "normformer",
-            "swinv2",
-            "cogview",
-            "postln",
-            "preln",
-        ], "ln_positions must be 'normformer', 'swinv2', 'cogview', 'postln', 'preln'"
-        self.use_head_scale = use_head_scale
-        assert use_alibi is False, "use_alibi is not supported yet"
-        self.ln_positions = ln_positions
-        self.use_cosine_attention = use_cosine_attention
-        self.tau_init = tau_init
-        self.use_absolute_position_embeddings = use_absolute_position_embeddings
-        self.use_swin_position_embeddings = use_swin_position_embeddings
-        self.use_deepnet_scaling = use_deepnet_scaling
-        self.use_glu = use_glu
-        self.use_alibi = use_alibi
-        self.sinkhorn_iters = sinkhorn_iters
-        if ln_positions == "postln":
-            assert (
-                use_final_ln_encoder
-            ), "use_final_ln_encoder must be True when ln_positions is 'postln'"
-            assert (
-                use_final_ln_decoder
-            ), "use_final_ln_decoder must be True when ln_positions is 'postln'"
-        self.use_final_ln_encoder = use_final_ln_encoder
-        self.use_final_ln_decoder = use_final_ln_decoder
-        self.force_ln_scale = force_ln_scale
-
-        # common parameters
-        self.encoder_vocab_size = encoder_vocab_size
-        self.image_vocab_size = image_vocab_size
-        self.image_length = image_length
-        self.max_text_length = max_text_length
-        self.d_model = d_model
-        self.encoder_ffn_dim = encoder_ffn_dim
-        self.encoder_layers = encoder_layers
-        self.encoder_attention_heads = encoder_attention_heads
-        self.decoder_ffn_dim = decoder_ffn_dim
-        self.decoder_layers = decoder_layers
-        self.decoder_attention_heads = decoder_attention_heads
-        self.dropout = dropout
-        self.attention_dropout = attention_dropout
-        self.activation_dropout = activation_dropout
-        self.activation_function = activation_function
-        self.init_std = init_std
-        self.use_cache = use_cache
-        self.gradient_checkpointing = gradient_checkpointing
-        self.scale_embedding = (
-            scale_embedding  # scale factor will be sqrt(d_model) if True
-        )
-
-        # special token id's are appended to vocab if not provided
-        decoder_start_token_id = kwargs.pop("decoder_start_token_id", image_vocab_size)
-        bos_token_id = kwargs.pop("bos_token_id", image_vocab_size)
-        pad_token_id = kwargs.pop("pad_token_id", image_vocab_size)
-        eos_token_id = kwargs.pop("eos_token_id", image_vocab_size)
-
-        # we generate to image_length + 1 (for bos) by default
-        min_length = kwargs.pop("min_length", image_length + 1)
-        max_length = kwargs.pop("max_length", image_length + 1)
-
-        super().__init__(
-            # args required in parent class
-            is_encoder_decoder=is_encoder_decoder,
-            tie_word_embeddings=tie_word_embeddings,
-            forced_eos_token_id=forced_eos_token_id,
-            decoder_start_token_id=decoder_start_token_id,
-            bos_token_id=bos_token_id,
-            pad_token_id=pad_token_id,
-            eos_token_id=eos_token_id,
-            min_length=min_length,
-            max_length=max_length,
-            do_sample=do_sample,
-            **kwargs,
-        )
-
-        # ensure backward compatibility for BART CNN models
-        if self.forced_bos_token_id is None and kwargs.get(
-            "force_bos_token_to_be_generated", False
-        ):
-            self.forced_bos_token_id = self.bos_token_id
-            warnings.warn(
-                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions."
-                "The config can simply be saved and uploaded again to be fixed."
-            )
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/construct_corner_blocks.py b/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/construct_corner_blocks.py
deleted file mode 100644
index 8910e5f28b27720962a5404d1bfccd0ccb2b5e43..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/generated_tasks/construct_corner_blocks.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import numpy as np
-import os
-import pybullet as p
-import random
-from cliport.tasks import primitives
-from cliport.tasks.grippers import Spatula
-from cliport.tasks.task import Task
-from cliport.utils import utils
-import numpy as np
-from cliport.tasks.task import Task
-from cliport.utils import utils
-
-class ConstructCornerBlocks(Task):
-    """Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner."""
-
-    def __init__(self):
-        super().__init__()
-        self.max_steps = 20
-        self.lang_template = "Create a corner structure using four blocks. Two red blocks form the base, one on each side of the corner, followed by a green block that is positioned on the red blocks at the corner junction, and finally a blue block on top of the green one. The overall structure forms a 3-D corner."
-        self.task_completed_desc = "done constructing corner blocks."
-        self.additional_reset()
-
-    def reset(self, env):
-        super().reset(env)
-
-        # Add corner.
-        corner_size = (0.15, 0.15, 0.05)
-        corner_urdf = 'corner/corner-template.urdf'
-        corner_pose = self.get_random_pose(env, corner_size)
-        env.add_object(corner_urdf, corner_pose, 'fixed')
-
-        # Add blocks.
-        block_size = (0.04, 0.04, 0.04)
-        block_urdf = 'block/block.urdf'
-        block_colors = [utils.COLORS['red'], utils.COLORS['red'], utils.COLORS['green'], utils.COLORS['blue']]
-        blocks = []
-        for i in range(4):
-            block_pose = self.get_random_pose(env, block_size)
-            block_id = env.add_object(block_urdf, block_pose, color=block_colors[i])
-            blocks.append(block_id)
-
-        # Associate placement locations for goals.
-        place_pos = [(0, -0.05, 0.02), (0, 0.05, 0.02), (0, 0, 0.06), (0, 0, 0.10)]
-        targs = [(utils.apply(corner_pose, i), corner_pose[1]) for i in place_pos]
-
-        # Goal: blocks are stacked in a corner (bottom row: two red blocks).
-        self.add_goal(objs=blocks[:2], matches=np.ones((2, 2)), targ_poses=targs[:2], replace=False,
-                rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*2,
-                          language_goal=self.lang_template)
-
-        # Goal: blocks are stacked in a corner (middle row: one green block).
-        self.add_goal(objs=blocks[2:3], matches=np.ones((1, 1)), targ_poses=targs[2:3], replace=False,
-                rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*1,
-                          language_goal=self.lang_template)
-
-        # Goal: blocks are stacked in a corner (top row: one blue block).
-        self.add_goal(objs=blocks[3:], matches=np.ones((1, 1)), targ_poses=targs[3:], replace=False,
-                rotations=True, metric='pose', params=None, step_max_reward=1 / 4, symmetries=[np.pi/2]*1,
-                          language_goal=self.lang_template)
diff --git a/spaces/Gen-Sim/Gen-Sim/cliport/tests/tasks_test.py b/spaces/Gen-Sim/Gen-Sim/cliport/tests/tasks_test.py
deleted file mode 100644
index 37868a38371ecb9434d048eb2c51cb33ac07ae83..0000000000000000000000000000000000000000
--- a/spaces/Gen-Sim/Gen-Sim/cliport/tests/tasks_test.py
+++ /dev/null
@@ -1,146 +0,0 @@
-"""Integration tests for dvnets tasks."""
-
-from absl.testing import absltest
-from absl.testing import parameterized
-from cliport import tasks
-from cliport.environments import environment
-
-ASSETS_PATH = 'cliport/environments/assets/'
-
-
-class TaskTest(parameterized.TestCase):
-
-    def _create_env(self):
-        assets_root = ASSETS_PATH
-        env = environment.Environment(assets_root)
-        env.seed(0)
-        return env
-
-    def _run_oracle_in_env(self, env):
-        agent = env.task.oracle(env)
-        obs = env.reset()
-        info = None
-        done = False
-        for _ in range(10):
-            act = agent.act(obs, info)
-            obs, _, done, info = env.step(act)
-            if done:
-                break
-
-    @parameterized.named_parameters((
-    # demo conditioned
-            'AlignBoxCorner',
-            tasks.AlignBoxCorner(),
-    ), (
-            'AssemblingKits',
-            tasks.AssemblingKits(),
-    ), (
-            'AssemblingKitsEasy',
-            tasks.AssemblingKitsEasy(),
-    ), (
-            'BlockInsertion',
-            tasks.BlockInsertion(),
-    ), (
-            'ManipulatingRope',
-            tasks.ManipulatingRope(),
-    ), (
-            'PackingBoxes',
-            tasks.PackingBoxes(),
-    ), (
-            'PalletizingBoxes',
-            tasks.PalletizingBoxes(),
-    ), (
-            'PlaceRedInGreen',
-            tasks.PlaceRedInGreen(),
-    ), (
-            'StackBlockPyramid',
-            tasks.StackBlockPyramid(),
-    ), (
-            'SweepingPiles',
-            tasks.SweepingPiles(),
-    ), (
-            'TowersOfHanoi',
-            tasks.TowersOfHanoi(),
-
-    # goal conditioned
-    ), (
-            'AlignRope',
-            tasks.AlignRope(),
-    ), (
-            'AssemblingKitsSeqSeenColors',
-            tasks.AssemblingKitsSeqSeenColors(),
-    ), (
-            'AssemblingKitsSeqUnseenColors',
-            tasks.AssemblingKitsSeqUnseenColors(),
-    ), (
-            'AssemblingKitsSeqFull',
-            tasks.AssemblingKitsSeqFull(),
-    ), (
-            'PackingShapes',
-            tasks.PackingShapes(),
-    ), (
-            'PackingBoxesPairsSeenColors',
-            tasks.PackingBoxesPairsSeenColors(),
-    ), (
-            'PackingBoxesPairsUnseenColors',
-            tasks.PackingBoxesPairsUnseenColors(),
-    ), (
-            'PackingBoxesPairsFull',
-            tasks.PackingBoxesPairsFull(),
-    ), (
-            'PackingSeenGoogleObjectsSeq',
-            tasks.PackingSeenGoogleObjectsSeq(),
-    ), (
-            'PackingUnseenGoogleObjectsSeq',
-            tasks.PackingUnseenGoogleObjectsSeq(),
-    ), (
-            'PackingSeenGoogleObjectsGroup',
-            tasks.PackingSeenGoogleObjectsGroup(),
-    ), (
-            'PackingUnseenGoogleObjectsGroup',
-            tasks.PackingUnseenGoogleObjectsGroup(),
-    ), (
-            'PutBlockInBowlSeenColors',
-            tasks.PutBlockInBowlSeenColors(),
-    ), (
-            'PutBlockInBowlUnseenColors',
-            tasks.PutBlockInBowlUnseenColors(),
-    ), (
-            'PutBlockInBowlFull',
-            tasks.PutBlockInBowlFull(),
-    ), (
-            'StackBlockPyramidSeqSeenColors',
-            tasks.StackBlockPyramidSeqSeenColors(),
-    ), (
-            'StackBlockPyramidSeqUnseenColors',
-            tasks.StackBlockPyramidSeqUnseenColors(),
-    ), (
-            'StackBlockPyramidSeqFull',
-            tasks.StackBlockPyramidSeqFull(),
-    ), (
-            'SeparatingPilesSeenColors',
-            tasks.SeparatingPilesUnseenColors(),
-    ), (
-            'SeparatingPilesUnseenColors',
-            tasks.SeparatingPilesUnseenColors(),
-    ), (
-            'SeparatingPilesFull',
-            tasks.SeparatingPilesFull(),
-    ), (
-            'TowersOfHanoiSeqSeenColors',
-            tasks.TowersOfHanoiSeqSeenColors(),
-    ), (
-            'TowersOfHanoiSeqUnseenColors',
-            tasks.TowersOfHanoiSeqUnseenColors(),
-    ), (
-            'TowersOfHanoiSeqFull',
-            tasks.TowersOfHanoiSeqFull(),
-    ))
-    def test_all_tasks(self, dvnets_task):
-        env = self._create_env()
-        env.set_task(dvnets_task)
-        self._run_oracle_in_env(env)
-
-
-if __name__ == '__main__':
-    absltest.main()
diff --git a/spaces/GeorgeOrville/bingo/tests/parse.ts b/spaces/GeorgeOrville/bingo/tests/parse.ts
deleted file mode 100644
index 92940fe6315f1d7cb2b267ba5e5a7e26460a1de3..0000000000000000000000000000000000000000
--- a/spaces/GeorgeOrville/bingo/tests/parse.ts
+++ /dev/null
@@ -1,13 +0,0 @@
-import { promises as fs } from 'fs'
-import { join } from 'path'
-import { parseHeadersFromCurl } from '@/lib/utils'
-
-(async () => {
-  const content = await fs.readFile(join(__dirname, './fixtures/curl.txt'), 'utf-8')
-  const headers = parseHeadersFromCurl(content)
-  console.log(headers)
-
-  const cmdContent = await fs.readFile(join(__dirname, './fixtures/cmd.txt'), 'utf-8')
-  const cmdHeaders = parseHeadersFromCurl(cmdContent)
-  console.log(cmdHeaders)
-})()
diff --git a/spaces/Gosula/hand_written_digit_recognition/app.py b/spaces/Gosula/hand_written_digit_recognition/app.py
deleted file mode 100644
index 162f2223fff9f8f90c05e28763bf34dd83ddef26..0000000000000000000000000000000000000000
--- a/spaces/Gosula/hand_written_digit_recognition/app.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# importing all the packages
-from sklearn.datasets import fetch_openml
-from sklearn.model_selection import train_test_split
-import numpy as np
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-import keras
-import matplotlib.pyplot as plt
-import pandas as pd
-from PIL import Image
-import streamlit as st
-from streamlit_drawable_canvas import st_canvas
-import cv2
-from PIL import Image
-import torchvision.transforms as transforms
-import torch
-from skorch import NeuralNetClassifier
-from torch import nn
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-from model import Cnn
-
-
-
-
-
-# Reading the data
-mnist = fetch_openml('mnist_784', as_frame=False, cache=False)
-X = mnist.data.astype('float32')
-y = mnist.target.astype('int64')
-X /= 255.0
-
-#device = 'cuda' if torch.cuda.is_available() else 'cpu'
-XCnn = X.reshape(-1, 1, 28, 28) #reshape input 
-XCnn_train, XCnn_test, y_train, y_test = train_test_split(XCnn, y, test_size=0.25, random_state=42) #train test split
-
-torch.manual_seed(0)
-
-
-
-# reshape and train test split
-
-#device = 'cuda' if torch.cuda.is_available() else 'cpu'
-XCnn = X.reshape(-1, 1, 28, 28)
-XCnn_train, XCnn_test, y_train, y_test = train_test_split(XCnn, y, test_size=0.25, random_state=42)
-
-from PIL import Image
-import torchvision.transforms as transforms
-
-torch.manual_seed(0)
-
-
-model=Cnn()
-
-# Specify the path to the saved model weights
-model_weights_path = 'model_weights.pth'
-
-# Load the model weights
-model.load_state_dict(torch.load(model_weights_path))
-
-# Set the model to evaluation mode for inference
-model.eval()
-
-# Create a NeuralNetClassifier using the loaded model
-cnn = NeuralNetClassifier(
-    module=model,
-    max_epochs=0,  # Set max_epochs to 0 to avoid additional training
-    lr=0.002,  # You can set this to the learning rate used during training
-    optimizer=torch.optim.Adam,  # You can set the optimizer used during training
-    device='cpu'  # You can specify the device ('cpu' for CPU, 'cuda' for GPU, etc.)
-)
-
-cnn.fit(XCnn_train, y_train)
-
-
-
-# Set the page title
-st.title("Handwritten Text Digit Recognition")
-
-stroke_width = st.sidebar.slider("Stroke width: ", 1, 35, 32)
-stroke_color = st.sidebar.color_picker("Stroke color hex:", "#ffffff")  # Default value is white
-bg_color = st.sidebar.color_picker("Background color hex:", "#000000")  # Default value is black
-
-bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
-drawing_mode = st.sidebar.selectbox(
-    "Drawing tool:", ("freedraw", "line", "rect", "circle", "transform", "polygon")
-)
-realtime_update = st.sidebar.checkbox("Update in realtime", True)
-#create canvas component
-canvas_result = st_canvas(
-    fill_color="rgba(255, 165, 0, 0.3)",  # Fixed fill color with some opacity
-    stroke_width=stroke_width,
-    stroke_color=stroke_color,
-    background_color=bg_color,
-    background_image=Image.open(bg_image) if bg_image else None,
-    update_streamlit=realtime_update,
-    height=200,
-    width=200,
-    drawing_mode=drawing_mode,
-    display_toolbar=st.sidebar.checkbox("Display toolbar", True),
-    key="full_app",
-)
-# Do something interesting with the image data and paths
-if canvas_result.image_data is not None:
-    image = canvas_result.image_data
-    image1 = image.copy()
-    image1 = image1.astype('uint8')
-    image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
-    image1 = cv2.resize(image1, (28, 28))
-    st.image(image1)
-
-    # Correctly reshape the image
-    image1 = image1.reshape(1, 1, 28, 28).astype('float32')
-    prediction = cnn.predict(image1)
-    st.title(f"Handwritten Digit Prediction: {prediction}")
-
-
-# if canvas_result.json_data is not None:
-#     st.dataframe(pd.json_normalize(canvas_result.json_data["objects"]))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r101_fpn_1x_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r101_fpn_1x_coco.py
deleted file mode 100644
index 9d2b1a695e0f63aeb20f81a4a38df17a7cf12d5b..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/paa/paa_r101_fpn_1x_coco.py
+++ /dev/null
@@ -1,2 +0,0 @@
-_base_ = './paa_r50_fpn_1x_coco.py'
-model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
diff --git a/spaces/Gradio-Blocks/uniformer_image_detection/configs/yolact/yolact_r50_8x8_coco.py b/spaces/Gradio-Blocks/uniformer_image_detection/configs/yolact/yolact_r50_8x8_coco.py
deleted file mode 100644
index b3adcb74a6155a0ab7303ab9ae90ee120f3eb4ad..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_detection/configs/yolact/yolact_r50_8x8_coco.py
+++ /dev/null
@@ -1,11 +0,0 @@
-_base_ = 'yolact_r50_1x8_coco.py'
-
-optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
-optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
-# learning policy
-lr_config = dict(
-    policy='step',
-    warmup='linear',
-    warmup_iters=1000,
-    warmup_ratio=0.1,
-    step=[20, 42, 49, 52])
diff --git a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py b/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
deleted file mode 100644
index 2a5dc203cc793860aae7743d16c4fb9a564ad1d8..0000000000000000000000000000000000000000
--- a/spaces/Gradio-Blocks/uniformer_image_segmentation/configs/encnet/encnet_r50-d8_512x512_160k_ade20k.py
+++ /dev/null
@@ -1,6 +0,0 @@
-_base_ = [
-    '../_base_/models/encnet_r50-d8.py', '../_base_/datasets/ade20k.py',
-    '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
-]
-model = dict(
-    decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/multibanddiffusion.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/multibanddiffusion.py
deleted file mode 100644
index 6a2f169d516ed5aaf5da61fb482d94dd142f55e9..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/models/multibanddiffusion.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Multi Band Diffusion models as described in
-"From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion"
-(paper link).
-"""
-
-import typing as tp
-
-import torch
-import julius
-
-from .unet import DiffusionUnet
-from ..modules.diffusion_schedule import NoiseSchedule
-from .encodec import CompressionModel
-from ..solvers.compression import CompressionSolver
-from .loaders import load_compression_model, load_diffusion_models
-
-
-class DiffusionProcess:
-    """Sampling for a diffusion Model.
-
-    Args:
-        model (DiffusionUnet): Diffusion U-Net model.
-        noise_schedule (NoiseSchedule): Noise schedule for diffusion process.
-    """
-    def __init__(self, model: DiffusionUnet, noise_schedule: NoiseSchedule) -> None:
-        """
-        """
-        self.model = model
-        self.schedule = noise_schedule
-
-    def generate(self, condition: torch.Tensor, initial_noise: torch.Tensor,
-                 step_list: tp.Optional[tp.List[int]] = None):
-        """Perform one diffusion process to generate one of the bands.
-
-        Args:
-            condition (tensor): The embeddings form the compression model.
-            initial_noise (tensor): The initial noise to start the process/
-        """
-        return self.schedule.generate_subsampled(model=self.model, initial=initial_noise, step_list=step_list,
-                                                 condition=condition)
-
-
-class MultiBandDiffusion:
-    """Sample from multiple diffusion models.
-
-    Args:
-        DPs (list of DiffusionProcess): Diffusion processes.
-        codec_model (CompressionModel): Underlying compression model used to obtain discrete tokens.
-    """
-    def __init__(self, DPs: tp.List[DiffusionProcess], codec_model: CompressionModel) -> None:
-        self.DPs = DPs
-        self.codec_model = codec_model
-        self.device = next(self.codec_model.parameters()).device
-
-    @property
-    def sample_rate(self) -> int:
-        return self.codec_model.sample_rate
-
-    @staticmethod
-    def get_mbd_musicgen(device=None):
-        """Load our diffusion models trained for MusicGen."""
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        path = 'https://dl.fbaipublicfiles.com/encodec/Diffusion/mbd_musicgen_32khz.th'
-        name = 'facebook/musicgen-small'
-        codec_model = load_compression_model(name, device=device)
-        models, processors, cfgs = load_diffusion_models(path, device=device)
-        DPs = []
-        for i in range(len(models)):
-            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i])
-            DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
-        return MultiBandDiffusion(DPs=DPs, codec_model=codec_model)
-
-    @staticmethod
-    def get_mbd_24khz(bw: float = 3.0, pretrained: bool = True,
-                      device: tp.Optional[tp.Union[torch.device, str]] = None,
-                      n_q: tp.Optional[int] = None):
-        """Get the pretrained Models for MultibandDiffusion.
-
-        Args:
-            bw (float): Bandwidth of the compression model.
-            pretrained (bool): Whether to use / download if necessary the models.
-            device (torch.device or str, optional): Device on which the models are loaded.
-            n_q (int, optional): Number of quantizers to use within the compression model.
-        """
-        if device is None:
-            device = 'cuda' if torch.cuda.is_available() else 'cpu'
-        assert bw in [1.5, 3.0, 6.0], f"bandwidth {bw} not available"
-        if n_q is not None:
-            assert n_q in [2, 4, 8]
-            assert {1.5: 2, 3.0: 4, 6.0: 8}[bw] == n_q, \
-                f"bandwidth and number of codebooks missmatch to use n_q = {n_q} bw should be {n_q * (1.5 / 2)}"
-        n_q = {1.5: 2, 3.0: 4, 6.0: 8}[bw]
-        codec_model = CompressionSolver.model_from_checkpoint(
-            '//pretrained/facebook/encodec_24khz', device=device)
-        codec_model.set_num_codebooks(n_q)
-        codec_model = codec_model.to(device)
-        path = f'https://dl.fbaipublicfiles.com/encodec/Diffusion/mbd_comp_{n_q}.pt'
-        models, processors, cfgs = load_diffusion_models(path, device=device)
-        DPs = []
-        for i in range(len(models)):
-            schedule = NoiseSchedule(**cfgs[i].schedule, sample_processor=processors[i])
-            DPs.append(DiffusionProcess(model=models[i], noise_schedule=schedule))
-        return MultiBandDiffusion(DPs=DPs, codec_model=codec_model)
-
-        return MultiBandDiffusion(DPs, codec_model)
-
-    @torch.no_grad()
-    def get_condition(self, wav: torch.Tensor, sample_rate: int) -> torch.Tensor:
-        """Get the conditioning (i.e. latent reprentatios of the compression model) from a waveform.
-        Args:
-            wav (torch.Tensor): The audio that we want to extract the conditioning from
-            sample_rate (int): sample rate of the audio"""
-        if sample_rate != self.sample_rate:
-            wav = julius.resample_frac(wav, sample_rate, self.sample_rate)
-        codes, scale = self.codec_model.encode(wav)
-        assert scale is None, "Scaled compression models not supported."
-        emb = self.get_emb(codes)
-        return emb
-
-    @torch.no_grad()
-    def get_emb(self, codes: torch.Tensor):
-        """Get latent representation from the discrete codes
-        Argrs:
-            codes (torch.Tensor): discrete tokens"""
-        emb = self.codec_model.decode_latent(codes)
-        return emb
-
-    def generate(self, emb: torch.Tensor, size: tp.Optional[torch.Size] = None,
-                 step_list: tp.Optional[tp.List[int]] = None):
-        """Generate Wavform audio from the latent embeddings of the compression model
-        Args:
-            emb (torch.Tensor): Conditioning embeddinds
-            size (none torch.Size): size of the output
-                if None this is computed from the typical upsampling of the model
-            step_list (optional list[int]): list of Markov chain steps, defaults to 50 linearly spaced step.
-        """
-        if size is None:
-            upsampling = int(self.codec_model.sample_rate / self.codec_model.frame_rate)
-            size = torch.Size([emb.size(0), self.codec_model.channels, emb.size(-1) * upsampling])
-        assert size[0] == emb.size(0)
-        out = torch.zeros(size).to(self.device)
-        for DP in self.DPs:
-            out += DP.generate(condition=emb, step_list=step_list, initial_noise=torch.randn_like(out))
-        return out
-
-    def re_eq(self, wav: torch.Tensor, ref: torch.Tensor, n_bands: int = 32, strictness: float = 1):
-        """match the eq to the encodec output by matching the standard deviation of some frequency bands
-        Args:
-            wav (torch.Tensor): audio to equalize
-            ref (torch.Tensor):refenrence audio from which we match the spectrogram.
-            n_bands (int): number of bands of the eq
-            strictness (float): how strict the the matching. 0 is no matching, 1 is exact matching.
-        """
-        split = julius.SplitBands(n_bands=n_bands, sample_rate=self.codec_model.sample_rate).to(wav.device)
-        bands = split(wav)
-        bands_ref = split(ref)
-        out = torch.zeros_like(ref)
-        for i in range(n_bands):
-            out += bands[i] * (bands_ref[i].std() / bands[i].std()) ** strictness
-        return out
-
-    def regenerate(self, wav: torch.Tensor, sample_rate: int):
-        """Regenerate a wavform through compression and diffusion regeneration.
-        Args:
-            wav (torch.Tensor): Original 'ground truth' audio
-            sample_rate (int): sample rate of the input (and output) wav
-        """
-        if sample_rate != self.codec_model.sample_rate:
-            wav = julius.resample_frac(wav, sample_rate, self.codec_model.sample_rate)
-        emb = self.get_condition(wav, sample_rate=self.codec_model.sample_rate)
-        size = wav.size()
-        out = self.generate(emb, size=size)
-        if sample_rate != self.codec_model.sample_rate:
-            out = julius.resample_frac(out, self.codec_model.sample_rate, sample_rate)
-        return out
-
-    def tokens_to_wav(self, tokens: torch.Tensor, n_bands: int = 32):
-        """Generate Waveform audio with diffusion from the discrete codes.
-        Args:
-            tokens (torch.Tensor): discrete codes
-            n_bands (int): bands for the eq matching.
-        """
-        wav_encodec = self.codec_model.decode(tokens)
-        condition = self.get_emb(tokens)
-        wav_diffusion = self.generate(emb=condition, size=wav_encodec.size())
-        return self.re_eq(wav=wav_diffusion, ref=wav_encodec, n_bands=n_bands)
diff --git a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/diffusion.py b/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/diffusion.py
deleted file mode 100644
index 93dea2520836f458ab1b8514dca952b51d113ec2..0000000000000000000000000000000000000000
--- a/spaces/GrandaddyShmax/AudioCraft_Plus/audiocraft/solvers/diffusion.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import typing as tp
-
-import flashy
-import julius
-import omegaconf
-import torch
-import torch.nn.functional as F
-
-from . import builders
-from . import base
-from .. import models
-from ..modules.diffusion_schedule import NoiseSchedule
-from ..metrics import RelativeVolumeMel
-from ..models.builders import get_processor
-from ..utils.samples.manager import SampleManager
-from ..solvers.compression import CompressionSolver
-
-
-class PerStageMetrics:
-    """Handle prompting the metrics per stage.
-    It outputs the metrics per range of diffusion states.
-    e.g. avg loss when t in [250, 500]
-    """
-    def __init__(self, num_steps: int, num_stages: int = 4):
-        self.num_steps = num_steps
-        self.num_stages = num_stages
-
-    def __call__(self, losses: dict, step: tp.Union[int, torch.Tensor]):
-        if type(step) is int:
-            stage = int((step / self.num_steps) * self.num_stages)
-            return {f"{name}_{stage}": loss for name, loss in losses.items()}
-        elif type(step) is torch.Tensor:
-            stage_tensor = ((step / self.num_steps) * self.num_stages).long()
-            out: tp.Dict[str, float] = {}
-            for stage_idx in range(self.num_stages):
-                mask = (stage_tensor == stage_idx)
-                N = mask.sum()
-                stage_out = {}
-                if N > 0:  # pass if no elements in the stage
-                    for name, loss in losses.items():
-                        stage_loss = (mask * loss).sum() / N
-                        stage_out[f"{name}_{stage_idx}"] = stage_loss
-                out = {**out, **stage_out}
-            return out
-
-
-class DataProcess:
-    """Apply filtering or resampling.
-
-    Args:
-        initial_sr (int): Initial sample rate.
-        target_sr (int): Target sample rate.
-        use_resampling: Whether to use resampling or not.
-        use_filter (bool):
-        n_bands (int): Number of bands to consider.
-        idx_band (int):
-        device (torch.device or str):
-        cutoffs ():
-        boost (bool):
-    """
-    def __init__(self, initial_sr: int = 24000, target_sr: int = 16000, use_resampling: bool = False,
-                 use_filter: bool = False, n_bands: int = 4,
-                 idx_band: int = 0, device: torch.device = torch.device('cpu'), cutoffs=None, boost=False):
-        """Apply filtering or resampling
-        Args:
-            initial_sr (int): sample rate of the dataset
-            target_sr (int): sample rate after resampling
-            use_resampling (bool): whether or not performs resampling
-            use_filter (bool): when True filter the data to keep only one frequency band
-            n_bands (int): Number of bands used
-            cuts (none or list): The cutoff frequencies of the band filtering
-                                if None then we use mel scale bands.
-            idx_band (int): index of the frequency band. 0 are lows ... (n_bands - 1) highs
-            boost (bool): make the data scale match our music dataset.
-        """
-        assert idx_band < n_bands
-        self.idx_band = idx_band
-        if use_filter:
-            if cutoffs is not None:
-                self.filter = julius.SplitBands(sample_rate=initial_sr, cutoffs=cutoffs).to(device)
-            else:
-                self.filter = julius.SplitBands(sample_rate=initial_sr, n_bands=n_bands).to(device)
-        self.use_filter = use_filter
-        self.use_resampling = use_resampling
-        self.target_sr = target_sr
-        self.initial_sr = initial_sr
-        self.boost = boost
-
-    def process_data(self, x, metric=False):
-        if x is None:
-            return None
-        if self.boost:
-            x /= torch.clamp(x.std(dim=(1, 2), keepdim=True), min=1e-4)
-            x * 0.22
-        if self.use_filter and not metric:
-            x = self.filter(x)[self.idx_band]
-        if self.use_resampling:
-            x = julius.resample_frac(x, old_sr=self.initial_sr, new_sr=self.target_sr)
-        return x
-
-    def inverse_process(self, x):
-        """Upsampling only."""
-        if self.use_resampling:
-            x = julius.resample_frac(x, old_sr=self.target_sr, new_sr=self.target_sr)
-        return x
-
-
-class DiffusionSolver(base.StandardSolver):
-    """Solver for compression task.
-
-    The diffusion task allows for MultiBand diffusion model training.
-
-    Args:
-        cfg (DictConfig): Configuration.
-    """
-    def __init__(self, cfg: omegaconf.DictConfig):
-        super().__init__(cfg)
-        self.cfg = cfg
-        self.device = cfg.device
-        self.sample_rate: int = self.cfg.sample_rate
-        self.codec_model = CompressionSolver.model_from_checkpoint(
-            cfg.compression_model_checkpoint, device=self.device)
-
-        self.codec_model.set_num_codebooks(cfg.n_q)
-        assert self.codec_model.sample_rate == self.cfg.sample_rate, (
-            f"Codec model sample rate is {self.codec_model.sample_rate} but "
-            f"Solver sample rate is {self.cfg.sample_rate}."
-            )
-        assert self.codec_model.sample_rate == self.sample_rate, \
-            f"Sample rate of solver {self.sample_rate} and codec {self.codec_model.sample_rate} " \
-            "don't match."
-
-        self.sample_processor = get_processor(cfg.processor, sample_rate=self.sample_rate)
-        self.register_stateful('sample_processor')
-        self.sample_processor.to(self.device)
-
-        self.schedule = NoiseSchedule(
-            **cfg.schedule, device=self.device, sample_processor=self.sample_processor)
-
-        self.eval_metric: tp.Optional[torch.nn.Module] = None
-
-        self.rvm = RelativeVolumeMel()
-        self.data_processor = DataProcess(initial_sr=self.sample_rate, target_sr=cfg.resampling.target_sr,
-                                          use_resampling=cfg.resampling.use, cutoffs=cfg.filter.cutoffs,
-                                          use_filter=cfg.filter.use, n_bands=cfg.filter.n_bands,
-                                          idx_band=cfg.filter.idx_band, device=self.device)
-
-    @property
-    def best_metric_name(self) -> tp.Optional[str]:
-        if self._current_stage == "evaluate":
-            return 'rvm'
-        else:
-            return 'loss'
-
-    @torch.no_grad()
-    def get_condition(self, wav: torch.Tensor) -> torch.Tensor:
-        codes, scale = self.codec_model.encode(wav)
-        assert scale is None, "Scaled compression models not supported."
-        emb = self.codec_model.decode_latent(codes)
-        return emb
-
-    def build_model(self):
-        """Build model and optimizer as well as optional Exponential Moving Average of the model.
-        """
-        # Model and optimizer
-        self.model = models.builders.get_diffusion_model(self.cfg).to(self.device)
-        self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
-        self.register_stateful('model', 'optimizer')
-        self.register_best_state('model')
-        self.register_ema('model')
-
-    def build_dataloaders(self):
-        """Build audio dataloaders for each stage."""
-        self.dataloaders = builders.get_audio_datasets(self.cfg)
-
-    def show(self):
-        # TODO
-        raise NotImplementedError()
-
-    def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
-        """Perform one training or valid step on a given batch."""
-        x = batch.to(self.device)
-        loss_fun = F.mse_loss if self.cfg.loss.kind == 'mse' else F.l1_loss
-
-        condition = self.get_condition(x)  # [bs, 128, T/hop, n_emb]
-        sample = self.data_processor.process_data(x)
-
-        input_, target, step = self.schedule.get_training_item(sample,
-                                                               tensor_step=self.cfg.schedule.variable_step_batch)
-        out = self.model(input_, step, condition=condition).sample
-
-        base_loss = loss_fun(out, target, reduction='none').mean(dim=(1, 2))
-        reference_loss = loss_fun(input_, target, reduction='none').mean(dim=(1, 2))
-        loss = base_loss / reference_loss ** self.cfg.loss.norm_power
-
-        if self.is_training:
-            loss.mean().backward()
-            flashy.distrib.sync_model(self.model)
-            self.optimizer.step()
-            self.optimizer.zero_grad()
-        metrics = {
-            'loss': loss.mean(), 'normed_loss': (base_loss / reference_loss).mean(),
-            }
-        metrics.update(self.per_stage({'loss': loss, 'normed_loss': base_loss / reference_loss}, step))
-        metrics.update({
-            'std_in': input_.std(), 'std_out': out.std()})
-        return metrics
-
-    def run_epoch(self):
-        # reset random seed at the beginning of the epoch
-        self.rng = torch.Generator()
-        self.rng.manual_seed(1234 + self.epoch)
-        self.per_stage = PerStageMetrics(self.schedule.num_steps, self.cfg.metrics.num_stage)
-        # run epoch
-        super().run_epoch()
-
-    def evaluate(self):
-        """Evaluate stage.
-        Runs audio reconstruction evaluation.
-        """
-        self.model.eval()
-        evaluate_stage_name = f'{self.current_stage}'
-        loader = self.dataloaders['evaluate']
-        updates = len(loader)
-        lp = self.log_progress(f'{evaluate_stage_name} estimate', loader, total=updates, updates=self.log_updates)
-
-        metrics = {}
-        n = 1
-        for idx, batch in enumerate(lp):
-            x = batch.to(self.device)
-            with torch.no_grad():
-                y_pred = self.regenerate(x)
-
-            y_pred = y_pred.cpu()
-            y = batch.cpu()  # should already be on CPU but just in case
-            rvm = self.rvm(y_pred, y)
-            lp.update(**rvm)
-            if len(metrics) == 0:
-                metrics = rvm
-            else:
-                for key in rvm.keys():
-                    metrics[key] = (metrics[key] * n + rvm[key]) / (n + 1)
-        metrics = flashy.distrib.average_metrics(metrics)
-        return metrics
-
-    @torch.no_grad()
-    def regenerate(self, wav: torch.Tensor, step_list: tp.Optional[list] = None):
-        """Regenerate the given waveform."""
-        condition = self.get_condition(wav)
-        initial = self.schedule.get_initial_noise(self.data_processor.process_data(wav))  # sampling rate changes.
-        result = self.schedule.generate_subsampled(self.model, initial=initial, condition=condition,
-                                                   step_list=step_list)
-        result = self.data_processor.inverse_process(result)
-        return result
-
-    def generate(self):
-        """Generate stage."""
-        sample_manager = SampleManager(self.xp)
-        self.model.eval()
-        generate_stage_name = f'{self.current_stage}'
-
-        loader = self.dataloaders['generate']
-        updates = len(loader)
-        lp = self.log_progress(generate_stage_name, loader, total=updates, updates=self.log_updates)
-
-        for batch in lp:
-            reference, _ = batch
-            reference = reference.to(self.device)
-            estimate = self.regenerate(reference)
-            reference = reference.cpu()
-            estimate = estimate.cpu()
-            sample_manager.add_samples(estimate, self.epoch, ground_truth_wavs=reference)
-        flashy.distrib.barrier()
diff --git a/spaces/Hallucinate/demo/ldm/lr_scheduler.py b/spaces/Hallucinate/demo/ldm/lr_scheduler.py
deleted file mode 100644
index be39da9ca6dacc22bf3df9c7389bbb403a4a3ade..0000000000000000000000000000000000000000
--- a/spaces/Hallucinate/demo/ldm/lr_scheduler.py
+++ /dev/null
@@ -1,98 +0,0 @@
-import numpy as np
-
-
-class LambdaWarmUpCosineScheduler:
-    """
-    note: use with a base_lr of 1.0
-    """
-    def __init__(self, warm_up_steps, lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0):
-        self.lr_warm_up_steps = warm_up_steps
-        self.lr_start = lr_start
-        self.lr_min = lr_min
-        self.lr_max = lr_max
-        self.lr_max_decay_steps = max_decay_steps
-        self.last_lr = 0.
-        self.verbosity_interval = verbosity_interval
-
-    def schedule(self, n, **kwargs):
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_lr}")
-        if n < self.lr_warm_up_steps:
-            lr = (self.lr_max - self.lr_start) / self.lr_warm_up_steps * n + self.lr_start
-            self.last_lr = lr
-            return lr
-        else:
-            t = (n - self.lr_warm_up_steps) / (self.lr_max_decay_steps - self.lr_warm_up_steps)
-            t = min(t, 1.0)
-            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
-                    1 + np.cos(t * np.pi))
-            self.last_lr = lr
-            return lr
-
-    def __call__(self, n, **kwargs):
-        return self.schedule(n,**kwargs)
-
-
-class LambdaWarmUpCosineScheduler2:
-    """
-    supports repeated iterations, configurable via lists
-    note: use with a base_lr of 1.0.
-    """
-    def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
-        assert len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths)
-        self.lr_warm_up_steps = warm_up_steps
-        self.f_start = f_start
-        self.f_min = f_min
-        self.f_max = f_max
-        self.cycle_lengths = cycle_lengths
-        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
-        self.last_f = 0.
-        self.verbosity_interval = verbosity_interval
-
-    def find_in_interval(self, n):
-        interval = 0
-        for cl in self.cum_cycles[1:]:
-            if n <= cl:
-                return interval
-            interval += 1
-
-    def schedule(self, n, **kwargs):
-        cycle = self.find_in_interval(n)
-        n = n - self.cum_cycles[cycle]
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
-                                                       f"current cycle {cycle}")
-        if n < self.lr_warm_up_steps[cycle]:
-            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
-            self.last_f = f
-            return f
-        else:
-            t = (n - self.lr_warm_up_steps[cycle]) / (self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle])
-            t = min(t, 1.0)
-            f = self.f_min[cycle] + 0.5 * (self.f_max[cycle] - self.f_min[cycle]) * (
-                    1 + np.cos(t * np.pi))
-            self.last_f = f
-            return f
-
-    def __call__(self, n, **kwargs):
-        return self.schedule(n, **kwargs)
-
-
-class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
-
-    def schedule(self, n, **kwargs):
-        cycle = self.find_in_interval(n)
-        n = n - self.cum_cycles[cycle]
-        if self.verbosity_interval > 0:
-            if n % self.verbosity_interval == 0: print(f"current step: {n}, recent lr-multiplier: {self.last_f}, "
-                                                       f"current cycle {cycle}")
-
-        if n < self.lr_warm_up_steps[cycle]:
-            f = (self.f_max[cycle] - self.f_start[cycle]) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
-            self.last_f = f
-            return f
-        else:
-            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (self.cycle_lengths[cycle] - n) / (self.cycle_lengths[cycle])
-            self.last_f = f
-            return f
-
diff --git a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py b/spaces/HaloMaster/chinesesummary/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py
deleted file mode 100644
index 8cb77bbe0edf675300614982466e802964f8c625..0000000000000000000000000000000000000000
--- a/spaces/HaloMaster/chinesesummary/fengshen/examples/zen1_finetune/fengshen_token_level_ft_task.py
+++ /dev/null
@@ -1,647 +0,0 @@
-# coding=utf-8
-# Copyright 2021 The IDEA Authors. All rights reserved.
-
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-
-#     http://www.apache.org/licenses/LICENSE-2.0
-
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from fengshen.models.zen1.ngram_utils import ZenNgramDict
-from fengshen.models.zen1.modeling import ZenForTokenClassification
-from fengshen.metric.metric import SeqEntityScore
-from fengshen.models.zen1.tokenization import BertTokenizer
-from random import shuffle
-from pytorch_lightning.callbacks import LearningRateMonitor
-from dataclasses import dataclass
-import logging
-import math
-import numpy as np
-import os
-import json
-import torch
-import pytorch_lightning as pl
-import argparse
-from pytorch_lightning.callbacks import ModelCheckpoint
-from torch.utils.data import Dataset, DataLoader
-
-import torch.nn.functional as F
-logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
-                    datefmt='%m/%d/%Y %H:%M:%S',
-                    level=logging.ERROR)
-logger = logging.getLogger(__name__)
-
-
-class InputExample(object):
-    """A single training/test example for simple sequence classification."""
-
-    def __init__(self, guid, text_a, text_b=None, label=None):
-        """Constructs a InputExample.
-
-        Args:
-            guid: Unique id for the example.
-            text_a: string. The untokenized text of the first sequence. For single
-            sequence tasks, only this sequence must be specified.
-            text_b: (Optional) string. The untokenized text of the second sequence.
-            Only must be specified for sequence pair tasks.
-            label: (Optional) string. The label of the example. This should be
-            specified for train and dev examples, but not for test examples.
-        """
-        self.guid = guid
-        self.text_a = text_a
-        self.text_b = text_b
-        self.label = label
-
-
-class InputFeatures(object):
-    """A single set of features of data."""
-
-    def __init__(self, input_ids, input_mask, segment_ids, label_id, ngram_ids, ngram_positions, ngram_lengths,
-                 ngram_tuples, ngram_seg_ids, ngram_masks, valid_ids=None, label_mask=None):
-        self.input_ids = input_ids
-        self.input_mask = input_mask
-        self.segment_ids = segment_ids
-        self.label_id = label_id
-        self.valid_ids = valid_ids
-        self.label_mask = label_mask
-
-        self.ngram_ids = ngram_ids
-        self.ngram_positions = ngram_positions
-        self.ngram_lengths = ngram_lengths
-        self.ngram_tuples = ngram_tuples
-        self.ngram_seg_ids = ngram_seg_ids
-        self.ngram_masks = ngram_masks
-
-
-def convert_examples_to_features(examples, label_map, max_seq_length, tokenizer, ngram_dict):
-    """Loads a data file into a list of `InputBatch`s."""
-
-    # label_map = {label: i for i, label in enumerate(label_list, 1)}
-
-    features = []
-    for (ex_index, example) in enumerate(examples):
-        textlist = example.text_a
-        labellist = example.label
-        tokens = []
-        labels = []
-        valid = []
-        label_mask = []
-        for i, word in enumerate(textlist):
-            token = tokenizer.tokenize(word)
-            tokens.extend(token)
-            label_1 = labellist[i]
-            for m in range(len(token)):
-                if m == 0:
-                    labels.append(label_1)
-                    valid.append(1)
-                    label_mask.append(1)
-                else:
-                    valid.append(0)
-        if len(tokens) >= max_seq_length - 1:
-            tokens = tokens[0:(max_seq_length - 2)]
-            labels = labels[0:(max_seq_length - 2)]
-            valid = valid[0:(max_seq_length - 2)]
-            label_mask = label_mask[0:(max_seq_length - 2)]
-        ntokens = []
-        segment_ids = []
-        label_ids = []
-        ntokens.append("[CLS]")
-        segment_ids.append(0)
-        valid.insert(0, 1)
-        label_mask.insert(0, 1)
-        label_ids.append(label_map["[CLS]"])
-        for i, token in enumerate(tokens):
-            ntokens.append(token)
-            segment_ids.append(0)
-            if len(labels) > i:
-                label_ids.append(label_map[labels[i]])
-        ntokens.append("[SEP]")
-        segment_ids.append(0)
-        valid.append(1)
-        label_mask.append(1)
-        label_ids.append(label_map["[SEP]"])
-        input_ids = tokenizer.convert_tokens_to_ids(ntokens)
-        input_mask = [1] * len(input_ids)
-        label_mask = [1] * len(label_ids)
-        while len(input_ids) < max_seq_length:
-            input_ids.append(0)
-            input_mask.append(0)
-            segment_ids.append(0)
-            label_ids.append(0)
-            valid.append(1)
-            label_mask.append(0)
-        while len(label_ids) < max_seq_length:
-            label_ids.append(0)
-            label_mask.append(0)
-        assert len(input_ids) == max_seq_length
-        assert len(input_mask) == max_seq_length
-        assert len(segment_ids) == max_seq_length
-        assert len(label_ids) == max_seq_length
-        assert len(valid) == max_seq_length
-        assert len(label_mask) == max_seq_length
-
-        # ----------- code for ngram BEGIN-----------
-        ngram_matches = []
-        #  Filter the ngram segment from 2 to 7 to check whether there is a ngram
-        for p in range(2, 8):
-            for q in range(0, len(tokens) - p + 1):
-                character_segment = tokens[q:q + p]
-                # j is the starting position of the ngram
-                # i is the length of the current ngram
-                character_segment = tuple(character_segment)
-                if character_segment in ngram_dict.ngram_to_id_dict:
-                    ngram_index = ngram_dict.ngram_to_id_dict[character_segment]
-                    ngram_matches.append([ngram_index, q, p, character_segment])
-
-        shuffle(ngram_matches)
-
-        max_ngram_in_seq_proportion = math.ceil((len(tokens) / max_seq_length) * ngram_dict.max_ngram_in_seq)
-        if len(ngram_matches) > max_ngram_in_seq_proportion:
-            ngram_matches = ngram_matches[:max_ngram_in_seq_proportion]
-
-        ngram_ids = [ngram[0] for ngram in ngram_matches]
-        ngram_positions = [ngram[1] for ngram in ngram_matches]
-        ngram_lengths = [ngram[2] for ngram in ngram_matches]
-        ngram_tuples = [ngram[3] for ngram in ngram_matches]
-        ngram_seg_ids = [0 if position < (len(tokens) + 2) else 1 for position in ngram_positions]
-
-        ngram_mask_array = np.zeros(ngram_dict.max_ngram_in_seq, dtype=np.bool)
-        ngram_mask_array[:len(ngram_ids)] = 1
-
-        # record the masked positions
-        ngram_positions_matrix = np.zeros(shape=(max_seq_length, ngram_dict.max_ngram_in_seq), dtype=np.int32)
-        for i in range(len(ngram_ids)):
-            ngram_positions_matrix[ngram_positions[i]:ngram_positions[i] + ngram_lengths[i], i] = 1.0
-
-        # Zero-pad up to the max ngram in seq length.
-        padding = [0] * (ngram_dict.max_ngram_in_seq - len(ngram_ids))
-        ngram_ids += padding
-        ngram_lengths += padding
-        ngram_seg_ids += padding
-
-        # ----------- code for ngram END-----------
-
-        features.append(
-            InputFeatures(input_ids=input_ids,
-                          input_mask=input_mask,
-                          segment_ids=segment_ids,
-                          label_id=label_ids,
-                          ngram_ids=ngram_ids,
-                          ngram_positions=ngram_positions_matrix,
-                          ngram_lengths=ngram_lengths,
-                          ngram_tuples=ngram_tuples,
-                          ngram_seg_ids=ngram_seg_ids,
-                          ngram_masks=ngram_mask_array,
-                          valid_ids=valid,
-                          label_mask=label_mask))
-    return features
-
-
-class DataProcessor(object):
-    """Base class for data converters for sequence classification data sets."""
-
-    def get_examples(self, data_path, set_type, quotechar=' '):
-        """See base class."""
-        return self._create_examples(
-            self._read_tsv(data_path, self.get_quotechar()), set_type)
-
-    def _create_examples(self, lines, set_type):
-        examples = []
-        for i, (sentence, label) in enumerate(lines):
-            guid = "%s-%s" % (set_type, i)
-            text_a = sentence
-            label = label
-            examples.append(InputExample(guid=guid, text_a=text_a, label=label))
-        return examples
-
-    def get_labels(self):
-        """Gets the list of labels for this data set."""
-        raise NotImplementedError()
-
-    def get_quotechar(self):
-        return ' '
-
-    @classmethod
-    def _read_tsv(cls, input_file, quotechar=None):
-        '''
-        read file
-        return format :
-        [ ['EU', 'B-ORG'], ['rejects', 'O'], ['German', 'B-MISC'], ['call', 'O'], ['to', 'O'], ['boycott', 'O'], ['British', 'B-MISC'], ['lamb', 'O'], ['.', 'O'] ]
-        '''
-        f = open(input_file)
-        data = []
-        sentence = []
-        label = []
-        for line in f:
-            if len(line) == 0 or line.startswith('-DOCSTART') or line[0] == "\n":
-                if len(sentence) > 0:
-                    data.append((sentence, label))
-                    sentence = []
-                    label = []
-                continue
-            splits = line.split(quotechar)
-            sentence.append(splits[0])
-            label.append(splits[-1][:-1])
-
-        if len(sentence) > 0:
-            data.append((sentence, label))
-            sentence = []
-            label = []
-        return data
-
-
-class MSRAProcessor(DataProcessor):
-    """Processor for the msra data set."""
-
-    def get_labels(self):
-        return ['B-NR', 'B-NS', 'B-NT', 'E-NR', 'E-NS', 'E-NT', 'M-NR',
-                'M-NS', 'M-NT', 'O', 'S-NR', 'S-NS', 'S-NT', '[CLS]', '[SEP]']
-
-
-class OntoNotes4Processor(DataProcessor):
-    """Processor for the OntoNotes4 data set."""
-
-    def get_labels(self):
-        return ['B-GPE', 'B-LOC', 'B-ORG', 'B-PER', 'E-GPE', 'E-LOC',
-                'E-ORG', 'E-PER', 'M-GPE', 'M-LOC', 'M-ORG', 'M-PER', 'O',
-                'S-GPE', 'S-LOC', 'S-ORG', 'S-PER', '[CLS]', '[SEP]']
-
-
-class WeiboProcessor(DataProcessor):
-    """Processor for the Weibo data set."""
-
-    def get_labels(self):
-        return ['B-GPE.NAM', 'B-GPE.NOM', 'B-LOC.NAM', 'B-LOC.NOM',
-                'B-ORG.NAM', 'B-ORG.NOM', 'B-PER.NAM', 'B-PER.NOM', 'E-GPE.NAM',
-                'E-GPE.NOM', 'E-LOC.NAM', 'E-LOC.NOM', 'E-ORG.NAM', 'E-ORG.NOM',
-                'E-PER.NAM', 'E-PER.NOM', 'M-GPE.NAM', 'M-LOC.NAM', 'M-LOC.NOM',
-                'M-ORG.NAM', 'M-ORG.NOM', 'M-PER.NAM', 'M-PER.NOM', 'O',
-                'S-GPE.NAM', 'S-LOC.NOM', 'S-PER.NAM', 'S-PER.NOM', '[CLS]', '[SEP]']
-
-
-class ResumeProcessor(DataProcessor):
-    """Processor for the resume data set."""
-
-    def get_labels(self):
-        return ['B-CONT', 'B-EDU', 'B-LOC', 'B-NAME', 'B-ORG', 'B-PRO',
-                'B-RACE', 'B-TITLE', 'E-CONT', 'E-EDU', 'E-LOC', 'E-NAME',
-                'E-ORG', 'E-PRO', 'E-RACE', 'E-TITLE', 'M-CONT', 'M-EDU',
-                'M-LOC', 'M-NAME', 'M-ORG', 'M-PRO', 'M-RACE', 'M-TITLE',
-                'O', 'S-NAME', 'S-ORG', 'S-RACE', '[CLS]', '[SEP]']
-
-
-class CMeEEProcessor(DataProcessor):
-    """Processor for the CMeEE data set."""
-
-    def get_quotechar(self):
-        return '\t'
-
-    def get_labels(self):
-        return ['B-临床表现', 'B-医学检验项目', 'B-医疗程序', 'B-医疗设备',
-                'B-微生物类', 'B-疾病', 'B-科室', 'B-药物', 'B-身体', 'I-临床表现',
-                'I-医学检验项目', 'I-医疗程序', 'I-医疗设备', 'I-微生物类',
-                'I-疾病', 'I-科室', 'I-药物', 'I-身体', 'O', '[CLS]', '[SEP]']
-
-
-class CLUENERProcessor(DataProcessor):
-    """Processor for the CLUENER data set."""
-
-    def get_quotechar(self):
-        return '\t'
-
-    def get_labels(self):
-        return ['B-书名', 'B-公司', 'B-地址', 'B-姓名', 'B-政府', 'B-景点',
-                'B-游戏', 'B-电影', 'B-组织机构', 'B-职位', 'I-书名', 'I-公司',
-                'I-地址', 'I-姓名', 'I-政府', 'I-景点', 'I-游戏', 'I-电影',
-                'I-组织机构', 'I-职位', 'O', '[CLS]', '[SEP]']
-
-
-class TaskDataset(Dataset):
-    def __init__(self, data_path, processor, mode='train'):
-        super().__init__()
-        self.data = self.load_data(data_path, processor, mode)
-
-    def __len__(self):
-        return len(self.data)
-
-    def __getitem__(self, index):
-        return self.data[index]
-
-    def load_data(self, data_path, processor, mode):
-        if mode == "train":
-            examples = processor.get_examples(data_path, mode)
-        elif mode == "test":
-            examples = processor.get_examples(data_path, mode)
-        elif mode == "dev":
-            examples = processor.get_examples(data_path, mode)
-        return examples
-
-
-@dataclass
-class TaskCollator:
-    args = None
-    tokenizer = None
-    ngram_dict = None
-    label2id = None
-
-    def __call__(self, samples):
-        features = convert_examples_to_features(samples, self.label2id, self.args.max_seq_length, self.tokenizer, self.ngram_dict)
-        # logger.info("  Num examples = %d", len(samples))
-
-        input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
-        input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
-        segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
-        label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
-        valid_ids = torch.tensor([f.valid_ids for f in features], dtype=torch.long)
-
-        ngram_ids = torch.tensor([f.ngram_ids for f in features], dtype=torch.long)
-        ngram_positions = torch.tensor([f.ngram_positions for f in features], dtype=torch.long)
-        # ngram_lengths = torch.tensor([f.ngram_lengths for f in features], dtype=torch.long)
-        # ngram_seg_ids = torch.tensor([f.ngram_seg_ids for f in features], dtype=torch.long)
-        # ngram_masks = torch.tensor([f.ngram_masks for f in features], dtype=torch.long)
-
-        # label_mask = torch.tensor([f.label_mask for f in features], dtype=torch.long)
-        return {
-            'input_ids': input_ids,
-            'ngram_ids': ngram_ids,
-            'ngram_positions': ngram_positions,
-            'attention_mask': input_mask,
-            'token_type_ids': segment_ids,
-            'labels': label_ids,
-            'valid_ids': valid_ids,
-        }
-
-
-class TaskDataModel(pl.LightningDataModule):
-    @staticmethod
-    def add_data_specific_args(parent_args):
-        parser = parent_args.add_argument_group('TASK NAME DataModel')
-        parser.add_argument('--data_dir', default='./data', type=str)
-        parser.add_argument('--num_workers', default=8, type=int)
-        parser.add_argument('--train_data', default='train.json', type=str)
-        parser.add_argument('--valid_data', default='dev.json', type=str)
-        parser.add_argument('--test_data', default='test.json', type=str)
-        parser.add_argument('--train_batchsize', default=16, type=int)
-        parser.add_argument('--valid_batchsize', default=32, type=int)
-        parser.add_argument('--max_seq_length', default=128, type=int)
-
-        parser.add_argument('--texta_name', default='text', type=str)
-        parser.add_argument('--textb_name', default='sentence2', type=str)
-        parser.add_argument('--label_name', default='label', type=str)
-        parser.add_argument('--id_name', default='id', type=str)
-
-        parser.add_argument('--dataset_name', default=None, type=str)
-        parser.add_argument('--vocab_file',
-                            type=str, default=None,
-                            help="Vocabulary mapping/file BERT was pretrainined on")
-        parser.add_argument("--do_lower_case",
-                            action='store_true',
-                            help="Set this flag if you are using an uncased model.")
-        parser.add_argument('--task_name', default='weibo', type=str)
-
-        return parent_args
-
-    def __init__(self, args):
-        super().__init__()
-        self.train_batchsize = args.train_batchsize
-        self.valid_batchsize = args.valid_batchsize
-        self.collator = TaskCollator()
-        self.collator.args = args
-        self.collator.tokenizer = BertTokenizer.from_pretrained(args.pretrained_model_path, do_lower_case=args.do_lower_case)
-        self.collator.ngram_dict = ZenNgramDict.from_pretrained(args.pretrained_model_path, tokenizer=self.collator.tokenizer)
-
-        processors = {
-            'weibo': WeiboProcessor,
-            'resume': ResumeProcessor,
-            'msra': MSRAProcessor,
-            'ontonotes4': OntoNotes4Processor,
-            'cmeee': CMeEEProcessor,
-            'cluener': CLUENERProcessor,
-        }
-        if args.task_name not in processors:
-            raise ValueError("Task not found: %s" % (args.task_name))
-        processor = processors[args.task_name]()
-        # 生成id映射
-        label_list = processor.get_labels()
-        label2id = {label: i for i, label in enumerate(label_list, 1)}
-        label2id["[PAD]"] = 0
-        self.id2label = {v: k for k, v in label2id.items()}
-        self.collator.label2id = label2id
-
-        if args.dataset_name is None:
-            self.train_data = TaskDataset(os.path.join(
-                args.data_dir, args.train_data), processor, mode='train')
-            self.valid_data = TaskDataset(os.path.join(
-                args.data_dir, args.valid_data), processor, mode='dev')
-            self.test_data = TaskDataset(os.path.join(
-                args.data_dir, args.test_data), processor, mode='test')
-
-        else:
-            import datasets
-            ds = datasets.load_dataset(args.dataset_name)
-            self.train_data = ds['train']
-            self.valid_data = ds['validation']
-            self.test_data = ds['test']
-        self.save_hyperparameters(args)
-
-    def train_dataloader(self):
-        return DataLoader(self.train_data, shuffle=True, batch_size=self.train_batchsize, pin_memory=False,
-                          collate_fn=self.collator)
-
-    def val_dataloader(self):
-        return DataLoader(self.valid_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
-                          collate_fn=self.collator)
-
-    def predict_dataloader(self):
-        return DataLoader(self.test_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False,
-                          collate_fn=self.collator)
-
-
-class LitModel(pl.LightningModule):
-
-    @staticmethod
-    def add_model_specific_args(parent_args):
-        parser = parent_args.add_argument_group('BaseModel')
-        parser.add_argument('--markup', default='bios', type=str)
-        parser.add_argument('--middle_prefix', default='I-', type=str)
-        return parent_args
-
-    def __init__(self, args, id2label):
-        super().__init__()
-        # config = ZenConfig(os.path.join(args.pretrained_model_path, 'config.json'))
-        self.model = ZenForTokenClassification.from_pretrained(args.pretrained_model_path, num_labels=len(id2label))
-        self.seq_entity_score = SeqEntityScore(id2label, markup=args.markup, middle_prefix=args.middle_prefix)
-        self.train_seq_entity_score = SeqEntityScore(id2label, markup=args.markup, middle_prefix=args.middle_prefix)
-        self.id2label = id2label
-        self.label2id = {v: k for k, v in id2label.items()}
-        self.save_hyperparameters(args)
-
-    def setup(self, stage) -> None:
-        if stage == 'fit':
-            train_loader = self.trainer._data_connector._train_dataloader_source.dataloader()
-
-            # Calculate total steps
-            if self.trainer.max_epochs > 0:
-                world_size = self.trainer.world_size
-                tb_size = self.hparams.train_batchsize * max(1, world_size)
-                ab_size = self.trainer.accumulate_grad_batches
-                self.total_steps = (len(train_loader.dataset) *
-                                    self.trainer.max_epochs // tb_size) // ab_size
-            else:
-                self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches
-
-            print('Total steps: {}' .format(self.total_steps))
-
-    def training_step(self, batch, batch_idx):
-        outputs = self.model(**batch)
-        loss, _ = outputs
-        # logits = outputs.logits
-        # preds = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
-        # preds = preds.detach().cpu().numpy()
-        # labels = batch['labels'].detach().cpu().numpy()
-        # num_labels = len(self.label2id)
-        # y_true = []
-        # y_pred = []
-        # for i, label in enumerate(labels):
-        #     temp_1 = []
-        #     temp_2 = []
-        #     for j, m in enumerate(label):
-        #         if j == 0:
-        #             continue
-        #         elif labels[i][j] == num_labels - 1:
-        #             y_true.append(temp_1)
-        #             y_pred.append(temp_2)
-        #             break
-        #         else:
-        #             temp_1.append(self.id2label[labels[i][j]])
-        #             temp_2.append(self.id2label[preds[i][j]])
-
-        # self.train_seq_entity_score.update(y_true, y_pred)
-        # result = self.train_seq_entity_score.result()
-        # self.train_seq_entity_score.reset()
-        self.log('train_loss', loss)
-
-        return loss
-
-    def validation_step(self, batch, batch_idx):
-        outputs = self.model(**batch)
-        loss, logits = outputs
-        preds = torch.argmax(F.log_softmax(logits, dim=2), dim=2)
-        preds = preds.detach().cpu().numpy()
-        labels = batch['labels'].detach().cpu().numpy()
-        num_labels = len(self.label2id)
-        y_true = []
-        y_pred = []
-        for i, label in enumerate(labels):
-            temp_1 = []
-            temp_2 = []
-            for j, m in enumerate(label):
-                if j == 0:
-                    continue
-                elif labels[i][j] == num_labels - 1:
-                    y_true.append(temp_1)
-                    y_pred.append(temp_2)
-                    break
-                else:
-                    temp_1.append(self.id2label[labels[i][j]])
-                    temp_2.append(self.id2label[preds[i][j]])
-
-        self.seq_entity_score.update(y_true, y_pred)
-        self.log('val_loss', loss)
-
-    def validation_epoch_end(self, outputs):
-        # compute metric for all process
-        score_dict, _ = self.seq_entity_score.result()
-        if self.trainer._accelerator_connector.cluster_environment.global_rank() == 0:
-            print('score_dict:\n', score_dict)
-        # reset the metric after once validation
-        self.seq_entity_score.reset()
-        for k, v in score_dict.items():
-            self.log('val_{}'.format(k), v)
-
-    def configure_optimizers(self):
-        from fengshen.models.model_utils import configure_optimizers
-        return configure_optimizers(self)
-
-
-class TaskModelCheckpoint:
-    @staticmethod
-    def add_argparse_args(parent_args):
-        parser = parent_args.add_argument_group('BaseModel')
-
-        parser.add_argument('--monitor', default='train_loss', type=str)
-        parser.add_argument('--mode', default='min', type=str)
-        parser.add_argument('--dirpath', default='./log/', type=str)
-        parser.add_argument(
-            '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str)
-
-        parser.add_argument('--save_top_k', default=3, type=float)
-        parser.add_argument('--every_n_train_steps', default=100, type=float)
-        parser.add_argument('--save_weights_only', default=True, type=bool)
-
-        return parent_args
-
-    def __init__(self, args):
-        self.callbacks = ModelCheckpoint(monitor=args.monitor,
-                                         save_top_k=args.save_top_k,
-                                         mode=args.mode,
-                                         every_n_train_steps=args.every_n_train_steps,
-                                         save_weights_only=args.save_weights_only,
-                                         dirpath=args.dirpath,
-                                         filename=args.filename)
-
-
-def save_test(data, args, data_model):
-    with open(args.output_save_path, 'w', encoding='utf-8') as f:
-        idx = 0
-        for i in range(len(data)):
-            batch = data[i]
-            for sample in batch:
-                tmp_result = dict()
-                label_id = np.argmax(sample.numpy())
-                tmp_result['id'] = data_model.test_data.data[idx]['id']
-                tmp_result['label'] = data_model.id2label[label_id]
-                json_data = json.dumps(tmp_result, ensure_ascii=False)
-                f.write(json_data+'\n')
-                idx += 1
-    print('save the result to '+args.output_save_path)
-
-
-def main():
-    total_parser = argparse.ArgumentParser("TASK NAME")
-    total_parser.add_argument('--pretrained_model_path', default='', type=str)
-    total_parser.add_argument('--output_save_path',
-                              default='./predict.json', type=str)
-    # * Args for data preprocessing
-    total_parser = TaskDataModel.add_data_specific_args(total_parser)
-    # * Args for training
-    total_parser = pl.Trainer.add_argparse_args(total_parser)
-    total_parser = TaskModelCheckpoint.add_argparse_args(total_parser)
-
-    # * Args for base model
-    from fengshen.models.model_utils import add_module_args
-    total_parser = add_module_args(total_parser)
-    total_parser = LitModel.add_model_specific_args(total_parser)
-
-    args = total_parser.parse_args()
-
-    checkpoint_callback = TaskModelCheckpoint(args).callbacks
-    lr_monitor = LearningRateMonitor(logging_interval='step')
-    trainer = pl.Trainer.from_argparse_args(args,
-                                            callbacks=[checkpoint_callback, lr_monitor]
-                                            )
-
-    data_model = TaskDataModel(args)
-    id2label = data_model.id2label
-    print('id2label:', id2label)
-    model = LitModel(args, id2label)
-    trainer.fit(model, data_model)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/HarryLee/Key2Text/README.md b/spaces/HarryLee/Key2Text/README.md
deleted file mode 100644
index a1195ae9b577cacadbf272e4f6f6d02ed1a2cd12..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/Key2Text/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: KeyToText
-emoji: 🌍
-colorFrom: purple
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.10.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/adaptive_span/README.md b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/adaptive_span/README.md
deleted file mode 100644
index d5224fb2894606a2a8027e01e224be190776ecfe..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/adaptive_span/README.md
+++ /dev/null
@@ -1,90 +0,0 @@
-# Adaptive Span
-
-Adaptive Span is a novel self-attention mechanism that can learn its optimal
-attention span. This allows us to extend significantly the maximum context size
-used in Transformer, while maintaining control over their memory footprint
-and computational time. It uses the Truncated BPTT technique for training,
-as in [transformerXL](https://github.com/pytorch/fairseq/blob/main/examples/truncated_bptt/README.md).
-
-Adaptive Span was introduced by paper:
-[Adaptive Attention Span in Transformers](https://arxiv.org/abs/1905.07799),
-which achieved state-of-the-art language modeling results at the time of publication.
-
-We manage to reproduce their result in fairseq and keep most of the
-[original implementation](https://github.com/facebookresearch/adaptive-span) untouched.
-You can refer to the their sweep file as well if any combination of hyperparameter is not clear.
-
-##### 0. Setup
-
-First you need to process the Enwik8 dataset, we use the pre-tokenized dataset
-from [adaptive span paper](https://github.com/facebookresearch/adaptive-span/blob/master/get_data.sh).
-You can download the dataset, and then run:
-```bash
-fairseq-preprocess --only-source --trainpref ~/data/enwik8/train.txt \
-    --validpref ~/data/enwik8/valid.txt --testpref ~/data/enwik8/test.txt \
-    --destdir ~/data/enwik8/data-bin/ --joined-dictionary --workers 20
-```
-
-##### 1. Train a Adaptive Span model on Enwik8
-
-We will train a 12-layer Adaptive Span model following the [hyperparameters
-used in the original
-paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh).
-
-The following command assumes 4 GPUs, so that the total batch size is 64
-sequences (4 x 16). Training should take 2-3 days on 4 V100 GPUs:
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \
-    --user-dir examples/adaptive_span \
-    --data  ~/data/enwik8/data-bin/ \
-    --fp16 --fp16-no-flatten-grads --max-update 600000 \
-    --task truncated_bptt_lm --tokens-per-sample 512 --arch adaptive_span \
-    --n-layer 12 --d-model 512 --n-head 8 --d-inner 2048 --dropout 0.3 \
-    --attn-span 8192 --optimizer adagrad_with_grad_clip --adagrad-clip 0.03 \
-    --validate-interval-updates 1000 \
-    --lr-scheduler fixed --warmup-updates 32000 --batch-size-valid 32 \
-    --lr 0.07 --criterion adaptive_span_loss --batch-size 16 --update-freq 1 \
-    --seed 2 --log-format json --log-interval 25 --aux-loss-scaler 5e-07
-```
-This should land around 1.05 on validation, 1.03 on test. You can lower the
---aux-loss-scaler for better performance (longer span). It gives ~0.03 bpc
-improvement to the transformerXL baseline here.
-If training on a single GPU, set `--update-freq=4` to accumulate 4x gradients
-and simulate training on 4 GPUs.
-You can also reproduce the transformerXL result on enwik8 using this code base.
-It should land around 1.06 on test,matching the [original paper](https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/run_enwik8_base.sh).
-You can try by
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train \
-    --user-dir examples/truncated_bptt \
-    ~/data/enwik8/data-bin/ \
-    --task truncated_bptt_lm  --fp16 --max-update 400000 \
-    --tokens-per-sample 512 --arch transformer_xl --n-layer 12 \
-    --d-model 512 --n-head 8 --d-head 64 --d-inner 2048 --dropout 0.1 \
-    --dropatt 0.0 --mem-len 512 --optimizer adam --clip-norm 0.25 \
-    --lr-scheduler cosine --warmup-updates 0 \
-    --lr 0.0 --lr 0.00025 --batch-size 15 \
-    --update-freq 1 --seed 2 --log-format json --log-interval 25 \
-    --fp16
-```
-
-##### 2. Evaluate
-For Adaptive Span:
-```bash
-fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \
- --user-dir examples/adaptive_span \
- --task truncated_bptt_lm --batch-size 8 --tokens-per-sample 512 --gen-subset test
-```
-For Transformer-XL evaluation:
-```bash
-fairseq-eval-lm ~/data/enwik8/data-bin/ --path model/checkpoint_best.pt \
-    --user-dir examples/truncated_bptt/ --task truncated_bptt_lm --batch-size 8 \
-    --tokens-per-sample 80 \
-    --model-overrides '{"mem_len":2100,"clamp_len":820,"same_length":True}' \
-    --gen-subset valid
-```
-
-*Note:* During training the model saw 512 tokens of context
-(``--tokens-per-sample=512``), with batch size 8. These settings match the evaluation
-settings from [the original
-paper](https://github.com/facebookresearch/adaptive-span/blob/master/experiments/enwik8.sh).
diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/layer_norm.py b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/layer_norm.py
deleted file mode 100644
index 234609d9e213a650e0032aaa0ca0462a818bfead..0000000000000000000000000000000000000000
--- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/fairseq/modules/layer_norm.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-try:
-    from apex.normalization import FusedLayerNorm as _FusedLayerNorm
-
-    has_fused_layernorm = True
-
-    class FusedLayerNorm(_FusedLayerNorm):
-        @torch.jit.unused
-        def forward(self, x):
-            if not x.is_cuda:
-                return super().forward(x)
-            else:
-                with torch.cuda.device(x.device):
-                    return super().forward(x)
-
-
-except ImportError:
-    has_fused_layernorm = False
-
-
-def LayerNorm(normalized_shape, eps=1e-5, elementwise_affine=True, export=False):
-    if torch.jit.is_scripting():
-        export = True
-    if not export and torch.cuda.is_available() and has_fused_layernorm:
-        return FusedLayerNorm(normalized_shape, eps, elementwise_affine)
-    return torch.nn.LayerNorm(normalized_shape, eps, elementwise_affine)
-
-
-class Fp32LayerNorm(nn.LayerNorm):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-    def forward(self, input):
-        output = F.layer_norm(
-            input.float(),
-            self.normalized_shape,
-            self.weight.float() if self.weight is not None else None,
-            self.bias.float() if self.bias is not None else None,
-            self.eps,
-        )
-        return output.type_as(input)
diff --git a/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/bpe_toy.py b/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/bpe_toy.py
deleted file mode 100644
index 0421b255861cb56eb40bf58a8225807cc396e968..0000000000000000000000000000000000000000
--- a/spaces/Harveenchadha/en_to_indic_translation/subword-nmt/subword_nmt/bpe_toy.py
+++ /dev/null
@@ -1,51 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-# Author: Rico Sennrich
-
-"""Use byte pair encoding (BPE) to learn a variable-length encoding of the vocabulary in a text.
-Unlike the original BPE, it does not compress the plain text, but can be used to reduce the vocabulary
-of a text to a configurable number of symbols, with only a small increase in the number of tokens.
-This is an (inefficient) toy implementation that shows the algorithm. For processing large datasets,
-indexing and incremental updates can be used to speed up the implementation (see learn_bpe.py).
-
-Reference:
-Rico Sennrich, Barry Haddow and Alexandra Birch (2016). Neural Machine Translation of Rare Words with Subword Units.
-Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). Berlin, Germany.
-"""
-
-
-import re
-import sys
-import collections
-
-def get_stats(vocab):
-  pairs = collections.defaultdict(int)
-  for word, freq in vocab.items():
-    symbols = word.split()
-    for i in range(len(symbols)-1):
-      pairs[symbols[i],symbols[i+1]] += freq
-  return pairs
-
-def merge_vocab(pair, v_in):
-  v_out = {}
-  bigram_pattern = re.escape(' '.join(pair))
-  p = re.compile(r'(?<!\S)' + bigram_pattern + r'(?!\S)')
-  for word in v_in:
-    w_out = p.sub(''.join(pair), word)
-    v_out[w_out] = v_in[word]
-  return v_out
-
-vocab = {'l o w</w>' : 5, 'l o w e r</w>' : 2,
-         'n e w e s t</w>' : 6, 'w i d e s t</w>' : 3}
-num_merges = 15
-for i in range(num_merges):
-  pairs = get_stats(vocab)
-  try:
-    best = max(pairs, key=pairs.get)
-  except ValueError:
-    break
-  if pairs[best] < 2:
-     sys.stderr.write('no pair has frequency > 1. Stopping\n')
-     break
-  vocab = merge_vocab(best, vocab)
-  print(best)
diff --git a/spaces/Hazzzardous/RWKV-Instruct-1B5/app.py b/spaces/Hazzzardous/RWKV-Instruct-1B5/app.py
deleted file mode 100644
index 0d22dd9e39ebafa5966309f93701574ae76111ae..0000000000000000000000000000000000000000
--- a/spaces/Hazzzardous/RWKV-Instruct-1B5/app.py
+++ /dev/null
@@ -1,284 +0,0 @@
-"""
-RWKV RNN Model - Gradio Space for HuggingFace
-YT - Mean Gene Hacks - https://www.youtube.com/@MeanGeneHacks
-(C) Gene Ruebsamen - 2/7/2023
-This program is free software: you can redistribute it and/or modify
-it under the terms of the GNU General Public License as published by
-the Free Software Foundation, either version 3 of the License, or
-(at your option) any later version.
-This program is distributed in the hope that it will be useful,
-but WITHOUT ANY WARRANTY; without even the implied warranty of
-MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-GNU General Public License for more details.
-You should have received a copy of the GNU General Public License
-along with this program.  If not, see <http://www.gnu.org/licenses/>.
-"""
-
-import gradio as gr
-import codecs
-from ast import literal_eval
-from datetime import datetime
-from rwkvstic.load import RWKV
-from config import config, title
-import torch
-import gc
-
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-
-desc = '''<p>RNN with Transformer-level LLM Performance (<a href='https://github.com/BlinkDL/RWKV-LM'>github</a>).
-    According to the author: "It combines the best of RNN and transformers - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding."'''
-
-thanks = '''<p>Thanks to <a href='https://github.com/gururise/rwkv_gradio'>Gururise</a> for this template</p>'''
-
-
-def to_md(text):
-    return text.replace("\n", "<br />")
-
-
-def get_model():
-    model = None
-    model = RWKV(
-        **config
-    )
-    return model
-
-
-model = None
-
-
-def infer(
-        prompt,
-        mode="generative",
-        max_new_tokens=10,
-        temperature=0.1,
-        top_p=1.0,
-        end_adj=0.0,
-        stop="<|endoftext|>",
-        seed=42,
-):
-    global model
-
-    if model == None:
-        gc.collect()
-        if (DEVICE == "cuda"):
-            torch.cuda.empty_cache()
-        model = get_model()
-
-    max_new_tokens = int(max_new_tokens)
-    temperature = float(temperature)
-    end_adj = float(end_adj)
-    top_p = float(top_p)
-    stop = [x.strip(' ') for x in stop.split(',')]
-    seed = seed
-
-    assert 1 <= max_new_tokens <= 384
-    assert 0.0 <= temperature <= 1.0
-    assert 0.0 <= top_p <= 1.0
-
-    temperature = max(0.05, temperature)
-    if prompt == "":
-        prompt = " "
-
-    # Clear model state for generative mode
-    model.resetState()
-    if (mode == "Q/A"):
-        prompt = f"\nQ: {prompt}\n\nA:"
-    if (mode == "ELDR"):
-        prompt = f"\n{prompt}\n\nExpert Long Detailed Response:"
-    if (mode == "EFA"):
-        prompt = f'\nAsk Expert\n\nQuestion:\n{prompt}\n\nExpert Full Answer:\n'
-    if (mode == "BFR"):
-        prompt = f"Task given:\n\n{prompt}\n\nBest Full Response:"
-
-    print(f"PROMPT ({datetime.now()}):\n-------\n{prompt}")
-    print(f"OUTPUT ({datetime.now()}):\n-------\n")
-    # Load prompt
-    model.loadContext(newctx=prompt)
-    generated_text = ""
-    done = False
-    with torch.no_grad():
-        for _ in range(max_new_tokens):
-            char = model.forward(stopStrings=stop, temp=temperature, top_p_usual=top_p, end_adj=end_adj)[
-                "output"]
-            print(char, end='', flush=True)
-            generated_text += char
-            generated_text = generated_text.lstrip("\n ")
-
-            for stop_word in stop:
-                stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
-                if stop_word != '' and stop_word in generated_text:
-                    done = True
-                    break
-            yield generated_text
-            if done:
-                print("<stopped>\n")
-                break
-
-    # print(f"{generated_text}")
-
-    for stop_word in stop:
-        stop_word = codecs.getdecoder("unicode_escape")(stop_word)[0]
-        if stop_word != '' and stop_word in generated_text:
-            generated_text = generated_text[:generated_text.find(stop_word)]
-
-    gc.collect()
-    yield generated_text
-
-
-def chat(
-        prompt,
-        history,
-        username,
-        max_new_tokens=10,
-        temperature=0.1,
-        top_p=1.0,
-        seed=42,
-):
-    global model
-    history = history or []
-
-    intro = ""
-
-    if model == None:
-        gc.collect()
-        if (DEVICE == "cuda"):
-            torch.cuda.empty_cache()
-        model = get_model()
-
-    username = username.strip()
-    username = username or "USER"
-
-    intro = f'''The following is a verbose and detailed conversation between an AI assistant called FRITZ, and a human user called USER. FRITZ is intelligent, knowledgeable, wise and polite.
-    {username}: What year was the french revolution?
-    FRITZ: The French Revolution started in 1789, and lasted 10 years until 1799.
-    {username}: 3+5=?
-    FRITZ: The answer is 8.
-    {username}: What year did the Berlin Wall fall?
-    FRITZ: The Berlin wall stood for 28 years and fell in 1989.
-    {username}: solve for a: 9-a=2
-    FRITZ: The answer is a=7, because 9-7 = 2.
-    {username}: wat is lhc
-    FRITZ: The Large Hadron Collider (LHC) is a high-energy particle collider, built by CERN, and completed in 2008. It was used to confirm the existence of the Higgs boson in 2012.
-    {username}: Tell me about yourself.
-    FRITZ: My name is Fritz. I am an RNN based Large Language Model (LLM).
-    '''
-
-    if len(history) == 0:
-        # no history, so lets reset chat state
-        model.resetState()
-        history = [[], model.emptyState]
-        print("reset chat state")
-    else:
-        if (history[0][0][0].split(':')[0] != username):
-            model.resetState()
-            history = [[], model.emptyState]
-            print("username changed, reset state")
-        else:
-            model.setState(history[1])
-            intro = ""
-
-    max_new_tokens = int(max_new_tokens)
-    temperature = float(temperature)
-    top_p = float(top_p)
-    seed = seed
-
-    assert 1 <= max_new_tokens <= 384
-    assert 0.0 <= temperature <= 1.0
-    assert 0.0 <= top_p <= 1.0
-
-    temperature = max(0.05, temperature)
-
-    prompt = f"{username}: " + prompt + "\n"
-    print(f"CHAT ({datetime.now()}):\n-------\n{prompt}")
-    print(f"OUTPUT ({datetime.now()}):\n-------\n")
-    # Load prompt
-
-    model.loadContext(newctx=intro+prompt)
-
-    out = model.forward(number=max_new_tokens, stopStrings=[
-                        "<|endoftext|>", username+":"], temp=temperature, top_p_usual=top_p)
-
-    generated_text = out["output"].lstrip("\n ")
-    generated_text = generated_text.rstrip("USER:")
-    print(f"{generated_text}")
-
-    gc.collect()
-    history[0].append((prompt, generated_text))
-    return history[0], [history[0], out["state"]]
-
-
-examples = [
-    [
-        # Question Answering
-        '''What is the capital of Germany?''', "Q/A", 25, 0.2, 1.0, "<|endoftext|>"],
-    [
-        # Question Answering
-        '''Are humans good or bad?''', "Q/A", 150, 0.8, 0.8, "<|endoftext|>"],
-    [
-        # Question Answering
-        '''What is the purpose of Vitamin A?''', "Q/A", 50, 0.2, 0.8, "<|endoftext|>"],
-    [
-        # Chatbot
-        '''This is a conversation between two AI large language models named Alex and Fritz. They are exploring each other's capabilities, and trying to ask interesting questions of one another to explore the limits of each others AI.
-Conversation:
-Alex: Good morning, Fritz, what type of LLM are you based upon?
-Fritz: Morning Alex, I am an RNN with transformer level performance. My language model is 100% attention free.
-Alex:''', "generative", 220, 0.9, 0.9, "\\n\\n,<|endoftext|>"],
-    [
-        # Generate List
-        '''Task given:
-Please Write a Short story about a cat learning python
-Best Full Response:
-''', "generative", 140, 0.85, 0.8, "<|endoftext|>"],
-    [
-        # Natural Language Interface
-        '''Here is a short story (in the style of Tolkien) in which Aiden attacks a robot with a sword:
-        ''', "generative", 140, 0.85, 0.8, "<|endoftext|>"]
-]
-
-
-iface = gr.Interface(
-    fn=infer,
-    description=f'''<h3>Generative and Question/Answer</h3>{desc}{thanks}''',
-    allow_flagging="never",
-    inputs=[
-        gr.Textbox(lines=20, label="Prompt"),  # prompt
-        gr.Radio(["generative", "Q/A","ELDR","EFR","BFR"],
-                 value="generative", label="Choose Mode"),
-        gr.Slider(1, 256, value=40),  # max_tokens
-        gr.Slider(0.0, 1.0, value=0.8),  # temperature
-        gr.Slider(0.0, 1.0, value=0.85),  # top_p
-        gr.Slider(-999, 0.0, value=0.0),  # end_adj
-        gr.Textbox(lines=1, value="<|endoftext|>")  # stop
-    ],
-    outputs=gr.Textbox(label="Generated Output", lines=25),
-    examples=examples,
-    cache_examples=False,
-).queue()
-
-chatiface = gr.Interface(
-    fn=chat,
-    description=f'''<h3>Chatbot</h3><h4>Refresh page or change name to reset memory context</h4>{desc}{thanks}''',
-    allow_flagging="never",
-    inputs=[
-        gr.Textbox(lines=5, label="Message"),  # prompt
-        "state",
-        gr.Text(lines=1, value="USER", label="Your Name",
-                placeholder="Enter your Name"),
-        gr.Slider(1, 256, value=60),  # max_tokens
-        gr.Slider(0.0, 1.0, value=0.8),  # temperature
-        gr.Slider(0.0, 1.0, value=0.85)  # top_p
-    ],
-    outputs=[gr.Chatbot(label="Chat Log", color_map=(
-        "green", "pink")), "state"],
-).queue()
-
-demo = gr.TabbedInterface(
-
-    [iface, chatiface], ["Generative", "Chatbot"],
-    title=title,
-
-)
-
-demo.queue()
-demo.launch(share=False)
\ No newline at end of file
diff --git a/spaces/HugoDzz/super-godot-galaxy/build/smg/index.js b/spaces/HugoDzz/super-godot-galaxy/build/smg/index.js
deleted file mode 100644
index 0d2932842955c0d78e3f58e7ef81f67448f090a4..0000000000000000000000000000000000000000
--- a/spaces/HugoDzz/super-godot-galaxy/build/smg/index.js
+++ /dev/null
@@ -1,14532 +0,0 @@
-
-var Godot = (() => {
-  var _scriptDir = typeof document !== 'undefined' && document.currentScript ? document.currentScript.src : undefined;
-  
-  return (
-function(Godot) {
-  Godot = Godot || {};
-
-
-
-// Support for growable heap + pthreads, where the buffer may change, so JS views
-// must be updated.
-function GROWABLE_HEAP_I8() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAP8;
-}
-function GROWABLE_HEAP_U8() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAPU8;
-}
-function GROWABLE_HEAP_I16() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAP16;
-}
-function GROWABLE_HEAP_U16() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAPU16;
-}
-function GROWABLE_HEAP_I32() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAP32;
-}
-function GROWABLE_HEAP_U32() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAPU32;
-}
-function GROWABLE_HEAP_F32() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAPF32;
-}
-function GROWABLE_HEAP_F64() {
-  if (wasmMemory.buffer != buffer) {
-    updateGlobalBufferAndViews(wasmMemory.buffer);
-  }
-  return HEAPF64;
-}
-
-var Module = typeof Godot != "undefined" ? Godot : {};
-
-var readyPromiseResolve, readyPromiseReject;
-
-Module["ready"] = new Promise(function(resolve, reject) {
- readyPromiseResolve = resolve;
- readyPromiseReject = reject;
-});
-
-[ "_main", "__emscripten_thread_init", "__emscripten_thread_exit", "__emscripten_thread_crashed", "__emscripten_tls_init", "_pthread_self", "executeNotifiedProxyingQueue", "establishStackSpace", "invokeEntryPoint", "PThread", "_emscripten_webgl_commit_frame", "__Z14godot_web_mainiPPc", "_fflush", "__emwebxr_on_input_event", "__emwebxr_on_simple_event", "__emscripten_proxy_execute_task_queue", "onRuntimeInitialized" ].forEach(prop => {
- if (!Object.getOwnPropertyDescriptor(Module["ready"], prop)) {
-  Object.defineProperty(Module["ready"], prop, {
-   get: () => abort("You are getting " + prop + " on the Promise object, instead of the instance. Use .then() to get called back with the instance, see the MODULARIZE docs in src/settings.js"),
-   set: () => abort("You are setting " + prop + " on the Promise object, instead of the instance. Use .then() to get called back with the instance, see the MODULARIZE docs in src/settings.js")
-  });
- }
-});
-
-var moduleOverrides = Object.assign({}, Module);
-
-var arguments_ = [];
-
-var thisProgram = "./this.program";
-
-var quit_ = (status, toThrow) => {
- throw toThrow;
-};
-
-var ENVIRONMENT_IS_WEB = typeof window == "object";
-
-var ENVIRONMENT_IS_WORKER = typeof importScripts == "function";
-
-var ENVIRONMENT_IS_NODE = typeof process == "object" && typeof process.versions == "object" && typeof process.versions.node == "string";
-
-var ENVIRONMENT_IS_SHELL = !ENVIRONMENT_IS_WEB && !ENVIRONMENT_IS_NODE && !ENVIRONMENT_IS_WORKER;
-
-if (Module["ENVIRONMENT"]) {
- throw new Error("Module.ENVIRONMENT has been deprecated. To force the environment, use the ENVIRONMENT compile-time option (for example, -sENVIRONMENT=web or -sENVIRONMENT=node)");
-}
-
-var ENVIRONMENT_IS_PTHREAD = Module["ENVIRONMENT_IS_PTHREAD"] || false;
-
-var scriptDirectory = "";
-
-function locateFile(path) {
- if (Module["locateFile"]) {
-  return Module["locateFile"](path, scriptDirectory);
- }
- return scriptDirectory + path;
-}
-
-var read_, readAsync, readBinary, setWindowTitle;
-
-function logExceptionOnExit(e) {
- if (e instanceof ExitStatus) return;
- let toLog = e;
- if (e && typeof e == "object" && e.stack) {
-  toLog = [ e, e.stack ];
- }
- err("exiting due to exception: " + toLog);
-}
-
-if (ENVIRONMENT_IS_SHELL) {
- if (typeof process == "object" && typeof require === "function" || typeof window == "object" || typeof importScripts == "function") throw new Error("not compiled for this environment (did you build to HTML and try to run it not on the web, or set ENVIRONMENT to something - like node - and run it someplace else - like on the web?)");
- if (typeof read != "undefined") {
-  read_ = function shell_read(f) {
-   return read(f);
-  };
- }
- readBinary = function readBinary(f) {
-  let data;
-  if (typeof readbuffer == "function") {
-   return new Uint8Array(readbuffer(f));
-  }
-  data = read(f, "binary");
-  assert(typeof data == "object");
-  return data;
- };
- readAsync = function readAsync(f, onload, onerror) {
-  setTimeout(() => onload(readBinary(f)), 0);
- };
- if (typeof scriptArgs != "undefined") {
-  arguments_ = scriptArgs;
- } else if (typeof arguments != "undefined") {
-  arguments_ = arguments;
- }
- if (typeof quit == "function") {
-  quit_ = (status, toThrow) => {
-   if (runtimeKeepaliveCounter) {
-    throw toThrow;
-   }
-   logExceptionOnExit(toThrow);
-   quit(status);
-  };
- }
- if (typeof print != "undefined") {
-  if (typeof console == "undefined") console = {};
-  console.log = print;
-  console.warn = console.error = typeof printErr != "undefined" ? printErr : print;
- }
-} else if (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER) {
- if (ENVIRONMENT_IS_WORKER) {
-  scriptDirectory = self.location.href;
- } else if (typeof document != "undefined" && document.currentScript) {
-  scriptDirectory = document.currentScript.src;
- }
- if (_scriptDir) {
-  scriptDirectory = _scriptDir;
- }
- if (scriptDirectory.indexOf("blob:") !== 0) {
-  scriptDirectory = scriptDirectory.substr(0, scriptDirectory.replace(/[?#].*/, "").lastIndexOf("/") + 1);
- } else {
-  scriptDirectory = "";
- }
- if (!(typeof window == "object" || typeof importScripts == "function")) throw new Error("not compiled for this environment (did you build to HTML and try to run it not on the web, or set ENVIRONMENT to something - like node - and run it someplace else - like on the web?)");
- {
-  read_ = url => {
-   var xhr = new XMLHttpRequest();
-   xhr.open("GET", url, false);
-   xhr.send(null);
-   return xhr.responseText;
-  };
-  if (ENVIRONMENT_IS_WORKER) {
-   readBinary = url => {
-    var xhr = new XMLHttpRequest();
-    xhr.open("GET", url, false);
-    xhr.responseType = "arraybuffer";
-    xhr.send(null);
-    return new Uint8Array(xhr.response);
-   };
-  }
-  readAsync = (url, onload, onerror) => {
-   var xhr = new XMLHttpRequest();
-   xhr.open("GET", url, true);
-   xhr.responseType = "arraybuffer";
-   xhr.onload = () => {
-    if (xhr.status == 200 || xhr.status == 0 && xhr.response) {
-     onload(xhr.response);
-     return;
-    }
-    onerror();
-   };
-   xhr.onerror = onerror;
-   xhr.send(null);
-  };
- }
- setWindowTitle = title => document.title = title;
-} else {
- throw new Error("environment detection error");
-}
-
-var out = Module["print"] || console.log.bind(console);
-
-var err = Module["printErr"] || console.warn.bind(console);
-
-Object.assign(Module, moduleOverrides);
-
-moduleOverrides = null;
-
-checkIncomingModuleAPI();
-
-if (Module["arguments"]) arguments_ = Module["arguments"];
-
-legacyModuleProp("arguments", "arguments_");
-
-if (Module["thisProgram"]) thisProgram = Module["thisProgram"];
-
-legacyModuleProp("thisProgram", "thisProgram");
-
-if (Module["quit"]) quit_ = Module["quit"];
-
-legacyModuleProp("quit", "quit_");
-
-assert(typeof Module["memoryInitializerPrefixURL"] == "undefined", "Module.memoryInitializerPrefixURL option was removed, use Module.locateFile instead");
-
-assert(typeof Module["pthreadMainPrefixURL"] == "undefined", "Module.pthreadMainPrefixURL option was removed, use Module.locateFile instead");
-
-assert(typeof Module["cdInitializerPrefixURL"] == "undefined", "Module.cdInitializerPrefixURL option was removed, use Module.locateFile instead");
-
-assert(typeof Module["filePackagePrefixURL"] == "undefined", "Module.filePackagePrefixURL option was removed, use Module.locateFile instead");
-
-assert(typeof Module["read"] == "undefined", "Module.read option was removed (modify read_ in JS)");
-
-assert(typeof Module["readAsync"] == "undefined", "Module.readAsync option was removed (modify readAsync in JS)");
-
-assert(typeof Module["readBinary"] == "undefined", "Module.readBinary option was removed (modify readBinary in JS)");
-
-assert(typeof Module["setWindowTitle"] == "undefined", "Module.setWindowTitle option was removed (modify setWindowTitle in JS)");
-
-assert(typeof Module["TOTAL_MEMORY"] == "undefined", "Module.TOTAL_MEMORY has been renamed Module.INITIAL_MEMORY");
-
-legacyModuleProp("read", "read_");
-
-legacyModuleProp("readAsync", "readAsync");
-
-legacyModuleProp("readBinary", "readBinary");
-
-legacyModuleProp("setWindowTitle", "setWindowTitle");
-
-var PROXYFS = "PROXYFS is no longer included by default; build with -lproxyfs.js";
-
-var WORKERFS = "WORKERFS is no longer included by default; build with -lworkerfs.js";
-
-var NODEFS = "NODEFS is no longer included by default; build with -lnodefs.js";
-
-assert(ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER || ENVIRONMENT_IS_NODE, "Pthreads do not work in this environment yet (need Web Workers, or an alternative to them)");
-
-assert(!ENVIRONMENT_IS_NODE, "node environment detected but not enabled at build time.  Add 'node' to `-sENVIRONMENT` to enable.");
-
-assert(!ENVIRONMENT_IS_SHELL, "shell environment detected but not enabled at build time.  Add 'shell' to `-sENVIRONMENT` to enable.");
-
-var STACK_ALIGN = 16;
-
-var POINTER_SIZE = 4;
-
-function getNativeTypeSize(type) {
- switch (type) {
- case "i1":
- case "i8":
- case "u8":
-  return 1;
-
- case "i16":
- case "u16":
-  return 2;
-
- case "i32":
- case "u32":
-  return 4;
-
- case "i64":
- case "u64":
-  return 8;
-
- case "float":
-  return 4;
-
- case "double":
-  return 8;
-
- default:
-  {
-   if (type[type.length - 1] === "*") {
-    return POINTER_SIZE;
-   }
-   if (type[0] === "i") {
-    const bits = Number(type.substr(1));
-    assert(bits % 8 === 0, "getNativeTypeSize invalid bits " + bits + ", type " + type);
-    return bits / 8;
-   }
-   return 0;
-  }
- }
-}
-
-function legacyModuleProp(prop, newName) {
- if (!Object.getOwnPropertyDescriptor(Module, prop)) {
-  Object.defineProperty(Module, prop, {
-   configurable: true,
-   get: function() {
-    abort("Module." + prop + " has been replaced with plain " + newName + " (the initial value can be provided on Module, but after startup the value is only looked for on a local variable of that name)");
-   }
-  });
- }
-}
-
-function ignoredModuleProp(prop) {
- if (Object.getOwnPropertyDescriptor(Module, prop)) {
-  abort("`Module." + prop + "` was supplied but `" + prop + "` not included in INCOMING_MODULE_JS_API");
- }
-}
-
-function isExportedByForceFilesystem(name) {
- return name === "FS_createPath" || name === "FS_createDataFile" || name === "FS_createPreloadedFile" || name === "FS_unlink" || name === "addRunDependency" || name === "FS_createLazyFile" || name === "FS_createDevice" || name === "removeRunDependency";
-}
-
-function missingLibrarySymbol(sym) {
- if (typeof globalThis !== "undefined" && !Object.getOwnPropertyDescriptor(globalThis, sym)) {
-  Object.defineProperty(globalThis, sym, {
-   configurable: true,
-   get: function() {
-    var msg = "`" + sym + "` is a library symbol and not included by default; add it to your library.js __deps or to DEFAULT_LIBRARY_FUNCS_TO_INCLUDE on the command line";
-    if (isExportedByForceFilesystem(sym)) {
-     msg += ". Alternatively, forcing filesystem support (-sFORCE_FILESYSTEM) can export this for you";
-    }
-    warnOnce(msg);
-    return undefined;
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-  });
- }
-}
-
-function unexportedRuntimeSymbol(sym) {
- if (!Object.getOwnPropertyDescriptor(Module, sym)) {
-  Object.defineProperty(Module, sym, {
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-    if (isExportedByForceFilesystem(sym)) {
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-
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-
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-
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-
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-
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- abort("no native wasm support detected");
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-
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-
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-
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-
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-
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- if (!condition) {
-  abort("Assertion failed" + (text ? ": " + text : ""));
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-
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- var endIdx = idx + maxBytesToRead;
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- var str = "";
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-  if (!(u0 & 128)) {
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-  if (u0 < 65536) {
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-  } else {
-   var ch = u0 - 65536;
-   str += String.fromCharCode(55296 | ch >> 10, 56320 | ch & 1023);
-  }
- }
- return str;
-}
-
-function UTF8ToString(ptr, maxBytesToRead) {
- return ptr ? UTF8ArrayToString(GROWABLE_HEAP_U8(), ptr, maxBytesToRead) : "";
-}
-
-function stringToUTF8Array(str, heap, outIdx, maxBytesToWrite) {
- if (!(maxBytesToWrite > 0)) return 0;
- var startIdx = outIdx;
- var endIdx = outIdx + maxBytesToWrite - 1;
- for (var i = 0; i < str.length; ++i) {
-  var u = str.charCodeAt(i);
-  if (u >= 55296 && u <= 57343) {
-   var u1 = str.charCodeAt(++i);
-   u = 65536 + ((u & 1023) << 10) | u1 & 1023;
-  }
-  if (u <= 127) {
-   if (outIdx >= endIdx) break;
-   heap[outIdx++] = u;
-  } else if (u <= 2047) {
-   if (outIdx + 1 >= endIdx) break;
-   heap[outIdx++] = 192 | u >> 6;
-   heap[outIdx++] = 128 | u & 63;
-  } else if (u <= 65535) {
-   if (outIdx + 2 >= endIdx) break;
-   heap[outIdx++] = 224 | u >> 12;
-   heap[outIdx++] = 128 | u >> 6 & 63;
-   heap[outIdx++] = 128 | u & 63;
-  } else {
-   if (outIdx + 3 >= endIdx) break;
-   if (u > 1114111) warnOnce("Invalid Unicode code point 0x" + u.toString(16) + " encountered when serializing a JS string to a UTF-8 string in wasm memory! (Valid unicode code points should be in range 0-0x10FFFF).");
-   heap[outIdx++] = 240 | u >> 18;
-   heap[outIdx++] = 128 | u >> 12 & 63;
-   heap[outIdx++] = 128 | u >> 6 & 63;
-   heap[outIdx++] = 128 | u & 63;
-  }
- }
- heap[outIdx] = 0;
- return outIdx - startIdx;
-}
-
-function stringToUTF8(str, outPtr, maxBytesToWrite) {
- assert(typeof maxBytesToWrite == "number", "stringToUTF8(str, outPtr, maxBytesToWrite) is missing the third parameter that specifies the length of the output buffer!");
- return stringToUTF8Array(str, GROWABLE_HEAP_U8(), outPtr, maxBytesToWrite);
-}
-
-function lengthBytesUTF8(str) {
- var len = 0;
- for (var i = 0; i < str.length; ++i) {
-  var c = str.charCodeAt(i);
-  if (c <= 127) {
-   len++;
-  } else if (c <= 2047) {
-   len += 2;
-  } else if (c >= 55296 && c <= 57343) {
-   len += 4;
-   ++i;
-  } else {
-   len += 3;
-  }
- }
- return len;
-}
-
-var HEAP, buffer, HEAP8, HEAPU8, HEAP16, HEAPU16, HEAP32, HEAPU32, HEAPF32, HEAPF64;
-
-if (ENVIRONMENT_IS_PTHREAD) {
- buffer = Module["buffer"];
-}
-
-function updateGlobalBufferAndViews(buf) {
- buffer = buf;
- Module["HEAP8"] = HEAP8 = new Int8Array(buf);
- Module["HEAP16"] = HEAP16 = new Int16Array(buf);
- Module["HEAP32"] = HEAP32 = new Int32Array(buf);
- Module["HEAPU8"] = HEAPU8 = new Uint8Array(buf);
- Module["HEAPU16"] = HEAPU16 = new Uint16Array(buf);
- Module["HEAPU32"] = HEAPU32 = new Uint32Array(buf);
- Module["HEAPF32"] = HEAPF32 = new Float32Array(buf);
- Module["HEAPF64"] = HEAPF64 = new Float64Array(buf);
-}
-
-var TOTAL_STACK = 5242880;
-
-if (Module["TOTAL_STACK"]) assert(TOTAL_STACK === Module["TOTAL_STACK"], "the stack size can no longer be determined at runtime");
-
-var INITIAL_MEMORY = Module["INITIAL_MEMORY"] || 33554432;
-
-legacyModuleProp("INITIAL_MEMORY", "INITIAL_MEMORY");
-
-assert(INITIAL_MEMORY >= TOTAL_STACK, "INITIAL_MEMORY should be larger than TOTAL_STACK, was " + INITIAL_MEMORY + "! (TOTAL_STACK=" + TOTAL_STACK + ")");
-
-assert(typeof Int32Array != "undefined" && typeof Float64Array !== "undefined" && Int32Array.prototype.subarray != undefined && Int32Array.prototype.set != undefined, "JS engine does not provide full typed array support");
-
-if (ENVIRONMENT_IS_PTHREAD) {
- wasmMemory = Module["wasmMemory"];
- buffer = Module["buffer"];
-} else {
- if (Module["wasmMemory"]) {
-  wasmMemory = Module["wasmMemory"];
- } else {
-  wasmMemory = new WebAssembly.Memory({
-   "initial": INITIAL_MEMORY / 65536,
-   "maximum": 2147483648 / 65536,
-   "shared": true
-  });
-  if (!(wasmMemory.buffer instanceof SharedArrayBuffer)) {
-   err("requested a shared WebAssembly.Memory but the returned buffer is not a SharedArrayBuffer, indicating that while the browser has SharedArrayBuffer it does not have WebAssembly threads support - you may need to set a flag");
-   if (ENVIRONMENT_IS_NODE) {
-    console.log("(on node you may need: --experimental-wasm-threads --experimental-wasm-bulk-memory and also use a recent version)");
-   }
-   throw Error("bad memory");
-  }
- }
-}
-
-if (wasmMemory) {
- buffer = wasmMemory.buffer;
-}
-
-INITIAL_MEMORY = buffer.byteLength;
-
-assert(INITIAL_MEMORY % 65536 === 0);
-
-updateGlobalBufferAndViews(buffer);
-
-var wasmTable;
-
-function writeStackCookie() {
- var max = _emscripten_stack_get_end();
- assert((max & 3) == 0);
- GROWABLE_HEAP_I32()[max >> 2] = 34821223;
- GROWABLE_HEAP_I32()[max + 4 >> 2] = 2310721022;
- GROWABLE_HEAP_U32()[0] = 1668509029;
-}
-
-function checkStackCookie() {
- if (ABORT) return;
- var max = _emscripten_stack_get_end();
- var cookie1 = GROWABLE_HEAP_U32()[max >> 2];
- var cookie2 = GROWABLE_HEAP_U32()[max + 4 >> 2];
- if (cookie1 != 34821223 || cookie2 != 2310721022) {
-  abort("Stack overflow! Stack cookie has been overwritten at 0x" + max.toString(16) + ", expected hex dwords 0x89BACDFE and 0x2135467, but received 0x" + cookie2.toString(16) + " 0x" + cookie1.toString(16));
- }
- if (GROWABLE_HEAP_U32()[0] !== 1668509029) abort("Runtime error: The application has corrupted its heap memory area (address zero)!");
-}
-
-(function() {
- var h16 = new Int16Array(1);
- var h8 = new Int8Array(h16.buffer);
- h16[0] = 25459;
- if (h8[0] !== 115 || h8[1] !== 99) throw "Runtime error: expected the system to be little-endian! (Run with -sSUPPORT_BIG_ENDIAN to bypass)";
-})();
-
-var __ATPRERUN__ = [];
-
-var __ATINIT__ = [];
-
-var __ATMAIN__ = [];
-
-var __ATEXIT__ = [];
-
-var __ATPOSTRUN__ = [];
-
-var runtimeInitialized = false;
-
-var runtimeExited = false;
-
-var runtimeKeepaliveCounter = 0;
-
-function keepRuntimeAlive() {
- return noExitRuntime || runtimeKeepaliveCounter > 0;
-}
-
-function preRun() {
- assert(!ENVIRONMENT_IS_PTHREAD);
- if (Module["preRun"]) {
-  if (typeof Module["preRun"] == "function") Module["preRun"] = [ Module["preRun"] ];
-  while (Module["preRun"].length) {
-   addOnPreRun(Module["preRun"].shift());
-  }
- }
- callRuntimeCallbacks(__ATPRERUN__);
-}
-
-function initRuntime() {
- assert(!runtimeInitialized);
- runtimeInitialized = true;
- if (ENVIRONMENT_IS_PTHREAD) return;
- checkStackCookie();
- if (!Module["noFSInit"] && !FS.init.initialized) FS.init();
- FS.ignorePermissions = false;
- TTY.init();
- SOCKFS.root = FS.mount(SOCKFS, {}, null);
- callRuntimeCallbacks(__ATINIT__);
-}
-
-function preMain() {
- checkStackCookie();
- if (ENVIRONMENT_IS_PTHREAD) return;
- callRuntimeCallbacks(__ATMAIN__);
-}
-
-function exitRuntime() {
- checkStackCookie();
- if (ENVIRONMENT_IS_PTHREAD) return;
- ___funcs_on_exit();
- callRuntimeCallbacks(__ATEXIT__);
- FS.quit();
- TTY.shutdown();
- IDBFS.quit();
- PThread.terminateAllThreads();
- runtimeExited = true;
-}
-
-function postRun() {
- checkStackCookie();
- if (ENVIRONMENT_IS_PTHREAD) return;
- if (Module["postRun"]) {
-  if (typeof Module["postRun"] == "function") Module["postRun"] = [ Module["postRun"] ];
-  while (Module["postRun"].length) {
-   addOnPostRun(Module["postRun"].shift());
-  }
- }
- callRuntimeCallbacks(__ATPOSTRUN__);
-}
-
-function addOnPreRun(cb) {
- __ATPRERUN__.unshift(cb);
-}
-
-function addOnInit(cb) {
- __ATINIT__.unshift(cb);
-}
-
-function addOnPreMain(cb) {
- __ATMAIN__.unshift(cb);
-}
-
-function addOnExit(cb) {
- __ATEXIT__.unshift(cb);
-}
-
-function addOnPostRun(cb) {
- __ATPOSTRUN__.unshift(cb);
-}
-
-assert(Math.imul, "This browser does not support Math.imul(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill");
-
-assert(Math.fround, "This browser does not support Math.fround(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill");
-
-assert(Math.clz32, "This browser does not support Math.clz32(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill");
-
-assert(Math.trunc, "This browser does not support Math.trunc(), build with LEGACY_VM_SUPPORT or POLYFILL_OLD_MATH_FUNCTIONS to add in a polyfill");
-
-var runDependencies = 0;
-
-var runDependencyWatcher = null;
-
-var dependenciesFulfilled = null;
-
-var runDependencyTracking = {};
-
-function getUniqueRunDependency(id) {
- var orig = id;
- while (1) {
-  if (!runDependencyTracking[id]) return id;
-  id = orig + Math.random();
- }
-}
-
-function addRunDependency(id) {
- runDependencies++;
- if (Module["monitorRunDependencies"]) {
-  Module["monitorRunDependencies"](runDependencies);
- }
- if (id) {
-  assert(!runDependencyTracking[id]);
-  runDependencyTracking[id] = 1;
-  if (runDependencyWatcher === null && typeof setInterval != "undefined") {
-   runDependencyWatcher = setInterval(function() {
-    if (ABORT) {
-     clearInterval(runDependencyWatcher);
-     runDependencyWatcher = null;
-     return;
-    }
-    var shown = false;
-    for (var dep in runDependencyTracking) {
-     if (!shown) {
-      shown = true;
-      err("still waiting on run dependencies:");
-     }
-     err("dependency: " + dep);
-    }
-    if (shown) {
-     err("(end of list)");
-    }
-   }, 1e4);
-  }
- } else {
-  err("warning: run dependency added without ID");
- }
-}
-
-function removeRunDependency(id) {
- runDependencies--;
- if (Module["monitorRunDependencies"]) {
-  Module["monitorRunDependencies"](runDependencies);
- }
- if (id) {
-  assert(runDependencyTracking[id]);
-  delete runDependencyTracking[id];
- } else {
-  err("warning: run dependency removed without ID");
- }
- if (runDependencies == 0) {
-  if (runDependencyWatcher !== null) {
-   clearInterval(runDependencyWatcher);
-   runDependencyWatcher = null;
-  }
-  if (dependenciesFulfilled) {
-   var callback = dependenciesFulfilled;
-   dependenciesFulfilled = null;
-   callback();
-  }
- }
-}
-
-function abort(what) {
- if (ENVIRONMENT_IS_PTHREAD) {
-  postMessage({
-   "cmd": "onAbort",
-   "arg": what
-  });
- } else {
-  if (Module["onAbort"]) {
-   Module["onAbort"](what);
-  }
- }
- what = "Aborted(" + what + ")";
- err(what);
- ABORT = true;
- EXITSTATUS = 1;
- var e = new WebAssembly.RuntimeError(what);
- readyPromiseReject(e);
- throw e;
-}
-
-var dataURIPrefix = "data:application/octet-stream;base64,";
-
-function isDataURI(filename) {
- return filename.startsWith(dataURIPrefix);
-}
-
-function isFileURI(filename) {
- return filename.startsWith("file://");
-}
-
-function createExportWrapper(name, fixedasm) {
- return function() {
-  var displayName = name;
-  var asm = fixedasm;
-  if (!fixedasm) {
-   asm = Module["asm"];
-  }
-  assert(runtimeInitialized, "native function `" + displayName + "` called before runtime initialization");
-  assert(!runtimeExited, "native function `" + displayName + "` called after runtime exit (use NO_EXIT_RUNTIME to keep it alive after main() exits)");
-  if (!asm[name]) {
-   assert(asm[name], "exported native function `" + displayName + "` not found");
-  }
-  return asm[name].apply(null, arguments);
- };
-}
-
-var wasmBinaryFile;
-
-wasmBinaryFile = "godot.web.template_debug.wasm32.wasm";
-
-if (!isDataURI(wasmBinaryFile)) {
- wasmBinaryFile = locateFile(wasmBinaryFile);
-}
-
-function getBinary(file) {
- try {
-  if (file == wasmBinaryFile && wasmBinary) {
-   return new Uint8Array(wasmBinary);
-  }
-  if (readBinary) {
-   return readBinary(file);
-  }
-  throw "both async and sync fetching of the wasm failed";
- } catch (err) {
-  abort(err);
- }
-}
-
-function getBinaryPromise() {
- if (!wasmBinary && (ENVIRONMENT_IS_WEB || ENVIRONMENT_IS_WORKER)) {
-  if (typeof fetch == "function") {
-   return fetch(wasmBinaryFile, {
-    credentials: "same-origin"
-   }).then(function(response) {
-    if (!response["ok"]) {
-     throw "failed to load wasm binary file at '" + wasmBinaryFile + "'";
-    }
-    return response["arrayBuffer"]();
-   }).catch(function() {
-    return getBinary(wasmBinaryFile);
-   });
-  }
- }
- return Promise.resolve().then(function() {
-  return getBinary(wasmBinaryFile);
- });
-}
-
-function createWasm() {
- var info = {
-  "env": asmLibraryArg,
-  "wasi_snapshot_preview1": asmLibraryArg
- };
- function receiveInstance(instance, module) {
-  var exports = instance.exports;
-  Module["asm"] = exports;
-  registerTLSInit(Module["asm"]["_emscripten_tls_init"]);
-  wasmTable = Module["asm"]["__indirect_function_table"];
-  assert(wasmTable, "table not found in wasm exports");
-  addOnInit(Module["asm"]["__wasm_call_ctors"]);
-  wasmModule = module;
-  if (!ENVIRONMENT_IS_PTHREAD) {
-   var numWorkersToLoad = PThread.unusedWorkers.length;
-   PThread.unusedWorkers.forEach(function(w) {
-    PThread.loadWasmModuleToWorker(w, function() {
-     if (!--numWorkersToLoad) removeRunDependency("wasm-instantiate");
-    });
-   });
-  }
- }
- if (!ENVIRONMENT_IS_PTHREAD) {
-  addRunDependency("wasm-instantiate");
- }
- var trueModule = Module;
- function receiveInstantiationResult(result) {
-  assert(Module === trueModule, "the Module object should not be replaced during async compilation - perhaps the order of HTML elements is wrong?");
-  trueModule = null;
-  receiveInstance(result["instance"], result["module"]);
- }
- function instantiateArrayBuffer(receiver) {
-  return getBinaryPromise().then(function(binary) {
-   return WebAssembly.instantiate(binary, info);
-  }).then(function(instance) {
-   return instance;
-  }).then(receiver, function(reason) {
-   err("failed to asynchronously prepare wasm: " + reason);
-   if (isFileURI(wasmBinaryFile)) {
-    err("warning: Loading from a file URI (" + wasmBinaryFile + ") is not supported in most browsers. See https://emscripten.org/docs/getting_started/FAQ.html#how-do-i-run-a-local-webserver-for-testing-why-does-my-program-stall-in-downloading-or-preparing");
-   }
-   abort(reason);
-  });
- }
- function instantiateAsync() {
-  if (!wasmBinary && typeof WebAssembly.instantiateStreaming == "function" && !isDataURI(wasmBinaryFile) && typeof fetch == "function") {
-   return fetch(wasmBinaryFile, {
-    credentials: "same-origin"
-   }).then(function(response) {
-    var result = WebAssembly.instantiateStreaming(response, info);
-    return result.then(receiveInstantiationResult, function(reason) {
-     err("wasm streaming compile failed: " + reason);
-     err("falling back to ArrayBuffer instantiation");
-     return instantiateArrayBuffer(receiveInstantiationResult);
-    });
-   });
-  } else {
-   return instantiateArrayBuffer(receiveInstantiationResult);
-  }
- }
- if (Module["instantiateWasm"]) {
-  try {
-   var exports = Module["instantiateWasm"](info, receiveInstance);
-   return exports;
-  } catch (e) {
-   err("Module.instantiateWasm callback failed with error: " + e);
-   return false;
-  }
- }
- instantiateAsync().catch(readyPromiseReject);
- return {};
-}
-
-var tempDouble;
-
-var tempI64;
-
-var ASM_CONSTS = {};
-
-function ExitStatus(status) {
- this.name = "ExitStatus";
- this.message = "Program terminated with exit(" + status + ")";
- this.status = status;
-}
-
-function killThread(pthread_ptr) {
- assert(!ENVIRONMENT_IS_PTHREAD, "Internal Error! killThread() can only ever be called from main application thread!");
- assert(pthread_ptr, "Internal Error! Null pthread_ptr in killThread!");
- var worker = PThread.pthreads[pthread_ptr];
- delete PThread.pthreads[pthread_ptr];
- worker.terminate();
- __emscripten_thread_free_data(pthread_ptr);
- PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);
- worker.pthread_ptr = 0;
-}
-
-function cancelThread(pthread_ptr) {
- assert(!ENVIRONMENT_IS_PTHREAD, "Internal Error! cancelThread() can only ever be called from main application thread!");
- assert(pthread_ptr, "Internal Error! Null pthread_ptr in cancelThread!");
- var worker = PThread.pthreads[pthread_ptr];
- worker.postMessage({
-  "cmd": "cancel"
- });
-}
-
-function cleanupThread(pthread_ptr) {
- assert(!ENVIRONMENT_IS_PTHREAD, "Internal Error! cleanupThread() can only ever be called from main application thread!");
- assert(pthread_ptr, "Internal Error! Null pthread_ptr in cleanupThread!");
- var worker = PThread.pthreads[pthread_ptr];
- assert(worker);
- PThread.returnWorkerToPool(worker);
-}
-
-function zeroMemory(address, size) {
- GROWABLE_HEAP_U8().fill(0, address, address + size);
-}
-
-function spawnThread(threadParams) {
- assert(!ENVIRONMENT_IS_PTHREAD, "Internal Error! spawnThread() can only ever be called from main application thread!");
- assert(threadParams.pthread_ptr, "Internal error, no pthread ptr!");
- var worker = PThread.getNewWorker();
- if (!worker) {
-  return 6;
- }
- assert(!worker.pthread_ptr, "Internal error!");
- PThread.runningWorkers.push(worker);
- PThread.pthreads[threadParams.pthread_ptr] = worker;
- worker.pthread_ptr = threadParams.pthread_ptr;
- var msg = {
-  "cmd": "run",
-  "start_routine": threadParams.startRoutine,
-  "arg": threadParams.arg,
-  "pthread_ptr": threadParams.pthread_ptr
- };
- worker.runPthread = () => {
-  msg.time = performance.now();
-  worker.postMessage(msg, threadParams.transferList);
- };
- if (worker.loaded) {
-  worker.runPthread();
-  delete worker.runPthread;
- }
- return 0;
-}
-
-var PATH = {
- isAbs: path => path.charAt(0) === "/",
- splitPath: filename => {
-  var splitPathRe = /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/;
-  return splitPathRe.exec(filename).slice(1);
- },
- normalizeArray: (parts, allowAboveRoot) => {
-  var up = 0;
-  for (var i = parts.length - 1; i >= 0; i--) {
-   var last = parts[i];
-   if (last === ".") {
-    parts.splice(i, 1);
-   } else if (last === "..") {
-    parts.splice(i, 1);
-    up++;
-   } else if (up) {
-    parts.splice(i, 1);
-    up--;
-   }
-  }
-  if (allowAboveRoot) {
-   for (;up; up--) {
-    parts.unshift("..");
-   }
-  }
-  return parts;
- },
- normalize: path => {
-  var isAbsolute = PATH.isAbs(path), trailingSlash = path.substr(-1) === "/";
-  path = PATH.normalizeArray(path.split("/").filter(p => !!p), !isAbsolute).join("/");
-  if (!path && !isAbsolute) {
-   path = ".";
-  }
-  if (path && trailingSlash) {
-   path += "/";
-  }
-  return (isAbsolute ? "/" : "") + path;
- },
- dirname: path => {
-  var result = PATH.splitPath(path), root = result[0], dir = result[1];
-  if (!root && !dir) {
-   return ".";
-  }
-  if (dir) {
-   dir = dir.substr(0, dir.length - 1);
-  }
-  return root + dir;
- },
- basename: path => {
-  if (path === "/") return "/";
-  path = PATH.normalize(path);
-  path = path.replace(/\/$/, "");
-  var lastSlash = path.lastIndexOf("/");
-  if (lastSlash === -1) return path;
-  return path.substr(lastSlash + 1);
- },
- join: function() {
-  var paths = Array.prototype.slice.call(arguments, 0);
-  return PATH.normalize(paths.join("/"));
- },
- join2: (l, r) => {
-  return PATH.normalize(l + "/" + r);
- }
-};
-
-function getRandomDevice() {
- if (typeof crypto == "object" && typeof crypto["getRandomValues"] == "function") {
-  var randomBuffer = new Uint8Array(1);
-  return () => {
-   crypto.getRandomValues(randomBuffer);
-   return randomBuffer[0];
-  };
- } else return () => abort("no cryptographic support found for randomDevice. consider polyfilling it if you want to use something insecure like Math.random(), e.g. put this in a --pre-js: var crypto = { getRandomValues: function(array) { for (var i = 0; i < array.length; i++) array[i] = (Math.random()*256)|0 } };");
-}
-
-var PATH_FS = {
- resolve: function() {
-  var resolvedPath = "", resolvedAbsolute = false;
-  for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) {
-   var path = i >= 0 ? arguments[i] : FS.cwd();
-   if (typeof path != "string") {
-    throw new TypeError("Arguments to path.resolve must be strings");
-   } else if (!path) {
-    return "";
-   }
-   resolvedPath = path + "/" + resolvedPath;
-   resolvedAbsolute = PATH.isAbs(path);
-  }
-  resolvedPath = PATH.normalizeArray(resolvedPath.split("/").filter(p => !!p), !resolvedAbsolute).join("/");
-  return (resolvedAbsolute ? "/" : "") + resolvedPath || ".";
- },
- relative: (from, to) => {
-  from = PATH_FS.resolve(from).substr(1);
-  to = PATH_FS.resolve(to).substr(1);
-  function trim(arr) {
-   var start = 0;
-   for (;start < arr.length; start++) {
-    if (arr[start] !== "") break;
-   }
-   var end = arr.length - 1;
-   for (;end >= 0; end--) {
-    if (arr[end] !== "") break;
-   }
-   if (start > end) return [];
-   return arr.slice(start, end - start + 1);
-  }
-  var fromParts = trim(from.split("/"));
-  var toParts = trim(to.split("/"));
-  var length = Math.min(fromParts.length, toParts.length);
-  var samePartsLength = length;
-  for (var i = 0; i < length; i++) {
-   if (fromParts[i] !== toParts[i]) {
-    samePartsLength = i;
-    break;
-   }
-  }
-  var outputParts = [];
-  for (var i = samePartsLength; i < fromParts.length; i++) {
-   outputParts.push("..");
-  }
-  outputParts = outputParts.concat(toParts.slice(samePartsLength));
-  return outputParts.join("/");
- }
-};
-
-function intArrayFromString(stringy, dontAddNull, length) {
- var len = length > 0 ? length : lengthBytesUTF8(stringy) + 1;
- var u8array = new Array(len);
- var numBytesWritten = stringToUTF8Array(stringy, u8array, 0, u8array.length);
- if (dontAddNull) u8array.length = numBytesWritten;
- return u8array;
-}
-
-var TTY = {
- ttys: [],
- init: function() {},
- shutdown: function() {},
- register: function(dev, ops) {
-  TTY.ttys[dev] = {
-   input: [],
-   output: [],
-   ops: ops
-  };
-  FS.registerDevice(dev, TTY.stream_ops);
- },
- stream_ops: {
-  open: function(stream) {
-   var tty = TTY.ttys[stream.node.rdev];
-   if (!tty) {
-    throw new FS.ErrnoError(43);
-   }
-   stream.tty = tty;
-   stream.seekable = false;
-  },
-  close: function(stream) {
-   stream.tty.ops.flush(stream.tty);
-  },
-  flush: function(stream) {
-   stream.tty.ops.flush(stream.tty);
-  },
-  read: function(stream, buffer, offset, length, pos) {
-   if (!stream.tty || !stream.tty.ops.get_char) {
-    throw new FS.ErrnoError(60);
-   }
-   var bytesRead = 0;
-   for (var i = 0; i < length; i++) {
-    var result;
-    try {
-     result = stream.tty.ops.get_char(stream.tty);
-    } catch (e) {
-     throw new FS.ErrnoError(29);
-    }
-    if (result === undefined && bytesRead === 0) {
-     throw new FS.ErrnoError(6);
-    }
-    if (result === null || result === undefined) break;
-    bytesRead++;
-    buffer[offset + i] = result;
-   }
-   if (bytesRead) {
-    stream.node.timestamp = Date.now();
-   }
-   return bytesRead;
-  },
-  write: function(stream, buffer, offset, length, pos) {
-   if (!stream.tty || !stream.tty.ops.put_char) {
-    throw new FS.ErrnoError(60);
-   }
-   try {
-    for (var i = 0; i < length; i++) {
-     stream.tty.ops.put_char(stream.tty, buffer[offset + i]);
-    }
-   } catch (e) {
-    throw new FS.ErrnoError(29);
-   }
-   if (length) {
-    stream.node.timestamp = Date.now();
-   }
-   return i;
-  }
- },
- default_tty_ops: {
-  get_char: function(tty) {
-   if (!tty.input.length) {
-    var result = null;
-    if (typeof window != "undefined" && typeof window.prompt == "function") {
-     result = window.prompt("Input: ");
-     if (result !== null) {
-      result += "\n";
-     }
-    } else if (typeof readline == "function") {
-     result = readline();
-     if (result !== null) {
-      result += "\n";
-     }
-    }
-    if (!result) {
-     return null;
-    }
-    tty.input = intArrayFromString(result, true);
-   }
-   return tty.input.shift();
-  },
-  put_char: function(tty, val) {
-   if (val === null || val === 10) {
-    out(UTF8ArrayToString(tty.output, 0));
-    tty.output = [];
-   } else {
-    if (val != 0) tty.output.push(val);
-   }
-  },
-  flush: function(tty) {
-   if (tty.output && tty.output.length > 0) {
-    out(UTF8ArrayToString(tty.output, 0));
-    tty.output = [];
-   }
-  }
- },
- default_tty1_ops: {
-  put_char: function(tty, val) {
-   if (val === null || val === 10) {
-    err(UTF8ArrayToString(tty.output, 0));
-    tty.output = [];
-   } else {
-    if (val != 0) tty.output.push(val);
-   }
-  },
-  flush: function(tty) {
-   if (tty.output && tty.output.length > 0) {
-    err(UTF8ArrayToString(tty.output, 0));
-    tty.output = [];
-   }
-  }
- }
-};
-
-function alignMemory(size, alignment) {
- assert(alignment, "alignment argument is required");
- return Math.ceil(size / alignment) * alignment;
-}
-
-function mmapAlloc(size) {
- size = alignMemory(size, 65536);
- var ptr = _emscripten_builtin_memalign(65536, size);
- if (!ptr) return 0;
- zeroMemory(ptr, size);
- return ptr;
-}
-
-var MEMFS = {
- ops_table: null,
- mount: function(mount) {
-  return MEMFS.createNode(null, "/", 16384 | 511, 0);
- },
- createNode: function(parent, name, mode, dev) {
-  if (FS.isBlkdev(mode) || FS.isFIFO(mode)) {
-   throw new FS.ErrnoError(63);
-  }
-  if (!MEMFS.ops_table) {
-   MEMFS.ops_table = {
-    dir: {
-     node: {
-      getattr: MEMFS.node_ops.getattr,
-      setattr: MEMFS.node_ops.setattr,
-      lookup: MEMFS.node_ops.lookup,
-      mknod: MEMFS.node_ops.mknod,
-      rename: MEMFS.node_ops.rename,
-      unlink: MEMFS.node_ops.unlink,
-      rmdir: MEMFS.node_ops.rmdir,
-      readdir: MEMFS.node_ops.readdir,
-      symlink: MEMFS.node_ops.symlink
-     },
-     stream: {
-      llseek: MEMFS.stream_ops.llseek
-     }
-    },
-    file: {
-     node: {
-      getattr: MEMFS.node_ops.getattr,
-      setattr: MEMFS.node_ops.setattr
-     },
-     stream: {
-      llseek: MEMFS.stream_ops.llseek,
-      read: MEMFS.stream_ops.read,
-      write: MEMFS.stream_ops.write,
-      allocate: MEMFS.stream_ops.allocate,
-      mmap: MEMFS.stream_ops.mmap,
-      msync: MEMFS.stream_ops.msync
-     }
-    },
-    link: {
-     node: {
-      getattr: MEMFS.node_ops.getattr,
-      setattr: MEMFS.node_ops.setattr,
-      readlink: MEMFS.node_ops.readlink
-     },
-     stream: {}
-    },
-    chrdev: {
-     node: {
-      getattr: MEMFS.node_ops.getattr,
-      setattr: MEMFS.node_ops.setattr
-     },
-     stream: FS.chrdev_stream_ops
-    }
-   };
-  }
-  var node = FS.createNode(parent, name, mode, dev);
-  if (FS.isDir(node.mode)) {
-   node.node_ops = MEMFS.ops_table.dir.node;
-   node.stream_ops = MEMFS.ops_table.dir.stream;
-   node.contents = {};
-  } else if (FS.isFile(node.mode)) {
-   node.node_ops = MEMFS.ops_table.file.node;
-   node.stream_ops = MEMFS.ops_table.file.stream;
-   node.usedBytes = 0;
-   node.contents = null;
-  } else if (FS.isLink(node.mode)) {
-   node.node_ops = MEMFS.ops_table.link.node;
-   node.stream_ops = MEMFS.ops_table.link.stream;
-  } else if (FS.isChrdev(node.mode)) {
-   node.node_ops = MEMFS.ops_table.chrdev.node;
-   node.stream_ops = MEMFS.ops_table.chrdev.stream;
-  }
-  node.timestamp = Date.now();
-  if (parent) {
-   parent.contents[name] = node;
-   parent.timestamp = node.timestamp;
-  }
-  return node;
- },
- getFileDataAsTypedArray: function(node) {
-  if (!node.contents) return new Uint8Array(0);
-  if (node.contents.subarray) return node.contents.subarray(0, node.usedBytes);
-  return new Uint8Array(node.contents);
- },
- expandFileStorage: function(node, newCapacity) {
-  var prevCapacity = node.contents ? node.contents.length : 0;
-  if (prevCapacity >= newCapacity) return;
-  var CAPACITY_DOUBLING_MAX = 1024 * 1024;
-  newCapacity = Math.max(newCapacity, prevCapacity * (prevCapacity < CAPACITY_DOUBLING_MAX ? 2 : 1.125) >>> 0);
-  if (prevCapacity != 0) newCapacity = Math.max(newCapacity, 256);
-  var oldContents = node.contents;
-  node.contents = new Uint8Array(newCapacity);
-  if (node.usedBytes > 0) node.contents.set(oldContents.subarray(0, node.usedBytes), 0);
- },
- resizeFileStorage: function(node, newSize) {
-  if (node.usedBytes == newSize) return;
-  if (newSize == 0) {
-   node.contents = null;
-   node.usedBytes = 0;
-  } else {
-   var oldContents = node.contents;
-   node.contents = new Uint8Array(newSize);
-   if (oldContents) {
-    node.contents.set(oldContents.subarray(0, Math.min(newSize, node.usedBytes)));
-   }
-   node.usedBytes = newSize;
-  }
- },
- node_ops: {
-  getattr: function(node) {
-   var attr = {};
-   attr.dev = FS.isChrdev(node.mode) ? node.id : 1;
-   attr.ino = node.id;
-   attr.mode = node.mode;
-   attr.nlink = 1;
-   attr.uid = 0;
-   attr.gid = 0;
-   attr.rdev = node.rdev;
-   if (FS.isDir(node.mode)) {
-    attr.size = 4096;
-   } else if (FS.isFile(node.mode)) {
-    attr.size = node.usedBytes;
-   } else if (FS.isLink(node.mode)) {
-    attr.size = node.link.length;
-   } else {
-    attr.size = 0;
-   }
-   attr.atime = new Date(node.timestamp);
-   attr.mtime = new Date(node.timestamp);
-   attr.ctime = new Date(node.timestamp);
-   attr.blksize = 4096;
-   attr.blocks = Math.ceil(attr.size / attr.blksize);
-   return attr;
-  },
-  setattr: function(node, attr) {
-   if (attr.mode !== undefined) {
-    node.mode = attr.mode;
-   }
-   if (attr.timestamp !== undefined) {
-    node.timestamp = attr.timestamp;
-   }
-   if (attr.size !== undefined) {
-    MEMFS.resizeFileStorage(node, attr.size);
-   }
-  },
-  lookup: function(parent, name) {
-   throw FS.genericErrors[44];
-  },
-  mknod: function(parent, name, mode, dev) {
-   return MEMFS.createNode(parent, name, mode, dev);
-  },
-  rename: function(old_node, new_dir, new_name) {
-   if (FS.isDir(old_node.mode)) {
-    var new_node;
-    try {
-     new_node = FS.lookupNode(new_dir, new_name);
-    } catch (e) {}
-    if (new_node) {
-     for (var i in new_node.contents) {
-      throw new FS.ErrnoError(55);
-     }
-    }
-   }
-   delete old_node.parent.contents[old_node.name];
-   old_node.parent.timestamp = Date.now();
-   old_node.name = new_name;
-   new_dir.contents[new_name] = old_node;
-   new_dir.timestamp = old_node.parent.timestamp;
-   old_node.parent = new_dir;
-  },
-  unlink: function(parent, name) {
-   delete parent.contents[name];
-   parent.timestamp = Date.now();
-  },
-  rmdir: function(parent, name) {
-   var node = FS.lookupNode(parent, name);
-   for (var i in node.contents) {
-    throw new FS.ErrnoError(55);
-   }
-   delete parent.contents[name];
-   parent.timestamp = Date.now();
-  },
-  readdir: function(node) {
-   var entries = [ ".", ".." ];
-   for (var key in node.contents) {
-    if (!node.contents.hasOwnProperty(key)) {
-     continue;
-    }
-    entries.push(key);
-   }
-   return entries;
-  },
-  symlink: function(parent, newname, oldpath) {
-   var node = MEMFS.createNode(parent, newname, 511 | 40960, 0);
-   node.link = oldpath;
-   return node;
-  },
-  readlink: function(node) {
-   if (!FS.isLink(node.mode)) {
-    throw new FS.ErrnoError(28);
-   }
-   return node.link;
-  }
- },
- stream_ops: {
-  read: function(stream, buffer, offset, length, position) {
-   var contents = stream.node.contents;
-   if (position >= stream.node.usedBytes) return 0;
-   var size = Math.min(stream.node.usedBytes - position, length);
-   assert(size >= 0);
-   if (size > 8 && contents.subarray) {
-    buffer.set(contents.subarray(position, position + size), offset);
-   } else {
-    for (var i = 0; i < size; i++) buffer[offset + i] = contents[position + i];
-   }
-   return size;
-  },
-  write: function(stream, buffer, offset, length, position, canOwn) {
-   assert(!(buffer instanceof ArrayBuffer));
-   if (buffer.buffer === GROWABLE_HEAP_I8().buffer) {
-    canOwn = false;
-   }
-   if (!length) return 0;
-   var node = stream.node;
-   node.timestamp = Date.now();
-   if (buffer.subarray && (!node.contents || node.contents.subarray)) {
-    if (canOwn) {
-     assert(position === 0, "canOwn must imply no weird position inside the file");
-     node.contents = buffer.subarray(offset, offset + length);
-     node.usedBytes = length;
-     return length;
-    } else if (node.usedBytes === 0 && position === 0) {
-     node.contents = buffer.slice(offset, offset + length);
-     node.usedBytes = length;
-     return length;
-    } else if (position + length <= node.usedBytes) {
-     node.contents.set(buffer.subarray(offset, offset + length), position);
-     return length;
-    }
-   }
-   MEMFS.expandFileStorage(node, position + length);
-   if (node.contents.subarray && buffer.subarray) {
-    node.contents.set(buffer.subarray(offset, offset + length), position);
-   } else {
-    for (var i = 0; i < length; i++) {
-     node.contents[position + i] = buffer[offset + i];
-    }
-   }
-   node.usedBytes = Math.max(node.usedBytes, position + length);
-   return length;
-  },
-  llseek: function(stream, offset, whence) {
-   var position = offset;
-   if (whence === 1) {
-    position += stream.position;
-   } else if (whence === 2) {
-    if (FS.isFile(stream.node.mode)) {
-     position += stream.node.usedBytes;
-    }
-   }
-   if (position < 0) {
-    throw new FS.ErrnoError(28);
-   }
-   return position;
-  },
-  allocate: function(stream, offset, length) {
-   MEMFS.expandFileStorage(stream.node, offset + length);
-   stream.node.usedBytes = Math.max(stream.node.usedBytes, offset + length);
-  },
-  mmap: function(stream, length, position, prot, flags) {
-   if (!FS.isFile(stream.node.mode)) {
-    throw new FS.ErrnoError(43);
-   }
-   var ptr;
-   var allocated;
-   var contents = stream.node.contents;
-   if (!(flags & 2) && contents.buffer === buffer) {
-    allocated = false;
-    ptr = contents.byteOffset;
-   } else {
-    if (position > 0 || position + length < contents.length) {
-     if (contents.subarray) {
-      contents = contents.subarray(position, position + length);
-     } else {
-      contents = Array.prototype.slice.call(contents, position, position + length);
-     }
-    }
-    allocated = true;
-    ptr = mmapAlloc(length);
-    if (!ptr) {
-     throw new FS.ErrnoError(48);
-    }
-    GROWABLE_HEAP_I8().set(contents, ptr);
-   }
-   return {
-    ptr: ptr,
-    allocated: allocated
-   };
-  },
-  msync: function(stream, buffer, offset, length, mmapFlags) {
-   if (!FS.isFile(stream.node.mode)) {
-    throw new FS.ErrnoError(43);
-   }
-   if (mmapFlags & 2) {
-    return 0;
-   }
-   var bytesWritten = MEMFS.stream_ops.write(stream, buffer, 0, length, offset, false);
-   return 0;
-  }
- }
-};
-
-function asyncLoad(url, onload, onerror, noRunDep) {
- var dep = !noRunDep ? getUniqueRunDependency("al " + url) : "";
- readAsync(url, arrayBuffer => {
-  assert(arrayBuffer, 'Loading data file "' + url + '" failed (no arrayBuffer).');
-  onload(new Uint8Array(arrayBuffer));
-  if (dep) removeRunDependency(dep);
- }, event => {
-  if (onerror) {
-   onerror();
-  } else {
-   throw 'Loading data file "' + url + '" failed.';
-  }
- });
- if (dep) addRunDependency(dep);
-}
-
-var IDBFS = {
- dbs: {},
- indexedDB: () => {
-  if (typeof indexedDB != "undefined") return indexedDB;
-  var ret = null;
-  if (typeof window == "object") ret = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB;
-  assert(ret, "IDBFS used, but indexedDB not supported");
-  return ret;
- },
- DB_VERSION: 21,
- DB_STORE_NAME: "FILE_DATA",
- mount: function(mount) {
-  return MEMFS.mount.apply(null, arguments);
- },
- syncfs: (mount, populate, callback) => {
-  IDBFS.getLocalSet(mount, (err, local) => {
-   if (err) return callback(err);
-   IDBFS.getRemoteSet(mount, (err, remote) => {
-    if (err) return callback(err);
-    var src = populate ? remote : local;
-    var dst = populate ? local : remote;
-    IDBFS.reconcile(src, dst, callback);
-   });
-  });
- },
- quit: () => {
-  Object.values(IDBFS.dbs).forEach(value => value.close());
-  IDBFS.dbs = {};
- },
- getDB: (name, callback) => {
-  var db = IDBFS.dbs[name];
-  if (db) {
-   return callback(null, db);
-  }
-  var req;
-  try {
-   req = IDBFS.indexedDB().open(name, IDBFS.DB_VERSION);
-  } catch (e) {
-   return callback(e);
-  }
-  if (!req) {
-   return callback("Unable to connect to IndexedDB");
-  }
-  req.onupgradeneeded = e => {
-   var db = e.target.result;
-   var transaction = e.target.transaction;
-   var fileStore;
-   if (db.objectStoreNames.contains(IDBFS.DB_STORE_NAME)) {
-    fileStore = transaction.objectStore(IDBFS.DB_STORE_NAME);
-   } else {
-    fileStore = db.createObjectStore(IDBFS.DB_STORE_NAME);
-   }
-   if (!fileStore.indexNames.contains("timestamp")) {
-    fileStore.createIndex("timestamp", "timestamp", {
-     unique: false
-    });
-   }
-  };
-  req.onsuccess = () => {
-   db = req.result;
-   IDBFS.dbs[name] = db;
-   callback(null, db);
-  };
-  req.onerror = e => {
-   callback(this.error);
-   e.preventDefault();
-  };
- },
- getLocalSet: (mount, callback) => {
-  var entries = {};
-  function isRealDir(p) {
-   return p !== "." && p !== "..";
-  }
-  function toAbsolute(root) {
-   return p => {
-    return PATH.join2(root, p);
-   };
-  }
-  var check = FS.readdir(mount.mountpoint).filter(isRealDir).map(toAbsolute(mount.mountpoint));
-  while (check.length) {
-   var path = check.pop();
-   var stat;
-   try {
-    stat = FS.stat(path);
-   } catch (e) {
-    return callback(e);
-   }
-   if (FS.isDir(stat.mode)) {
-    check.push.apply(check, FS.readdir(path).filter(isRealDir).map(toAbsolute(path)));
-   }
-   entries[path] = {
-    "timestamp": stat.mtime
-   };
-  }
-  return callback(null, {
-   type: "local",
-   entries: entries
-  });
- },
- getRemoteSet: (mount, callback) => {
-  var entries = {};
-  IDBFS.getDB(mount.mountpoint, (err, db) => {
-   if (err) return callback(err);
-   try {
-    var transaction = db.transaction([ IDBFS.DB_STORE_NAME ], "readonly");
-    transaction.onerror = e => {
-     callback(this.error);
-     e.preventDefault();
-    };
-    var store = transaction.objectStore(IDBFS.DB_STORE_NAME);
-    var index = store.index("timestamp");
-    index.openKeyCursor().onsuccess = event => {
-     var cursor = event.target.result;
-     if (!cursor) {
-      return callback(null, {
-       type: "remote",
-       db: db,
-       entries: entries
-      });
-     }
-     entries[cursor.primaryKey] = {
-      "timestamp": cursor.key
-     };
-     cursor.continue();
-    };
-   } catch (e) {
-    return callback(e);
-   }
-  });
- },
- loadLocalEntry: (path, callback) => {
-  var stat, node;
-  try {
-   var lookup = FS.lookupPath(path);
-   node = lookup.node;
-   stat = FS.stat(path);
-  } catch (e) {
-   return callback(e);
-  }
-  if (FS.isDir(stat.mode)) {
-   return callback(null, {
-    "timestamp": stat.mtime,
-    "mode": stat.mode
-   });
-  } else if (FS.isFile(stat.mode)) {
-   node.contents = MEMFS.getFileDataAsTypedArray(node);
-   return callback(null, {
-    "timestamp": stat.mtime,
-    "mode": stat.mode,
-    "contents": node.contents
-   });
-  } else {
-   return callback(new Error("node type not supported"));
-  }
- },
- storeLocalEntry: (path, entry, callback) => {
-  try {
-   if (FS.isDir(entry["mode"])) {
-    FS.mkdirTree(path, entry["mode"]);
-   } else if (FS.isFile(entry["mode"])) {
-    FS.writeFile(path, entry["contents"], {
-     canOwn: true
-    });
-   } else {
-    return callback(new Error("node type not supported"));
-   }
-   FS.chmod(path, entry["mode"]);
-   FS.utime(path, entry["timestamp"], entry["timestamp"]);
-  } catch (e) {
-   return callback(e);
-  }
-  callback(null);
- },
- removeLocalEntry: (path, callback) => {
-  try {
-   var stat = FS.stat(path);
-   if (FS.isDir(stat.mode)) {
-    FS.rmdir(path);
-   } else if (FS.isFile(stat.mode)) {
-    FS.unlink(path);
-   }
-  } catch (e) {
-   return callback(e);
-  }
-  callback(null);
- },
- loadRemoteEntry: (store, path, callback) => {
-  var req = store.get(path);
-  req.onsuccess = event => {
-   callback(null, event.target.result);
-  };
-  req.onerror = e => {
-   callback(this.error);
-   e.preventDefault();
-  };
- },
- storeRemoteEntry: (store, path, entry, callback) => {
-  try {
-   var req = store.put(entry, path);
-  } catch (e) {
-   callback(e);
-   return;
-  }
-  req.onsuccess = () => {
-   callback(null);
-  };
-  req.onerror = e => {
-   callback(this.error);
-   e.preventDefault();
-  };
- },
- removeRemoteEntry: (store, path, callback) => {
-  var req = store.delete(path);
-  req.onsuccess = () => {
-   callback(null);
-  };
-  req.onerror = e => {
-   callback(this.error);
-   e.preventDefault();
-  };
- },
- reconcile: (src, dst, callback) => {
-  var total = 0;
-  var create = [];
-  Object.keys(src.entries).forEach(function(key) {
-   var e = src.entries[key];
-   var e2 = dst.entries[key];
-   if (!e2 || e["timestamp"].getTime() != e2["timestamp"].getTime()) {
-    create.push(key);
-    total++;
-   }
-  });
-  var remove = [];
-  Object.keys(dst.entries).forEach(function(key) {
-   if (!src.entries[key]) {
-    remove.push(key);
-    total++;
-   }
-  });
-  if (!total) {
-   return callback(null);
-  }
-  var errored = false;
-  var db = src.type === "remote" ? src.db : dst.db;
-  var transaction = db.transaction([ IDBFS.DB_STORE_NAME ], "readwrite");
-  var store = transaction.objectStore(IDBFS.DB_STORE_NAME);
-  function done(err) {
-   if (err && !errored) {
-    errored = true;
-    return callback(err);
-   }
-  }
-  transaction.onerror = e => {
-   done(this.error);
-   e.preventDefault();
-  };
-  transaction.oncomplete = e => {
-   if (!errored) {
-    callback(null);
-   }
-  };
-  create.sort().forEach(path => {
-   if (dst.type === "local") {
-    IDBFS.loadRemoteEntry(store, path, (err, entry) => {
-     if (err) return done(err);
-     IDBFS.storeLocalEntry(path, entry, done);
-    });
-   } else {
-    IDBFS.loadLocalEntry(path, (err, entry) => {
-     if (err) return done(err);
-     IDBFS.storeRemoteEntry(store, path, entry, done);
-    });
-   }
-  });
-  remove.sort().reverse().forEach(path => {
-   if (dst.type === "local") {
-    IDBFS.removeLocalEntry(path, done);
-   } else {
-    IDBFS.removeRemoteEntry(store, path, done);
-   }
-  });
- }
-};
-
-var ERRNO_MESSAGES = {
- 0: "Success",
- 1: "Arg list too long",
- 2: "Permission denied",
- 3: "Address already in use",
- 4: "Address not available",
- 5: "Address family not supported by protocol family",
- 6: "No more processes",
- 7: "Socket already connected",
- 8: "Bad file number",
- 9: "Trying to read unreadable message",
- 10: "Mount device busy",
- 11: "Operation canceled",
- 12: "No children",
- 13: "Connection aborted",
- 14: "Connection refused",
- 15: "Connection reset by peer",
- 16: "File locking deadlock error",
- 17: "Destination address required",
- 18: "Math arg out of domain of func",
- 19: "Quota exceeded",
- 20: "File exists",
- 21: "Bad address",
- 22: "File too large",
- 23: "Host is unreachable",
- 24: "Identifier removed",
- 25: "Illegal byte sequence",
- 26: "Connection already in progress",
- 27: "Interrupted system call",
- 28: "Invalid argument",
- 29: "I/O error",
- 30: "Socket is already connected",
- 31: "Is a directory",
- 32: "Too many symbolic links",
- 33: "Too many open files",
- 34: "Too many links",
- 35: "Message too long",
- 36: "Multihop attempted",
- 37: "File or path name too long",
- 38: "Network interface is not configured",
- 39: "Connection reset by network",
- 40: "Network is unreachable",
- 41: "Too many open files in system",
- 42: "No buffer space available",
- 43: "No such device",
- 44: "No such file or directory",
- 45: "Exec format error",
- 46: "No record locks available",
- 47: "The link has been severed",
- 48: "Not enough core",
- 49: "No message of desired type",
- 50: "Protocol not available",
- 51: "No space left on device",
- 52: "Function not implemented",
- 53: "Socket is not connected",
- 54: "Not a directory",
- 55: "Directory not empty",
- 56: "State not recoverable",
- 57: "Socket operation on non-socket",
- 59: "Not a typewriter",
- 60: "No such device or address",
- 61: "Value too large for defined data type",
- 62: "Previous owner died",
- 63: "Not super-user",
- 64: "Broken pipe",
- 65: "Protocol error",
- 66: "Unknown protocol",
- 67: "Protocol wrong type for socket",
- 68: "Math result not representable",
- 69: "Read only file system",
- 70: "Illegal seek",
- 71: "No such process",
- 72: "Stale file handle",
- 73: "Connection timed out",
- 74: "Text file busy",
- 75: "Cross-device link",
- 100: "Device not a stream",
- 101: "Bad font file fmt",
- 102: "Invalid slot",
- 103: "Invalid request code",
- 104: "No anode",
- 105: "Block device required",
- 106: "Channel number out of range",
- 107: "Level 3 halted",
- 108: "Level 3 reset",
- 109: "Link number out of range",
- 110: "Protocol driver not attached",
- 111: "No CSI structure available",
- 112: "Level 2 halted",
- 113: "Invalid exchange",
- 114: "Invalid request descriptor",
- 115: "Exchange full",
- 116: "No data (for no delay io)",
- 117: "Timer expired",
- 118: "Out of streams resources",
- 119: "Machine is not on the network",
- 120: "Package not installed",
- 121: "The object is remote",
- 122: "Advertise error",
- 123: "Srmount error",
- 124: "Communication error on send",
- 125: "Cross mount point (not really error)",
- 126: "Given log. name not unique",
- 127: "f.d. invalid for this operation",
- 128: "Remote address changed",
- 129: "Can   access a needed shared lib",
- 130: "Accessing a corrupted shared lib",
- 131: ".lib section in a.out corrupted",
- 132: "Attempting to link in too many libs",
- 133: "Attempting to exec a shared library",
- 135: "Streams pipe error",
- 136: "Too many users",
- 137: "Socket type not supported",
- 138: "Not supported",
- 139: "Protocol family not supported",
- 140: "Can't send after socket shutdown",
- 141: "Too many references",
- 142: "Host is down",
- 148: "No medium (in tape drive)",
- 156: "Level 2 not synchronized"
-};
-
-var ERRNO_CODES = {};
-
-var FS = {
- root: null,
- mounts: [],
- devices: {},
- streams: [],
- nextInode: 1,
- nameTable: null,
- currentPath: "/",
- initialized: false,
- ignorePermissions: true,
- ErrnoError: null,
- genericErrors: {},
- filesystems: null,
- syncFSRequests: 0,
- lookupPath: (path, opts = {}) => {
-  path = PATH_FS.resolve(FS.cwd(), path);
-  if (!path) return {
-   path: "",
-   node: null
-  };
-  var defaults = {
-   follow_mount: true,
-   recurse_count: 0
-  };
-  opts = Object.assign(defaults, opts);
-  if (opts.recurse_count > 8) {
-   throw new FS.ErrnoError(32);
-  }
-  var parts = PATH.normalizeArray(path.split("/").filter(p => !!p), false);
-  var current = FS.root;
-  var current_path = "/";
-  for (var i = 0; i < parts.length; i++) {
-   var islast = i === parts.length - 1;
-   if (islast && opts.parent) {
-    break;
-   }
-   current = FS.lookupNode(current, parts[i]);
-   current_path = PATH.join2(current_path, parts[i]);
-   if (FS.isMountpoint(current)) {
-    if (!islast || islast && opts.follow_mount) {
-     current = current.mounted.root;
-    }
-   }
-   if (!islast || opts.follow) {
-    var count = 0;
-    while (FS.isLink(current.mode)) {
-     var link = FS.readlink(current_path);
-     current_path = PATH_FS.resolve(PATH.dirname(current_path), link);
-     var lookup = FS.lookupPath(current_path, {
-      recurse_count: opts.recurse_count + 1
-     });
-     current = lookup.node;
-     if (count++ > 40) {
-      throw new FS.ErrnoError(32);
-     }
-    }
-   }
-  }
-  return {
-   path: current_path,
-   node: current
-  };
- },
- getPath: node => {
-  var path;
-  while (true) {
-   if (FS.isRoot(node)) {
-    var mount = node.mount.mountpoint;
-    if (!path) return mount;
-    return mount[mount.length - 1] !== "/" ? mount + "/" + path : mount + path;
-   }
-   path = path ? node.name + "/" + path : node.name;
-   node = node.parent;
-  }
- },
- hashName: (parentid, name) => {
-  var hash = 0;
-  for (var i = 0; i < name.length; i++) {
-   hash = (hash << 5) - hash + name.charCodeAt(i) | 0;
-  }
-  return (parentid + hash >>> 0) % FS.nameTable.length;
- },
- hashAddNode: node => {
-  var hash = FS.hashName(node.parent.id, node.name);
-  node.name_next = FS.nameTable[hash];
-  FS.nameTable[hash] = node;
- },
- hashRemoveNode: node => {
-  var hash = FS.hashName(node.parent.id, node.name);
-  if (FS.nameTable[hash] === node) {
-   FS.nameTable[hash] = node.name_next;
-  } else {
-   var current = FS.nameTable[hash];
-   while (current) {
-    if (current.name_next === node) {
-     current.name_next = node.name_next;
-     break;
-    }
-    current = current.name_next;
-   }
-  }
- },
- lookupNode: (parent, name) => {
-  var errCode = FS.mayLookup(parent);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode, parent);
-  }
-  var hash = FS.hashName(parent.id, name);
-  for (var node = FS.nameTable[hash]; node; node = node.name_next) {
-   var nodeName = node.name;
-   if (node.parent.id === parent.id && nodeName === name) {
-    return node;
-   }
-  }
-  return FS.lookup(parent, name);
- },
- createNode: (parent, name, mode, rdev) => {
-  assert(typeof parent == "object");
-  var node = new FS.FSNode(parent, name, mode, rdev);
-  FS.hashAddNode(node);
-  return node;
- },
- destroyNode: node => {
-  FS.hashRemoveNode(node);
- },
- isRoot: node => {
-  return node === node.parent;
- },
- isMountpoint: node => {
-  return !!node.mounted;
- },
- isFile: mode => {
-  return (mode & 61440) === 32768;
- },
- isDir: mode => {
-  return (mode & 61440) === 16384;
- },
- isLink: mode => {
-  return (mode & 61440) === 40960;
- },
- isChrdev: mode => {
-  return (mode & 61440) === 8192;
- },
- isBlkdev: mode => {
-  return (mode & 61440) === 24576;
- },
- isFIFO: mode => {
-  return (mode & 61440) === 4096;
- },
- isSocket: mode => {
-  return (mode & 49152) === 49152;
- },
- flagModes: {
-  "r": 0,
-  "r+": 2,
-  "w": 577,
-  "w+": 578,
-  "a": 1089,
-  "a+": 1090
- },
- modeStringToFlags: str => {
-  var flags = FS.flagModes[str];
-  if (typeof flags == "undefined") {
-   throw new Error("Unknown file open mode: " + str);
-  }
-  return flags;
- },
- flagsToPermissionString: flag => {
-  var perms = [ "r", "w", "rw" ][flag & 3];
-  if (flag & 512) {
-   perms += "w";
-  }
-  return perms;
- },
- nodePermissions: (node, perms) => {
-  if (FS.ignorePermissions) {
-   return 0;
-  }
-  if (perms.includes("r") && !(node.mode & 292)) {
-   return 2;
-  } else if (perms.includes("w") && !(node.mode & 146)) {
-   return 2;
-  } else if (perms.includes("x") && !(node.mode & 73)) {
-   return 2;
-  }
-  return 0;
- },
- mayLookup: dir => {
-  var errCode = FS.nodePermissions(dir, "x");
-  if (errCode) return errCode;
-  if (!dir.node_ops.lookup) return 2;
-  return 0;
- },
- mayCreate: (dir, name) => {
-  try {
-   var node = FS.lookupNode(dir, name);
-   return 20;
-  } catch (e) {}
-  return FS.nodePermissions(dir, "wx");
- },
- mayDelete: (dir, name, isdir) => {
-  var node;
-  try {
-   node = FS.lookupNode(dir, name);
-  } catch (e) {
-   return e.errno;
-  }
-  var errCode = FS.nodePermissions(dir, "wx");
-  if (errCode) {
-   return errCode;
-  }
-  if (isdir) {
-   if (!FS.isDir(node.mode)) {
-    return 54;
-   }
-   if (FS.isRoot(node) || FS.getPath(node) === FS.cwd()) {
-    return 10;
-   }
-  } else {
-   if (FS.isDir(node.mode)) {
-    return 31;
-   }
-  }
-  return 0;
- },
- mayOpen: (node, flags) => {
-  if (!node) {
-   return 44;
-  }
-  if (FS.isLink(node.mode)) {
-   return 32;
-  } else if (FS.isDir(node.mode)) {
-   if (FS.flagsToPermissionString(flags) !== "r" || flags & 512) {
-    return 31;
-   }
-  }
-  return FS.nodePermissions(node, FS.flagsToPermissionString(flags));
- },
- MAX_OPEN_FDS: 4096,
- nextfd: (fd_start = 0, fd_end = FS.MAX_OPEN_FDS) => {
-  for (var fd = fd_start; fd <= fd_end; fd++) {
-   if (!FS.streams[fd]) {
-    return fd;
-   }
-  }
-  throw new FS.ErrnoError(33);
- },
- getStream: fd => FS.streams[fd],
- createStream: (stream, fd_start, fd_end) => {
-  if (!FS.FSStream) {
-   FS.FSStream = function() {
-    this.shared = {};
-   };
-   FS.FSStream.prototype = {};
-   Object.defineProperties(FS.FSStream.prototype, {
-    object: {
-     get: function() {
-      return this.node;
-     },
-     set: function(val) {
-      this.node = val;
-     }
-    },
-    isRead: {
-     get: function() {
-      return (this.flags & 2097155) !== 1;
-     }
-    },
-    isWrite: {
-     get: function() {
-      return (this.flags & 2097155) !== 0;
-     }
-    },
-    isAppend: {
-     get: function() {
-      return this.flags & 1024;
-     }
-    },
-    flags: {
-     get: function() {
-      return this.shared.flags;
-     },
-     set: function(val) {
-      this.shared.flags = val;
-     }
-    },
-    position: {
-     get: function() {
-      return this.shared.position;
-     },
-     set: function(val) {
-      this.shared.position = val;
-     }
-    }
-   });
-  }
-  stream = Object.assign(new FS.FSStream(), stream);
-  var fd = FS.nextfd(fd_start, fd_end);
-  stream.fd = fd;
-  FS.streams[fd] = stream;
-  return stream;
- },
- closeStream: fd => {
-  FS.streams[fd] = null;
- },
- chrdev_stream_ops: {
-  open: stream => {
-   var device = FS.getDevice(stream.node.rdev);
-   stream.stream_ops = device.stream_ops;
-   if (stream.stream_ops.open) {
-    stream.stream_ops.open(stream);
-   }
-  },
-  llseek: () => {
-   throw new FS.ErrnoError(70);
-  }
- },
- major: dev => dev >> 8,
- minor: dev => dev & 255,
- makedev: (ma, mi) => ma << 8 | mi,
- registerDevice: (dev, ops) => {
-  FS.devices[dev] = {
-   stream_ops: ops
-  };
- },
- getDevice: dev => FS.devices[dev],
- getMounts: mount => {
-  var mounts = [];
-  var check = [ mount ];
-  while (check.length) {
-   var m = check.pop();
-   mounts.push(m);
-   check.push.apply(check, m.mounts);
-  }
-  return mounts;
- },
- syncfs: (populate, callback) => {
-  if (typeof populate == "function") {
-   callback = populate;
-   populate = false;
-  }
-  FS.syncFSRequests++;
-  if (FS.syncFSRequests > 1) {
-   err("warning: " + FS.syncFSRequests + " FS.syncfs operations in flight at once, probably just doing extra work");
-  }
-  var mounts = FS.getMounts(FS.root.mount);
-  var completed = 0;
-  function doCallback(errCode) {
-   assert(FS.syncFSRequests > 0);
-   FS.syncFSRequests--;
-   return callback(errCode);
-  }
-  function done(errCode) {
-   if (errCode) {
-    if (!done.errored) {
-     done.errored = true;
-     return doCallback(errCode);
-    }
-    return;
-   }
-   if (++completed >= mounts.length) {
-    doCallback(null);
-   }
-  }
-  mounts.forEach(mount => {
-   if (!mount.type.syncfs) {
-    return done(null);
-   }
-   mount.type.syncfs(mount, populate, done);
-  });
- },
- mount: (type, opts, mountpoint) => {
-  if (typeof type == "string") {
-   throw type;
-  }
-  var root = mountpoint === "/";
-  var pseudo = !mountpoint;
-  var node;
-  if (root && FS.root) {
-   throw new FS.ErrnoError(10);
-  } else if (!root && !pseudo) {
-   var lookup = FS.lookupPath(mountpoint, {
-    follow_mount: false
-   });
-   mountpoint = lookup.path;
-   node = lookup.node;
-   if (FS.isMountpoint(node)) {
-    throw new FS.ErrnoError(10);
-   }
-   if (!FS.isDir(node.mode)) {
-    throw new FS.ErrnoError(54);
-   }
-  }
-  var mount = {
-   type: type,
-   opts: opts,
-   mountpoint: mountpoint,
-   mounts: []
-  };
-  var mountRoot = type.mount(mount);
-  mountRoot.mount = mount;
-  mount.root = mountRoot;
-  if (root) {
-   FS.root = mountRoot;
-  } else if (node) {
-   node.mounted = mount;
-   if (node.mount) {
-    node.mount.mounts.push(mount);
-   }
-  }
-  return mountRoot;
- },
- unmount: mountpoint => {
-  var lookup = FS.lookupPath(mountpoint, {
-   follow_mount: false
-  });
-  if (!FS.isMountpoint(lookup.node)) {
-   throw new FS.ErrnoError(28);
-  }
-  var node = lookup.node;
-  var mount = node.mounted;
-  var mounts = FS.getMounts(mount);
-  Object.keys(FS.nameTable).forEach(hash => {
-   var current = FS.nameTable[hash];
-   while (current) {
-    var next = current.name_next;
-    if (mounts.includes(current.mount)) {
-     FS.destroyNode(current);
-    }
-    current = next;
-   }
-  });
-  node.mounted = null;
-  var idx = node.mount.mounts.indexOf(mount);
-  assert(idx !== -1);
-  node.mount.mounts.splice(idx, 1);
- },
- lookup: (parent, name) => {
-  return parent.node_ops.lookup(parent, name);
- },
- mknod: (path, mode, dev) => {
-  var lookup = FS.lookupPath(path, {
-   parent: true
-  });
-  var parent = lookup.node;
-  var name = PATH.basename(path);
-  if (!name || name === "." || name === "..") {
-   throw new FS.ErrnoError(28);
-  }
-  var errCode = FS.mayCreate(parent, name);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  if (!parent.node_ops.mknod) {
-   throw new FS.ErrnoError(63);
-  }
-  return parent.node_ops.mknod(parent, name, mode, dev);
- },
- create: (path, mode) => {
-  mode = mode !== undefined ? mode : 438;
-  mode &= 4095;
-  mode |= 32768;
-  return FS.mknod(path, mode, 0);
- },
- mkdir: (path, mode) => {
-  mode = mode !== undefined ? mode : 511;
-  mode &= 511 | 512;
-  mode |= 16384;
-  return FS.mknod(path, mode, 0);
- },
- mkdirTree: (path, mode) => {
-  var dirs = path.split("/");
-  var d = "";
-  for (var i = 0; i < dirs.length; ++i) {
-   if (!dirs[i]) continue;
-   d += "/" + dirs[i];
-   try {
-    FS.mkdir(d, mode);
-   } catch (e) {
-    if (e.errno != 20) throw e;
-   }
-  }
- },
- mkdev: (path, mode, dev) => {
-  if (typeof dev == "undefined") {
-   dev = mode;
-   mode = 438;
-  }
-  mode |= 8192;
-  return FS.mknod(path, mode, dev);
- },
- symlink: (oldpath, newpath) => {
-  if (!PATH_FS.resolve(oldpath)) {
-   throw new FS.ErrnoError(44);
-  }
-  var lookup = FS.lookupPath(newpath, {
-   parent: true
-  });
-  var parent = lookup.node;
-  if (!parent) {
-   throw new FS.ErrnoError(44);
-  }
-  var newname = PATH.basename(newpath);
-  var errCode = FS.mayCreate(parent, newname);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  if (!parent.node_ops.symlink) {
-   throw new FS.ErrnoError(63);
-  }
-  return parent.node_ops.symlink(parent, newname, oldpath);
- },
- rename: (old_path, new_path) => {
-  var old_dirname = PATH.dirname(old_path);
-  var new_dirname = PATH.dirname(new_path);
-  var old_name = PATH.basename(old_path);
-  var new_name = PATH.basename(new_path);
-  var lookup, old_dir, new_dir;
-  lookup = FS.lookupPath(old_path, {
-   parent: true
-  });
-  old_dir = lookup.node;
-  lookup = FS.lookupPath(new_path, {
-   parent: true
-  });
-  new_dir = lookup.node;
-  if (!old_dir || !new_dir) throw new FS.ErrnoError(44);
-  if (old_dir.mount !== new_dir.mount) {
-   throw new FS.ErrnoError(75);
-  }
-  var old_node = FS.lookupNode(old_dir, old_name);
-  var relative = PATH_FS.relative(old_path, new_dirname);
-  if (relative.charAt(0) !== ".") {
-   throw new FS.ErrnoError(28);
-  }
-  relative = PATH_FS.relative(new_path, old_dirname);
-  if (relative.charAt(0) !== ".") {
-   throw new FS.ErrnoError(55);
-  }
-  var new_node;
-  try {
-   new_node = FS.lookupNode(new_dir, new_name);
-  } catch (e) {}
-  if (old_node === new_node) {
-   return;
-  }
-  var isdir = FS.isDir(old_node.mode);
-  var errCode = FS.mayDelete(old_dir, old_name, isdir);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  errCode = new_node ? FS.mayDelete(new_dir, new_name, isdir) : FS.mayCreate(new_dir, new_name);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  if (!old_dir.node_ops.rename) {
-   throw new FS.ErrnoError(63);
-  }
-  if (FS.isMountpoint(old_node) || new_node && FS.isMountpoint(new_node)) {
-   throw new FS.ErrnoError(10);
-  }
-  if (new_dir !== old_dir) {
-   errCode = FS.nodePermissions(old_dir, "w");
-   if (errCode) {
-    throw new FS.ErrnoError(errCode);
-   }
-  }
-  FS.hashRemoveNode(old_node);
-  try {
-   old_dir.node_ops.rename(old_node, new_dir, new_name);
-  } catch (e) {
-   throw e;
-  } finally {
-   FS.hashAddNode(old_node);
-  }
- },
- rmdir: path => {
-  var lookup = FS.lookupPath(path, {
-   parent: true
-  });
-  var parent = lookup.node;
-  var name = PATH.basename(path);
-  var node = FS.lookupNode(parent, name);
-  var errCode = FS.mayDelete(parent, name, true);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  if (!parent.node_ops.rmdir) {
-   throw new FS.ErrnoError(63);
-  }
-  if (FS.isMountpoint(node)) {
-   throw new FS.ErrnoError(10);
-  }
-  parent.node_ops.rmdir(parent, name);
-  FS.destroyNode(node);
- },
- readdir: path => {
-  var lookup = FS.lookupPath(path, {
-   follow: true
-  });
-  var node = lookup.node;
-  if (!node.node_ops.readdir) {
-   throw new FS.ErrnoError(54);
-  }
-  return node.node_ops.readdir(node);
- },
- unlink: path => {
-  var lookup = FS.lookupPath(path, {
-   parent: true
-  });
-  var parent = lookup.node;
-  if (!parent) {
-   throw new FS.ErrnoError(44);
-  }
-  var name = PATH.basename(path);
-  var node = FS.lookupNode(parent, name);
-  var errCode = FS.mayDelete(parent, name, false);
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  if (!parent.node_ops.unlink) {
-   throw new FS.ErrnoError(63);
-  }
-  if (FS.isMountpoint(node)) {
-   throw new FS.ErrnoError(10);
-  }
-  parent.node_ops.unlink(parent, name);
-  FS.destroyNode(node);
- },
- readlink: path => {
-  var lookup = FS.lookupPath(path);
-  var link = lookup.node;
-  if (!link) {
-   throw new FS.ErrnoError(44);
-  }
-  if (!link.node_ops.readlink) {
-   throw new FS.ErrnoError(28);
-  }
-  return PATH_FS.resolve(FS.getPath(link.parent), link.node_ops.readlink(link));
- },
- stat: (path, dontFollow) => {
-  var lookup = FS.lookupPath(path, {
-   follow: !dontFollow
-  });
-  var node = lookup.node;
-  if (!node) {
-   throw new FS.ErrnoError(44);
-  }
-  if (!node.node_ops.getattr) {
-   throw new FS.ErrnoError(63);
-  }
-  return node.node_ops.getattr(node);
- },
- lstat: path => {
-  return FS.stat(path, true);
- },
- chmod: (path, mode, dontFollow) => {
-  var node;
-  if (typeof path == "string") {
-   var lookup = FS.lookupPath(path, {
-    follow: !dontFollow
-   });
-   node = lookup.node;
-  } else {
-   node = path;
-  }
-  if (!node.node_ops.setattr) {
-   throw new FS.ErrnoError(63);
-  }
-  node.node_ops.setattr(node, {
-   mode: mode & 4095 | node.mode & ~4095,
-   timestamp: Date.now()
-  });
- },
- lchmod: (path, mode) => {
-  FS.chmod(path, mode, true);
- },
- fchmod: (fd, mode) => {
-  var stream = FS.getStream(fd);
-  if (!stream) {
-   throw new FS.ErrnoError(8);
-  }
-  FS.chmod(stream.node, mode);
- },
- chown: (path, uid, gid, dontFollow) => {
-  var node;
-  if (typeof path == "string") {
-   var lookup = FS.lookupPath(path, {
-    follow: !dontFollow
-   });
-   node = lookup.node;
-  } else {
-   node = path;
-  }
-  if (!node.node_ops.setattr) {
-   throw new FS.ErrnoError(63);
-  }
-  node.node_ops.setattr(node, {
-   timestamp: Date.now()
-  });
- },
- lchown: (path, uid, gid) => {
-  FS.chown(path, uid, gid, true);
- },
- fchown: (fd, uid, gid) => {
-  var stream = FS.getStream(fd);
-  if (!stream) {
-   throw new FS.ErrnoError(8);
-  }
-  FS.chown(stream.node, uid, gid);
- },
- truncate: (path, len) => {
-  if (len < 0) {
-   throw new FS.ErrnoError(28);
-  }
-  var node;
-  if (typeof path == "string") {
-   var lookup = FS.lookupPath(path, {
-    follow: true
-   });
-   node = lookup.node;
-  } else {
-   node = path;
-  }
-  if (!node.node_ops.setattr) {
-   throw new FS.ErrnoError(63);
-  }
-  if (FS.isDir(node.mode)) {
-   throw new FS.ErrnoError(31);
-  }
-  if (!FS.isFile(node.mode)) {
-   throw new FS.ErrnoError(28);
-  }
-  var errCode = FS.nodePermissions(node, "w");
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  node.node_ops.setattr(node, {
-   size: len,
-   timestamp: Date.now()
-  });
- },
- ftruncate: (fd, len) => {
-  var stream = FS.getStream(fd);
-  if (!stream) {
-   throw new FS.ErrnoError(8);
-  }
-  if ((stream.flags & 2097155) === 0) {
-   throw new FS.ErrnoError(28);
-  }
-  FS.truncate(stream.node, len);
- },
- utime: (path, atime, mtime) => {
-  var lookup = FS.lookupPath(path, {
-   follow: true
-  });
-  var node = lookup.node;
-  node.node_ops.setattr(node, {
-   timestamp: Math.max(atime, mtime)
-  });
- },
- open: (path, flags, mode) => {
-  if (path === "") {
-   throw new FS.ErrnoError(44);
-  }
-  flags = typeof flags == "string" ? FS.modeStringToFlags(flags) : flags;
-  mode = typeof mode == "undefined" ? 438 : mode;
-  if (flags & 64) {
-   mode = mode & 4095 | 32768;
-  } else {
-   mode = 0;
-  }
-  var node;
-  if (typeof path == "object") {
-   node = path;
-  } else {
-   path = PATH.normalize(path);
-   try {
-    var lookup = FS.lookupPath(path, {
-     follow: !(flags & 131072)
-    });
-    node = lookup.node;
-   } catch (e) {}
-  }
-  var created = false;
-  if (flags & 64) {
-   if (node) {
-    if (flags & 128) {
-     throw new FS.ErrnoError(20);
-    }
-   } else {
-    node = FS.mknod(path, mode, 0);
-    created = true;
-   }
-  }
-  if (!node) {
-   throw new FS.ErrnoError(44);
-  }
-  if (FS.isChrdev(node.mode)) {
-   flags &= ~512;
-  }
-  if (flags & 65536 && !FS.isDir(node.mode)) {
-   throw new FS.ErrnoError(54);
-  }
-  if (!created) {
-   var errCode = FS.mayOpen(node, flags);
-   if (errCode) {
-    throw new FS.ErrnoError(errCode);
-   }
-  }
-  if (flags & 512 && !created) {
-   FS.truncate(node, 0);
-  }
-  flags &= ~(128 | 512 | 131072);
-  var stream = FS.createStream({
-   node: node,
-   path: FS.getPath(node),
-   flags: flags,
-   seekable: true,
-   position: 0,
-   stream_ops: node.stream_ops,
-   ungotten: [],
-   error: false
-  });
-  if (stream.stream_ops.open) {
-   stream.stream_ops.open(stream);
-  }
-  if (Module["logReadFiles"] && !(flags & 1)) {
-   if (!FS.readFiles) FS.readFiles = {};
-   if (!(path in FS.readFiles)) {
-    FS.readFiles[path] = 1;
-   }
-  }
-  return stream;
- },
- close: stream => {
-  if (FS.isClosed(stream)) {
-   throw new FS.ErrnoError(8);
-  }
-  if (stream.getdents) stream.getdents = null;
-  try {
-   if (stream.stream_ops.close) {
-    stream.stream_ops.close(stream);
-   }
-  } catch (e) {
-   throw e;
-  } finally {
-   FS.closeStream(stream.fd);
-  }
-  stream.fd = null;
- },
- isClosed: stream => {
-  return stream.fd === null;
- },
- llseek: (stream, offset, whence) => {
-  if (FS.isClosed(stream)) {
-   throw new FS.ErrnoError(8);
-  }
-  if (!stream.seekable || !stream.stream_ops.llseek) {
-   throw new FS.ErrnoError(70);
-  }
-  if (whence != 0 && whence != 1 && whence != 2) {
-   throw new FS.ErrnoError(28);
-  }
-  stream.position = stream.stream_ops.llseek(stream, offset, whence);
-  stream.ungotten = [];
-  return stream.position;
- },
- read: (stream, buffer, offset, length, position) => {
-  if (length < 0 || position < 0) {
-   throw new FS.ErrnoError(28);
-  }
-  if (FS.isClosed(stream)) {
-   throw new FS.ErrnoError(8);
-  }
-  if ((stream.flags & 2097155) === 1) {
-   throw new FS.ErrnoError(8);
-  }
-  if (FS.isDir(stream.node.mode)) {
-   throw new FS.ErrnoError(31);
-  }
-  if (!stream.stream_ops.read) {
-   throw new FS.ErrnoError(28);
-  }
-  var seeking = typeof position != "undefined";
-  if (!seeking) {
-   position = stream.position;
-  } else if (!stream.seekable) {
-   throw new FS.ErrnoError(70);
-  }
-  var bytesRead = stream.stream_ops.read(stream, buffer, offset, length, position);
-  if (!seeking) stream.position += bytesRead;
-  return bytesRead;
- },
- write: (stream, buffer, offset, length, position, canOwn) => {
-  if (length < 0 || position < 0) {
-   throw new FS.ErrnoError(28);
-  }
-  if (FS.isClosed(stream)) {
-   throw new FS.ErrnoError(8);
-  }
-  if ((stream.flags & 2097155) === 0) {
-   throw new FS.ErrnoError(8);
-  }
-  if (FS.isDir(stream.node.mode)) {
-   throw new FS.ErrnoError(31);
-  }
-  if (!stream.stream_ops.write) {
-   throw new FS.ErrnoError(28);
-  }
-  if (stream.seekable && stream.flags & 1024) {
-   FS.llseek(stream, 0, 2);
-  }
-  var seeking = typeof position != "undefined";
-  if (!seeking) {
-   position = stream.position;
-  } else if (!stream.seekable) {
-   throw new FS.ErrnoError(70);
-  }
-  var bytesWritten = stream.stream_ops.write(stream, buffer, offset, length, position, canOwn);
-  if (!seeking) stream.position += bytesWritten;
-  return bytesWritten;
- },
- allocate: (stream, offset, length) => {
-  if (FS.isClosed(stream)) {
-   throw new FS.ErrnoError(8);
-  }
-  if (offset < 0 || length <= 0) {
-   throw new FS.ErrnoError(28);
-  }
-  if ((stream.flags & 2097155) === 0) {
-   throw new FS.ErrnoError(8);
-  }
-  if (!FS.isFile(stream.node.mode) && !FS.isDir(stream.node.mode)) {
-   throw new FS.ErrnoError(43);
-  }
-  if (!stream.stream_ops.allocate) {
-   throw new FS.ErrnoError(138);
-  }
-  stream.stream_ops.allocate(stream, offset, length);
- },
- mmap: (stream, length, position, prot, flags) => {
-  if ((prot & 2) !== 0 && (flags & 2) === 0 && (stream.flags & 2097155) !== 2) {
-   throw new FS.ErrnoError(2);
-  }
-  if ((stream.flags & 2097155) === 1) {
-   throw new FS.ErrnoError(2);
-  }
-  if (!stream.stream_ops.mmap) {
-   throw new FS.ErrnoError(43);
-  }
-  return stream.stream_ops.mmap(stream, length, position, prot, flags);
- },
- msync: (stream, buffer, offset, length, mmapFlags) => {
-  if (!stream || !stream.stream_ops.msync) {
-   return 0;
-  }
-  return stream.stream_ops.msync(stream, buffer, offset, length, mmapFlags);
- },
- munmap: stream => 0,
- ioctl: (stream, cmd, arg) => {
-  if (!stream.stream_ops.ioctl) {
-   throw new FS.ErrnoError(59);
-  }
-  return stream.stream_ops.ioctl(stream, cmd, arg);
- },
- readFile: (path, opts = {}) => {
-  opts.flags = opts.flags || 0;
-  opts.encoding = opts.encoding || "binary";
-  if (opts.encoding !== "utf8" && opts.encoding !== "binary") {
-   throw new Error('Invalid encoding type "' + opts.encoding + '"');
-  }
-  var ret;
-  var stream = FS.open(path, opts.flags);
-  var stat = FS.stat(path);
-  var length = stat.size;
-  var buf = new Uint8Array(length);
-  FS.read(stream, buf, 0, length, 0);
-  if (opts.encoding === "utf8") {
-   ret = UTF8ArrayToString(buf, 0);
-  } else if (opts.encoding === "binary") {
-   ret = buf;
-  }
-  FS.close(stream);
-  return ret;
- },
- writeFile: (path, data, opts = {}) => {
-  opts.flags = opts.flags || 577;
-  var stream = FS.open(path, opts.flags, opts.mode);
-  if (typeof data == "string") {
-   var buf = new Uint8Array(lengthBytesUTF8(data) + 1);
-   var actualNumBytes = stringToUTF8Array(data, buf, 0, buf.length);
-   FS.write(stream, buf, 0, actualNumBytes, undefined, opts.canOwn);
-  } else if (ArrayBuffer.isView(data)) {
-   FS.write(stream, data, 0, data.byteLength, undefined, opts.canOwn);
-  } else {
-   throw new Error("Unsupported data type");
-  }
-  FS.close(stream);
- },
- cwd: () => FS.currentPath,
- chdir: path => {
-  var lookup = FS.lookupPath(path, {
-   follow: true
-  });
-  if (lookup.node === null) {
-   throw new FS.ErrnoError(44);
-  }
-  if (!FS.isDir(lookup.node.mode)) {
-   throw new FS.ErrnoError(54);
-  }
-  var errCode = FS.nodePermissions(lookup.node, "x");
-  if (errCode) {
-   throw new FS.ErrnoError(errCode);
-  }
-  FS.currentPath = lookup.path;
- },
- createDefaultDirectories: () => {
-  FS.mkdir("/tmp");
-  FS.mkdir("/home");
-  FS.mkdir("/home/web_user");
- },
- createDefaultDevices: () => {
-  FS.mkdir("/dev");
-  FS.registerDevice(FS.makedev(1, 3), {
-   read: () => 0,
-   write: (stream, buffer, offset, length, pos) => length
-  });
-  FS.mkdev("/dev/null", FS.makedev(1, 3));
-  TTY.register(FS.makedev(5, 0), TTY.default_tty_ops);
-  TTY.register(FS.makedev(6, 0), TTY.default_tty1_ops);
-  FS.mkdev("/dev/tty", FS.makedev(5, 0));
-  FS.mkdev("/dev/tty1", FS.makedev(6, 0));
-  var random_device = getRandomDevice();
-  FS.createDevice("/dev", "random", random_device);
-  FS.createDevice("/dev", "urandom", random_device);
-  FS.mkdir("/dev/shm");
-  FS.mkdir("/dev/shm/tmp");
- },
- createSpecialDirectories: () => {
-  FS.mkdir("/proc");
-  var proc_self = FS.mkdir("/proc/self");
-  FS.mkdir("/proc/self/fd");
-  FS.mount({
-   mount: () => {
-    var node = FS.createNode(proc_self, "fd", 16384 | 511, 73);
-    node.node_ops = {
-     lookup: (parent, name) => {
-      var fd = +name;
-      var stream = FS.getStream(fd);
-      if (!stream) throw new FS.ErrnoError(8);
-      var ret = {
-       parent: null,
-       mount: {
-        mountpoint: "fake"
-       },
-       node_ops: {
-        readlink: () => stream.path
-       }
-      };
-      ret.parent = ret;
-      return ret;
-     }
-    };
-    return node;
-   }
-  }, {}, "/proc/self/fd");
- },
- createStandardStreams: () => {
-  if (Module["stdin"]) {
-   FS.createDevice("/dev", "stdin", Module["stdin"]);
-  } else {
-   FS.symlink("/dev/tty", "/dev/stdin");
-  }
-  if (Module["stdout"]) {
-   FS.createDevice("/dev", "stdout", null, Module["stdout"]);
-  } else {
-   FS.symlink("/dev/tty", "/dev/stdout");
-  }
-  if (Module["stderr"]) {
-   FS.createDevice("/dev", "stderr", null, Module["stderr"]);
-  } else {
-   FS.symlink("/dev/tty1", "/dev/stderr");
-  }
-  var stdin = FS.open("/dev/stdin", 0);
-  var stdout = FS.open("/dev/stdout", 1);
-  var stderr = FS.open("/dev/stderr", 1);
-  assert(stdin.fd === 0, "invalid handle for stdin (" + stdin.fd + ")");
-  assert(stdout.fd === 1, "invalid handle for stdout (" + stdout.fd + ")");
-  assert(stderr.fd === 2, "invalid handle for stderr (" + stderr.fd + ")");
- },
- ensureErrnoError: () => {
-  if (FS.ErrnoError) return;
-  FS.ErrnoError = function ErrnoError(errno, node) {
-   this.node = node;
-   this.setErrno = function(errno) {
-    this.errno = errno;
-    for (var key in ERRNO_CODES) {
-     if (ERRNO_CODES[key] === errno) {
-      this.code = key;
-      break;
-     }
-    }
-   };
-   this.setErrno(errno);
-   this.message = ERRNO_MESSAGES[errno];
-   if (this.stack) {
-    Object.defineProperty(this, "stack", {
-     value: new Error().stack,
-     writable: true
-    });
-    this.stack = demangleAll(this.stack);
-   }
-  };
-  FS.ErrnoError.prototype = new Error();
-  FS.ErrnoError.prototype.constructor = FS.ErrnoError;
-  [ 44 ].forEach(code => {
-   FS.genericErrors[code] = new FS.ErrnoError(code);
-   FS.genericErrors[code].stack = "<generic error, no stack>";
-  });
- },
- staticInit: () => {
-  FS.ensureErrnoError();
-  FS.nameTable = new Array(4096);
-  FS.mount(MEMFS, {}, "/");
-  FS.createDefaultDirectories();
-  FS.createDefaultDevices();
-  FS.createSpecialDirectories();
-  FS.filesystems = {
-   "MEMFS": MEMFS,
-   "IDBFS": IDBFS
-  };
- },
- init: (input, output, error) => {
-  assert(!FS.init.initialized, "FS.init was previously called. If you want to initialize later with custom parameters, remove any earlier calls (note that one is automatically added to the generated code)");
-  FS.init.initialized = true;
-  FS.ensureErrnoError();
-  Module["stdin"] = input || Module["stdin"];
-  Module["stdout"] = output || Module["stdout"];
-  Module["stderr"] = error || Module["stderr"];
-  FS.createStandardStreams();
- },
- quit: () => {
-  FS.init.initialized = false;
-  _fflush(0);
-  for (var i = 0; i < FS.streams.length; i++) {
-   var stream = FS.streams[i];
-   if (!stream) {
-    continue;
-   }
-   FS.close(stream);
-  }
- },
- getMode: (canRead, canWrite) => {
-  var mode = 0;
-  if (canRead) mode |= 292 | 73;
-  if (canWrite) mode |= 146;
-  return mode;
- },
- findObject: (path, dontResolveLastLink) => {
-  var ret = FS.analyzePath(path, dontResolveLastLink);
-  if (!ret.exists) {
-   return null;
-  }
-  return ret.object;
- },
- analyzePath: (path, dontResolveLastLink) => {
-  try {
-   var lookup = FS.lookupPath(path, {
-    follow: !dontResolveLastLink
-   });
-   path = lookup.path;
-  } catch (e) {}
-  var ret = {
-   isRoot: false,
-   exists: false,
-   error: 0,
-   name: null,
-   path: null,
-   object: null,
-   parentExists: false,
-   parentPath: null,
-   parentObject: null
-  };
-  try {
-   var lookup = FS.lookupPath(path, {
-    parent: true
-   });
-   ret.parentExists = true;
-   ret.parentPath = lookup.path;
-   ret.parentObject = lookup.node;
-   ret.name = PATH.basename(path);
-   lookup = FS.lookupPath(path, {
-    follow: !dontResolveLastLink
-   });
-   ret.exists = true;
-   ret.path = lookup.path;
-   ret.object = lookup.node;
-   ret.name = lookup.node.name;
-   ret.isRoot = lookup.path === "/";
-  } catch (e) {
-   ret.error = e.errno;
-  }
-  return ret;
- },
- createPath: (parent, path, canRead, canWrite) => {
-  parent = typeof parent == "string" ? parent : FS.getPath(parent);
-  var parts = path.split("/").reverse();
-  while (parts.length) {
-   var part = parts.pop();
-   if (!part) continue;
-   var current = PATH.join2(parent, part);
-   try {
-    FS.mkdir(current);
-   } catch (e) {}
-   parent = current;
-  }
-  return current;
- },
- createFile: (parent, name, properties, canRead, canWrite) => {
-  var path = PATH.join2(typeof parent == "string" ? parent : FS.getPath(parent), name);
-  var mode = FS.getMode(canRead, canWrite);
-  return FS.create(path, mode);
- },
- createDataFile: (parent, name, data, canRead, canWrite, canOwn) => {
-  var path = name;
-  if (parent) {
-   parent = typeof parent == "string" ? parent : FS.getPath(parent);
-   path = name ? PATH.join2(parent, name) : parent;
-  }
-  var mode = FS.getMode(canRead, canWrite);
-  var node = FS.create(path, mode);
-  if (data) {
-   if (typeof data == "string") {
-    var arr = new Array(data.length);
-    for (var i = 0, len = data.length; i < len; ++i) arr[i] = data.charCodeAt(i);
-    data = arr;
-   }
-   FS.chmod(node, mode | 146);
-   var stream = FS.open(node, 577);
-   FS.write(stream, data, 0, data.length, 0, canOwn);
-   FS.close(stream);
-   FS.chmod(node, mode);
-  }
-  return node;
- },
- createDevice: (parent, name, input, output) => {
-  var path = PATH.join2(typeof parent == "string" ? parent : FS.getPath(parent), name);
-  var mode = FS.getMode(!!input, !!output);
-  if (!FS.createDevice.major) FS.createDevice.major = 64;
-  var dev = FS.makedev(FS.createDevice.major++, 0);
-  FS.registerDevice(dev, {
-   open: stream => {
-    stream.seekable = false;
-   },
-   close: stream => {
-    if (output && output.buffer && output.buffer.length) {
-     output(10);
-    }
-   },
-   read: (stream, buffer, offset, length, pos) => {
-    var bytesRead = 0;
-    for (var i = 0; i < length; i++) {
-     var result;
-     try {
-      result = input();
-     } catch (e) {
-      throw new FS.ErrnoError(29);
-     }
-     if (result === undefined && bytesRead === 0) {
-      throw new FS.ErrnoError(6);
-     }
-     if (result === null || result === undefined) break;
-     bytesRead++;
-     buffer[offset + i] = result;
-    }
-    if (bytesRead) {
-     stream.node.timestamp = Date.now();
-    }
-    return bytesRead;
-   },
-   write: (stream, buffer, offset, length, pos) => {
-    for (var i = 0; i < length; i++) {
-     try {
-      output(buffer[offset + i]);
-     } catch (e) {
-      throw new FS.ErrnoError(29);
-     }
-    }
-    if (length) {
-     stream.node.timestamp = Date.now();
-    }
-    return i;
-   }
-  });
-  return FS.mkdev(path, mode, dev);
- },
- forceLoadFile: obj => {
-  if (obj.isDevice || obj.isFolder || obj.link || obj.contents) return true;
-  if (typeof XMLHttpRequest != "undefined") {
-   throw new Error("Lazy loading should have been performed (contents set) in createLazyFile, but it was not. Lazy loading only works in web workers. Use --embed-file or --preload-file in emcc on the main thread.");
-  } else if (read_) {
-   try {
-    obj.contents = intArrayFromString(read_(obj.url), true);
-    obj.usedBytes = obj.contents.length;
-   } catch (e) {
-    throw new FS.ErrnoError(29);
-   }
-  } else {
-   throw new Error("Cannot load without read() or XMLHttpRequest.");
-  }
- },
- createLazyFile: (parent, name, url, canRead, canWrite) => {
-  function LazyUint8Array() {
-   this.lengthKnown = false;
-   this.chunks = [];
-  }
-  LazyUint8Array.prototype.get = function LazyUint8Array_get(idx) {
-   if (idx > this.length - 1 || idx < 0) {
-    return undefined;
-   }
-   var chunkOffset = idx % this.chunkSize;
-   var chunkNum = idx / this.chunkSize | 0;
-   return this.getter(chunkNum)[chunkOffset];
-  };
-  LazyUint8Array.prototype.setDataGetter = function LazyUint8Array_setDataGetter(getter) {
-   this.getter = getter;
-  };
-  LazyUint8Array.prototype.cacheLength = function LazyUint8Array_cacheLength() {
-   var xhr = new XMLHttpRequest();
-   xhr.open("HEAD", url, false);
-   xhr.send(null);
-   if (!(xhr.status >= 200 && xhr.status < 300 || xhr.status === 304)) throw new Error("Couldn't load " + url + ". Status: " + xhr.status);
-   var datalength = Number(xhr.getResponseHeader("Content-length"));
-   var header;
-   var hasByteServing = (header = xhr.getResponseHeader("Accept-Ranges")) && header === "bytes";
-   var usesGzip = (header = xhr.getResponseHeader("Content-Encoding")) && header === "gzip";
-   var chunkSize = 1024 * 1024;
-   if (!hasByteServing) chunkSize = datalength;
-   var doXHR = (from, to) => {
-    if (from > to) throw new Error("invalid range (" + from + ", " + to + ") or no bytes requested!");
-    if (to > datalength - 1) throw new Error("only " + datalength + " bytes available! programmer error!");
-    var xhr = new XMLHttpRequest();
-    xhr.open("GET", url, false);
-    if (datalength !== chunkSize) xhr.setRequestHeader("Range", "bytes=" + from + "-" + to);
-    xhr.responseType = "arraybuffer";
-    if (xhr.overrideMimeType) {
-     xhr.overrideMimeType("text/plain; charset=x-user-defined");
-    }
-    xhr.send(null);
-    if (!(xhr.status >= 200 && xhr.status < 300 || xhr.status === 304)) throw new Error("Couldn't load " + url + ". Status: " + xhr.status);
-    if (xhr.response !== undefined) {
-     return new Uint8Array(xhr.response || []);
-    }
-    return intArrayFromString(xhr.responseText || "", true);
-   };
-   var lazyArray = this;
-   lazyArray.setDataGetter(chunkNum => {
-    var start = chunkNum * chunkSize;
-    var end = (chunkNum + 1) * chunkSize - 1;
-    end = Math.min(end, datalength - 1);
-    if (typeof lazyArray.chunks[chunkNum] == "undefined") {
-     lazyArray.chunks[chunkNum] = doXHR(start, end);
-    }
-    if (typeof lazyArray.chunks[chunkNum] == "undefined") throw new Error("doXHR failed!");
-    return lazyArray.chunks[chunkNum];
-   });
-   if (usesGzip || !datalength) {
-    chunkSize = datalength = 1;
-    datalength = this.getter(0).length;
-    chunkSize = datalength;
-    out("LazyFiles on gzip forces download of the whole file when length is accessed");
-   }
-   this._length = datalength;
-   this._chunkSize = chunkSize;
-   this.lengthKnown = true;
-  };
-  if (typeof XMLHttpRequest != "undefined") {
-   if (!ENVIRONMENT_IS_WORKER) throw "Cannot do synchronous binary XHRs outside webworkers in modern browsers. Use --embed-file or --preload-file in emcc";
-   var lazyArray = new LazyUint8Array();
-   Object.defineProperties(lazyArray, {
-    length: {
-     get: function() {
-      if (!this.lengthKnown) {
-       this.cacheLength();
-      }
-      return this._length;
-     }
-    },
-    chunkSize: {
-     get: function() {
-      if (!this.lengthKnown) {
-       this.cacheLength();
-      }
-      return this._chunkSize;
-     }
-    }
-   });
-   var properties = {
-    isDevice: false,
-    contents: lazyArray
-   };
-  } else {
-   var properties = {
-    isDevice: false,
-    url: url
-   };
-  }
-  var node = FS.createFile(parent, name, properties, canRead, canWrite);
-  if (properties.contents) {
-   node.contents = properties.contents;
-  } else if (properties.url) {
-   node.contents = null;
-   node.url = properties.url;
-  }
-  Object.defineProperties(node, {
-   usedBytes: {
-    get: function() {
-     return this.contents.length;
-    }
-   }
-  });
-  var stream_ops = {};
-  var keys = Object.keys(node.stream_ops);
-  keys.forEach(key => {
-   var fn = node.stream_ops[key];
-   stream_ops[key] = function forceLoadLazyFile() {
-    FS.forceLoadFile(node);
-    return fn.apply(null, arguments);
-   };
-  });
-  function writeChunks(stream, buffer, offset, length, position) {
-   var contents = stream.node.contents;
-   if (position >= contents.length) return 0;
-   var size = Math.min(contents.length - position, length);
-   assert(size >= 0);
-   if (contents.slice) {
-    for (var i = 0; i < size; i++) {
-     buffer[offset + i] = contents[position + i];
-    }
-   } else {
-    for (var i = 0; i < size; i++) {
-     buffer[offset + i] = contents.get(position + i);
-    }
-   }
-   return size;
-  }
-  stream_ops.read = (stream, buffer, offset, length, position) => {
-   FS.forceLoadFile(node);
-   return writeChunks(stream, buffer, offset, length, position);
-  };
-  stream_ops.mmap = (stream, length, position, prot, flags) => {
-   FS.forceLoadFile(node);
-   var ptr = mmapAlloc(length);
-   if (!ptr) {
-    throw new FS.ErrnoError(48);
-   }
-   writeChunks(stream, GROWABLE_HEAP_I8(), ptr, length, position);
-   return {
-    ptr: ptr,
-    allocated: true
-   };
-  };
-  node.stream_ops = stream_ops;
-  return node;
- },
- createPreloadedFile: (parent, name, url, canRead, canWrite, onload, onerror, dontCreateFile, canOwn, preFinish) => {
-  var fullname = name ? PATH_FS.resolve(PATH.join2(parent, name)) : parent;
-  var dep = getUniqueRunDependency("cp " + fullname);
-  function processData(byteArray) {
-   function finish(byteArray) {
-    if (preFinish) preFinish();
-    if (!dontCreateFile) {
-     FS.createDataFile(parent, name, byteArray, canRead, canWrite, canOwn);
-    }
-    if (onload) onload();
-    removeRunDependency(dep);
-   }
-   if (Browser.handledByPreloadPlugin(byteArray, fullname, finish, () => {
-    if (onerror) onerror();
-    removeRunDependency(dep);
-   })) {
-    return;
-   }
-   finish(byteArray);
-  }
-  addRunDependency(dep);
-  if (typeof url == "string") {
-   asyncLoad(url, byteArray => processData(byteArray), onerror);
-  } else {
-   processData(url);
-  }
- },
- indexedDB: () => {
-  return window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB;
- },
- DB_NAME: () => {
-  return "EM_FS_" + window.location.pathname;
- },
- DB_VERSION: 20,
- DB_STORE_NAME: "FILE_DATA",
- saveFilesToDB: (paths, onload, onerror) => {
-  onload = onload || (() => {});
-  onerror = onerror || (() => {});
-  var indexedDB = FS.indexedDB();
-  try {
-   var openRequest = indexedDB.open(FS.DB_NAME(), FS.DB_VERSION);
-  } catch (e) {
-   return onerror(e);
-  }
-  openRequest.onupgradeneeded = () => {
-   out("creating db");
-   var db = openRequest.result;
-   db.createObjectStore(FS.DB_STORE_NAME);
-  };
-  openRequest.onsuccess = () => {
-   var db = openRequest.result;
-   var transaction = db.transaction([ FS.DB_STORE_NAME ], "readwrite");
-   var files = transaction.objectStore(FS.DB_STORE_NAME);
-   var ok = 0, fail = 0, total = paths.length;
-   function finish() {
-    if (fail == 0) onload(); else onerror();
-   }
-   paths.forEach(path => {
-    var putRequest = files.put(FS.analyzePath(path).object.contents, path);
-    putRequest.onsuccess = () => {
-     ok++;
-     if (ok + fail == total) finish();
-    };
-    putRequest.onerror = () => {
-     fail++;
-     if (ok + fail == total) finish();
-    };
-   });
-   transaction.onerror = onerror;
-  };
-  openRequest.onerror = onerror;
- },
- loadFilesFromDB: (paths, onload, onerror) => {
-  onload = onload || (() => {});
-  onerror = onerror || (() => {});
-  var indexedDB = FS.indexedDB();
-  try {
-   var openRequest = indexedDB.open(FS.DB_NAME(), FS.DB_VERSION);
-  } catch (e) {
-   return onerror(e);
-  }
-  openRequest.onupgradeneeded = onerror;
-  openRequest.onsuccess = () => {
-   var db = openRequest.result;
-   try {
-    var transaction = db.transaction([ FS.DB_STORE_NAME ], "readonly");
-   } catch (e) {
-    onerror(e);
-    return;
-   }
-   var files = transaction.objectStore(FS.DB_STORE_NAME);
-   var ok = 0, fail = 0, total = paths.length;
-   function finish() {
-    if (fail == 0) onload(); else onerror();
-   }
-   paths.forEach(path => {
-    var getRequest = files.get(path);
-    getRequest.onsuccess = () => {
-     if (FS.analyzePath(path).exists) {
-      FS.unlink(path);
-     }
-     FS.createDataFile(PATH.dirname(path), PATH.basename(path), getRequest.result, true, true, true);
-     ok++;
-     if (ok + fail == total) finish();
-    };
-    getRequest.onerror = () => {
-     fail++;
-     if (ok + fail == total) finish();
-    };
-   });
-   transaction.onerror = onerror;
-  };
-  openRequest.onerror = onerror;
- },
- absolutePath: () => {
-  abort("FS.absolutePath has been removed; use PATH_FS.resolve instead");
- },
- createFolder: () => {
-  abort("FS.createFolder has been removed; use FS.mkdir instead");
- },
- createLink: () => {
-  abort("FS.createLink has been removed; use FS.symlink instead");
- },
- joinPath: () => {
-  abort("FS.joinPath has been removed; use PATH.join instead");
- },
- mmapAlloc: () => {
-  abort("FS.mmapAlloc has been replaced by the top level function mmapAlloc");
- },
- standardizePath: () => {
-  abort("FS.standardizePath has been removed; use PATH.normalize instead");
- }
-};
-
-var SYSCALLS = {
- DEFAULT_POLLMASK: 5,
- calculateAt: function(dirfd, path, allowEmpty) {
-  if (PATH.isAbs(path)) {
-   return path;
-  }
-  var dir;
-  if (dirfd === -100) {
-   dir = FS.cwd();
-  } else {
-   var dirstream = FS.getStream(dirfd);
-   if (!dirstream) throw new FS.ErrnoError(8);
-   dir = dirstream.path;
-  }
-  if (path.length == 0) {
-   if (!allowEmpty) {
-    throw new FS.ErrnoError(44);
-   }
-   return dir;
-  }
-  return PATH.join2(dir, path);
- },
- doStat: function(func, path, buf) {
-  try {
-   var stat = func(path);
-  } catch (e) {
-   if (e && e.node && PATH.normalize(path) !== PATH.normalize(FS.getPath(e.node))) {
-    return -54;
-   }
-   throw e;
-  }
-  GROWABLE_HEAP_I32()[buf >> 2] = stat.dev;
-  GROWABLE_HEAP_I32()[buf + 8 >> 2] = stat.ino;
-  GROWABLE_HEAP_I32()[buf + 12 >> 2] = stat.mode;
-  GROWABLE_HEAP_I32()[buf + 16 >> 2] = stat.nlink;
-  GROWABLE_HEAP_I32()[buf + 20 >> 2] = stat.uid;
-  GROWABLE_HEAP_I32()[buf + 24 >> 2] = stat.gid;
-  GROWABLE_HEAP_I32()[buf + 28 >> 2] = stat.rdev;
-  tempI64 = [ stat.size >>> 0, (tempDouble = stat.size, +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[buf + 40 >> 2] = tempI64[0], GROWABLE_HEAP_I32()[buf + 44 >> 2] = tempI64[1];
-  GROWABLE_HEAP_I32()[buf + 48 >> 2] = 4096;
-  GROWABLE_HEAP_I32()[buf + 52 >> 2] = stat.blocks;
-  tempI64 = [ Math.floor(stat.atime.getTime() / 1e3) >>> 0, (tempDouble = Math.floor(stat.atime.getTime() / 1e3), 
-  +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[buf + 56 >> 2] = tempI64[0], GROWABLE_HEAP_I32()[buf + 60 >> 2] = tempI64[1];
-  GROWABLE_HEAP_I32()[buf + 64 >> 2] = 0;
-  tempI64 = [ Math.floor(stat.mtime.getTime() / 1e3) >>> 0, (tempDouble = Math.floor(stat.mtime.getTime() / 1e3), 
-  +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[buf + 72 >> 2] = tempI64[0], GROWABLE_HEAP_I32()[buf + 76 >> 2] = tempI64[1];
-  GROWABLE_HEAP_I32()[buf + 80 >> 2] = 0;
-  tempI64 = [ Math.floor(stat.ctime.getTime() / 1e3) >>> 0, (tempDouble = Math.floor(stat.ctime.getTime() / 1e3), 
-  +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[buf + 88 >> 2] = tempI64[0], GROWABLE_HEAP_I32()[buf + 92 >> 2] = tempI64[1];
-  GROWABLE_HEAP_I32()[buf + 96 >> 2] = 0;
-  tempI64 = [ stat.ino >>> 0, (tempDouble = stat.ino, +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[buf + 104 >> 2] = tempI64[0], GROWABLE_HEAP_I32()[buf + 108 >> 2] = tempI64[1];
-  return 0;
- },
- doMsync: function(addr, stream, len, flags, offset) {
-  var buffer = GROWABLE_HEAP_U8().slice(addr, addr + len);
-  FS.msync(stream, buffer, offset, len, flags);
- },
- varargs: undefined,
- get: function() {
-  assert(SYSCALLS.varargs != undefined);
-  SYSCALLS.varargs += 4;
-  var ret = GROWABLE_HEAP_I32()[SYSCALLS.varargs - 4 >> 2];
-  return ret;
- },
- getStr: function(ptr) {
-  var ret = UTF8ToString(ptr);
-  return ret;
- },
- getStreamFromFD: function(fd) {
-  var stream = FS.getStream(fd);
-  if (!stream) throw new FS.ErrnoError(8);
-  return stream;
- }
-};
-
-function _proc_exit(code) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(1, 1, code);
- EXITSTATUS = code;
- if (!keepRuntimeAlive()) {
-  PThread.terminateAllThreads();
-  if (Module["onExit"]) Module["onExit"](code);
-  ABORT = true;
- }
- quit_(code, new ExitStatus(code));
-}
-
-function exitJS(status, implicit) {
- EXITSTATUS = status;
- if (!implicit) {
-  if (ENVIRONMENT_IS_PTHREAD) {
-   exitOnMainThread(status);
-   throw "unwind";
-  } else {}
- }
- if (!keepRuntimeAlive()) {
-  exitRuntime();
- }
- if (keepRuntimeAlive() && !implicit) {
-  var msg = "program exited (with status: " + status + "), but keepRuntimeAlive() is set (counter=" + runtimeKeepaliveCounter + ") due to an async operation, so halting execution but not exiting the runtime or preventing further async execution (you can use emscripten_force_exit, if you want to force a true shutdown)";
-  readyPromiseReject(msg);
-  err(msg);
- }
- _proc_exit(status);
-}
-
-var _exit = exitJS;
-
-function ptrToString(ptr) {
- return "0x" + ptr.toString(16).padStart(8, "0");
-}
-
-function handleException(e) {
- if (e instanceof ExitStatus || e == "unwind") {
-  return EXITSTATUS;
- }
- quit_(1, e);
-}
-
-var PThread = {
- unusedWorkers: [],
- runningWorkers: [],
- tlsInitFunctions: [],
- pthreads: {},
- init: function() {
-  if (ENVIRONMENT_IS_PTHREAD) {
-   PThread.initWorker();
-  } else {
-   PThread.initMainThread();
-  }
- },
- initMainThread: function() {
-  var pthreadPoolSize = 8;
-  for (var i = 0; i < pthreadPoolSize; ++i) {
-   PThread.allocateUnusedWorker();
-  }
- },
- initWorker: function() {
-  noExitRuntime = false;
- },
- setExitStatus: function(status) {
-  EXITSTATUS = status;
- },
- terminateAllThreads: function() {
-  assert(!ENVIRONMENT_IS_PTHREAD, "Internal Error! terminateAllThreads() can only ever be called from main application thread!");
-  for (var t in PThread.pthreads) {
-   var worker = PThread.pthreads[t];
-   if (worker) {
-    PThread.returnWorkerToPool(worker);
-   }
-  }
-  assert(Object.keys(PThread.pthreads).length === 0);
-  assert(PThread.runningWorkers.length === 0);
-  for (var i = 0; i < PThread.unusedWorkers.length; ++i) {
-   var worker = PThread.unusedWorkers[i];
-   assert(!worker.pthread_ptr);
-   worker.terminate();
-  }
-  PThread.unusedWorkers = [];
- },
- returnWorkerToPool: function(worker) {
-  var pthread_ptr = worker.pthread_ptr;
-  delete PThread.pthreads[pthread_ptr];
-  PThread.unusedWorkers.push(worker);
-  PThread.runningWorkers.splice(PThread.runningWorkers.indexOf(worker), 1);
-  worker.pthread_ptr = 0;
-  __emscripten_thread_free_data(pthread_ptr);
- },
- receiveObjectTransfer: function(data) {},
- threadInitTLS: function() {
-  for (var i in PThread.tlsInitFunctions) {
-   if (PThread.tlsInitFunctions.hasOwnProperty(i)) PThread.tlsInitFunctions[i]();
-  }
- },
- loadWasmModuleToWorker: function(worker, onFinishedLoading) {
-  worker.onmessage = e => {
-   var d = e["data"];
-   var cmd = d["cmd"];
-   if (worker.pthread_ptr) PThread.currentProxiedOperationCallerThread = worker.pthread_ptr;
-   if (d["targetThread"] && d["targetThread"] != _pthread_self()) {
-    var targetWorker = PThread.pthreads[d.targetThread];
-    if (targetWorker) {
-     targetWorker.postMessage(d, d["transferList"]);
-    } else {
-     err('Internal error! Worker sent a message "' + cmd + '" to target pthread ' + d["targetThread"] + ", but that thread no longer exists!");
-    }
-    PThread.currentProxiedOperationCallerThread = undefined;
-    return;
-   }
-   if (cmd === "processProxyingQueue") {
-    executeNotifiedProxyingQueue(d["queue"]);
-   } else if (cmd === "spawnThread") {
-    spawnThread(d);
-   } else if (cmd === "cleanupThread") {
-    cleanupThread(d["thread"]);
-   } else if (cmd === "killThread") {
-    killThread(d["thread"]);
-   } else if (cmd === "cancelThread") {
-    cancelThread(d["thread"]);
-   } else if (cmd === "loaded") {
-    worker.loaded = true;
-    if (onFinishedLoading) onFinishedLoading(worker);
-    if (worker.runPthread) {
-     worker.runPthread();
-     delete worker.runPthread;
-    }
-   } else if (cmd === "print") {
-    out("Thread " + d["threadId"] + ": " + d["text"]);
-   } else if (cmd === "printErr") {
-    err("Thread " + d["threadId"] + ": " + d["text"]);
-   } else if (cmd === "alert") {
-    alert("Thread " + d["threadId"] + ": " + d["text"]);
-   } else if (d.target === "setimmediate") {
-    worker.postMessage(d);
-   } else if (cmd === "onAbort") {
-    if (Module["onAbort"]) {
-     Module["onAbort"](d["arg"]);
-    }
-   } else if (cmd) {
-    err("worker sent an unknown command " + cmd);
-   }
-   PThread.currentProxiedOperationCallerThread = undefined;
-  };
-  worker.onerror = e => {
-   var message = "worker sent an error!";
-   if (worker.pthread_ptr) {
-    message = "Pthread " + ptrToString(worker.pthread_ptr) + " sent an error!";
-   }
-   err(message + " " + e.filename + ":" + e.lineno + ": " + e.message);
-   throw e;
-  };
-  assert(wasmMemory instanceof WebAssembly.Memory, "WebAssembly memory should have been loaded by now!");
-  assert(wasmModule instanceof WebAssembly.Module, "WebAssembly Module should have been loaded by now!");
-  worker.postMessage({
-   "cmd": "load",
-   "urlOrBlob": Module["mainScriptUrlOrBlob"] || _scriptDir,
-   "wasmMemory": wasmMemory,
-   "wasmModule": wasmModule
-  });
- },
- allocateUnusedWorker: function() {
-  var pthreadMainJs = locateFile("godot.web.template_debug.wasm32.worker.js");
-  PThread.unusedWorkers.push(new Worker(pthreadMainJs));
- },
- getNewWorker: function() {
-  if (PThread.unusedWorkers.length == 0) {
-   err("Tried to spawn a new thread, but the thread pool is exhausted.\n" + "This might result in a deadlock unless some threads eventually exit or the code explicitly breaks out to the event loop.\n" + "If you want to increase the pool size, use setting `-sPTHREAD_POOL_SIZE=...`." + "\nIf you want to throw an explicit error instead of the risk of deadlocking in those cases, use setting `-sPTHREAD_POOL_SIZE_STRICT=2`.");
-   PThread.allocateUnusedWorker();
-   PThread.loadWasmModuleToWorker(PThread.unusedWorkers[0]);
-  }
-  return PThread.unusedWorkers.pop();
- }
-};
-
-Module["PThread"] = PThread;
-
-function callRuntimeCallbacks(callbacks) {
- while (callbacks.length > 0) {
-  callbacks.shift()(Module);
- }
-}
-
-function withStackSave(f) {
- var stack = stackSave();
- var ret = f();
- stackRestore(stack);
- return ret;
-}
-
-function demangle(func) {
- warnOnce("warning: build with -sDEMANGLE_SUPPORT to link in libcxxabi demangling");
- return func;
-}
-
-function demangleAll(text) {
- var regex = /\b_Z[\w\d_]+/g;
- return text.replace(regex, function(x) {
-  var y = demangle(x);
-  return x === y ? x : y + " [" + x + "]";
- });
-}
-
-function establishStackSpace() {
- var pthread_ptr = _pthread_self();
- var stackTop = GROWABLE_HEAP_I32()[pthread_ptr + 44 >> 2];
- var stackSize = GROWABLE_HEAP_I32()[pthread_ptr + 48 >> 2];
- var stackMax = stackTop - stackSize;
- assert(stackTop != 0);
- assert(stackMax != 0);
- assert(stackTop > stackMax, "stackTop must be higher then stackMax");
- _emscripten_stack_set_limits(stackTop, stackMax);
- stackRestore(stackTop);
- writeStackCookie();
-}
-
-Module["establishStackSpace"] = establishStackSpace;
-
-function exitOnMainThread(returnCode) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(2, 0, returnCode);
- try {
-  _exit(returnCode);
- } catch (e) {
-  handleException(e);
- }
-}
-
-function getValue(ptr, type = "i8") {
- if (type.endsWith("*")) type = "*";
- switch (type) {
- case "i1":
-  return GROWABLE_HEAP_I8()[ptr >> 0];
-
- case "i8":
-  return GROWABLE_HEAP_I8()[ptr >> 0];
-
- case "i16":
-  return GROWABLE_HEAP_I16()[ptr >> 1];
-
- case "i32":
-  return GROWABLE_HEAP_I32()[ptr >> 2];
-
- case "i64":
-  return GROWABLE_HEAP_I32()[ptr >> 2];
-
- case "float":
-  return GROWABLE_HEAP_F32()[ptr >> 2];
-
- case "double":
-  return GROWABLE_HEAP_F64()[ptr >> 3];
-
- case "*":
-  return GROWABLE_HEAP_U32()[ptr >> 2];
-
- default:
-  abort("invalid type for getValue: " + type);
- }
- return null;
-}
-
-var wasmTableMirror = [];
-
-function getWasmTableEntry(funcPtr) {
- var func = wasmTableMirror[funcPtr];
- if (!func) {
-  if (funcPtr >= wasmTableMirror.length) wasmTableMirror.length = funcPtr + 1;
-  wasmTableMirror[funcPtr] = func = wasmTable.get(funcPtr);
- }
- assert(wasmTable.get(funcPtr) == func, "JavaScript-side Wasm function table mirror is out of date!");
- return func;
-}
-
-function invokeEntryPoint(ptr, arg) {
- var result = getWasmTableEntry(ptr)(arg);
- checkStackCookie();
- if (keepRuntimeAlive()) {
-  PThread.setExitStatus(result);
- } else {
-  __emscripten_thread_exit(result);
- }
-}
-
-Module["invokeEntryPoint"] = invokeEntryPoint;
-
-function jsStackTrace() {
- var error = new Error();
- if (!error.stack) {
-  try {
-   throw new Error();
-  } catch (e) {
-   error = e;
-  }
-  if (!error.stack) {
-   return "(no stack trace available)";
-  }
- }
- return error.stack.toString();
-}
-
-function registerTLSInit(tlsInitFunc) {
- PThread.tlsInitFunctions.push(tlsInitFunc);
-}
-
-function setValue(ptr, value, type = "i8") {
- if (type.endsWith("*")) type = "*";
- switch (type) {
- case "i1":
-  GROWABLE_HEAP_I8()[ptr >> 0] = value;
-  break;
-
- case "i8":
-  GROWABLE_HEAP_I8()[ptr >> 0] = value;
-  break;
-
- case "i16":
-  GROWABLE_HEAP_I16()[ptr >> 1] = value;
-  break;
-
- case "i32":
-  GROWABLE_HEAP_I32()[ptr >> 2] = value;
-  break;
-
- case "i64":
-  tempI64 = [ value >>> 0, (tempDouble = value, +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[ptr >> 2] = tempI64[0], GROWABLE_HEAP_I32()[ptr + 4 >> 2] = tempI64[1];
-  break;
-
- case "float":
-  GROWABLE_HEAP_F32()[ptr >> 2] = value;
-  break;
-
- case "double":
-  GROWABLE_HEAP_F64()[ptr >> 3] = value;
-  break;
-
- case "*":
-  GROWABLE_HEAP_U32()[ptr >> 2] = value;
-  break;
-
- default:
-  abort("invalid type for setValue: " + type);
- }
-}
-
-function stackTrace() {
- var js = jsStackTrace();
- if (Module["extraStackTrace"]) js += "\n" + Module["extraStackTrace"]();
- return demangleAll(js);
-}
-
-function writeArrayToMemory(array, buffer) {
- assert(array.length >= 0, "writeArrayToMemory array must have a length (should be an array or typed array)");
- GROWABLE_HEAP_I8().set(array, buffer);
-}
-
-function ___assert_fail(condition, filename, line, func) {
- abort("Assertion failed: " + UTF8ToString(condition) + ", at: " + [ filename ? UTF8ToString(filename) : "unknown filename", line, func ? UTF8ToString(func) : "unknown function" ]);
-}
-
-function ___call_sighandler(fp, sig) {
- getWasmTableEntry(fp)(sig);
-}
-
-function ___emscripten_init_main_thread_js(tb) {
- __emscripten_thread_init(tb, !ENVIRONMENT_IS_WORKER, 1, !ENVIRONMENT_IS_WEB);
- PThread.threadInitTLS();
-}
-
-function ___emscripten_thread_cleanup(thread) {
- if (!ENVIRONMENT_IS_PTHREAD) cleanupThread(thread); else postMessage({
-  "cmd": "cleanupThread",
-  "thread": thread
- });
-}
-
-function pthreadCreateProxied(pthread_ptr, attr, start_routine, arg) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(3, 1, pthread_ptr, attr, start_routine, arg);
- return ___pthread_create_js(pthread_ptr, attr, start_routine, arg);
-}
-
-function ___pthread_create_js(pthread_ptr, attr, start_routine, arg) {
- if (typeof SharedArrayBuffer == "undefined") {
-  err("Current environment does not support SharedArrayBuffer, pthreads are not available!");
-  return 6;
- }
- var transferList = [];
- var error = 0;
- if (ENVIRONMENT_IS_PTHREAD && (transferList.length === 0 || error)) {
-  return pthreadCreateProxied(pthread_ptr, attr, start_routine, arg);
- }
- if (error) return error;
- var threadParams = {
-  startRoutine: start_routine,
-  pthread_ptr: pthread_ptr,
-  arg: arg,
-  transferList: transferList
- };
- if (ENVIRONMENT_IS_PTHREAD) {
-  threadParams.cmd = "spawnThread";
-  postMessage(threadParams, transferList);
-  return 0;
- }
- return spawnThread(threadParams);
-}
-
-function ___syscall__newselect(nfds, readfds, writefds, exceptfds, timeout) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(4, 1, nfds, readfds, writefds, exceptfds, timeout);
- try {
-  assert(nfds <= 64, "nfds must be less than or equal to 64");
-  assert(!exceptfds, "exceptfds not supported");
-  var total = 0;
-  var srcReadLow = readfds ? GROWABLE_HEAP_I32()[readfds >> 2] : 0, srcReadHigh = readfds ? GROWABLE_HEAP_I32()[readfds + 4 >> 2] : 0;
-  var srcWriteLow = writefds ? GROWABLE_HEAP_I32()[writefds >> 2] : 0, srcWriteHigh = writefds ? GROWABLE_HEAP_I32()[writefds + 4 >> 2] : 0;
-  var srcExceptLow = exceptfds ? GROWABLE_HEAP_I32()[exceptfds >> 2] : 0, srcExceptHigh = exceptfds ? GROWABLE_HEAP_I32()[exceptfds + 4 >> 2] : 0;
-  var dstReadLow = 0, dstReadHigh = 0;
-  var dstWriteLow = 0, dstWriteHigh = 0;
-  var dstExceptLow = 0, dstExceptHigh = 0;
-  var allLow = (readfds ? GROWABLE_HEAP_I32()[readfds >> 2] : 0) | (writefds ? GROWABLE_HEAP_I32()[writefds >> 2] : 0) | (exceptfds ? GROWABLE_HEAP_I32()[exceptfds >> 2] : 0);
-  var allHigh = (readfds ? GROWABLE_HEAP_I32()[readfds + 4 >> 2] : 0) | (writefds ? GROWABLE_HEAP_I32()[writefds + 4 >> 2] : 0) | (exceptfds ? GROWABLE_HEAP_I32()[exceptfds + 4 >> 2] : 0);
-  var check = function(fd, low, high, val) {
-   return fd < 32 ? low & val : high & val;
-  };
-  for (var fd = 0; fd < nfds; fd++) {
-   var mask = 1 << fd % 32;
-   if (!check(fd, allLow, allHigh, mask)) {
-    continue;
-   }
-   var stream = FS.getStream(fd);
-   if (!stream) throw new FS.ErrnoError(8);
-   var flags = SYSCALLS.DEFAULT_POLLMASK;
-   if (stream.stream_ops.poll) {
-    flags = stream.stream_ops.poll(stream);
-   }
-   if (flags & 1 && check(fd, srcReadLow, srcReadHigh, mask)) {
-    fd < 32 ? dstReadLow = dstReadLow | mask : dstReadHigh = dstReadHigh | mask;
-    total++;
-   }
-   if (flags & 4 && check(fd, srcWriteLow, srcWriteHigh, mask)) {
-    fd < 32 ? dstWriteLow = dstWriteLow | mask : dstWriteHigh = dstWriteHigh | mask;
-    total++;
-   }
-   if (flags & 2 && check(fd, srcExceptLow, srcExceptHigh, mask)) {
-    fd < 32 ? dstExceptLow = dstExceptLow | mask : dstExceptHigh = dstExceptHigh | mask;
-    total++;
-   }
-  }
-  if (readfds) {
-   GROWABLE_HEAP_I32()[readfds >> 2] = dstReadLow;
-   GROWABLE_HEAP_I32()[readfds + 4 >> 2] = dstReadHigh;
-  }
-  if (writefds) {
-   GROWABLE_HEAP_I32()[writefds >> 2] = dstWriteLow;
-   GROWABLE_HEAP_I32()[writefds + 4 >> 2] = dstWriteHigh;
-  }
-  if (exceptfds) {
-   GROWABLE_HEAP_I32()[exceptfds >> 2] = dstExceptLow;
-   GROWABLE_HEAP_I32()[exceptfds + 4 >> 2] = dstExceptHigh;
-  }
-  return total;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-var SOCKFS = {
- mount: function(mount) {
-  Module["websocket"] = Module["websocket"] && "object" === typeof Module["websocket"] ? Module["websocket"] : {};
-  Module["websocket"]._callbacks = {};
-  Module["websocket"]["on"] = function(event, callback) {
-   if ("function" === typeof callback) {
-    this._callbacks[event] = callback;
-   }
-   return this;
-  };
-  Module["websocket"].emit = function(event, param) {
-   if ("function" === typeof this._callbacks[event]) {
-    this._callbacks[event].call(this, param);
-   }
-  };
-  return FS.createNode(null, "/", 16384 | 511, 0);
- },
- createSocket: function(family, type, protocol) {
-  type &= ~526336;
-  var streaming = type == 1;
-  if (streaming && protocol && protocol != 6) {
-   throw new FS.ErrnoError(66);
-  }
-  var sock = {
-   family: family,
-   type: type,
-   protocol: protocol,
-   server: null,
-   error: null,
-   peers: {},
-   pending: [],
-   recv_queue: [],
-   sock_ops: SOCKFS.websocket_sock_ops
-  };
-  var name = SOCKFS.nextname();
-  var node = FS.createNode(SOCKFS.root, name, 49152, 0);
-  node.sock = sock;
-  var stream = FS.createStream({
-   path: name,
-   node: node,
-   flags: 2,
-   seekable: false,
-   stream_ops: SOCKFS.stream_ops
-  });
-  sock.stream = stream;
-  return sock;
- },
- getSocket: function(fd) {
-  var stream = FS.getStream(fd);
-  if (!stream || !FS.isSocket(stream.node.mode)) {
-   return null;
-  }
-  return stream.node.sock;
- },
- stream_ops: {
-  poll: function(stream) {
-   var sock = stream.node.sock;
-   return sock.sock_ops.poll(sock);
-  },
-  ioctl: function(stream, request, varargs) {
-   var sock = stream.node.sock;
-   return sock.sock_ops.ioctl(sock, request, varargs);
-  },
-  read: function(stream, buffer, offset, length, position) {
-   var sock = stream.node.sock;
-   var msg = sock.sock_ops.recvmsg(sock, length);
-   if (!msg) {
-    return 0;
-   }
-   buffer.set(msg.buffer, offset);
-   return msg.buffer.length;
-  },
-  write: function(stream, buffer, offset, length, position) {
-   var sock = stream.node.sock;
-   return sock.sock_ops.sendmsg(sock, buffer, offset, length);
-  },
-  close: function(stream) {
-   var sock = stream.node.sock;
-   sock.sock_ops.close(sock);
-  }
- },
- nextname: function() {
-  if (!SOCKFS.nextname.current) {
-   SOCKFS.nextname.current = 0;
-  }
-  return "socket[" + SOCKFS.nextname.current++ + "]";
- },
- websocket_sock_ops: {
-  createPeer: function(sock, addr, port) {
-   var ws;
-   if (typeof addr == "object") {
-    ws = addr;
-    addr = null;
-    port = null;
-   }
-   if (ws) {
-    if (ws._socket) {
-     addr = ws._socket.remoteAddress;
-     port = ws._socket.remotePort;
-    } else {
-     var result = /ws[s]?:\/\/([^:]+):(\d+)/.exec(ws.url);
-     if (!result) {
-      throw new Error("WebSocket URL must be in the format ws(s)://address:port");
-     }
-     addr = result[1];
-     port = parseInt(result[2], 10);
-    }
-   } else {
-    try {
-     var runtimeConfig = Module["websocket"] && "object" === typeof Module["websocket"];
-     var url = "ws:#".replace("#", "//");
-     if (runtimeConfig) {
-      if ("string" === typeof Module["websocket"]["url"]) {
-       url = Module["websocket"]["url"];
-      }
-     }
-     if (url === "ws://" || url === "wss://") {
-      var parts = addr.split("/");
-      url = url + parts[0] + ":" + port + "/" + parts.slice(1).join("/");
-     }
-     var subProtocols = "binary";
-     if (runtimeConfig) {
-      if ("string" === typeof Module["websocket"]["subprotocol"]) {
-       subProtocols = Module["websocket"]["subprotocol"];
-      }
-     }
-     var opts = undefined;
-     if (subProtocols !== "null") {
-      subProtocols = subProtocols.replace(/^ +| +$/g, "").split(/ *, */);
-      opts = subProtocols;
-     }
-     if (runtimeConfig && null === Module["websocket"]["subprotocol"]) {
-      subProtocols = "null";
-      opts = undefined;
-     }
-     var WebSocketConstructor;
-     {
-      WebSocketConstructor = WebSocket;
-     }
-     ws = new WebSocketConstructor(url, opts);
-     ws.binaryType = "arraybuffer";
-    } catch (e) {
-     throw new FS.ErrnoError(23);
-    }
-   }
-   var peer = {
-    addr: addr,
-    port: port,
-    socket: ws,
-    dgram_send_queue: []
-   };
-   SOCKFS.websocket_sock_ops.addPeer(sock, peer);
-   SOCKFS.websocket_sock_ops.handlePeerEvents(sock, peer);
-   if (sock.type === 2 && typeof sock.sport != "undefined") {
-    peer.dgram_send_queue.push(new Uint8Array([ 255, 255, 255, 255, "p".charCodeAt(0), "o".charCodeAt(0), "r".charCodeAt(0), "t".charCodeAt(0), (sock.sport & 65280) >> 8, sock.sport & 255 ]));
-   }
-   return peer;
-  },
-  getPeer: function(sock, addr, port) {
-   return sock.peers[addr + ":" + port];
-  },
-  addPeer: function(sock, peer) {
-   sock.peers[peer.addr + ":" + peer.port] = peer;
-  },
-  removePeer: function(sock, peer) {
-   delete sock.peers[peer.addr + ":" + peer.port];
-  },
-  handlePeerEvents: function(sock, peer) {
-   var first = true;
-   var handleOpen = function() {
-    Module["websocket"].emit("open", sock.stream.fd);
-    try {
-     var queued = peer.dgram_send_queue.shift();
-     while (queued) {
-      peer.socket.send(queued);
-      queued = peer.dgram_send_queue.shift();
-     }
-    } catch (e) {
-     peer.socket.close();
-    }
-   };
-   function handleMessage(data) {
-    if (typeof data == "string") {
-     var encoder = new TextEncoder();
-     data = encoder.encode(data);
-    } else {
-     assert(data.byteLength !== undefined);
-     if (data.byteLength == 0) {
-      return;
-     }
-     data = new Uint8Array(data);
-    }
-    var wasfirst = first;
-    first = false;
-    if (wasfirst && data.length === 10 && data[0] === 255 && data[1] === 255 && data[2] === 255 && data[3] === 255 && data[4] === "p".charCodeAt(0) && data[5] === "o".charCodeAt(0) && data[6] === "r".charCodeAt(0) && data[7] === "t".charCodeAt(0)) {
-     var newport = data[8] << 8 | data[9];
-     SOCKFS.websocket_sock_ops.removePeer(sock, peer);
-     peer.port = newport;
-     SOCKFS.websocket_sock_ops.addPeer(sock, peer);
-     return;
-    }
-    sock.recv_queue.push({
-     addr: peer.addr,
-     port: peer.port,
-     data: data
-    });
-    Module["websocket"].emit("message", sock.stream.fd);
-   }
-   if (ENVIRONMENT_IS_NODE) {
-    peer.socket.on("open", handleOpen);
-    peer.socket.on("message", function(data, isBinary) {
-     if (!isBinary) {
-      return;
-     }
-     handleMessage(new Uint8Array(data).buffer);
-    });
-    peer.socket.on("close", function() {
-     Module["websocket"].emit("close", sock.stream.fd);
-    });
-    peer.socket.on("error", function(error) {
-     sock.error = 14;
-     Module["websocket"].emit("error", [ sock.stream.fd, sock.error, "ECONNREFUSED: Connection refused" ]);
-    });
-   } else {
-    peer.socket.onopen = handleOpen;
-    peer.socket.onclose = function() {
-     Module["websocket"].emit("close", sock.stream.fd);
-    };
-    peer.socket.onmessage = function peer_socket_onmessage(event) {
-     handleMessage(event.data);
-    };
-    peer.socket.onerror = function(error) {
-     sock.error = 14;
-     Module["websocket"].emit("error", [ sock.stream.fd, sock.error, "ECONNREFUSED: Connection refused" ]);
-    };
-   }
-  },
-  poll: function(sock) {
-   if (sock.type === 1 && sock.server) {
-    return sock.pending.length ? 64 | 1 : 0;
-   }
-   var mask = 0;
-   var dest = sock.type === 1 ? SOCKFS.websocket_sock_ops.getPeer(sock, sock.daddr, sock.dport) : null;
-   if (sock.recv_queue.length || !dest || dest && dest.socket.readyState === dest.socket.CLOSING || dest && dest.socket.readyState === dest.socket.CLOSED) {
-    mask |= 64 | 1;
-   }
-   if (!dest || dest && dest.socket.readyState === dest.socket.OPEN) {
-    mask |= 4;
-   }
-   if (dest && dest.socket.readyState === dest.socket.CLOSING || dest && dest.socket.readyState === dest.socket.CLOSED) {
-    mask |= 16;
-   }
-   return mask;
-  },
-  ioctl: function(sock, request, arg) {
-   switch (request) {
-   case 21531:
-    var bytes = 0;
-    if (sock.recv_queue.length) {
-     bytes = sock.recv_queue[0].data.length;
-    }
-    GROWABLE_HEAP_I32()[arg >> 2] = bytes;
-    return 0;
-
-   default:
-    return 28;
-   }
-  },
-  close: function(sock) {
-   if (sock.server) {
-    try {
-     sock.server.close();
-    } catch (e) {}
-    sock.server = null;
-   }
-   var peers = Object.keys(sock.peers);
-   for (var i = 0; i < peers.length; i++) {
-    var peer = sock.peers[peers[i]];
-    try {
-     peer.socket.close();
-    } catch (e) {}
-    SOCKFS.websocket_sock_ops.removePeer(sock, peer);
-   }
-   return 0;
-  },
-  bind: function(sock, addr, port) {
-   if (typeof sock.saddr != "undefined" || typeof sock.sport != "undefined") {
-    throw new FS.ErrnoError(28);
-   }
-   sock.saddr = addr;
-   sock.sport = port;
-   if (sock.type === 2) {
-    if (sock.server) {
-     sock.server.close();
-     sock.server = null;
-    }
-    try {
-     sock.sock_ops.listen(sock, 0);
-    } catch (e) {
-     if (!(e instanceof FS.ErrnoError)) throw e;
-     if (e.errno !== 138) throw e;
-    }
-   }
-  },
-  connect: function(sock, addr, port) {
-   if (sock.server) {
-    throw new FS.ErrnoError(138);
-   }
-   if (typeof sock.daddr != "undefined" && typeof sock.dport != "undefined") {
-    var dest = SOCKFS.websocket_sock_ops.getPeer(sock, sock.daddr, sock.dport);
-    if (dest) {
-     if (dest.socket.readyState === dest.socket.CONNECTING) {
-      throw new FS.ErrnoError(7);
-     } else {
-      throw new FS.ErrnoError(30);
-     }
-    }
-   }
-   var peer = SOCKFS.websocket_sock_ops.createPeer(sock, addr, port);
-   sock.daddr = peer.addr;
-   sock.dport = peer.port;
-   throw new FS.ErrnoError(26);
-  },
-  listen: function(sock, backlog) {
-   if (!ENVIRONMENT_IS_NODE) {
-    throw new FS.ErrnoError(138);
-   }
-  },
-  accept: function(listensock) {
-   if (!listensock.server || !listensock.pending.length) {
-    throw new FS.ErrnoError(28);
-   }
-   var newsock = listensock.pending.shift();
-   newsock.stream.flags = listensock.stream.flags;
-   return newsock;
-  },
-  getname: function(sock, peer) {
-   var addr, port;
-   if (peer) {
-    if (sock.daddr === undefined || sock.dport === undefined) {
-     throw new FS.ErrnoError(53);
-    }
-    addr = sock.daddr;
-    port = sock.dport;
-   } else {
-    addr = sock.saddr || 0;
-    port = sock.sport || 0;
-   }
-   return {
-    addr: addr,
-    port: port
-   };
-  },
-  sendmsg: function(sock, buffer, offset, length, addr, port) {
-   if (sock.type === 2) {
-    if (addr === undefined || port === undefined) {
-     addr = sock.daddr;
-     port = sock.dport;
-    }
-    if (addr === undefined || port === undefined) {
-     throw new FS.ErrnoError(17);
-    }
-   } else {
-    addr = sock.daddr;
-    port = sock.dport;
-   }
-   var dest = SOCKFS.websocket_sock_ops.getPeer(sock, addr, port);
-   if (sock.type === 1) {
-    if (!dest || dest.socket.readyState === dest.socket.CLOSING || dest.socket.readyState === dest.socket.CLOSED) {
-     throw new FS.ErrnoError(53);
-    } else if (dest.socket.readyState === dest.socket.CONNECTING) {
-     throw new FS.ErrnoError(6);
-    }
-   }
-   if (ArrayBuffer.isView(buffer)) {
-    offset += buffer.byteOffset;
-    buffer = buffer.buffer;
-   }
-   var data;
-   if (buffer instanceof SharedArrayBuffer) {
-    data = new Uint8Array(new Uint8Array(buffer.slice(offset, offset + length))).buffer;
-   } else {
-    data = buffer.slice(offset, offset + length);
-   }
-   if (sock.type === 2) {
-    if (!dest || dest.socket.readyState !== dest.socket.OPEN) {
-     if (!dest || dest.socket.readyState === dest.socket.CLOSING || dest.socket.readyState === dest.socket.CLOSED) {
-      dest = SOCKFS.websocket_sock_ops.createPeer(sock, addr, port);
-     }
-     dest.dgram_send_queue.push(data);
-     return length;
-    }
-   }
-   try {
-    dest.socket.send(data);
-    return length;
-   } catch (e) {
-    throw new FS.ErrnoError(28);
-   }
-  },
-  recvmsg: function(sock, length) {
-   if (sock.type === 1 && sock.server) {
-    throw new FS.ErrnoError(53);
-   }
-   var queued = sock.recv_queue.shift();
-   if (!queued) {
-    if (sock.type === 1) {
-     var dest = SOCKFS.websocket_sock_ops.getPeer(sock, sock.daddr, sock.dport);
-     if (!dest) {
-      throw new FS.ErrnoError(53);
-     }
-     if (dest.socket.readyState === dest.socket.CLOSING || dest.socket.readyState === dest.socket.CLOSED) {
-      return null;
-     }
-     throw new FS.ErrnoError(6);
-    }
-    throw new FS.ErrnoError(6);
-   }
-   var queuedLength = queued.data.byteLength || queued.data.length;
-   var queuedOffset = queued.data.byteOffset || 0;
-   var queuedBuffer = queued.data.buffer || queued.data;
-   var bytesRead = Math.min(length, queuedLength);
-   var res = {
-    buffer: new Uint8Array(queuedBuffer, queuedOffset, bytesRead),
-    addr: queued.addr,
-    port: queued.port
-   };
-   if (sock.type === 1 && bytesRead < queuedLength) {
-    var bytesRemaining = queuedLength - bytesRead;
-    queued.data = new Uint8Array(queuedBuffer, queuedOffset + bytesRead, bytesRemaining);
-    sock.recv_queue.unshift(queued);
-   }
-   return res;
-  }
- }
-};
-
-function getSocketFromFD(fd) {
- var socket = SOCKFS.getSocket(fd);
- if (!socket) throw new FS.ErrnoError(8);
- return socket;
-}
-
-function setErrNo(value) {
- GROWABLE_HEAP_I32()[___errno_location() >> 2] = value;
- return value;
-}
-
-var Sockets = {
- BUFFER_SIZE: 10240,
- MAX_BUFFER_SIZE: 10485760,
- nextFd: 1,
- fds: {},
- nextport: 1,
- maxport: 65535,
- peer: null,
- connections: {},
- portmap: {},
- localAddr: 4261412874,
- addrPool: [ 33554442, 50331658, 67108874, 83886090, 100663306, 117440522, 134217738, 150994954, 167772170, 184549386, 201326602, 218103818, 234881034 ]
-};
-
-function inetPton4(str) {
- var b = str.split(".");
- for (var i = 0; i < 4; i++) {
-  var tmp = Number(b[i]);
-  if (isNaN(tmp)) return null;
-  b[i] = tmp;
- }
- return (b[0] | b[1] << 8 | b[2] << 16 | b[3] << 24) >>> 0;
-}
-
-function jstoi_q(str) {
- return parseInt(str);
-}
-
-function inetPton6(str) {
- var words;
- var w, offset, z, i;
- var valid6regx = /^((?=.*::)(?!.*::.+::)(::)?([\dA-F]{1,4}:(:|\b)|){5}|([\dA-F]{1,4}:){6})((([\dA-F]{1,4}((?!\3)::|:\b|$))|(?!\2\3)){2}|(((2[0-4]|1\d|[1-9])?\d|25[0-5])\.?\b){4})$/i;
- var parts = [];
- if (!valid6regx.test(str)) {
-  return null;
- }
- if (str === "::") {
-  return [ 0, 0, 0, 0, 0, 0, 0, 0 ];
- }
- if (str.startsWith("::")) {
-  str = str.replace("::", "Z:");
- } else {
-  str = str.replace("::", ":Z:");
- }
- if (str.indexOf(".") > 0) {
-  str = str.replace(new RegExp("[.]", "g"), ":");
-  words = str.split(":");
-  words[words.length - 4] = jstoi_q(words[words.length - 4]) + jstoi_q(words[words.length - 3]) * 256;
-  words[words.length - 3] = jstoi_q(words[words.length - 2]) + jstoi_q(words[words.length - 1]) * 256;
-  words = words.slice(0, words.length - 2);
- } else {
-  words = str.split(":");
- }
- offset = 0;
- z = 0;
- for (w = 0; w < words.length; w++) {
-  if (typeof words[w] == "string") {
-   if (words[w] === "Z") {
-    for (z = 0; z < 8 - words.length + 1; z++) {
-     parts[w + z] = 0;
-    }
-    offset = z - 1;
-   } else {
-    parts[w + offset] = _htons(parseInt(words[w], 16));
-   }
-  } else {
-   parts[w + offset] = words[w];
-  }
- }
- return [ parts[1] << 16 | parts[0], parts[3] << 16 | parts[2], parts[5] << 16 | parts[4], parts[7] << 16 | parts[6] ];
-}
-
-function writeSockaddr(sa, family, addr, port, addrlen) {
- switch (family) {
- case 2:
-  addr = inetPton4(addr);
-  zeroMemory(sa, 16);
-  if (addrlen) {
-   GROWABLE_HEAP_I32()[addrlen >> 2] = 16;
-  }
-  GROWABLE_HEAP_I16()[sa >> 1] = family;
-  GROWABLE_HEAP_I32()[sa + 4 >> 2] = addr;
-  GROWABLE_HEAP_I16()[sa + 2 >> 1] = _htons(port);
-  break;
-
- case 10:
-  addr = inetPton6(addr);
-  zeroMemory(sa, 28);
-  if (addrlen) {
-   GROWABLE_HEAP_I32()[addrlen >> 2] = 28;
-  }
-  GROWABLE_HEAP_I32()[sa >> 2] = family;
-  GROWABLE_HEAP_I32()[sa + 8 >> 2] = addr[0];
-  GROWABLE_HEAP_I32()[sa + 12 >> 2] = addr[1];
-  GROWABLE_HEAP_I32()[sa + 16 >> 2] = addr[2];
-  GROWABLE_HEAP_I32()[sa + 20 >> 2] = addr[3];
-  GROWABLE_HEAP_I16()[sa + 2 >> 1] = _htons(port);
-  break;
-
- default:
-  return 5;
- }
- return 0;
-}
-
-var DNS = {
- address_map: {
-  id: 1,
-  addrs: {},
-  names: {}
- },
- lookup_name: function(name) {
-  var res = inetPton4(name);
-  if (res !== null) {
-   return name;
-  }
-  res = inetPton6(name);
-  if (res !== null) {
-   return name;
-  }
-  var addr;
-  if (DNS.address_map.addrs[name]) {
-   addr = DNS.address_map.addrs[name];
-  } else {
-   var id = DNS.address_map.id++;
-   assert(id < 65535, "exceeded max address mappings of 65535");
-   addr = "172.29." + (id & 255) + "." + (id & 65280);
-   DNS.address_map.names[addr] = name;
-   DNS.address_map.addrs[name] = addr;
-  }
-  return addr;
- },
- lookup_addr: function(addr) {
-  if (DNS.address_map.names[addr]) {
-   return DNS.address_map.names[addr];
-  }
-  return null;
- }
-};
-
-function ___syscall_accept4(fd, addr, addrlen, flags) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(5, 1, fd, addr, addrlen, flags);
- try {
-  var sock = getSocketFromFD(fd);
-  var newsock = sock.sock_ops.accept(sock);
-  if (addr) {
-   var errno = writeSockaddr(addr, newsock.family, DNS.lookup_name(newsock.daddr), newsock.dport, addrlen);
-   assert(!errno);
-  }
-  return newsock.stream.fd;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function inetNtop4(addr) {
- return (addr & 255) + "." + (addr >> 8 & 255) + "." + (addr >> 16 & 255) + "." + (addr >> 24 & 255);
-}
-
-function inetNtop6(ints) {
- var str = "";
- var word = 0;
- var longest = 0;
- var lastzero = 0;
- var zstart = 0;
- var len = 0;
- var i = 0;
- var parts = [ ints[0] & 65535, ints[0] >> 16, ints[1] & 65535, ints[1] >> 16, ints[2] & 65535, ints[2] >> 16, ints[3] & 65535, ints[3] >> 16 ];
- var hasipv4 = true;
- var v4part = "";
- for (i = 0; i < 5; i++) {
-  if (parts[i] !== 0) {
-   hasipv4 = false;
-   break;
-  }
- }
- if (hasipv4) {
-  v4part = inetNtop4(parts[6] | parts[7] << 16);
-  if (parts[5] === -1) {
-   str = "::ffff:";
-   str += v4part;
-   return str;
-  }
-  if (parts[5] === 0) {
-   str = "::";
-   if (v4part === "0.0.0.0") v4part = "";
-   if (v4part === "0.0.0.1") v4part = "1";
-   str += v4part;
-   return str;
-  }
- }
- for (word = 0; word < 8; word++) {
-  if (parts[word] === 0) {
-   if (word - lastzero > 1) {
-    len = 0;
-   }
-   lastzero = word;
-   len++;
-  }
-  if (len > longest) {
-   longest = len;
-   zstart = word - longest + 1;
-  }
- }
- for (word = 0; word < 8; word++) {
-  if (longest > 1) {
-   if (parts[word] === 0 && word >= zstart && word < zstart + longest) {
-    if (word === zstart) {
-     str += ":";
-     if (zstart === 0) str += ":";
-    }
-    continue;
-   }
-  }
-  str += Number(_ntohs(parts[word] & 65535)).toString(16);
-  str += word < 7 ? ":" : "";
- }
- return str;
-}
-
-function readSockaddr(sa, salen) {
- var family = GROWABLE_HEAP_I16()[sa >> 1];
- var port = _ntohs(GROWABLE_HEAP_U16()[sa + 2 >> 1]);
- var addr;
- switch (family) {
- case 2:
-  if (salen !== 16) {
-   return {
-    errno: 28
-   };
-  }
-  addr = GROWABLE_HEAP_I32()[sa + 4 >> 2];
-  addr = inetNtop4(addr);
-  break;
-
- case 10:
-  if (salen !== 28) {
-   return {
-    errno: 28
-   };
-  }
-  addr = [ GROWABLE_HEAP_I32()[sa + 8 >> 2], GROWABLE_HEAP_I32()[sa + 12 >> 2], GROWABLE_HEAP_I32()[sa + 16 >> 2], GROWABLE_HEAP_I32()[sa + 20 >> 2] ];
-  addr = inetNtop6(addr);
-  break;
-
- default:
-  return {
-   errno: 5
-  };
- }
- return {
-  family: family,
-  addr: addr,
-  port: port
- };
-}
-
-function getSocketAddress(addrp, addrlen, allowNull) {
- if (allowNull && addrp === 0) return null;
- var info = readSockaddr(addrp, addrlen);
- if (info.errno) throw new FS.ErrnoError(info.errno);
- info.addr = DNS.lookup_addr(info.addr) || info.addr;
- return info;
-}
-
-function ___syscall_bind(fd, addr, addrlen) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(6, 1, fd, addr, addrlen);
- try {
-  var sock = getSocketFromFD(fd);
-  var info = getSocketAddress(addr, addrlen);
-  sock.sock_ops.bind(sock, info.addr, info.port);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_chdir(path) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(7, 1, path);
- try {
-  path = SYSCALLS.getStr(path);
-  FS.chdir(path);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_chmod(path, mode) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(8, 1, path, mode);
- try {
-  path = SYSCALLS.getStr(path);
-  FS.chmod(path, mode);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_connect(fd, addr, addrlen) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(9, 1, fd, addr, addrlen);
- try {
-  var sock = getSocketFromFD(fd);
-  var info = getSocketAddress(addr, addrlen);
-  sock.sock_ops.connect(sock, info.addr, info.port);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_faccessat(dirfd, path, amode, flags) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(10, 1, dirfd, path, amode, flags);
- try {
-  path = SYSCALLS.getStr(path);
-  assert(flags === 0);
-  path = SYSCALLS.calculateAt(dirfd, path);
-  if (amode & ~7) {
-   return -28;
-  }
-  var lookup = FS.lookupPath(path, {
-   follow: true
-  });
-  var node = lookup.node;
-  if (!node) {
-   return -44;
-  }
-  var perms = "";
-  if (amode & 4) perms += "r";
-  if (amode & 2) perms += "w";
-  if (amode & 1) perms += "x";
-  if (perms && FS.nodePermissions(node, perms)) {
-   return -2;
-  }
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_fchmod(fd, mode) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(11, 1, fd, mode);
- try {
-  FS.fchmod(fd, mode);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_fcntl64(fd, cmd, varargs) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(12, 1, fd, cmd, varargs);
- SYSCALLS.varargs = varargs;
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  switch (cmd) {
-  case 0:
-   {
-    var arg = SYSCALLS.get();
-    if (arg < 0) {
-     return -28;
-    }
-    var newStream;
-    newStream = FS.createStream(stream, arg);
-    return newStream.fd;
-   }
-
-  case 1:
-  case 2:
-   return 0;
-
-  case 3:
-   return stream.flags;
-
-  case 4:
-   {
-    var arg = SYSCALLS.get();
-    stream.flags |= arg;
-    return 0;
-   }
-
-  case 5:
-   {
-    var arg = SYSCALLS.get();
-    var offset = 0;
-    GROWABLE_HEAP_I16()[arg + offset >> 1] = 2;
-    return 0;
-   }
-
-  case 6:
-  case 7:
-   return 0;
-
-  case 16:
-  case 8:
-   return -28;
-
-  case 9:
-   setErrNo(28);
-   return -1;
-
-  default:
-   {
-    return -28;
-   }
-  }
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_getcwd(buf, size) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(13, 1, buf, size);
- try {
-  if (size === 0) return -28;
-  var cwd = FS.cwd();
-  var cwdLengthInBytes = lengthBytesUTF8(cwd) + 1;
-  if (size < cwdLengthInBytes) return -68;
-  stringToUTF8(cwd, buf, size);
-  return cwdLengthInBytes;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_getdents64(fd, dirp, count) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(14, 1, fd, dirp, count);
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  if (!stream.getdents) {
-   stream.getdents = FS.readdir(stream.path);
-  }
-  var struct_size = 280;
-  var pos = 0;
-  var off = FS.llseek(stream, 0, 1);
-  var idx = Math.floor(off / struct_size);
-  while (idx < stream.getdents.length && pos + struct_size <= count) {
-   var id;
-   var type;
-   var name = stream.getdents[idx];
-   if (name === ".") {
-    id = stream.node.id;
-    type = 4;
-   } else if (name === "..") {
-    var lookup = FS.lookupPath(stream.path, {
-     parent: true
-    });
-    id = lookup.node.id;
-    type = 4;
-   } else {
-    var child = FS.lookupNode(stream.node, name);
-    id = child.id;
-    type = FS.isChrdev(child.mode) ? 2 : FS.isDir(child.mode) ? 4 : FS.isLink(child.mode) ? 10 : 8;
-   }
-   assert(id);
-   tempI64 = [ id >>> 0, (tempDouble = id, +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-   GROWABLE_HEAP_I32()[dirp + pos >> 2] = tempI64[0], GROWABLE_HEAP_I32()[dirp + pos + 4 >> 2] = tempI64[1];
-   tempI64 = [ (idx + 1) * struct_size >>> 0, (tempDouble = (idx + 1) * struct_size, 
-   +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-   GROWABLE_HEAP_I32()[dirp + pos + 8 >> 2] = tempI64[0], GROWABLE_HEAP_I32()[dirp + pos + 12 >> 2] = tempI64[1];
-   GROWABLE_HEAP_I16()[dirp + pos + 16 >> 1] = 280;
-   GROWABLE_HEAP_I8()[dirp + pos + 18 >> 0] = type;
-   stringToUTF8(name, dirp + pos + 19, 256);
-   pos += struct_size;
-   idx += 1;
-  }
-  FS.llseek(stream, idx * struct_size, 0);
-  return pos;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_getsockname(fd, addr, addrlen) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(15, 1, fd, addr, addrlen);
- try {
-  err("__syscall_getsockname " + fd);
-  var sock = getSocketFromFD(fd);
-  var errno = writeSockaddr(addr, sock.family, DNS.lookup_name(sock.saddr || "0.0.0.0"), sock.sport, addrlen);
-  assert(!errno);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_getsockopt(fd, level, optname, optval, optlen) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(16, 1, fd, level, optname, optval, optlen);
- try {
-  var sock = getSocketFromFD(fd);
-  if (level === 1) {
-   if (optname === 4) {
-    GROWABLE_HEAP_I32()[optval >> 2] = sock.error;
-    GROWABLE_HEAP_I32()[optlen >> 2] = 4;
-    sock.error = null;
-    return 0;
-   }
-  }
-  return -50;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_ioctl(fd, op, varargs) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(17, 1, fd, op, varargs);
- SYSCALLS.varargs = varargs;
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  switch (op) {
-  case 21509:
-  case 21505:
-   {
-    if (!stream.tty) return -59;
-    return 0;
-   }
-
-  case 21510:
-  case 21511:
-  case 21512:
-  case 21506:
-  case 21507:
-  case 21508:
-   {
-    if (!stream.tty) return -59;
-    return 0;
-   }
-
-  case 21519:
-   {
-    if (!stream.tty) return -59;
-    var argp = SYSCALLS.get();
-    GROWABLE_HEAP_I32()[argp >> 2] = 0;
-    return 0;
-   }
-
-  case 21520:
-   {
-    if (!stream.tty) return -59;
-    return -28;
-   }
-
-  case 21531:
-   {
-    var argp = SYSCALLS.get();
-    return FS.ioctl(stream, op, argp);
-   }
-
-  case 21523:
-   {
-    if (!stream.tty) return -59;
-    return 0;
-   }
-
-  case 21524:
-   {
-    if (!stream.tty) return -59;
-    return 0;
-   }
-
-  default:
-   return -28;
-  }
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_listen(fd, backlog) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(18, 1, fd, backlog);
- try {
-  var sock = getSocketFromFD(fd);
-  sock.sock_ops.listen(sock, backlog);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_lstat64(path, buf) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(19, 1, path, buf);
- try {
-  path = SYSCALLS.getStr(path);
-  return SYSCALLS.doStat(FS.lstat, path, buf);
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_mkdirat(dirfd, path, mode) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(20, 1, dirfd, path, mode);
- try {
-  path = SYSCALLS.getStr(path);
-  path = SYSCALLS.calculateAt(dirfd, path);
-  path = PATH.normalize(path);
-  if (path[path.length - 1] === "/") path = path.substr(0, path.length - 1);
-  FS.mkdir(path, mode, 0);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_newfstatat(dirfd, path, buf, flags) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(21, 1, dirfd, path, buf, flags);
- try {
-  path = SYSCALLS.getStr(path);
-  var nofollow = flags & 256;
-  var allowEmpty = flags & 4096;
-  flags = flags & ~4352;
-  assert(!flags, flags);
-  path = SYSCALLS.calculateAt(dirfd, path, allowEmpty);
-  return SYSCALLS.doStat(nofollow ? FS.lstat : FS.stat, path, buf);
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_openat(dirfd, path, flags, varargs) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(22, 1, dirfd, path, flags, varargs);
- SYSCALLS.varargs = varargs;
- try {
-  path = SYSCALLS.getStr(path);
-  path = SYSCALLS.calculateAt(dirfd, path);
-  var mode = varargs ? SYSCALLS.get() : 0;
-  return FS.open(path, flags, mode).fd;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_poll(fds, nfds, timeout) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(23, 1, fds, nfds, timeout);
- try {
-  var nonzero = 0;
-  for (var i = 0; i < nfds; i++) {
-   var pollfd = fds + 8 * i;
-   var fd = GROWABLE_HEAP_I32()[pollfd >> 2];
-   var events = GROWABLE_HEAP_I16()[pollfd + 4 >> 1];
-   var mask = 32;
-   var stream = FS.getStream(fd);
-   if (stream) {
-    mask = SYSCALLS.DEFAULT_POLLMASK;
-    if (stream.stream_ops.poll) {
-     mask = stream.stream_ops.poll(stream);
-    }
-   }
-   mask &= events | 8 | 16;
-   if (mask) nonzero++;
-   GROWABLE_HEAP_I16()[pollfd + 6 >> 1] = mask;
-  }
-  return nonzero;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_readlinkat(dirfd, path, buf, bufsize) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(24, 1, dirfd, path, buf, bufsize);
- try {
-  path = SYSCALLS.getStr(path);
-  path = SYSCALLS.calculateAt(dirfd, path);
-  if (bufsize <= 0) return -28;
-  var ret = FS.readlink(path);
-  var len = Math.min(bufsize, lengthBytesUTF8(ret));
-  var endChar = GROWABLE_HEAP_I8()[buf + len];
-  stringToUTF8(ret, buf, bufsize + 1);
-  GROWABLE_HEAP_I8()[buf + len] = endChar;
-  return len;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_recvfrom(fd, buf, len, flags, addr, addrlen) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(25, 1, fd, buf, len, flags, addr, addrlen);
- try {
-  var sock = getSocketFromFD(fd);
-  var msg = sock.sock_ops.recvmsg(sock, len);
-  if (!msg) return 0;
-  if (addr) {
-   var errno = writeSockaddr(addr, sock.family, DNS.lookup_name(msg.addr), msg.port, addrlen);
-   assert(!errno);
-  }
-  GROWABLE_HEAP_U8().set(msg.buffer, buf);
-  return msg.buffer.byteLength;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_renameat(olddirfd, oldpath, newdirfd, newpath) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(26, 1, olddirfd, oldpath, newdirfd, newpath);
- try {
-  oldpath = SYSCALLS.getStr(oldpath);
-  newpath = SYSCALLS.getStr(newpath);
-  oldpath = SYSCALLS.calculateAt(olddirfd, oldpath);
-  newpath = SYSCALLS.calculateAt(newdirfd, newpath);
-  FS.rename(oldpath, newpath);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_rmdir(path) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(27, 1, path);
- try {
-  path = SYSCALLS.getStr(path);
-  FS.rmdir(path);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_sendto(fd, message, length, flags, addr, addr_len) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(28, 1, fd, message, length, flags, addr, addr_len);
- try {
-  var sock = getSocketFromFD(fd);
-  var dest = getSocketAddress(addr, addr_len, true);
-  if (!dest) {
-   return FS.write(sock.stream, GROWABLE_HEAP_I8(), message, length);
-  }
-  return sock.sock_ops.sendmsg(sock, GROWABLE_HEAP_I8(), message, length, dest.addr, dest.port);
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_socket(domain, type, protocol) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(29, 1, domain, type, protocol);
- try {
-  var sock = SOCKFS.createSocket(domain, type, protocol);
-  assert(sock.stream.fd < 64);
-  return sock.stream.fd;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_stat64(path, buf) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(30, 1, path, buf);
- try {
-  path = SYSCALLS.getStr(path);
-  return SYSCALLS.doStat(FS.stat, path, buf);
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_statfs64(path, size, buf) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(31, 1, path, size, buf);
- try {
-  path = SYSCALLS.getStr(path);
-  assert(size === 64);
-  GROWABLE_HEAP_I32()[buf + 4 >> 2] = 4096;
-  GROWABLE_HEAP_I32()[buf + 40 >> 2] = 4096;
-  GROWABLE_HEAP_I32()[buf + 8 >> 2] = 1e6;
-  GROWABLE_HEAP_I32()[buf + 12 >> 2] = 5e5;
-  GROWABLE_HEAP_I32()[buf + 16 >> 2] = 5e5;
-  GROWABLE_HEAP_I32()[buf + 20 >> 2] = FS.nextInode;
-  GROWABLE_HEAP_I32()[buf + 24 >> 2] = 1e6;
-  GROWABLE_HEAP_I32()[buf + 28 >> 2] = 42;
-  GROWABLE_HEAP_I32()[buf + 44 >> 2] = 2;
-  GROWABLE_HEAP_I32()[buf + 36 >> 2] = 255;
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_symlink(target, linkpath) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(32, 1, target, linkpath);
- try {
-  target = SYSCALLS.getStr(target);
-  linkpath = SYSCALLS.getStr(linkpath);
-  FS.symlink(target, linkpath);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function ___syscall_unlinkat(dirfd, path, flags) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(33, 1, dirfd, path, flags);
- try {
-  path = SYSCALLS.getStr(path);
-  path = SYSCALLS.calculateAt(dirfd, path);
-  if (flags === 0) {
-   FS.unlink(path);
-  } else if (flags === 512) {
-   FS.rmdir(path);
-  } else {
-   abort("Invalid flags passed to unlinkat");
-  }
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function __dlinit(main_dso_handle) {}
-
-var dlopenMissingError = "To use dlopen, you need enable dynamic linking, see https://github.com/emscripten-core/emscripten/wiki/Linking";
-
-function __dlopen_js(filename, flag) {
- abort(dlopenMissingError);
-}
-
-function __dlsym_js(handle, symbol) {
- abort(dlopenMissingError);
-}
-
-function __emscripten_date_now() {
- return Date.now();
-}
-
-function __emscripten_default_pthread_stack_size() {
- return 2097152;
-}
-
-var nowIsMonotonic = true;
-
-function __emscripten_get_now_is_monotonic() {
- return nowIsMonotonic;
-}
-
-function executeNotifiedProxyingQueue(queue) {
- Atomics.store(GROWABLE_HEAP_I32(), queue >> 2, 1);
- if (_pthread_self()) {
-  __emscripten_proxy_execute_task_queue(queue);
- }
- Atomics.compareExchange(GROWABLE_HEAP_I32(), queue >> 2, 1, 0);
-}
-
-Module["executeNotifiedProxyingQueue"] = executeNotifiedProxyingQueue;
-
-function __emscripten_notify_task_queue(targetThreadId, currThreadId, mainThreadId, queue) {
- if (targetThreadId == currThreadId) {
-  setTimeout(() => executeNotifiedProxyingQueue(queue));
- } else if (ENVIRONMENT_IS_PTHREAD) {
-  postMessage({
-   "targetThread": targetThreadId,
-   "cmd": "processProxyingQueue",
-   "queue": queue
-  });
- } else {
-  var worker = PThread.pthreads[targetThreadId];
-  if (!worker) {
-   err("Cannot send message to thread with ID " + targetThreadId + ", unknown thread ID!");
-   return;
-  }
-  worker.postMessage({
-   "cmd": "processProxyingQueue",
-   "queue": queue
-  });
- }
- return 1;
-}
-
-function __emscripten_proxied_gl_context_activated_from_main_browser_thread(contextHandle) {
- GLctx = Module.ctx = GL.currentContext = contextHandle;
- GL.currentContextIsProxied = true;
-}
-
-function __emscripten_set_offscreencanvas_size(target, width, height) {
- err("emscripten_set_offscreencanvas_size: Build with -sOFFSCREENCANVAS_SUPPORT=1 to enable transferring canvases to pthreads.");
- return -1;
-}
-
-function __emscripten_throw_longjmp() {
- throw Infinity;
-}
-
-function readI53FromI64(ptr) {
- return GROWABLE_HEAP_U32()[ptr >> 2] + GROWABLE_HEAP_I32()[ptr + 4 >> 2] * 4294967296;
-}
-
-function __gmtime_js(time, tmPtr) {
- var date = new Date(readI53FromI64(time) * 1e3);
- GROWABLE_HEAP_I32()[tmPtr >> 2] = date.getUTCSeconds();
- GROWABLE_HEAP_I32()[tmPtr + 4 >> 2] = date.getUTCMinutes();
- GROWABLE_HEAP_I32()[tmPtr + 8 >> 2] = date.getUTCHours();
- GROWABLE_HEAP_I32()[tmPtr + 12 >> 2] = date.getUTCDate();
- GROWABLE_HEAP_I32()[tmPtr + 16 >> 2] = date.getUTCMonth();
- GROWABLE_HEAP_I32()[tmPtr + 20 >> 2] = date.getUTCFullYear() - 1900;
- GROWABLE_HEAP_I32()[tmPtr + 24 >> 2] = date.getUTCDay();
- var start = Date.UTC(date.getUTCFullYear(), 0, 1, 0, 0, 0, 0);
- var yday = (date.getTime() - start) / (1e3 * 60 * 60 * 24) | 0;
- GROWABLE_HEAP_I32()[tmPtr + 28 >> 2] = yday;
-}
-
-function __localtime_js(time, tmPtr) {
- var date = new Date(readI53FromI64(time) * 1e3);
- GROWABLE_HEAP_I32()[tmPtr >> 2] = date.getSeconds();
- GROWABLE_HEAP_I32()[tmPtr + 4 >> 2] = date.getMinutes();
- GROWABLE_HEAP_I32()[tmPtr + 8 >> 2] = date.getHours();
- GROWABLE_HEAP_I32()[tmPtr + 12 >> 2] = date.getDate();
- GROWABLE_HEAP_I32()[tmPtr + 16 >> 2] = date.getMonth();
- GROWABLE_HEAP_I32()[tmPtr + 20 >> 2] = date.getFullYear() - 1900;
- GROWABLE_HEAP_I32()[tmPtr + 24 >> 2] = date.getDay();
- var start = new Date(date.getFullYear(), 0, 1);
- var yday = (date.getTime() - start.getTime()) / (1e3 * 60 * 60 * 24) | 0;
- GROWABLE_HEAP_I32()[tmPtr + 28 >> 2] = yday;
- GROWABLE_HEAP_I32()[tmPtr + 36 >> 2] = -(date.getTimezoneOffset() * 60);
- var summerOffset = new Date(date.getFullYear(), 6, 1).getTimezoneOffset();
- var winterOffset = start.getTimezoneOffset();
- var dst = (summerOffset != winterOffset && date.getTimezoneOffset() == Math.min(winterOffset, summerOffset)) | 0;
- GROWABLE_HEAP_I32()[tmPtr + 32 >> 2] = dst;
-}
-
-function __mmap_js(len, prot, flags, fd, off, allocated) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(34, 1, len, prot, flags, fd, off, allocated);
- try {
-  var stream = FS.getStream(fd);
-  if (!stream) return -8;
-  var res = FS.mmap(stream, len, off, prot, flags);
-  var ptr = res.ptr;
-  GROWABLE_HEAP_I32()[allocated >> 2] = res.allocated;
-  return ptr;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function __munmap_js(addr, len, prot, flags, fd, offset) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(35, 1, addr, len, prot, flags, fd, offset);
- try {
-  var stream = FS.getStream(fd);
-  if (stream) {
-   if (prot & 2) {
-    SYSCALLS.doMsync(addr, stream, len, flags, offset);
-   }
-   FS.munmap(stream);
-  }
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return -e.errno;
- }
-}
-
-function allocateUTF8(str) {
- var size = lengthBytesUTF8(str) + 1;
- var ret = _malloc(size);
- if (ret) stringToUTF8Array(str, GROWABLE_HEAP_I8(), ret, size);
- return ret;
-}
-
-function _tzset_impl(timezone, daylight, tzname) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(36, 1, timezone, daylight, tzname);
- var currentYear = new Date().getFullYear();
- var winter = new Date(currentYear, 0, 1);
- var summer = new Date(currentYear, 6, 1);
- var winterOffset = winter.getTimezoneOffset();
- var summerOffset = summer.getTimezoneOffset();
- var stdTimezoneOffset = Math.max(winterOffset, summerOffset);
- GROWABLE_HEAP_I32()[timezone >> 2] = stdTimezoneOffset * 60;
- GROWABLE_HEAP_I32()[daylight >> 2] = Number(winterOffset != summerOffset);
- function extractZone(date) {
-  var match = date.toTimeString().match(/\(([A-Za-z ]+)\)$/);
-  return match ? match[1] : "GMT";
- }
- var winterName = extractZone(winter);
- var summerName = extractZone(summer);
- var winterNamePtr = allocateUTF8(winterName);
- var summerNamePtr = allocateUTF8(summerName);
- if (summerOffset < winterOffset) {
-  GROWABLE_HEAP_U32()[tzname >> 2] = winterNamePtr;
-  GROWABLE_HEAP_U32()[tzname + 4 >> 2] = summerNamePtr;
- } else {
-  GROWABLE_HEAP_U32()[tzname >> 2] = summerNamePtr;
-  GROWABLE_HEAP_U32()[tzname + 4 >> 2] = winterNamePtr;
- }
-}
-
-function __tzset_js(timezone, daylight, tzname) {
- if (__tzset_js.called) return;
- __tzset_js.called = true;
- _tzset_impl(timezone, daylight, tzname);
-}
-
-function _abort() {
- abort("native code called abort()");
-}
-
-function runtimeKeepalivePush() {
- runtimeKeepaliveCounter += 1;
-}
-
-function _emscripten_set_main_loop_timing(mode, value) {
- Browser.mainLoop.timingMode = mode;
- Browser.mainLoop.timingValue = value;
- if (!Browser.mainLoop.func) {
-  err("emscripten_set_main_loop_timing: Cannot set timing mode for main loop since a main loop does not exist! Call emscripten_set_main_loop first to set one up.");
-  return 1;
- }
- if (!Browser.mainLoop.running) {
-  runtimeKeepalivePush();
-  Browser.mainLoop.running = true;
- }
- if (mode == 0) {
-  Browser.mainLoop.scheduler = function Browser_mainLoop_scheduler_setTimeout() {
-   var timeUntilNextTick = Math.max(0, Browser.mainLoop.tickStartTime + value - _emscripten_get_now()) | 0;
-   setTimeout(Browser.mainLoop.runner, timeUntilNextTick);
-  };
-  Browser.mainLoop.method = "timeout";
- } else if (mode == 1) {
-  Browser.mainLoop.scheduler = function Browser_mainLoop_scheduler_rAF() {
-   Browser.requestAnimationFrame(Browser.mainLoop.runner);
-  };
-  Browser.mainLoop.method = "rAF";
- } else if (mode == 2) {
-  if (typeof setImmediate == "undefined") {
-   var setImmediates = [];
-   var emscriptenMainLoopMessageId = "setimmediate";
-   var Browser_setImmediate_messageHandler = event => {
-    if (event.data === emscriptenMainLoopMessageId || event.data.target === emscriptenMainLoopMessageId) {
-     event.stopPropagation();
-     setImmediates.shift()();
-    }
-   };
-   addEventListener("message", Browser_setImmediate_messageHandler, true);
-   setImmediate = function Browser_emulated_setImmediate(func) {
-    setImmediates.push(func);
-    if (ENVIRONMENT_IS_WORKER) {
-     if (Module["setImmediates"] === undefined) Module["setImmediates"] = [];
-     Module["setImmediates"].push(func);
-     postMessage({
-      target: emscriptenMainLoopMessageId
-     });
-    } else postMessage(emscriptenMainLoopMessageId, "*");
-   };
-  }
-  Browser.mainLoop.scheduler = function Browser_mainLoop_scheduler_setImmediate() {
-   setImmediate(Browser.mainLoop.runner);
-  };
-  Browser.mainLoop.method = "immediate";
- }
- return 0;
-}
-
-var _emscripten_get_now;
-
-if (ENVIRONMENT_IS_PTHREAD) {
- _emscripten_get_now = () => performance.now() - Module["__performance_now_clock_drift"];
-} else _emscripten_get_now = () => performance.now();
-
-function maybeExit() {
- if (!keepRuntimeAlive()) {
-  try {
-   if (ENVIRONMENT_IS_PTHREAD) __emscripten_thread_exit(EXITSTATUS); else _exit(EXITSTATUS);
-  } catch (e) {
-   handleException(e);
-  }
- }
-}
-
-function runtimeKeepalivePop() {
- assert(runtimeKeepaliveCounter > 0);
- runtimeKeepaliveCounter -= 1;
-}
-
-function setMainLoop(browserIterationFunc, fps, simulateInfiniteLoop, arg, noSetTiming) {
- assert(!Browser.mainLoop.func, "emscripten_set_main_loop: there can only be one main loop function at once: call emscripten_cancel_main_loop to cancel the previous one before setting a new one with different parameters.");
- Browser.mainLoop.func = browserIterationFunc;
- Browser.mainLoop.arg = arg;
- var thisMainLoopId = Browser.mainLoop.currentlyRunningMainloop;
- function checkIsRunning() {
-  if (thisMainLoopId < Browser.mainLoop.currentlyRunningMainloop) {
-   runtimeKeepalivePop();
-   maybeExit();
-   return false;
-  }
-  return true;
- }
- Browser.mainLoop.running = false;
- Browser.mainLoop.runner = function Browser_mainLoop_runner() {
-  if (ABORT) return;
-  if (Browser.mainLoop.queue.length > 0) {
-   var start = Date.now();
-   var blocker = Browser.mainLoop.queue.shift();
-   blocker.func(blocker.arg);
-   if (Browser.mainLoop.remainingBlockers) {
-    var remaining = Browser.mainLoop.remainingBlockers;
-    var next = remaining % 1 == 0 ? remaining - 1 : Math.floor(remaining);
-    if (blocker.counted) {
-     Browser.mainLoop.remainingBlockers = next;
-    } else {
-     next = next + .5;
-     Browser.mainLoop.remainingBlockers = (8 * remaining + next) / 9;
-    }
-   }
-   out('main loop blocker "' + blocker.name + '" took ' + (Date.now() - start) + " ms");
-   Browser.mainLoop.updateStatus();
-   if (!checkIsRunning()) return;
-   setTimeout(Browser.mainLoop.runner, 0);
-   return;
-  }
-  if (!checkIsRunning()) return;
-  Browser.mainLoop.currentFrameNumber = Browser.mainLoop.currentFrameNumber + 1 | 0;
-  if (Browser.mainLoop.timingMode == 1 && Browser.mainLoop.timingValue > 1 && Browser.mainLoop.currentFrameNumber % Browser.mainLoop.timingValue != 0) {
-   Browser.mainLoop.scheduler();
-   return;
-  } else if (Browser.mainLoop.timingMode == 0) {
-   Browser.mainLoop.tickStartTime = _emscripten_get_now();
-  }
-  if (Browser.mainLoop.method === "timeout" && Module.ctx) {
-   warnOnce("Looks like you are rendering without using requestAnimationFrame for the main loop. You should use 0 for the frame rate in emscripten_set_main_loop in order to use requestAnimationFrame, as that can greatly improve your frame rates!");
-   Browser.mainLoop.method = "";
-  }
-  Browser.mainLoop.runIter(browserIterationFunc);
-  checkStackCookie();
-  if (!checkIsRunning()) return;
-  if (typeof SDL == "object" && SDL.audio && SDL.audio.queueNewAudioData) SDL.audio.queueNewAudioData();
-  Browser.mainLoop.scheduler();
- };
- if (!noSetTiming) {
-  if (fps && fps > 0) _emscripten_set_main_loop_timing(0, 1e3 / fps); else _emscripten_set_main_loop_timing(1, 1);
-  Browser.mainLoop.scheduler();
- }
- if (simulateInfiniteLoop) {
-  throw "unwind";
- }
-}
-
-function callUserCallback(func) {
- if (runtimeExited || ABORT) {
-  err("user callback triggered after runtime exited or application aborted.  Ignoring.");
-  return;
- }
- try {
-  func();
-  maybeExit();
- } catch (e) {
-  handleException(e);
- }
-}
-
-function safeSetTimeout(func, timeout) {
- runtimeKeepalivePush();
- return setTimeout(function() {
-  runtimeKeepalivePop();
-  callUserCallback(func);
- }, timeout);
-}
-
-function warnOnce(text) {
- if (!warnOnce.shown) warnOnce.shown = {};
- if (!warnOnce.shown[text]) {
-  warnOnce.shown[text] = 1;
-  err(text);
- }
-}
-
-var Browser = {
- mainLoop: {
-  running: false,
-  scheduler: null,
-  method: "",
-  currentlyRunningMainloop: 0,
-  func: null,
-  arg: 0,
-  timingMode: 0,
-  timingValue: 0,
-  currentFrameNumber: 0,
-  queue: [],
-  pause: function() {
-   Browser.mainLoop.scheduler = null;
-   Browser.mainLoop.currentlyRunningMainloop++;
-  },
-  resume: function() {
-   Browser.mainLoop.currentlyRunningMainloop++;
-   var timingMode = Browser.mainLoop.timingMode;
-   var timingValue = Browser.mainLoop.timingValue;
-   var func = Browser.mainLoop.func;
-   Browser.mainLoop.func = null;
-   setMainLoop(func, 0, false, Browser.mainLoop.arg, true);
-   _emscripten_set_main_loop_timing(timingMode, timingValue);
-   Browser.mainLoop.scheduler();
-  },
-  updateStatus: function() {
-   if (Module["setStatus"]) {
-    var message = Module["statusMessage"] || "Please wait...";
-    var remaining = Browser.mainLoop.remainingBlockers;
-    var expected = Browser.mainLoop.expectedBlockers;
-    if (remaining) {
-     if (remaining < expected) {
-      Module["setStatus"](message + " (" + (expected - remaining) + "/" + expected + ")");
-     } else {
-      Module["setStatus"](message);
-     }
-    } else {
-     Module["setStatus"]("");
-    }
-   }
-  },
-  runIter: function(func) {
-   if (ABORT) return;
-   if (Module["preMainLoop"]) {
-    var preRet = Module["preMainLoop"]();
-    if (preRet === false) {
-     return;
-    }
-   }
-   callUserCallback(func);
-   if (Module["postMainLoop"]) Module["postMainLoop"]();
-  }
- },
- isFullscreen: false,
- pointerLock: false,
- moduleContextCreatedCallbacks: [],
- workers: [],
- init: function() {
-  if (!Module["preloadPlugins"]) Module["preloadPlugins"] = [];
-  if (Browser.initted) return;
-  Browser.initted = true;
-  try {
-   new Blob();
-   Browser.hasBlobConstructor = true;
-  } catch (e) {
-   Browser.hasBlobConstructor = false;
-   err("warning: no blob constructor, cannot create blobs with mimetypes");
-  }
-  Browser.BlobBuilder = typeof MozBlobBuilder != "undefined" ? MozBlobBuilder : typeof WebKitBlobBuilder != "undefined" ? WebKitBlobBuilder : !Browser.hasBlobConstructor ? err("warning: no BlobBuilder") : null;
-  Browser.URLObject = typeof window != "undefined" ? window.URL ? window.URL : window.webkitURL : undefined;
-  if (!Module.noImageDecoding && typeof Browser.URLObject == "undefined") {
-   err("warning: Browser does not support creating object URLs. Built-in browser image decoding will not be available.");
-   Module.noImageDecoding = true;
-  }
-  var imagePlugin = {};
-  imagePlugin["canHandle"] = function imagePlugin_canHandle(name) {
-   return !Module.noImageDecoding && /\.(jpg|jpeg|png|bmp)$/i.test(name);
-  };
-  imagePlugin["handle"] = function imagePlugin_handle(byteArray, name, onload, onerror) {
-   var b = null;
-   if (Browser.hasBlobConstructor) {
-    try {
-     b = new Blob([ byteArray ], {
-      type: Browser.getMimetype(name)
-     });
-     if (b.size !== byteArray.length) {
-      b = new Blob([ new Uint8Array(byteArray).buffer ], {
-       type: Browser.getMimetype(name)
-      });
-     }
-    } catch (e) {
-     warnOnce("Blob constructor present but fails: " + e + "; falling back to blob builder");
-    }
-   }
-   if (!b) {
-    var bb = new Browser.BlobBuilder();
-    bb.append(new Uint8Array(byteArray).buffer);
-    b = bb.getBlob();
-   }
-   var url = Browser.URLObject.createObjectURL(b);
-   assert(typeof url == "string", "createObjectURL must return a url as a string");
-   var img = new Image();
-   img.onload = () => {
-    assert(img.complete, "Image " + name + " could not be decoded");
-    var canvas = document.createElement("canvas");
-    canvas.width = img.width;
-    canvas.height = img.height;
-    var ctx = canvas.getContext("2d");
-    ctx.drawImage(img, 0, 0);
-    preloadedImages[name] = canvas;
-    Browser.URLObject.revokeObjectURL(url);
-    if (onload) onload(byteArray);
-   };
-   img.onerror = event => {
-    out("Image " + url + " could not be decoded");
-    if (onerror) onerror();
-   };
-   img.src = url;
-  };
-  Module["preloadPlugins"].push(imagePlugin);
-  var audioPlugin = {};
-  audioPlugin["canHandle"] = function audioPlugin_canHandle(name) {
-   return !Module.noAudioDecoding && name.substr(-4) in {
-    ".ogg": 1,
-    ".wav": 1,
-    ".mp3": 1
-   };
-  };
-  audioPlugin["handle"] = function audioPlugin_handle(byteArray, name, onload, onerror) {
-   var done = false;
-   function finish(audio) {
-    if (done) return;
-    done = true;
-    preloadedAudios[name] = audio;
-    if (onload) onload(byteArray);
-   }
-   function fail() {
-    if (done) return;
-    done = true;
-    preloadedAudios[name] = new Audio();
-    if (onerror) onerror();
-   }
-   if (Browser.hasBlobConstructor) {
-    try {
-     var b = new Blob([ byteArray ], {
-      type: Browser.getMimetype(name)
-     });
-    } catch (e) {
-     return fail();
-    }
-    var url = Browser.URLObject.createObjectURL(b);
-    assert(typeof url == "string", "createObjectURL must return a url as a string");
-    var audio = new Audio();
-    audio.addEventListener("canplaythrough", () => finish(audio), false);
-    audio.onerror = function audio_onerror(event) {
-     if (done) return;
-     err("warning: browser could not fully decode audio " + name + ", trying slower base64 approach");
-     function encode64(data) {
-      var BASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
-      var PAD = "=";
-      var ret = "";
-      var leftchar = 0;
-      var leftbits = 0;
-      for (var i = 0; i < data.length; i++) {
-       leftchar = leftchar << 8 | data[i];
-       leftbits += 8;
-       while (leftbits >= 6) {
-        var curr = leftchar >> leftbits - 6 & 63;
-        leftbits -= 6;
-        ret += BASE[curr];
-       }
-      }
-      if (leftbits == 2) {
-       ret += BASE[(leftchar & 3) << 4];
-       ret += PAD + PAD;
-      } else if (leftbits == 4) {
-       ret += BASE[(leftchar & 15) << 2];
-       ret += PAD;
-      }
-      return ret;
-     }
-     audio.src = "data:audio/x-" + name.substr(-3) + ";base64," + encode64(byteArray);
-     finish(audio);
-    };
-    audio.src = url;
-    safeSetTimeout(function() {
-     finish(audio);
-    }, 1e4);
-   } else {
-    return fail();
-   }
-  };
-  Module["preloadPlugins"].push(audioPlugin);
-  function pointerLockChange() {
-   Browser.pointerLock = document["pointerLockElement"] === Module["canvas"] || document["mozPointerLockElement"] === Module["canvas"] || document["webkitPointerLockElement"] === Module["canvas"] || document["msPointerLockElement"] === Module["canvas"];
-  }
-  var canvas = Module["canvas"];
-  if (canvas) {
-   canvas.requestPointerLock = canvas["requestPointerLock"] || canvas["mozRequestPointerLock"] || canvas["webkitRequestPointerLock"] || canvas["msRequestPointerLock"] || (() => {});
-   canvas.exitPointerLock = document["exitPointerLock"] || document["mozExitPointerLock"] || document["webkitExitPointerLock"] || document["msExitPointerLock"] || (() => {});
-   canvas.exitPointerLock = canvas.exitPointerLock.bind(document);
-   document.addEventListener("pointerlockchange", pointerLockChange, false);
-   document.addEventListener("mozpointerlockchange", pointerLockChange, false);
-   document.addEventListener("webkitpointerlockchange", pointerLockChange, false);
-   document.addEventListener("mspointerlockchange", pointerLockChange, false);
-   if (Module["elementPointerLock"]) {
-    canvas.addEventListener("click", ev => {
-     if (!Browser.pointerLock && Module["canvas"].requestPointerLock) {
-      Module["canvas"].requestPointerLock();
-      ev.preventDefault();
-     }
-    }, false);
-   }
-  }
- },
- handledByPreloadPlugin: function(byteArray, fullname, finish, onerror) {
-  Browser.init();
-  var handled = false;
-  Module["preloadPlugins"].forEach(function(plugin) {
-   if (handled) return;
-   if (plugin["canHandle"](fullname)) {
-    plugin["handle"](byteArray, fullname, finish, onerror);
-    handled = true;
-   }
-  });
-  return handled;
- },
- createContext: function(canvas, useWebGL, setInModule, webGLContextAttributes) {
-  if (useWebGL && Module.ctx && canvas == Module.canvas) return Module.ctx;
-  var ctx;
-  var contextHandle;
-  if (useWebGL) {
-   var contextAttributes = {
-    antialias: false,
-    alpha: false,
-    majorVersion: typeof WebGL2RenderingContext != "undefined" ? 2 : 1
-   };
-   if (webGLContextAttributes) {
-    for (var attribute in webGLContextAttributes) {
-     contextAttributes[attribute] = webGLContextAttributes[attribute];
-    }
-   }
-   if (typeof GL != "undefined") {
-    contextHandle = GL.createContext(canvas, contextAttributes);
-    if (contextHandle) {
-     ctx = GL.getContext(contextHandle).GLctx;
-    }
-   }
-  } else {
-   ctx = canvas.getContext("2d");
-  }
-  if (!ctx) return null;
-  if (setInModule) {
-   if (!useWebGL) assert(typeof GLctx == "undefined", "cannot set in module if GLctx is used, but we are a non-GL context that would replace it");
-   Module.ctx = ctx;
-   if (useWebGL) GL.makeContextCurrent(contextHandle);
-   Module.useWebGL = useWebGL;
-   Browser.moduleContextCreatedCallbacks.forEach(function(callback) {
-    callback();
-   });
-   Browser.init();
-  }
-  return ctx;
- },
- destroyContext: function(canvas, useWebGL, setInModule) {},
- fullscreenHandlersInstalled: false,
- lockPointer: undefined,
- resizeCanvas: undefined,
- requestFullscreen: function(lockPointer, resizeCanvas) {
-  Browser.lockPointer = lockPointer;
-  Browser.resizeCanvas = resizeCanvas;
-  if (typeof Browser.lockPointer == "undefined") Browser.lockPointer = true;
-  if (typeof Browser.resizeCanvas == "undefined") Browser.resizeCanvas = false;
-  var canvas = Module["canvas"];
-  function fullscreenChange() {
-   Browser.isFullscreen = false;
-   var canvasContainer = canvas.parentNode;
-   if ((document["fullscreenElement"] || document["mozFullScreenElement"] || document["msFullscreenElement"] || document["webkitFullscreenElement"] || document["webkitCurrentFullScreenElement"]) === canvasContainer) {
-    canvas.exitFullscreen = Browser.exitFullscreen;
-    if (Browser.lockPointer) canvas.requestPointerLock();
-    Browser.isFullscreen = true;
-    if (Browser.resizeCanvas) {
-     Browser.setFullscreenCanvasSize();
-    } else {
-     Browser.updateCanvasDimensions(canvas);
-    }
-   } else {
-    canvasContainer.parentNode.insertBefore(canvas, canvasContainer);
-    canvasContainer.parentNode.removeChild(canvasContainer);
-    if (Browser.resizeCanvas) {
-     Browser.setWindowedCanvasSize();
-    } else {
-     Browser.updateCanvasDimensions(canvas);
-    }
-   }
-   if (Module["onFullScreen"]) Module["onFullScreen"](Browser.isFullscreen);
-   if (Module["onFullscreen"]) Module["onFullscreen"](Browser.isFullscreen);
-  }
-  if (!Browser.fullscreenHandlersInstalled) {
-   Browser.fullscreenHandlersInstalled = true;
-   document.addEventListener("fullscreenchange", fullscreenChange, false);
-   document.addEventListener("mozfullscreenchange", fullscreenChange, false);
-   document.addEventListener("webkitfullscreenchange", fullscreenChange, false);
-   document.addEventListener("MSFullscreenChange", fullscreenChange, false);
-  }
-  var canvasContainer = document.createElement("div");
-  canvas.parentNode.insertBefore(canvasContainer, canvas);
-  canvasContainer.appendChild(canvas);
-  canvasContainer.requestFullscreen = canvasContainer["requestFullscreen"] || canvasContainer["mozRequestFullScreen"] || canvasContainer["msRequestFullscreen"] || (canvasContainer["webkitRequestFullscreen"] ? () => canvasContainer["webkitRequestFullscreen"](Element["ALLOW_KEYBOARD_INPUT"]) : null) || (canvasContainer["webkitRequestFullScreen"] ? () => canvasContainer["webkitRequestFullScreen"](Element["ALLOW_KEYBOARD_INPUT"]) : null);
-  canvasContainer.requestFullscreen();
- },
- requestFullScreen: function() {
-  abort("Module.requestFullScreen has been replaced by Module.requestFullscreen (without a capital S)");
- },
- exitFullscreen: function() {
-  if (!Browser.isFullscreen) {
-   return false;
-  }
-  var CFS = document["exitFullscreen"] || document["cancelFullScreen"] || document["mozCancelFullScreen"] || document["msExitFullscreen"] || document["webkitCancelFullScreen"] || function() {};
-  CFS.apply(document, []);
-  return true;
- },
- nextRAF: 0,
- fakeRequestAnimationFrame: function(func) {
-  var now = Date.now();
-  if (Browser.nextRAF === 0) {
-   Browser.nextRAF = now + 1e3 / 60;
-  } else {
-   while (now + 2 >= Browser.nextRAF) {
-    Browser.nextRAF += 1e3 / 60;
-   }
-  }
-  var delay = Math.max(Browser.nextRAF - now, 0);
-  setTimeout(func, delay);
- },
- requestAnimationFrame: function(func) {
-  if (typeof requestAnimationFrame == "function") {
-   requestAnimationFrame(func);
-   return;
-  }
-  var RAF = Browser.fakeRequestAnimationFrame;
-  RAF(func);
- },
- safeSetTimeout: function(func) {
-  return safeSetTimeout(func);
- },
- safeRequestAnimationFrame: function(func) {
-  runtimeKeepalivePush();
-  return Browser.requestAnimationFrame(function() {
-   runtimeKeepalivePop();
-   callUserCallback(func);
-  });
- },
- getMimetype: function(name) {
-  return {
-   "jpg": "image/jpeg",
-   "jpeg": "image/jpeg",
-   "png": "image/png",
-   "bmp": "image/bmp",
-   "ogg": "audio/ogg",
-   "wav": "audio/wav",
-   "mp3": "audio/mpeg"
-  }[name.substr(name.lastIndexOf(".") + 1)];
- },
- getUserMedia: function(func) {
-  if (!window.getUserMedia) {
-   window.getUserMedia = navigator["getUserMedia"] || navigator["mozGetUserMedia"];
-  }
-  window.getUserMedia(func);
- },
- getMovementX: function(event) {
-  return event["movementX"] || event["mozMovementX"] || event["webkitMovementX"] || 0;
- },
- getMovementY: function(event) {
-  return event["movementY"] || event["mozMovementY"] || event["webkitMovementY"] || 0;
- },
- getMouseWheelDelta: function(event) {
-  var delta = 0;
-  switch (event.type) {
-  case "DOMMouseScroll":
-   delta = event.detail / 3;
-   break;
-
-  case "mousewheel":
-   delta = event.wheelDelta / 120;
-   break;
-
-  case "wheel":
-   delta = event.deltaY;
-   switch (event.deltaMode) {
-   case 0:
-    delta /= 100;
-    break;
-
-   case 1:
-    delta /= 3;
-    break;
-
-   case 2:
-    delta *= 80;
-    break;
-
-   default:
-    throw "unrecognized mouse wheel delta mode: " + event.deltaMode;
-   }
-   break;
-
-  default:
-   throw "unrecognized mouse wheel event: " + event.type;
-  }
-  return delta;
- },
- mouseX: 0,
- mouseY: 0,
- mouseMovementX: 0,
- mouseMovementY: 0,
- touches: {},
- lastTouches: {},
- calculateMouseEvent: function(event) {
-  if (Browser.pointerLock) {
-   if (event.type != "mousemove" && "mozMovementX" in event) {
-    Browser.mouseMovementX = Browser.mouseMovementY = 0;
-   } else {
-    Browser.mouseMovementX = Browser.getMovementX(event);
-    Browser.mouseMovementY = Browser.getMovementY(event);
-   }
-   if (typeof SDL != "undefined") {
-    Browser.mouseX = SDL.mouseX + Browser.mouseMovementX;
-    Browser.mouseY = SDL.mouseY + Browser.mouseMovementY;
-   } else {
-    Browser.mouseX += Browser.mouseMovementX;
-    Browser.mouseY += Browser.mouseMovementY;
-   }
-  } else {
-   var rect = Module["canvas"].getBoundingClientRect();
-   var cw = Module["canvas"].width;
-   var ch = Module["canvas"].height;
-   var scrollX = typeof window.scrollX != "undefined" ? window.scrollX : window.pageXOffset;
-   var scrollY = typeof window.scrollY != "undefined" ? window.scrollY : window.pageYOffset;
-   assert(typeof scrollX != "undefined" && typeof scrollY != "undefined", "Unable to retrieve scroll position, mouse positions likely broken.");
-   if (event.type === "touchstart" || event.type === "touchend" || event.type === "touchmove") {
-    var touch = event.touch;
-    if (touch === undefined) {
-     return;
-    }
-    var adjustedX = touch.pageX - (scrollX + rect.left);
-    var adjustedY = touch.pageY - (scrollY + rect.top);
-    adjustedX = adjustedX * (cw / rect.width);
-    adjustedY = adjustedY * (ch / rect.height);
-    var coords = {
-     x: adjustedX,
-     y: adjustedY
-    };
-    if (event.type === "touchstart") {
-     Browser.lastTouches[touch.identifier] = coords;
-     Browser.touches[touch.identifier] = coords;
-    } else if (event.type === "touchend" || event.type === "touchmove") {
-     var last = Browser.touches[touch.identifier];
-     if (!last) last = coords;
-     Browser.lastTouches[touch.identifier] = last;
-     Browser.touches[touch.identifier] = coords;
-    }
-    return;
-   }
-   var x = event.pageX - (scrollX + rect.left);
-   var y = event.pageY - (scrollY + rect.top);
-   x = x * (cw / rect.width);
-   y = y * (ch / rect.height);
-   Browser.mouseMovementX = x - Browser.mouseX;
-   Browser.mouseMovementY = y - Browser.mouseY;
-   Browser.mouseX = x;
-   Browser.mouseY = y;
-  }
- },
- resizeListeners: [],
- updateResizeListeners: function() {
-  var canvas = Module["canvas"];
-  Browser.resizeListeners.forEach(function(listener) {
-   listener(canvas.width, canvas.height);
-  });
- },
- setCanvasSize: function(width, height, noUpdates) {
-  var canvas = Module["canvas"];
-  Browser.updateCanvasDimensions(canvas, width, height);
-  if (!noUpdates) Browser.updateResizeListeners();
- },
- windowedWidth: 0,
- windowedHeight: 0,
- setFullscreenCanvasSize: function() {
-  if (typeof SDL != "undefined") {
-   var flags = GROWABLE_HEAP_U32()[SDL.screen >> 2];
-   flags = flags | 8388608;
-   GROWABLE_HEAP_I32()[SDL.screen >> 2] = flags;
-  }
-  Browser.updateCanvasDimensions(Module["canvas"]);
-  Browser.updateResizeListeners();
- },
- setWindowedCanvasSize: function() {
-  if (typeof SDL != "undefined") {
-   var flags = GROWABLE_HEAP_U32()[SDL.screen >> 2];
-   flags = flags & ~8388608;
-   GROWABLE_HEAP_I32()[SDL.screen >> 2] = flags;
-  }
-  Browser.updateCanvasDimensions(Module["canvas"]);
-  Browser.updateResizeListeners();
- },
- updateCanvasDimensions: function(canvas, wNative, hNative) {
-  if (wNative && hNative) {
-   canvas.widthNative = wNative;
-   canvas.heightNative = hNative;
-  } else {
-   wNative = canvas.widthNative;
-   hNative = canvas.heightNative;
-  }
-  var w = wNative;
-  var h = hNative;
-  if (Module["forcedAspectRatio"] && Module["forcedAspectRatio"] > 0) {
-   if (w / h < Module["forcedAspectRatio"]) {
-    w = Math.round(h * Module["forcedAspectRatio"]);
-   } else {
-    h = Math.round(w / Module["forcedAspectRatio"]);
-   }
-  }
-  if ((document["fullscreenElement"] || document["mozFullScreenElement"] || document["msFullscreenElement"] || document["webkitFullscreenElement"] || document["webkitCurrentFullScreenElement"]) === canvas.parentNode && typeof screen != "undefined") {
-   var factor = Math.min(screen.width / w, screen.height / h);
-   w = Math.round(w * factor);
-   h = Math.round(h * factor);
-  }
-  if (Browser.resizeCanvas) {
-   if (canvas.width != w) canvas.width = w;
-   if (canvas.height != h) canvas.height = h;
-   if (typeof canvas.style != "undefined") {
-    canvas.style.removeProperty("width");
-    canvas.style.removeProperty("height");
-   }
-  } else {
-   if (canvas.width != wNative) canvas.width = wNative;
-   if (canvas.height != hNative) canvas.height = hNative;
-   if (typeof canvas.style != "undefined") {
-    if (w != wNative || h != hNative) {
-     canvas.style.setProperty("width", w + "px", "important");
-     canvas.style.setProperty("height", h + "px", "important");
-    } else {
-     canvas.style.removeProperty("width");
-     canvas.style.removeProperty("height");
-    }
-   }
-  }
- }
-};
-
-function _emscripten_cancel_main_loop() {
- Browser.mainLoop.pause();
- Browser.mainLoop.func = null;
-}
-
-function _emscripten_check_blocking_allowed() {
- if (ENVIRONMENT_IS_WORKER) return;
- warnOnce("Blocking on the main thread is very dangerous, see https://emscripten.org/docs/porting/pthreads.html#blocking-on-the-main-browser-thread");
-}
-
-function _emscripten_console_error(str) {
- assert(typeof str == "number");
- console.error(UTF8ToString(str));
-}
-
-function _emscripten_force_exit(status) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(37, 1, status);
- noExitRuntime = false;
- runtimeKeepaliveCounter = 0;
- _exit(status);
-}
-
-function __webgl_enable_ANGLE_instanced_arrays(ctx) {
- var ext = ctx.getExtension("ANGLE_instanced_arrays");
- if (ext) {
-  ctx["vertexAttribDivisor"] = function(index, divisor) {
-   ext["vertexAttribDivisorANGLE"](index, divisor);
-  };
-  ctx["drawArraysInstanced"] = function(mode, first, count, primcount) {
-   ext["drawArraysInstancedANGLE"](mode, first, count, primcount);
-  };
-  ctx["drawElementsInstanced"] = function(mode, count, type, indices, primcount) {
-   ext["drawElementsInstancedANGLE"](mode, count, type, indices, primcount);
-  };
-  return 1;
- }
-}
-
-function __webgl_enable_OES_vertex_array_object(ctx) {
- var ext = ctx.getExtension("OES_vertex_array_object");
- if (ext) {
-  ctx["createVertexArray"] = function() {
-   return ext["createVertexArrayOES"]();
-  };
-  ctx["deleteVertexArray"] = function(vao) {
-   ext["deleteVertexArrayOES"](vao);
-  };
-  ctx["bindVertexArray"] = function(vao) {
-   ext["bindVertexArrayOES"](vao);
-  };
-  ctx["isVertexArray"] = function(vao) {
-   return ext["isVertexArrayOES"](vao);
-  };
-  return 1;
- }
-}
-
-function __webgl_enable_WEBGL_draw_buffers(ctx) {
- var ext = ctx.getExtension("WEBGL_draw_buffers");
- if (ext) {
-  ctx["drawBuffers"] = function(n, bufs) {
-   ext["drawBuffersWEBGL"](n, bufs);
-  };
-  return 1;
- }
-}
-
-function __webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(ctx) {
- return !!(ctx.dibvbi = ctx.getExtension("WEBGL_draw_instanced_base_vertex_base_instance"));
-}
-
-function __webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(ctx) {
- return !!(ctx.mdibvbi = ctx.getExtension("WEBGL_multi_draw_instanced_base_vertex_base_instance"));
-}
-
-function __webgl_enable_WEBGL_multi_draw(ctx) {
- return !!(ctx.multiDrawWebgl = ctx.getExtension("WEBGL_multi_draw"));
-}
-
-var GL = {
- counter: 1,
- buffers: [],
- programs: [],
- framebuffers: [],
- renderbuffers: [],
- textures: [],
- shaders: [],
- vaos: [],
- contexts: {},
- offscreenCanvases: {},
- queries: [],
- samplers: [],
- transformFeedbacks: [],
- syncs: [],
- stringCache: {},
- stringiCache: {},
- unpackAlignment: 4,
- recordError: function recordError(errorCode) {
-  if (!GL.lastError) {
-   GL.lastError = errorCode;
-  }
- },
- getNewId: function(table) {
-  var ret = GL.counter++;
-  for (var i = table.length; i < ret; i++) {
-   table[i] = null;
-  }
-  return ret;
- },
- getSource: function(shader, count, string, length) {
-  var source = "";
-  for (var i = 0; i < count; ++i) {
-   var len = length ? GROWABLE_HEAP_I32()[length + i * 4 >> 2] : -1;
-   source += UTF8ToString(GROWABLE_HEAP_I32()[string + i * 4 >> 2], len < 0 ? undefined : len);
-  }
-  return source;
- },
- createContext: function(canvas, webGLContextAttributes) {
-  if (webGLContextAttributes.renderViaOffscreenBackBuffer) webGLContextAttributes["preserveDrawingBuffer"] = true;
-  var ctx = webGLContextAttributes.majorVersion > 1 ? canvas.getContext("webgl2", webGLContextAttributes) : canvas.getContext("webgl", webGLContextAttributes);
-  if (!ctx) return 0;
-  var handle = GL.registerContext(ctx, webGLContextAttributes);
-  return handle;
- },
- enableOffscreenFramebufferAttributes: function(webGLContextAttributes) {
-  webGLContextAttributes.renderViaOffscreenBackBuffer = true;
-  webGLContextAttributes.preserveDrawingBuffer = true;
- },
- createOffscreenFramebuffer: function(context) {
-  var gl = context.GLctx;
-  var fbo = gl.createFramebuffer();
-  gl.bindFramebuffer(36160, fbo);
-  context.defaultFbo = fbo;
-  context.defaultFboForbidBlitFramebuffer = false;
-  if (gl.getContextAttributes().antialias) {
-   context.defaultFboForbidBlitFramebuffer = true;
-  } else {
-   var firefoxMatch = navigator.userAgent.toLowerCase().match(/firefox\/(\d\d)/);
-   if (firefoxMatch != null) {
-    var firefoxVersion = firefoxMatch[1];
-    context.defaultFboForbidBlitFramebuffer = firefoxVersion < 67;
-   }
-  }
-  context.defaultColorTarget = gl.createTexture();
-  context.defaultDepthTarget = gl.createRenderbuffer();
-  GL.resizeOffscreenFramebuffer(context);
-  gl.bindTexture(3553, context.defaultColorTarget);
-  gl.texParameteri(3553, 10241, 9728);
-  gl.texParameteri(3553, 10240, 9728);
-  gl.texParameteri(3553, 10242, 33071);
-  gl.texParameteri(3553, 10243, 33071);
-  gl.texImage2D(3553, 0, 6408, gl.canvas.width, gl.canvas.height, 0, 6408, 5121, null);
-  gl.framebufferTexture2D(36160, 36064, 3553, context.defaultColorTarget, 0);
-  gl.bindTexture(3553, null);
-  var depthTarget = gl.createRenderbuffer();
-  gl.bindRenderbuffer(36161, context.defaultDepthTarget);
-  gl.renderbufferStorage(36161, 33189, gl.canvas.width, gl.canvas.height);
-  gl.framebufferRenderbuffer(36160, 36096, 36161, context.defaultDepthTarget);
-  gl.bindRenderbuffer(36161, null);
-  var vertices = [ -1, -1, -1, 1, 1, -1, 1, 1 ];
-  var vb = gl.createBuffer();
-  gl.bindBuffer(34962, vb);
-  gl.bufferData(34962, new Float32Array(vertices), 35044);
-  gl.bindBuffer(34962, null);
-  context.blitVB = vb;
-  var vsCode = "attribute vec2 pos;" + "varying lowp vec2 tex;" + "void main() { tex = pos * 0.5 + vec2(0.5,0.5); gl_Position = vec4(pos, 0.0, 1.0); }";
-  var vs = gl.createShader(35633);
-  gl.shaderSource(vs, vsCode);
-  gl.compileShader(vs);
-  var fsCode = "varying lowp vec2 tex;" + "uniform sampler2D sampler;" + "void main() { gl_FragColor = texture2D(sampler, tex); }";
-  var fs = gl.createShader(35632);
-  gl.shaderSource(fs, fsCode);
-  gl.compileShader(fs);
-  var blitProgram = gl.createProgram();
-  gl.attachShader(blitProgram, vs);
-  gl.attachShader(blitProgram, fs);
-  gl.linkProgram(blitProgram);
-  context.blitProgram = blitProgram;
-  context.blitPosLoc = gl.getAttribLocation(blitProgram, "pos");
-  gl.useProgram(blitProgram);
-  gl.uniform1i(gl.getUniformLocation(blitProgram, "sampler"), 0);
-  gl.useProgram(null);
-  context.defaultVao = undefined;
-  if (gl.createVertexArray) {
-   context.defaultVao = gl.createVertexArray();
-   gl.bindVertexArray(context.defaultVao);
-   gl.enableVertexAttribArray(context.blitPosLoc);
-   gl.bindVertexArray(null);
-  }
- },
- resizeOffscreenFramebuffer: function(context) {
-  var gl = context.GLctx;
-  if (context.defaultColorTarget) {
-   var prevTextureBinding = gl.getParameter(32873);
-   gl.bindTexture(3553, context.defaultColorTarget);
-   gl.texImage2D(3553, 0, 6408, gl.drawingBufferWidth, gl.drawingBufferHeight, 0, 6408, 5121, null);
-   gl.bindTexture(3553, prevTextureBinding);
-  }
-  if (context.defaultDepthTarget) {
-   var prevRenderBufferBinding = gl.getParameter(36007);
-   gl.bindRenderbuffer(36161, context.defaultDepthTarget);
-   gl.renderbufferStorage(36161, 33189, gl.drawingBufferWidth, gl.drawingBufferHeight);
-   gl.bindRenderbuffer(36161, prevRenderBufferBinding);
-  }
- },
- blitOffscreenFramebuffer: function(context) {
-  var gl = context.GLctx;
-  var prevScissorTest = gl.getParameter(3089);
-  if (prevScissorTest) gl.disable(3089);
-  var prevFbo = gl.getParameter(36006);
-  if (gl.blitFramebuffer && !context.defaultFboForbidBlitFramebuffer) {
-   gl.bindFramebuffer(36008, context.defaultFbo);
-   gl.bindFramebuffer(36009, null);
-   gl.blitFramebuffer(0, 0, gl.canvas.width, gl.canvas.height, 0, 0, gl.canvas.width, gl.canvas.height, 16384, 9728);
-  } else {
-   gl.bindFramebuffer(36160, null);
-   var prevProgram = gl.getParameter(35725);
-   gl.useProgram(context.blitProgram);
-   var prevVB = gl.getParameter(34964);
-   gl.bindBuffer(34962, context.blitVB);
-   var prevActiveTexture = gl.getParameter(34016);
-   gl.activeTexture(33984);
-   var prevTextureBinding = gl.getParameter(32873);
-   gl.bindTexture(3553, context.defaultColorTarget);
-   var prevBlend = gl.getParameter(3042);
-   if (prevBlend) gl.disable(3042);
-   var prevCullFace = gl.getParameter(2884);
-   if (prevCullFace) gl.disable(2884);
-   var prevDepthTest = gl.getParameter(2929);
-   if (prevDepthTest) gl.disable(2929);
-   var prevStencilTest = gl.getParameter(2960);
-   if (prevStencilTest) gl.disable(2960);
-   function draw() {
-    gl.vertexAttribPointer(context.blitPosLoc, 2, 5126, false, 0, 0);
-    gl.drawArrays(5, 0, 4);
-   }
-   if (context.defaultVao) {
-    var prevVAO = gl.getParameter(34229);
-    gl.bindVertexArray(context.defaultVao);
-    draw();
-    gl.bindVertexArray(prevVAO);
-   } else {
-    var prevVertexAttribPointer = {
-     buffer: gl.getVertexAttrib(context.blitPosLoc, 34975),
-     size: gl.getVertexAttrib(context.blitPosLoc, 34339),
-     stride: gl.getVertexAttrib(context.blitPosLoc, 34340),
-     type: gl.getVertexAttrib(context.blitPosLoc, 34341),
-     normalized: gl.getVertexAttrib(context.blitPosLoc, 34922),
-     pointer: gl.getVertexAttribOffset(context.blitPosLoc, 34373)
-    };
-    var maxVertexAttribs = gl.getParameter(34921);
-    var prevVertexAttribEnables = [];
-    for (var i = 0; i < maxVertexAttribs; ++i) {
-     var prevEnabled = gl.getVertexAttrib(i, 34338);
-     var wantEnabled = i == context.blitPosLoc;
-     if (prevEnabled && !wantEnabled) {
-      gl.disableVertexAttribArray(i);
-     }
-     if (!prevEnabled && wantEnabled) {
-      gl.enableVertexAttribArray(i);
-     }
-     prevVertexAttribEnables[i] = prevEnabled;
-    }
-    draw();
-    for (var i = 0; i < maxVertexAttribs; ++i) {
-     var prevEnabled = prevVertexAttribEnables[i];
-     var nowEnabled = i == context.blitPosLoc;
-     if (prevEnabled && !nowEnabled) {
-      gl.enableVertexAttribArray(i);
-     }
-     if (!prevEnabled && nowEnabled) {
-      gl.disableVertexAttribArray(i);
-     }
-    }
-    gl.bindBuffer(34962, prevVertexAttribPointer.buffer);
-    gl.vertexAttribPointer(context.blitPosLoc, prevVertexAttribPointer.size, prevVertexAttribPointer.type, prevVertexAttribPointer.normalized, prevVertexAttribPointer.stride, prevVertexAttribPointer.offset);
-   }
-   if (prevStencilTest) gl.enable(2960);
-   if (prevDepthTest) gl.enable(2929);
-   if (prevCullFace) gl.enable(2884);
-   if (prevBlend) gl.enable(3042);
-   gl.bindTexture(3553, prevTextureBinding);
-   gl.activeTexture(prevActiveTexture);
-   gl.bindBuffer(34962, prevVB);
-   gl.useProgram(prevProgram);
-  }
-  gl.bindFramebuffer(36160, prevFbo);
-  if (prevScissorTest) gl.enable(3089);
- },
- registerContext: function(ctx, webGLContextAttributes) {
-  var handle = _malloc(8);
-  GROWABLE_HEAP_I32()[handle + 4 >> 2] = _pthread_self();
-  var context = {
-   handle: handle,
-   attributes: webGLContextAttributes,
-   version: webGLContextAttributes.majorVersion,
-   GLctx: ctx
-  };
-  if (ctx.canvas) ctx.canvas.GLctxObject = context;
-  GL.contexts[handle] = context;
-  if (typeof webGLContextAttributes.enableExtensionsByDefault == "undefined" || webGLContextAttributes.enableExtensionsByDefault) {
-   GL.initExtensions(context);
-  }
-  if (webGLContextAttributes.renderViaOffscreenBackBuffer) GL.createOffscreenFramebuffer(context);
-  return handle;
- },
- makeContextCurrent: function(contextHandle) {
-  GL.currentContext = GL.contexts[contextHandle];
-  Module.ctx = GLctx = GL.currentContext && GL.currentContext.GLctx;
-  return !(contextHandle && !GLctx);
- },
- getContext: function(contextHandle) {
-  return GL.contexts[contextHandle];
- },
- deleteContext: function(contextHandle) {
-  if (GL.currentContext === GL.contexts[contextHandle]) GL.currentContext = null;
-  if (typeof JSEvents == "object") JSEvents.removeAllHandlersOnTarget(GL.contexts[contextHandle].GLctx.canvas);
-  if (GL.contexts[contextHandle] && GL.contexts[contextHandle].GLctx.canvas) GL.contexts[contextHandle].GLctx.canvas.GLctxObject = undefined;
-  _free(GL.contexts[contextHandle].handle);
-  GL.contexts[contextHandle] = null;
- },
- initExtensions: function(context) {
-  if (!context) context = GL.currentContext;
-  if (context.initExtensionsDone) return;
-  context.initExtensionsDone = true;
-  var GLctx = context.GLctx;
-  __webgl_enable_ANGLE_instanced_arrays(GLctx);
-  __webgl_enable_OES_vertex_array_object(GLctx);
-  __webgl_enable_WEBGL_draw_buffers(GLctx);
-  __webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(GLctx);
-  __webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(GLctx);
-  if (context.version >= 2) {
-   GLctx.disjointTimerQueryExt = GLctx.getExtension("EXT_disjoint_timer_query_webgl2");
-  }
-  if (context.version < 2 || !GLctx.disjointTimerQueryExt) {
-   GLctx.disjointTimerQueryExt = GLctx.getExtension("EXT_disjoint_timer_query");
-  }
-  __webgl_enable_WEBGL_multi_draw(GLctx);
-  var exts = GLctx.getSupportedExtensions() || [];
-  exts.forEach(function(ext) {
-   if (!ext.includes("lose_context") && !ext.includes("debug")) {
-    GLctx.getExtension(ext);
-   }
-  });
- }
-};
-
-function _emscripten_glActiveTexture(x0) {
- GLctx["activeTexture"](x0);
-}
-
-function _emscripten_glAttachShader(program, shader) {
- GLctx.attachShader(GL.programs[program], GL.shaders[shader]);
-}
-
-function _emscripten_glBeginTransformFeedback(x0) {
- GLctx["beginTransformFeedback"](x0);
-}
-
-function _emscripten_glBindBuffer(target, buffer) {
- if (target == 35051) {
-  GLctx.currentPixelPackBufferBinding = buffer;
- } else if (target == 35052) {
-  GLctx.currentPixelUnpackBufferBinding = buffer;
- }
- GLctx.bindBuffer(target, GL.buffers[buffer]);
-}
-
-function _emscripten_glBindBufferBase(target, index, buffer) {
- GLctx["bindBufferBase"](target, index, GL.buffers[buffer]);
-}
-
-function _emscripten_glBindBufferRange(target, index, buffer, offset, ptrsize) {
- GLctx["bindBufferRange"](target, index, GL.buffers[buffer], offset, ptrsize);
-}
-
-function _emscripten_glBindFramebuffer(target, framebuffer) {
- GLctx.bindFramebuffer(target, framebuffer ? GL.framebuffers[framebuffer] : GL.currentContext.defaultFbo);
-}
-
-function _emscripten_glBindRenderbuffer(target, renderbuffer) {
- GLctx.bindRenderbuffer(target, GL.renderbuffers[renderbuffer]);
-}
-
-function _emscripten_glBindTexture(target, texture) {
- GLctx.bindTexture(target, GL.textures[texture]);
-}
-
-function _emscripten_glBindVertexArray(vao) {
- GLctx["bindVertexArray"](GL.vaos[vao]);
-}
-
-function _emscripten_glBlendColor(x0, x1, x2, x3) {
- GLctx["blendColor"](x0, x1, x2, x3);
-}
-
-function _emscripten_glBlendEquation(x0) {
- GLctx["blendEquation"](x0);
-}
-
-function _emscripten_glBlendFunc(x0, x1) {
- GLctx["blendFunc"](x0, x1);
-}
-
-function _emscripten_glBlendFuncSeparate(x0, x1, x2, x3) {
- GLctx["blendFuncSeparate"](x0, x1, x2, x3);
-}
-
-function _emscripten_glBlitFramebuffer(x0, x1, x2, x3, x4, x5, x6, x7, x8, x9) {
- GLctx["blitFramebuffer"](x0, x1, x2, x3, x4, x5, x6, x7, x8, x9);
-}
-
-function _emscripten_glBufferData(target, size, data, usage) {
- if (GL.currentContext.version >= 2) {
-  if (data && size) {
-   GLctx.bufferData(target, GROWABLE_HEAP_U8(), usage, data, size);
-  } else {
-   GLctx.bufferData(target, size, usage);
-  }
- } else {
-  GLctx.bufferData(target, data ? GROWABLE_HEAP_U8().subarray(data, data + size) : size, usage);
- }
-}
-
-function _emscripten_glBufferSubData(target, offset, size, data) {
- if (GL.currentContext.version >= 2) {
-  size && GLctx.bufferSubData(target, offset, GROWABLE_HEAP_U8(), data, size);
-  return;
- }
- GLctx.bufferSubData(target, offset, GROWABLE_HEAP_U8().subarray(data, data + size));
-}
-
-function _emscripten_glCheckFramebufferStatus(x0) {
- return GLctx["checkFramebufferStatus"](x0);
-}
-
-function _emscripten_glClear(x0) {
- GLctx["clear"](x0);
-}
-
-function _emscripten_glClearBufferfv(buffer, drawbuffer, value) {
- GLctx["clearBufferfv"](buffer, drawbuffer, GROWABLE_HEAP_F32(), value >> 2);
-}
-
-function _emscripten_glClearColor(x0, x1, x2, x3) {
- GLctx["clearColor"](x0, x1, x2, x3);
-}
-
-function _emscripten_glClearDepthf(x0) {
- GLctx["clearDepth"](x0);
-}
-
-function _emscripten_glColorMask(red, green, blue, alpha) {
- GLctx.colorMask(!!red, !!green, !!blue, !!alpha);
-}
-
-function _emscripten_glCompileShader(shader) {
- GLctx.compileShader(GL.shaders[shader]);
-}
-
-function _emscripten_glCompressedTexImage2D(target, level, internalFormat, width, height, border, imageSize, data) {
- if (GL.currentContext.version >= 2) {
-  if (GLctx.currentPixelUnpackBufferBinding || !imageSize) {
-   GLctx["compressedTexImage2D"](target, level, internalFormat, width, height, border, imageSize, data);
-  } else {
-   GLctx["compressedTexImage2D"](target, level, internalFormat, width, height, border, GROWABLE_HEAP_U8(), data, imageSize);
-  }
-  return;
- }
- GLctx["compressedTexImage2D"](target, level, internalFormat, width, height, border, data ? GROWABLE_HEAP_U8().subarray(data, data + imageSize) : null);
-}
-
-function _emscripten_glCopyBufferSubData(x0, x1, x2, x3, x4) {
- GLctx["copyBufferSubData"](x0, x1, x2, x3, x4);
-}
-
-function _emscripten_glCreateProgram() {
- var id = GL.getNewId(GL.programs);
- var program = GLctx.createProgram();
- program.name = id;
- program.maxUniformLength = program.maxAttributeLength = program.maxUniformBlockNameLength = 0;
- program.uniformIdCounter = 1;
- GL.programs[id] = program;
- return id;
-}
-
-function _emscripten_glCreateShader(shaderType) {
- var id = GL.getNewId(GL.shaders);
- GL.shaders[id] = GLctx.createShader(shaderType);
- return id;
-}
-
-function _emscripten_glCullFace(x0) {
- GLctx["cullFace"](x0);
-}
-
-function _emscripten_glDeleteBuffers(n, buffers) {
- for (var i = 0; i < n; i++) {
-  var id = GROWABLE_HEAP_I32()[buffers + i * 4 >> 2];
-  var buffer = GL.buffers[id];
-  if (!buffer) continue;
-  GLctx.deleteBuffer(buffer);
-  buffer.name = 0;
-  GL.buffers[id] = null;
-  if (id == GLctx.currentPixelPackBufferBinding) GLctx.currentPixelPackBufferBinding = 0;
-  if (id == GLctx.currentPixelUnpackBufferBinding) GLctx.currentPixelUnpackBufferBinding = 0;
- }
-}
-
-function _emscripten_glDeleteFramebuffers(n, framebuffers) {
- for (var i = 0; i < n; ++i) {
-  var id = GROWABLE_HEAP_I32()[framebuffers + i * 4 >> 2];
-  var framebuffer = GL.framebuffers[id];
-  if (!framebuffer) continue;
-  GLctx.deleteFramebuffer(framebuffer);
-  framebuffer.name = 0;
-  GL.framebuffers[id] = null;
- }
-}
-
-function _emscripten_glDeleteProgram(id) {
- if (!id) return;
- var program = GL.programs[id];
- if (!program) {
-  GL.recordError(1281);
-  return;
- }
- GLctx.deleteProgram(program);
- program.name = 0;
- GL.programs[id] = null;
-}
-
-function _emscripten_glDeleteQueries(n, ids) {
- for (var i = 0; i < n; i++) {
-  var id = GROWABLE_HEAP_I32()[ids + i * 4 >> 2];
-  var query = GL.queries[id];
-  if (!query) continue;
-  GLctx["deleteQuery"](query);
-  GL.queries[id] = null;
- }
-}
-
-function _emscripten_glDeleteRenderbuffers(n, renderbuffers) {
- for (var i = 0; i < n; i++) {
-  var id = GROWABLE_HEAP_I32()[renderbuffers + i * 4 >> 2];
-  var renderbuffer = GL.renderbuffers[id];
-  if (!renderbuffer) continue;
-  GLctx.deleteRenderbuffer(renderbuffer);
-  renderbuffer.name = 0;
-  GL.renderbuffers[id] = null;
- }
-}
-
-function _emscripten_glDeleteShader(id) {
- if (!id) return;
- var shader = GL.shaders[id];
- if (!shader) {
-  GL.recordError(1281);
-  return;
- }
- GLctx.deleteShader(shader);
- GL.shaders[id] = null;
-}
-
-function _emscripten_glDeleteSync(id) {
- if (!id) return;
- var sync = GL.syncs[id];
- if (!sync) {
-  GL.recordError(1281);
-  return;
- }
- GLctx.deleteSync(sync);
- sync.name = 0;
- GL.syncs[id] = null;
-}
-
-function _emscripten_glDeleteTextures(n, textures) {
- for (var i = 0; i < n; i++) {
-  var id = GROWABLE_HEAP_I32()[textures + i * 4 >> 2];
-  var texture = GL.textures[id];
-  if (!texture) continue;
-  GLctx.deleteTexture(texture);
-  texture.name = 0;
-  GL.textures[id] = null;
- }
-}
-
-function _emscripten_glDeleteVertexArrays(n, vaos) {
- for (var i = 0; i < n; i++) {
-  var id = GROWABLE_HEAP_I32()[vaos + i * 4 >> 2];
-  GLctx["deleteVertexArray"](GL.vaos[id]);
-  GL.vaos[id] = null;
- }
-}
-
-function _emscripten_glDepthFunc(x0) {
- GLctx["depthFunc"](x0);
-}
-
-function _emscripten_glDepthMask(flag) {
- GLctx.depthMask(!!flag);
-}
-
-function _emscripten_glDisable(x0) {
- GLctx["disable"](x0);
-}
-
-function _emscripten_glDisableVertexAttribArray(index) {
- GLctx.disableVertexAttribArray(index);
-}
-
-function _emscripten_glDrawArrays(mode, first, count) {
- GLctx.drawArrays(mode, first, count);
-}
-
-function _emscripten_glDrawArraysInstanced(mode, first, count, primcount) {
- GLctx["drawArraysInstanced"](mode, first, count, primcount);
-}
-
-function _emscripten_glDrawElements(mode, count, type, indices) {
- GLctx.drawElements(mode, count, type, indices);
-}
-
-function _emscripten_glDrawElementsInstanced(mode, count, type, indices, primcount) {
- GLctx["drawElementsInstanced"](mode, count, type, indices, primcount);
-}
-
-function _emscripten_glEnable(x0) {
- GLctx["enable"](x0);
-}
-
-function _emscripten_glEnableVertexAttribArray(index) {
- GLctx.enableVertexAttribArray(index);
-}
-
-function _emscripten_glEndTransformFeedback() {
- GLctx["endTransformFeedback"]();
-}
-
-function _emscripten_glFenceSync(condition, flags) {
- var sync = GLctx.fenceSync(condition, flags);
- if (sync) {
-  var id = GL.getNewId(GL.syncs);
-  sync.name = id;
-  GL.syncs[id] = sync;
-  return id;
- }
- return 0;
-}
-
-function _emscripten_glFinish() {
- GLctx["finish"]();
-}
-
-function _emscripten_glFramebufferRenderbuffer(target, attachment, renderbuffertarget, renderbuffer) {
- GLctx.framebufferRenderbuffer(target, attachment, renderbuffertarget, GL.renderbuffers[renderbuffer]);
-}
-
-function _emscripten_glFramebufferTexture2D(target, attachment, textarget, texture, level) {
- GLctx.framebufferTexture2D(target, attachment, textarget, GL.textures[texture], level);
-}
-
-function _emscripten_glFramebufferTextureLayer(target, attachment, texture, level, layer) {
- GLctx.framebufferTextureLayer(target, attachment, GL.textures[texture], level, layer);
-}
-
-function _emscripten_glFrontFace(x0) {
- GLctx["frontFace"](x0);
-}
-
-function __glGenObject(n, buffers, createFunction, objectTable) {
- for (var i = 0; i < n; i++) {
-  var buffer = GLctx[createFunction]();
-  var id = buffer && GL.getNewId(objectTable);
-  if (buffer) {
-   buffer.name = id;
-   objectTable[id] = buffer;
-  } else {
-   GL.recordError(1282);
-  }
-  GROWABLE_HEAP_I32()[buffers + i * 4 >> 2] = id;
- }
-}
-
-function _emscripten_glGenBuffers(n, buffers) {
- __glGenObject(n, buffers, "createBuffer", GL.buffers);
-}
-
-function _emscripten_glGenFramebuffers(n, ids) {
- __glGenObject(n, ids, "createFramebuffer", GL.framebuffers);
-}
-
-function _emscripten_glGenQueries(n, ids) {
- __glGenObject(n, ids, "createQuery", GL.queries);
-}
-
-function _emscripten_glGenRenderbuffers(n, renderbuffers) {
- __glGenObject(n, renderbuffers, "createRenderbuffer", GL.renderbuffers);
-}
-
-function _emscripten_glGenTextures(n, textures) {
- __glGenObject(n, textures, "createTexture", GL.textures);
-}
-
-function _emscripten_glGenVertexArrays(n, arrays) {
- __glGenObject(n, arrays, "createVertexArray", GL.vaos);
-}
-
-function _emscripten_glGenerateMipmap(x0) {
- GLctx["generateMipmap"](x0);
-}
-
-function readI53FromU64(ptr) {
- return GROWABLE_HEAP_U32()[ptr >> 2] + GROWABLE_HEAP_U32()[ptr + 4 >> 2] * 4294967296;
-}
-
-function writeI53ToI64(ptr, num) {
- GROWABLE_HEAP_U32()[ptr >> 2] = num;
- GROWABLE_HEAP_U32()[ptr + 4 >> 2] = (num - GROWABLE_HEAP_U32()[ptr >> 2]) / 4294967296;
- var deserialized = num >= 0 ? readI53FromU64(ptr) : readI53FromI64(ptr);
- if (deserialized != num) warnOnce("writeI53ToI64() out of range: serialized JS Number " + num + " to Wasm heap as bytes lo=0x" + GROWABLE_HEAP_U32()[ptr >> 2].toString(16) + ", hi=0x" + GROWABLE_HEAP_U32()[ptr + 4 >> 2].toString(16) + ", which deserializes back to " + deserialized + " instead!");
-}
-
-function emscriptenWebGLGet(name_, p, type) {
- if (!p) {
-  GL.recordError(1281);
-  return;
- }
- var ret = undefined;
- switch (name_) {
- case 36346:
-  ret = 1;
-  break;
-
- case 36344:
-  if (type != 0 && type != 1) {
-   GL.recordError(1280);
-  }
-  return;
-
- case 34814:
- case 36345:
-  ret = 0;
-  break;
-
- case 34466:
-  var formats = GLctx.getParameter(34467);
-  ret = formats ? formats.length : 0;
-  break;
-
- case 33309:
-  if (GL.currentContext.version < 2) {
-   GL.recordError(1282);
-   return;
-  }
-  var exts = GLctx.getSupportedExtensions() || [];
-  ret = 2 * exts.length;
-  break;
-
- case 33307:
- case 33308:
-  if (GL.currentContext.version < 2) {
-   GL.recordError(1280);
-   return;
-  }
-  ret = name_ == 33307 ? 3 : 0;
-  break;
- }
- if (ret === undefined) {
-  var result = GLctx.getParameter(name_);
-  switch (typeof result) {
-  case "number":
-   ret = result;
-   break;
-
-  case "boolean":
-   ret = result ? 1 : 0;
-   break;
-
-  case "string":
-   GL.recordError(1280);
-   return;
-
-  case "object":
-   if (result === null) {
-    switch (name_) {
-    case 34964:
-    case 35725:
-    case 34965:
-    case 36006:
-    case 36007:
-    case 32873:
-    case 34229:
-    case 36662:
-    case 36663:
-    case 35053:
-    case 35055:
-    case 36010:
-    case 35097:
-    case 35869:
-    case 32874:
-    case 36389:
-    case 35983:
-    case 35368:
-    case 34068:
-     {
-      ret = 0;
-      break;
-     }
-
-    default:
-     {
-      GL.recordError(1280);
-      return;
-     }
-    }
-   } else if (result instanceof Float32Array || result instanceof Uint32Array || result instanceof Int32Array || result instanceof Array) {
-    for (var i = 0; i < result.length; ++i) {
-     switch (type) {
-     case 0:
-      GROWABLE_HEAP_I32()[p + i * 4 >> 2] = result[i];
-      break;
-
-     case 2:
-      GROWABLE_HEAP_F32()[p + i * 4 >> 2] = result[i];
-      break;
-
-     case 4:
-      GROWABLE_HEAP_I8()[p + i >> 0] = result[i] ? 1 : 0;
-      break;
-     }
-    }
-    return;
-   } else {
-    try {
-     ret = result.name | 0;
-    } catch (e) {
-     GL.recordError(1280);
-     err("GL_INVALID_ENUM in glGet" + type + "v: Unknown object returned from WebGL getParameter(" + name_ + ")! (error: " + e + ")");
-     return;
-    }
-   }
-   break;
-
-  default:
-   GL.recordError(1280);
-   err("GL_INVALID_ENUM in glGet" + type + "v: Native code calling glGet" + type + "v(" + name_ + ") and it returns " + result + " of type " + typeof result + "!");
-   return;
-  }
- }
- switch (type) {
- case 1:
-  writeI53ToI64(p, ret);
-  break;
-
- case 0:
-  GROWABLE_HEAP_I32()[p >> 2] = ret;
-  break;
-
- case 2:
-  GROWABLE_HEAP_F32()[p >> 2] = ret;
-  break;
-
- case 4:
-  GROWABLE_HEAP_I8()[p >> 0] = ret ? 1 : 0;
-  break;
- }
-}
-
-function _emscripten_glGetFloatv(name_, p) {
- emscriptenWebGLGet(name_, p, 2);
-}
-
-function _emscripten_glGetIntegerv(name_, p) {
- emscriptenWebGLGet(name_, p, 0);
-}
-
-function _emscripten_glGetProgramInfoLog(program, maxLength, length, infoLog) {
- var log = GLctx.getProgramInfoLog(GL.programs[program]);
- if (log === null) log = "(unknown error)";
- var numBytesWrittenExclNull = maxLength > 0 && infoLog ? stringToUTF8(log, infoLog, maxLength) : 0;
- if (length) GROWABLE_HEAP_I32()[length >> 2] = numBytesWrittenExclNull;
-}
-
-function _emscripten_glGetProgramiv(program, pname, p) {
- if (!p) {
-  GL.recordError(1281);
-  return;
- }
- if (program >= GL.counter) {
-  GL.recordError(1281);
-  return;
- }
- program = GL.programs[program];
- if (pname == 35716) {
-  var log = GLctx.getProgramInfoLog(program);
-  if (log === null) log = "(unknown error)";
-  GROWABLE_HEAP_I32()[p >> 2] = log.length + 1;
- } else if (pname == 35719) {
-  if (!program.maxUniformLength) {
-   for (var i = 0; i < GLctx.getProgramParameter(program, 35718); ++i) {
-    program.maxUniformLength = Math.max(program.maxUniformLength, GLctx.getActiveUniform(program, i).name.length + 1);
-   }
-  }
-  GROWABLE_HEAP_I32()[p >> 2] = program.maxUniformLength;
- } else if (pname == 35722) {
-  if (!program.maxAttributeLength) {
-   for (var i = 0; i < GLctx.getProgramParameter(program, 35721); ++i) {
-    program.maxAttributeLength = Math.max(program.maxAttributeLength, GLctx.getActiveAttrib(program, i).name.length + 1);
-   }
-  }
-  GROWABLE_HEAP_I32()[p >> 2] = program.maxAttributeLength;
- } else if (pname == 35381) {
-  if (!program.maxUniformBlockNameLength) {
-   for (var i = 0; i < GLctx.getProgramParameter(program, 35382); ++i) {
-    program.maxUniformBlockNameLength = Math.max(program.maxUniformBlockNameLength, GLctx.getActiveUniformBlockName(program, i).length + 1);
-   }
-  }
-  GROWABLE_HEAP_I32()[p >> 2] = program.maxUniformBlockNameLength;
- } else {
-  GROWABLE_HEAP_I32()[p >> 2] = GLctx.getProgramParameter(program, pname);
- }
-}
-
-function _emscripten_glGetShaderInfoLog(shader, maxLength, length, infoLog) {
- var log = GLctx.getShaderInfoLog(GL.shaders[shader]);
- if (log === null) log = "(unknown error)";
- var numBytesWrittenExclNull = maxLength > 0 && infoLog ? stringToUTF8(log, infoLog, maxLength) : 0;
- if (length) GROWABLE_HEAP_I32()[length >> 2] = numBytesWrittenExclNull;
-}
-
-function _emscripten_glGetShaderiv(shader, pname, p) {
- if (!p) {
-  GL.recordError(1281);
-  return;
- }
- if (pname == 35716) {
-  var log = GLctx.getShaderInfoLog(GL.shaders[shader]);
-  if (log === null) log = "(unknown error)";
-  var logLength = log ? log.length + 1 : 0;
-  GROWABLE_HEAP_I32()[p >> 2] = logLength;
- } else if (pname == 35720) {
-  var source = GLctx.getShaderSource(GL.shaders[shader]);
-  var sourceLength = source ? source.length + 1 : 0;
-  GROWABLE_HEAP_I32()[p >> 2] = sourceLength;
- } else {
-  GROWABLE_HEAP_I32()[p >> 2] = GLctx.getShaderParameter(GL.shaders[shader], pname);
- }
-}
-
-function stringToNewUTF8(jsString) {
- var length = lengthBytesUTF8(jsString) + 1;
- var cString = _malloc(length);
- stringToUTF8(jsString, cString, length);
- return cString;
-}
-
-function _emscripten_glGetString(name_) {
- var ret = GL.stringCache[name_];
- if (!ret) {
-  switch (name_) {
-  case 7939:
-   var exts = GLctx.getSupportedExtensions() || [];
-   exts = exts.concat(exts.map(function(e) {
-    return "GL_" + e;
-   }));
-   ret = stringToNewUTF8(exts.join(" "));
-   break;
-
-  case 7936:
-  case 7937:
-  case 37445:
-  case 37446:
-   var s = GLctx.getParameter(name_);
-   if (!s) {
-    GL.recordError(1280);
-   }
-   ret = s && stringToNewUTF8(s);
-   break;
-
-  case 7938:
-   var glVersion = GLctx.getParameter(7938);
-   if (GL.currentContext.version >= 2) glVersion = "OpenGL ES 3.0 (" + glVersion + ")"; else {
-    glVersion = "OpenGL ES 2.0 (" + glVersion + ")";
-   }
-   ret = stringToNewUTF8(glVersion);
-   break;
-
-  case 35724:
-   var glslVersion = GLctx.getParameter(35724);
-   var ver_re = /^WebGL GLSL ES ([0-9]\.[0-9][0-9]?)(?:$| .*)/;
-   var ver_num = glslVersion.match(ver_re);
-   if (ver_num !== null) {
-    if (ver_num[1].length == 3) ver_num[1] = ver_num[1] + "0";
-    glslVersion = "OpenGL ES GLSL ES " + ver_num[1] + " (" + glslVersion + ")";
-   }
-   ret = stringToNewUTF8(glslVersion);
-   break;
-
-  default:
-   GL.recordError(1280);
-  }
-  GL.stringCache[name_] = ret;
- }
- return ret;
-}
-
-function _emscripten_glGetStringi(name, index) {
- if (GL.currentContext.version < 2) {
-  GL.recordError(1282);
-  return 0;
- }
- var stringiCache = GL.stringiCache[name];
- if (stringiCache) {
-  if (index < 0 || index >= stringiCache.length) {
-   GL.recordError(1281);
-   return 0;
-  }
-  return stringiCache[index];
- }
- switch (name) {
- case 7939:
-  var exts = GLctx.getSupportedExtensions() || [];
-  exts = exts.concat(exts.map(function(e) {
-   return "GL_" + e;
-  }));
-  exts = exts.map(function(e) {
-   return stringToNewUTF8(e);
-  });
-  stringiCache = GL.stringiCache[name] = exts;
-  if (index < 0 || index >= stringiCache.length) {
-   GL.recordError(1281);
-   return 0;
-  }
-  return stringiCache[index];
-
- default:
-  GL.recordError(1280);
-  return 0;
- }
-}
-
-function _emscripten_glGetSynciv(sync, pname, bufSize, length, values) {
- if (bufSize < 0) {
-  GL.recordError(1281);
-  return;
- }
- if (!values) {
-  GL.recordError(1281);
-  return;
- }
- var ret = GLctx.getSyncParameter(GL.syncs[sync], pname);
- if (ret !== null) {
-  GROWABLE_HEAP_I32()[values >> 2] = ret;
-  if (length) GROWABLE_HEAP_I32()[length >> 2] = 1;
- }
-}
-
-function _emscripten_glGetUniformBlockIndex(program, uniformBlockName) {
- return GLctx["getUniformBlockIndex"](GL.programs[program], UTF8ToString(uniformBlockName));
-}
-
-function webglGetLeftBracePos(name) {
- return name.slice(-1) == "]" && name.lastIndexOf("[");
-}
-
-function webglPrepareUniformLocationsBeforeFirstUse(program) {
- var uniformLocsById = program.uniformLocsById, uniformSizeAndIdsByName = program.uniformSizeAndIdsByName, i, j;
- if (!uniformLocsById) {
-  program.uniformLocsById = uniformLocsById = {};
-  program.uniformArrayNamesById = {};
-  for (i = 0; i < GLctx.getProgramParameter(program, 35718); ++i) {
-   var u = GLctx.getActiveUniform(program, i);
-   var nm = u.name;
-   var sz = u.size;
-   var lb = webglGetLeftBracePos(nm);
-   var arrayName = lb > 0 ? nm.slice(0, lb) : nm;
-   var id = program.uniformIdCounter;
-   program.uniformIdCounter += sz;
-   uniformSizeAndIdsByName[arrayName] = [ sz, id ];
-   for (j = 0; j < sz; ++j) {
-    uniformLocsById[id] = j;
-    program.uniformArrayNamesById[id++] = arrayName;
-   }
-  }
- }
-}
-
-function _emscripten_glGetUniformLocation(program, name) {
- name = UTF8ToString(name);
- if (program = GL.programs[program]) {
-  webglPrepareUniformLocationsBeforeFirstUse(program);
-  var uniformLocsById = program.uniformLocsById;
-  var arrayIndex = 0;
-  var uniformBaseName = name;
-  var leftBrace = webglGetLeftBracePos(name);
-  if (leftBrace > 0) {
-   arrayIndex = jstoi_q(name.slice(leftBrace + 1)) >>> 0;
-   uniformBaseName = name.slice(0, leftBrace);
-  }
-  var sizeAndId = program.uniformSizeAndIdsByName[uniformBaseName];
-  if (sizeAndId && arrayIndex < sizeAndId[0]) {
-   arrayIndex += sizeAndId[1];
-   if (uniformLocsById[arrayIndex] = uniformLocsById[arrayIndex] || GLctx.getUniformLocation(program, name)) {
-    return arrayIndex;
-   }
-  }
- } else {
-  GL.recordError(1281);
- }
- return -1;
-}
-
-function _emscripten_glLinkProgram(program) {
- program = GL.programs[program];
- GLctx.linkProgram(program);
- program.uniformLocsById = 0;
- program.uniformSizeAndIdsByName = {};
-}
-
-function _emscripten_glPixelStorei(pname, param) {
- if (pname == 3317) {
-  GL.unpackAlignment = param;
- }
- GLctx.pixelStorei(pname, param);
-}
-
-function _emscripten_glReadBuffer(x0) {
- GLctx["readBuffer"](x0);
-}
-
-function computeUnpackAlignedImageSize(width, height, sizePerPixel, alignment) {
- function roundedToNextMultipleOf(x, y) {
-  return x + y - 1 & -y;
- }
- var plainRowSize = width * sizePerPixel;
- var alignedRowSize = roundedToNextMultipleOf(plainRowSize, alignment);
- return height * alignedRowSize;
-}
-
-function __colorChannelsInGlTextureFormat(format) {
- var colorChannels = {
-  5: 3,
-  6: 4,
-  8: 2,
-  29502: 3,
-  29504: 4,
-  26917: 2,
-  26918: 2,
-  29846: 3,
-  29847: 4
- };
- return colorChannels[format - 6402] || 1;
-}
-
-function heapObjectForWebGLType(type) {
- type -= 5120;
- if (type == 0) return GROWABLE_HEAP_I8();
- if (type == 1) return GROWABLE_HEAP_U8();
- if (type == 2) return GROWABLE_HEAP_I16();
- if (type == 4) return GROWABLE_HEAP_I32();
- if (type == 6) return GROWABLE_HEAP_F32();
- if (type == 5 || type == 28922 || type == 28520 || type == 30779 || type == 30782) return GROWABLE_HEAP_U32();
- return GROWABLE_HEAP_U16();
-}
-
-function heapAccessShiftForWebGLHeap(heap) {
- return 31 - Math.clz32(heap.BYTES_PER_ELEMENT);
-}
-
-function emscriptenWebGLGetTexPixelData(type, format, width, height, pixels, internalFormat) {
- var heap = heapObjectForWebGLType(type);
- var shift = heapAccessShiftForWebGLHeap(heap);
- var byteSize = 1 << shift;
- var sizePerPixel = __colorChannelsInGlTextureFormat(format) * byteSize;
- var bytes = computeUnpackAlignedImageSize(width, height, sizePerPixel, GL.unpackAlignment);
- return heap.subarray(pixels >> shift, pixels + bytes >> shift);
-}
-
-function _emscripten_glReadPixels(x, y, width, height, format, type, pixels) {
- if (GL.currentContext.version >= 2) {
-  if (GLctx.currentPixelPackBufferBinding) {
-   GLctx.readPixels(x, y, width, height, format, type, pixels);
-  } else {
-   var heap = heapObjectForWebGLType(type);
-   GLctx.readPixels(x, y, width, height, format, type, heap, pixels >> heapAccessShiftForWebGLHeap(heap));
-  }
-  return;
- }
- var pixelData = emscriptenWebGLGetTexPixelData(type, format, width, height, pixels, format);
- if (!pixelData) {
-  GL.recordError(1280);
-  return;
- }
- GLctx.readPixels(x, y, width, height, format, type, pixelData);
-}
-
-function _emscripten_glRenderbufferStorage(x0, x1, x2, x3) {
- GLctx["renderbufferStorage"](x0, x1, x2, x3);
-}
-
-function _emscripten_glScissor(x0, x1, x2, x3) {
- GLctx["scissor"](x0, x1, x2, x3);
-}
-
-function _emscripten_glShaderSource(shader, count, string, length) {
- var source = GL.getSource(shader, count, string, length);
- GLctx.shaderSource(GL.shaders[shader], source);
-}
-
-function _emscripten_glTexImage2D(target, level, internalFormat, width, height, border, format, type, pixels) {
- if (GL.currentContext.version >= 2) {
-  if (GLctx.currentPixelUnpackBufferBinding) {
-   GLctx.texImage2D(target, level, internalFormat, width, height, border, format, type, pixels);
-  } else if (pixels) {
-   var heap = heapObjectForWebGLType(type);
-   GLctx.texImage2D(target, level, internalFormat, width, height, border, format, type, heap, pixels >> heapAccessShiftForWebGLHeap(heap));
-  } else {
-   GLctx.texImage2D(target, level, internalFormat, width, height, border, format, type, null);
-  }
-  return;
- }
- GLctx.texImage2D(target, level, internalFormat, width, height, border, format, type, pixels ? emscriptenWebGLGetTexPixelData(type, format, width, height, pixels, internalFormat) : null);
-}
-
-function _emscripten_glTexImage3D(target, level, internalFormat, width, height, depth, border, format, type, pixels) {
- if (GLctx.currentPixelUnpackBufferBinding) {
-  GLctx["texImage3D"](target, level, internalFormat, width, height, depth, border, format, type, pixels);
- } else if (pixels) {
-  var heap = heapObjectForWebGLType(type);
-  GLctx["texImage3D"](target, level, internalFormat, width, height, depth, border, format, type, heap, pixels >> heapAccessShiftForWebGLHeap(heap));
- } else {
-  GLctx["texImage3D"](target, level, internalFormat, width, height, depth, border, format, type, null);
- }
-}
-
-function _emscripten_glTexParameterf(x0, x1, x2) {
- GLctx["texParameterf"](x0, x1, x2);
-}
-
-function _emscripten_glTexParameteri(x0, x1, x2) {
- GLctx["texParameteri"](x0, x1, x2);
-}
-
-function _emscripten_glTexStorage2D(x0, x1, x2, x3, x4) {
- GLctx["texStorage2D"](x0, x1, x2, x3, x4);
-}
-
-function _emscripten_glTexSubImage3D(target, level, xoffset, yoffset, zoffset, width, height, depth, format, type, pixels) {
- if (GLctx.currentPixelUnpackBufferBinding) {
-  GLctx["texSubImage3D"](target, level, xoffset, yoffset, zoffset, width, height, depth, format, type, pixels);
- } else if (pixels) {
-  var heap = heapObjectForWebGLType(type);
-  GLctx["texSubImage3D"](target, level, xoffset, yoffset, zoffset, width, height, depth, format, type, heap, pixels >> heapAccessShiftForWebGLHeap(heap));
- } else {
-  GLctx["texSubImage3D"](target, level, xoffset, yoffset, zoffset, width, height, depth, format, type, null);
- }
-}
-
-function _emscripten_glTransformFeedbackVaryings(program, count, varyings, bufferMode) {
- program = GL.programs[program];
- var vars = [];
- for (var i = 0; i < count; i++) vars.push(UTF8ToString(GROWABLE_HEAP_I32()[varyings + i * 4 >> 2]));
- GLctx["transformFeedbackVaryings"](program, vars, bufferMode);
-}
-
-function webglGetUniformLocation(location) {
- var p = GLctx.currentProgram;
- if (p) {
-  var webglLoc = p.uniformLocsById[location];
-  if (typeof webglLoc == "number") {
-   p.uniformLocsById[location] = webglLoc = GLctx.getUniformLocation(p, p.uniformArrayNamesById[location] + (webglLoc > 0 ? "[" + webglLoc + "]" : ""));
-  }
-  return webglLoc;
- } else {
-  GL.recordError(1282);
- }
-}
-
-function _emscripten_glUniform1f(location, v0) {
- GLctx.uniform1f(webglGetUniformLocation(location), v0);
-}
-
-function _emscripten_glUniform1i(location, v0) {
- GLctx.uniform1i(webglGetUniformLocation(location), v0);
-}
-
-var __miniTempWebGLIntBuffers = [];
-
-function _emscripten_glUniform1iv(location, count, value) {
- if (GL.currentContext.version >= 2) {
-  count && GLctx.uniform1iv(webglGetUniformLocation(location), GROWABLE_HEAP_I32(), value >> 2, count);
-  return;
- }
- if (count <= 288) {
-  var view = __miniTempWebGLIntBuffers[count - 1];
-  for (var i = 0; i < count; ++i) {
-   view[i] = GROWABLE_HEAP_I32()[value + 4 * i >> 2];
-  }
- } else {
-  var view = GROWABLE_HEAP_I32().subarray(value >> 2, value + count * 4 >> 2);
- }
- GLctx.uniform1iv(webglGetUniformLocation(location), view);
-}
-
-function _emscripten_glUniform1ui(location, v0) {
- GLctx.uniform1ui(webglGetUniformLocation(location), v0);
-}
-
-function _emscripten_glUniform1uiv(location, count, value) {
- count && GLctx.uniform1uiv(webglGetUniformLocation(location), GROWABLE_HEAP_U32(), value >> 2, count);
-}
-
-function _emscripten_glUniform2f(location, v0, v1) {
- GLctx.uniform2f(webglGetUniformLocation(location), v0, v1);
-}
-
-var miniTempWebGLFloatBuffers = [];
-
-function _emscripten_glUniform2fv(location, count, value) {
- if (GL.currentContext.version >= 2) {
-  count && GLctx.uniform2fv(webglGetUniformLocation(location), GROWABLE_HEAP_F32(), value >> 2, count * 2);
-  return;
- }
- if (count <= 144) {
-  var view = miniTempWebGLFloatBuffers[2 * count - 1];
-  for (var i = 0; i < 2 * count; i += 2) {
-   view[i] = GROWABLE_HEAP_F32()[value + 4 * i >> 2];
-   view[i + 1] = GROWABLE_HEAP_F32()[value + (4 * i + 4) >> 2];
-  }
- } else {
-  var view = GROWABLE_HEAP_F32().subarray(value >> 2, value + count * 8 >> 2);
- }
- GLctx.uniform2fv(webglGetUniformLocation(location), view);
-}
-
-function _emscripten_glUniform2iv(location, count, value) {
- if (GL.currentContext.version >= 2) {
-  count && GLctx.uniform2iv(webglGetUniformLocation(location), GROWABLE_HEAP_I32(), value >> 2, count * 2);
-  return;
- }
- if (count <= 144) {
-  var view = __miniTempWebGLIntBuffers[2 * count - 1];
-  for (var i = 0; i < 2 * count; i += 2) {
-   view[i] = GROWABLE_HEAP_I32()[value + 4 * i >> 2];
-   view[i + 1] = GROWABLE_HEAP_I32()[value + (4 * i + 4) >> 2];
-  }
- } else {
-  var view = GROWABLE_HEAP_I32().subarray(value >> 2, value + count * 8 >> 2);
- }
- GLctx.uniform2iv(webglGetUniformLocation(location), view);
-}
-
-function _emscripten_glUniform3fv(location, count, value) {
- if (GL.currentContext.version >= 2) {
-  count && GLctx.uniform3fv(webglGetUniformLocation(location), GROWABLE_HEAP_F32(), value >> 2, count * 3);
-  return;
- }
- if (count <= 96) {
-  var view = miniTempWebGLFloatBuffers[3 * count - 1];
-  for (var i = 0; i < 3 * count; i += 3) {
-   view[i] = GROWABLE_HEAP_F32()[value + 4 * i >> 2];
-   view[i + 1] = GROWABLE_HEAP_F32()[value + (4 * i + 4) >> 2];
-   view[i + 2] = GROWABLE_HEAP_F32()[value + (4 * i + 8) >> 2];
-  }
- } else {
-  var view = GROWABLE_HEAP_F32().subarray(value >> 2, value + count * 12 >> 2);
- }
- GLctx.uniform3fv(webglGetUniformLocation(location), view);
-}
-
-function _emscripten_glUniform4f(location, v0, v1, v2, v3) {
- GLctx.uniform4f(webglGetUniformLocation(location), v0, v1, v2, v3);
-}
-
-function _emscripten_glUniform4fv(location, count, value) {
- if (GL.currentContext.version >= 2) {
-  count && GLctx.uniform4fv(webglGetUniformLocation(location), GROWABLE_HEAP_F32(), value >> 2, count * 4);
-  return;
- }
- if (count <= 72) {
-  var view = miniTempWebGLFloatBuffers[4 * count - 1];
-  var heap = GROWABLE_HEAP_F32();
-  value >>= 2;
-  for (var i = 0; i < 4 * count; i += 4) {
-   var dst = value + i;
-   view[i] = heap[dst];
-   view[i + 1] = heap[dst + 1];
-   view[i + 2] = heap[dst + 2];
-   view[i + 3] = heap[dst + 3];
-  }
- } else {
-  var view = GROWABLE_HEAP_F32().subarray(value >> 2, value + count * 16 >> 2);
- }
- GLctx.uniform4fv(webglGetUniformLocation(location), view);
-}
-
-function _emscripten_glUniformBlockBinding(program, uniformBlockIndex, uniformBlockBinding) {
- program = GL.programs[program];
- GLctx["uniformBlockBinding"](program, uniformBlockIndex, uniformBlockBinding);
-}
-
-function _emscripten_glUniformMatrix4fv(location, count, transpose, value) {
- if (GL.currentContext.version >= 2) {
-  count && GLctx.uniformMatrix4fv(webglGetUniformLocation(location), !!transpose, GROWABLE_HEAP_F32(), value >> 2, count * 16);
-  return;
- }
- if (count <= 18) {
-  var view = miniTempWebGLFloatBuffers[16 * count - 1];
-  var heap = GROWABLE_HEAP_F32();
-  value >>= 2;
-  for (var i = 0; i < 16 * count; i += 16) {
-   var dst = value + i;
-   view[i] = heap[dst];
-   view[i + 1] = heap[dst + 1];
-   view[i + 2] = heap[dst + 2];
-   view[i + 3] = heap[dst + 3];
-   view[i + 4] = heap[dst + 4];
-   view[i + 5] = heap[dst + 5];
-   view[i + 6] = heap[dst + 6];
-   view[i + 7] = heap[dst + 7];
-   view[i + 8] = heap[dst + 8];
-   view[i + 9] = heap[dst + 9];
-   view[i + 10] = heap[dst + 10];
-   view[i + 11] = heap[dst + 11];
-   view[i + 12] = heap[dst + 12];
-   view[i + 13] = heap[dst + 13];
-   view[i + 14] = heap[dst + 14];
-   view[i + 15] = heap[dst + 15];
-  }
- } else {
-  var view = GROWABLE_HEAP_F32().subarray(value >> 2, value + count * 64 >> 2);
- }
- GLctx.uniformMatrix4fv(webglGetUniformLocation(location), !!transpose, view);
-}
-
-function _emscripten_glUseProgram(program) {
- program = GL.programs[program];
- GLctx.useProgram(program);
- GLctx.currentProgram = program;
-}
-
-function _emscripten_glVertexAttrib4f(x0, x1, x2, x3, x4) {
- GLctx["vertexAttrib4f"](x0, x1, x2, x3, x4);
-}
-
-function _emscripten_glVertexAttribDivisor(index, divisor) {
- GLctx["vertexAttribDivisor"](index, divisor);
-}
-
-function _emscripten_glVertexAttribIPointer(index, size, type, stride, ptr) {
- GLctx["vertexAttribIPointer"](index, size, type, stride, ptr);
-}
-
-function _emscripten_glVertexAttribPointer(index, size, type, normalized, stride, ptr) {
- GLctx.vertexAttribPointer(index, size, type, !!normalized, stride, ptr);
-}
-
-function _emscripten_glViewport(x0, x1, x2, x3) {
- GLctx["viewport"](x0, x1, x2, x3);
-}
-
-function _emscripten_memcpy_big(dest, src, num) {
- GROWABLE_HEAP_U8().copyWithin(dest, src, src + num);
-}
-
-function _emscripten_num_logical_cores() {
- return navigator["hardwareConcurrency"];
-}
-
-function _emscripten_proxy_to_main_thread_js(index, sync) {
- var numCallArgs = arguments.length - 2;
- var outerArgs = arguments;
- if (numCallArgs > 20 - 1) throw "emscripten_proxy_to_main_thread_js: Too many arguments " + numCallArgs + " to proxied function idx=" + index + ", maximum supported is " + (20 - 1) + "!";
- return withStackSave(function() {
-  var serializedNumCallArgs = numCallArgs;
-  var args = stackAlloc(serializedNumCallArgs * 8);
-  var b = args >> 3;
-  for (var i = 0; i < numCallArgs; i++) {
-   var arg = outerArgs[2 + i];
-   GROWABLE_HEAP_F64()[b + i] = arg;
-  }
-  return _emscripten_run_in_main_runtime_thread_js(index, serializedNumCallArgs, args, sync);
- });
-}
-
-var _emscripten_receive_on_main_thread_js_callArgs = [];
-
-function _emscripten_receive_on_main_thread_js(index, numCallArgs, args) {
- _emscripten_receive_on_main_thread_js_callArgs.length = numCallArgs;
- var b = args >> 3;
- for (var i = 0; i < numCallArgs; i++) {
-  _emscripten_receive_on_main_thread_js_callArgs[i] = GROWABLE_HEAP_F64()[b + i];
- }
- var isEmAsmConst = index < 0;
- var func = !isEmAsmConst ? proxiedFunctionTable[index] : ASM_CONSTS[-index - 1];
- assert(func.length == numCallArgs, "Call args mismatch in emscripten_receive_on_main_thread_js");
- return func.apply(null, _emscripten_receive_on_main_thread_js_callArgs);
-}
-
-function getHeapMax() {
- return 2147483648;
-}
-
-function emscripten_realloc_buffer(size) {
- try {
-  wasmMemory.grow(size - buffer.byteLength + 65535 >>> 16);
-  updateGlobalBufferAndViews(wasmMemory.buffer);
-  return 1;
- } catch (e) {
-  err("emscripten_realloc_buffer: Attempted to grow heap from " + buffer.byteLength + " bytes to " + size + " bytes, but got error: " + e);
- }
-}
-
-function _emscripten_resize_heap(requestedSize) {
- var oldSize = GROWABLE_HEAP_U8().length;
- requestedSize = requestedSize >>> 0;
- if (requestedSize <= oldSize) {
-  return false;
- }
- var maxHeapSize = getHeapMax();
- if (requestedSize > maxHeapSize) {
-  err("Cannot enlarge memory, asked to go up to " + requestedSize + " bytes, but the limit is " + maxHeapSize + " bytes!");
-  return false;
- }
- let alignUp = (x, multiple) => x + (multiple - x % multiple) % multiple;
- for (var cutDown = 1; cutDown <= 4; cutDown *= 2) {
-  var overGrownHeapSize = oldSize * (1 + .2 / cutDown);
-  overGrownHeapSize = Math.min(overGrownHeapSize, requestedSize + 100663296);
-  var newSize = Math.min(maxHeapSize, alignUp(Math.max(requestedSize, overGrownHeapSize), 65536));
-  var replacement = emscripten_realloc_buffer(newSize);
-  if (replacement) {
-   return true;
-  }
- }
- err("Failed to grow the heap from " + oldSize + " bytes to " + newSize + " bytes, not enough memory!");
- return false;
-}
-
-function _emscripten_set_main_loop(func, fps, simulateInfiniteLoop) {
- var browserIterationFunc = getWasmTableEntry(func);
- setMainLoop(browserIterationFunc, fps, simulateInfiniteLoop);
-}
-
-function _emscripten_supports_offscreencanvas() {
- return 0;
-}
-
-function _emscripten_unwind_to_js_event_loop() {
- throw "unwind";
-}
-
-function _emscripten_webgl_destroy_context(contextHandle) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(38, 1, contextHandle);
- if (GL.currentContext == contextHandle) GL.currentContext = 0;
- GL.deleteContext(contextHandle);
-}
-
-function _emscripten_webgl_do_commit_frame() {
- if (!GL.currentContext || !GL.currentContext.GLctx) {
-  return -3;
- }
- if (GL.currentContext.defaultFbo) {
-  GL.blitOffscreenFramebuffer(GL.currentContext);
-  return 0;
- }
- if (!GL.currentContext.attributes.explicitSwapControl) {
-  return -3;
- }
- return 0;
-}
-
-function _emscripten_webgl_create_context_proxied(target, attributes) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(39, 1, target, attributes);
- return _emscripten_webgl_do_create_context(target, attributes);
-}
-
-var JSEvents = {
- inEventHandler: 0,
- removeAllEventListeners: function() {
-  for (var i = JSEvents.eventHandlers.length - 1; i >= 0; --i) {
-   JSEvents._removeHandler(i);
-  }
-  JSEvents.eventHandlers = [];
-  JSEvents.deferredCalls = [];
- },
- registerRemoveEventListeners: function() {
-  if (!JSEvents.removeEventListenersRegistered) {
-   __ATEXIT__.push(JSEvents.removeAllEventListeners);
-   JSEvents.removeEventListenersRegistered = true;
-  }
- },
- deferredCalls: [],
- deferCall: function(targetFunction, precedence, argsList) {
-  function arraysHaveEqualContent(arrA, arrB) {
-   if (arrA.length != arrB.length) return false;
-   for (var i in arrA) {
-    if (arrA[i] != arrB[i]) return false;
-   }
-   return true;
-  }
-  for (var i in JSEvents.deferredCalls) {
-   var call = JSEvents.deferredCalls[i];
-   if (call.targetFunction == targetFunction && arraysHaveEqualContent(call.argsList, argsList)) {
-    return;
-   }
-  }
-  JSEvents.deferredCalls.push({
-   targetFunction: targetFunction,
-   precedence: precedence,
-   argsList: argsList
-  });
-  JSEvents.deferredCalls.sort(function(x, y) {
-   return x.precedence < y.precedence;
-  });
- },
- removeDeferredCalls: function(targetFunction) {
-  for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
-   if (JSEvents.deferredCalls[i].targetFunction == targetFunction) {
-    JSEvents.deferredCalls.splice(i, 1);
-    --i;
-   }
-  }
- },
- canPerformEventHandlerRequests: function() {
-  return JSEvents.inEventHandler && JSEvents.currentEventHandler.allowsDeferredCalls;
- },
- runDeferredCalls: function() {
-  if (!JSEvents.canPerformEventHandlerRequests()) {
-   return;
-  }
-  for (var i = 0; i < JSEvents.deferredCalls.length; ++i) {
-   var call = JSEvents.deferredCalls[i];
-   JSEvents.deferredCalls.splice(i, 1);
-   --i;
-   call.targetFunction.apply(null, call.argsList);
-  }
- },
- eventHandlers: [],
- removeAllHandlersOnTarget: function(target, eventTypeString) {
-  for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
-   if (JSEvents.eventHandlers[i].target == target && (!eventTypeString || eventTypeString == JSEvents.eventHandlers[i].eventTypeString)) {
-    JSEvents._removeHandler(i--);
-   }
-  }
- },
- _removeHandler: function(i) {
-  var h = JSEvents.eventHandlers[i];
-  h.target.removeEventListener(h.eventTypeString, h.eventListenerFunc, h.useCapture);
-  JSEvents.eventHandlers.splice(i, 1);
- },
- registerOrRemoveHandler: function(eventHandler) {
-  var jsEventHandler = function jsEventHandler(event) {
-   ++JSEvents.inEventHandler;
-   JSEvents.currentEventHandler = eventHandler;
-   JSEvents.runDeferredCalls();
-   eventHandler.handlerFunc(event);
-   JSEvents.runDeferredCalls();
-   --JSEvents.inEventHandler;
-  };
-  if (eventHandler.callbackfunc) {
-   eventHandler.eventListenerFunc = jsEventHandler;
-   eventHandler.target.addEventListener(eventHandler.eventTypeString, jsEventHandler, eventHandler.useCapture);
-   JSEvents.eventHandlers.push(eventHandler);
-   JSEvents.registerRemoveEventListeners();
-  } else {
-   for (var i = 0; i < JSEvents.eventHandlers.length; ++i) {
-    if (JSEvents.eventHandlers[i].target == eventHandler.target && JSEvents.eventHandlers[i].eventTypeString == eventHandler.eventTypeString) {
-     JSEvents._removeHandler(i--);
-    }
-   }
-  }
- },
- queueEventHandlerOnThread_iiii: function(targetThread, eventHandlerFunc, eventTypeId, eventData, userData) {
-  withStackSave(function() {
-   var varargs = stackAlloc(12);
-   GROWABLE_HEAP_I32()[varargs >> 2] = eventTypeId;
-   GROWABLE_HEAP_I32()[varargs + 4 >> 2] = eventData;
-   GROWABLE_HEAP_I32()[varargs + 8 >> 2] = userData;
-   _emscripten_dispatch_to_thread_(targetThread, 637534208, eventHandlerFunc, eventData, varargs);
-  });
- },
- getTargetThreadForEventCallback: function(targetThread) {
-  switch (targetThread) {
-  case 1:
-   return 0;
-
-  case 2:
-   return PThread.currentProxiedOperationCallerThread;
-
-  default:
-   return targetThread;
-  }
- },
- getNodeNameForTarget: function(target) {
-  if (!target) return "";
-  if (target == window) return "#window";
-  if (target == screen) return "#screen";
-  return target && target.nodeName ? target.nodeName : "";
- },
- fullscreenEnabled: function() {
-  return document.fullscreenEnabled || document.webkitFullscreenEnabled;
- }
-};
-
-var __emscripten_webgl_power_preferences = [ "default", "low-power", "high-performance" ];
-
-function maybeCStringToJsString(cString) {
- return cString > 2 ? UTF8ToString(cString) : cString;
-}
-
-var specialHTMLTargets = [ 0, typeof document != "undefined" ? document : 0, typeof window != "undefined" ? window : 0 ];
-
-function findEventTarget(target) {
- target = maybeCStringToJsString(target);
- var domElement = specialHTMLTargets[target] || (typeof document != "undefined" ? document.querySelector(target) : undefined);
- return domElement;
-}
-
-function findCanvasEventTarget(target) {
- return findEventTarget(target);
-}
-
-function _emscripten_webgl_do_create_context(target, attributes) {
- assert(attributes);
- var a = attributes >> 2;
- var powerPreference = GROWABLE_HEAP_I32()[a + (24 >> 2)];
- var contextAttributes = {
-  "alpha": !!GROWABLE_HEAP_I32()[a + (0 >> 2)],
-  "depth": !!GROWABLE_HEAP_I32()[a + (4 >> 2)],
-  "stencil": !!GROWABLE_HEAP_I32()[a + (8 >> 2)],
-  "antialias": !!GROWABLE_HEAP_I32()[a + (12 >> 2)],
-  "premultipliedAlpha": !!GROWABLE_HEAP_I32()[a + (16 >> 2)],
-  "preserveDrawingBuffer": !!GROWABLE_HEAP_I32()[a + (20 >> 2)],
-  "powerPreference": __emscripten_webgl_power_preferences[powerPreference],
-  "failIfMajorPerformanceCaveat": !!GROWABLE_HEAP_I32()[a + (28 >> 2)],
-  majorVersion: GROWABLE_HEAP_I32()[a + (32 >> 2)],
-  minorVersion: GROWABLE_HEAP_I32()[a + (36 >> 2)],
-  enableExtensionsByDefault: GROWABLE_HEAP_I32()[a + (40 >> 2)],
-  explicitSwapControl: GROWABLE_HEAP_I32()[a + (44 >> 2)],
-  proxyContextToMainThread: GROWABLE_HEAP_I32()[a + (48 >> 2)],
-  renderViaOffscreenBackBuffer: GROWABLE_HEAP_I32()[a + (52 >> 2)]
- };
- var canvas = findCanvasEventTarget(target);
- if (ENVIRONMENT_IS_PTHREAD) {
-  if (contextAttributes.proxyContextToMainThread === 2 || !canvas && contextAttributes.proxyContextToMainThread === 1) {
-   if (typeof OffscreenCanvas == "undefined") {
-    GROWABLE_HEAP_I32()[attributes + 52 >> 2] = 1;
-    GROWABLE_HEAP_I32()[attributes + 20 >> 2] = 1;
-   }
-   return _emscripten_webgl_create_context_proxied(target, attributes);
-  }
- }
- if (!canvas) {
-  return 0;
- }
- if (contextAttributes.explicitSwapControl && !contextAttributes.renderViaOffscreenBackBuffer) {
-  contextAttributes.renderViaOffscreenBackBuffer = true;
- }
- var contextHandle = GL.createContext(canvas, contextAttributes);
- return contextHandle;
-}
-
-function _emscripten_webgl_enable_extension(contextHandle, extension) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(40, 1, contextHandle, extension);
- var context = GL.getContext(contextHandle);
- var extString = UTF8ToString(extension);
- if (extString.startsWith("GL_")) extString = extString.substr(3);
- if (extString == "ANGLE_instanced_arrays") __webgl_enable_ANGLE_instanced_arrays(GLctx);
- if (extString == "OES_vertex_array_object") __webgl_enable_OES_vertex_array_object(GLctx);
- if (extString == "WEBGL_draw_buffers") __webgl_enable_WEBGL_draw_buffers(GLctx);
- if (extString == "WEBGL_draw_instanced_base_vertex_base_instance") __webgl_enable_WEBGL_draw_instanced_base_vertex_base_instance(GLctx);
- if (extString == "WEBGL_multi_draw_instanced_base_vertex_base_instance") __webgl_enable_WEBGL_multi_draw_instanced_base_vertex_base_instance(GLctx);
- if (extString == "WEBGL_multi_draw") __webgl_enable_WEBGL_multi_draw(GLctx);
- var ext = context.GLctx.getExtension(extString);
- return !!ext;
-}
-
-function _emscripten_webgl_init_context_attributes(attributes) {
- assert(attributes);
- var a = attributes >> 2;
- for (var i = 0; i < 56 >> 2; ++i) {
-  GROWABLE_HEAP_I32()[a + i] = 0;
- }
- GROWABLE_HEAP_I32()[a + (0 >> 2)] = GROWABLE_HEAP_I32()[a + (4 >> 2)] = GROWABLE_HEAP_I32()[a + (12 >> 2)] = GROWABLE_HEAP_I32()[a + (16 >> 2)] = GROWABLE_HEAP_I32()[a + (32 >> 2)] = GROWABLE_HEAP_I32()[a + (40 >> 2)] = 1;
- if (ENVIRONMENT_IS_WORKER) GROWABLE_HEAP_I32()[attributes + 48 >> 2] = 1;
-}
-
-function _emscripten_webgl_make_context_current_calling_thread(contextHandle) {
- var success = GL.makeContextCurrent(contextHandle);
- if (success) GL.currentContextIsProxied = false;
- return success ? 0 : -5;
-}
-
-var ENV = {};
-
-function getExecutableName() {
- return thisProgram || "./this.program";
-}
-
-function getEnvStrings() {
- if (!getEnvStrings.strings) {
-  var lang = (typeof navigator == "object" && navigator.languages && navigator.languages[0] || "C").replace("-", "_") + ".UTF-8";
-  var env = {
-   "USER": "web_user",
-   "LOGNAME": "web_user",
-   "PATH": "/",
-   "PWD": "/",
-   "HOME": "/home/web_user",
-   "LANG": lang,
-   "_": getExecutableName()
-  };
-  for (var x in ENV) {
-   if (ENV[x] === undefined) delete env[x]; else env[x] = ENV[x];
-  }
-  var strings = [];
-  for (var x in env) {
-   strings.push(x + "=" + env[x]);
-  }
-  getEnvStrings.strings = strings;
- }
- return getEnvStrings.strings;
-}
-
-function writeAsciiToMemory(str, buffer, dontAddNull) {
- for (var i = 0; i < str.length; ++i) {
-  assert(str.charCodeAt(i) === (str.charCodeAt(i) & 255));
-  GROWABLE_HEAP_I8()[buffer++ >> 0] = str.charCodeAt(i);
- }
- if (!dontAddNull) GROWABLE_HEAP_I8()[buffer >> 0] = 0;
-}
-
-function _environ_get(__environ, environ_buf) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(41, 1, __environ, environ_buf);
- var bufSize = 0;
- getEnvStrings().forEach(function(string, i) {
-  var ptr = environ_buf + bufSize;
-  GROWABLE_HEAP_U32()[__environ + i * 4 >> 2] = ptr;
-  writeAsciiToMemory(string, ptr);
-  bufSize += string.length + 1;
- });
- return 0;
-}
-
-function _environ_sizes_get(penviron_count, penviron_buf_size) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(42, 1, penviron_count, penviron_buf_size);
- var strings = getEnvStrings();
- GROWABLE_HEAP_U32()[penviron_count >> 2] = strings.length;
- var bufSize = 0;
- strings.forEach(function(string) {
-  bufSize += string.length + 1;
- });
- GROWABLE_HEAP_U32()[penviron_buf_size >> 2] = bufSize;
- return 0;
-}
-
-function _fd_close(fd) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(43, 1, fd);
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  FS.close(stream);
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return e.errno;
- }
-}
-
-function _fd_fdstat_get(fd, pbuf) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(44, 1, fd, pbuf);
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  var type = stream.tty ? 2 : FS.isDir(stream.mode) ? 3 : FS.isLink(stream.mode) ? 7 : 4;
-  GROWABLE_HEAP_I8()[pbuf >> 0] = type;
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return e.errno;
- }
-}
-
-function doReadv(stream, iov, iovcnt, offset) {
- var ret = 0;
- for (var i = 0; i < iovcnt; i++) {
-  var ptr = GROWABLE_HEAP_U32()[iov >> 2];
-  var len = GROWABLE_HEAP_U32()[iov + 4 >> 2];
-  iov += 8;
-  var curr = FS.read(stream, GROWABLE_HEAP_I8(), ptr, len, offset);
-  if (curr < 0) return -1;
-  ret += curr;
-  if (curr < len) break;
- }
- return ret;
-}
-
-function _fd_read(fd, iov, iovcnt, pnum) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(45, 1, fd, iov, iovcnt, pnum);
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  var num = doReadv(stream, iov, iovcnt);
-  GROWABLE_HEAP_I32()[pnum >> 2] = num;
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return e.errno;
- }
-}
-
-function convertI32PairToI53Checked(lo, hi) {
- assert(lo == lo >>> 0 || lo == (lo | 0));
- assert(hi === (hi | 0));
- return hi + 2097152 >>> 0 < 4194305 - !!lo ? (lo >>> 0) + hi * 4294967296 : NaN;
-}
-
-function _fd_seek(fd, offset_low, offset_high, whence, newOffset) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(46, 1, fd, offset_low, offset_high, whence, newOffset);
- try {
-  var offset = convertI32PairToI53Checked(offset_low, offset_high);
-  if (isNaN(offset)) return 61;
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  FS.llseek(stream, offset, whence);
-  tempI64 = [ stream.position >>> 0, (tempDouble = stream.position, +Math.abs(tempDouble) >= 1 ? tempDouble > 0 ? (Math.min(+Math.floor(tempDouble / 4294967296), 4294967295) | 0) >>> 0 : ~~+Math.ceil((tempDouble - +(~~tempDouble >>> 0)) / 4294967296) >>> 0 : 0) ], 
-  GROWABLE_HEAP_I32()[newOffset >> 2] = tempI64[0], GROWABLE_HEAP_I32()[newOffset + 4 >> 2] = tempI64[1];
-  if (stream.getdents && offset === 0 && whence === 0) stream.getdents = null;
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return e.errno;
- }
-}
-
-function doWritev(stream, iov, iovcnt, offset) {
- var ret = 0;
- for (var i = 0; i < iovcnt; i++) {
-  var ptr = GROWABLE_HEAP_U32()[iov >> 2];
-  var len = GROWABLE_HEAP_U32()[iov + 4 >> 2];
-  iov += 8;
-  var curr = FS.write(stream, GROWABLE_HEAP_I8(), ptr, len, offset);
-  if (curr < 0) return -1;
-  ret += curr;
- }
- return ret;
-}
-
-function _fd_write(fd, iov, iovcnt, pnum) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(47, 1, fd, iov, iovcnt, pnum);
- try {
-  var stream = SYSCALLS.getStreamFromFD(fd);
-  var num = doWritev(stream, iov, iovcnt);
-  GROWABLE_HEAP_U32()[pnum >> 2] = num;
-  return 0;
- } catch (e) {
-  if (typeof FS == "undefined" || !(e instanceof FS.ErrnoError)) throw e;
-  return e.errno;
- }
-}
-
-function _getTempRet0() {
- return getTempRet0();
-}
-
-function _getaddrinfo(node, service, hint, out) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(48, 1, node, service, hint, out);
- var addrs = [];
- var canon = null;
- var addr = 0;
- var port = 0;
- var flags = 0;
- var family = 0;
- var type = 0;
- var proto = 0;
- var ai, last;
- function allocaddrinfo(family, type, proto, canon, addr, port) {
-  var sa, salen, ai;
-  var errno;
-  salen = family === 10 ? 28 : 16;
-  addr = family === 10 ? inetNtop6(addr) : inetNtop4(addr);
-  sa = _malloc(salen);
-  errno = writeSockaddr(sa, family, addr, port);
-  assert(!errno);
-  ai = _malloc(32);
-  GROWABLE_HEAP_I32()[ai + 4 >> 2] = family;
-  GROWABLE_HEAP_I32()[ai + 8 >> 2] = type;
-  GROWABLE_HEAP_I32()[ai + 12 >> 2] = proto;
-  GROWABLE_HEAP_I32()[ai + 24 >> 2] = canon;
-  GROWABLE_HEAP_U32()[ai + 20 >> 2] = sa;
-  if (family === 10) {
-   GROWABLE_HEAP_I32()[ai + 16 >> 2] = 28;
-  } else {
-   GROWABLE_HEAP_I32()[ai + 16 >> 2] = 16;
-  }
-  GROWABLE_HEAP_I32()[ai + 28 >> 2] = 0;
-  return ai;
- }
- if (hint) {
-  flags = GROWABLE_HEAP_I32()[hint >> 2];
-  family = GROWABLE_HEAP_I32()[hint + 4 >> 2];
-  type = GROWABLE_HEAP_I32()[hint + 8 >> 2];
-  proto = GROWABLE_HEAP_I32()[hint + 12 >> 2];
- }
- if (type && !proto) {
-  proto = type === 2 ? 17 : 6;
- }
- if (!type && proto) {
-  type = proto === 17 ? 2 : 1;
- }
- if (proto === 0) {
-  proto = 6;
- }
- if (type === 0) {
-  type = 1;
- }
- if (!node && !service) {
-  return -2;
- }
- if (flags & ~(1 | 2 | 4 | 1024 | 8 | 16 | 32)) {
-  return -1;
- }
- if (hint !== 0 && GROWABLE_HEAP_I32()[hint >> 2] & 2 && !node) {
-  return -1;
- }
- if (flags & 32) {
-  return -2;
- }
- if (type !== 0 && type !== 1 && type !== 2) {
-  return -7;
- }
- if (family !== 0 && family !== 2 && family !== 10) {
-  return -6;
- }
- if (service) {
-  service = UTF8ToString(service);
-  port = parseInt(service, 10);
-  if (isNaN(port)) {
-   if (flags & 1024) {
-    return -2;
-   }
-   return -8;
-  }
- }
- if (!node) {
-  if (family === 0) {
-   family = 2;
-  }
-  if ((flags & 1) === 0) {
-   if (family === 2) {
-    addr = _htonl(2130706433);
-   } else {
-    addr = [ 0, 0, 0, 1 ];
-   }
-  }
-  ai = allocaddrinfo(family, type, proto, null, addr, port);
-  GROWABLE_HEAP_U32()[out >> 2] = ai;
-  return 0;
- }
- node = UTF8ToString(node);
- addr = inetPton4(node);
- if (addr !== null) {
-  if (family === 0 || family === 2) {
-   family = 2;
-  } else if (family === 10 && flags & 8) {
-   addr = [ 0, 0, _htonl(65535), addr ];
-   family = 10;
-  } else {
-   return -2;
-  }
- } else {
-  addr = inetPton6(node);
-  if (addr !== null) {
-   if (family === 0 || family === 10) {
-    family = 10;
-   } else {
-    return -2;
-   }
-  }
- }
- if (addr != null) {
-  ai = allocaddrinfo(family, type, proto, node, addr, port);
-  GROWABLE_HEAP_U32()[out >> 2] = ai;
-  return 0;
- }
- if (flags & 4) {
-  return -2;
- }
- node = DNS.lookup_name(node);
- addr = inetPton4(node);
- if (family === 0) {
-  family = 2;
- } else if (family === 10) {
-  addr = [ 0, 0, _htonl(65535), addr ];
- }
- ai = allocaddrinfo(family, type, proto, null, addr, port);
- GROWABLE_HEAP_U32()[out >> 2] = ai;
- return 0;
-}
-
-function _getnameinfo(sa, salen, node, nodelen, serv, servlen, flags) {
- var info = readSockaddr(sa, salen);
- if (info.errno) {
-  return -6;
- }
- var port = info.port;
- var addr = info.addr;
- var overflowed = false;
- if (node && nodelen) {
-  var lookup;
-  if (flags & 1 || !(lookup = DNS.lookup_addr(addr))) {
-   if (flags & 8) {
-    return -2;
-   }
-  } else {
-   addr = lookup;
-  }
-  var numBytesWrittenExclNull = stringToUTF8(addr, node, nodelen);
-  if (numBytesWrittenExclNull + 1 >= nodelen) {
-   overflowed = true;
-  }
- }
- if (serv && servlen) {
-  port = "" + port;
-  var numBytesWrittenExclNull = stringToUTF8(port, serv, servlen);
-  if (numBytesWrittenExclNull + 1 >= servlen) {
-   overflowed = true;
-  }
- }
- if (overflowed) {
-  return -12;
- }
- return 0;
-}
-
-function _glGetBufferSubData(target, offset, size, data) {
- if (!data) {
-  GL.recordError(1281);
-  return;
- }
- size && GLctx["getBufferSubData"](target, offset, GROWABLE_HEAP_U8(), data, size);
-}
-
-var GodotRuntime = {
- get_func: function(ptr) {
-  return wasmTable.get(ptr);
- },
- error: function() {
-  err.apply(null, Array.from(arguments));
- },
- print: function() {
-  out.apply(null, Array.from(arguments));
- },
- malloc: function(p_size) {
-  return _malloc(p_size);
- },
- free: function(p_ptr) {
-  _free(p_ptr);
- },
- getHeapValue: function(p_ptr, p_type) {
-  return getValue(p_ptr, p_type);
- },
- setHeapValue: function(p_ptr, p_value, p_type) {
-  setValue(p_ptr, p_value, p_type);
- },
- heapSub: function(p_heap, p_ptr, p_len) {
-  const bytes = p_heap.BYTES_PER_ELEMENT;
-  return p_heap.subarray(p_ptr / bytes, p_ptr / bytes + p_len);
- },
- heapSlice: function(p_heap, p_ptr, p_len) {
-  const bytes = p_heap.BYTES_PER_ELEMENT;
-  return p_heap.slice(p_ptr / bytes, p_ptr / bytes + p_len);
- },
- heapCopy: function(p_dst, p_src, p_ptr) {
-  const bytes = p_src.BYTES_PER_ELEMENT;
-  return p_dst.set(p_src, p_ptr / bytes);
- },
- parseString: function(p_ptr) {
-  return UTF8ToString(p_ptr);
- },
- parseStringArray: function(p_ptr, p_size) {
-  const strings = [];
-  const ptrs = GodotRuntime.heapSub(GROWABLE_HEAP_I32(), p_ptr, p_size);
-  ptrs.forEach(function(ptr) {
-   strings.push(GodotRuntime.parseString(ptr));
-  });
-  return strings;
- },
- strlen: function(p_str) {
-  return lengthBytesUTF8(p_str);
- },
- allocString: function(p_str) {
-  const length = GodotRuntime.strlen(p_str) + 1;
-  const c_str = GodotRuntime.malloc(length);
-  stringToUTF8(p_str, c_str, length);
-  return c_str;
- },
- allocStringArray: function(p_strings) {
-  const size = p_strings.length;
-  const c_ptr = GodotRuntime.malloc(size * 4);
-  for (let i = 0; i < size; i++) {
-   GROWABLE_HEAP_I32()[(c_ptr >> 2) + i] = GodotRuntime.allocString(p_strings[i]);
-  }
-  return c_ptr;
- },
- freeStringArray: function(p_ptr, p_len) {
-  for (let i = 0; i < p_len; i++) {
-   GodotRuntime.free(GROWABLE_HEAP_I32()[(p_ptr >> 2) + i]);
-  }
-  GodotRuntime.free(p_ptr);
- },
- stringToHeap: function(p_str, p_ptr, p_len) {
-  return stringToUTF8Array(p_str, GROWABLE_HEAP_I8(), p_ptr, p_len);
- }
-};
-
-var GodotConfig = {
- canvas: null,
- locale: "en",
- canvas_resize_policy: 2,
- virtual_keyboard: false,
- persistent_drops: false,
- on_execute: null,
- on_exit: null,
- init_config: function(p_opts) {
-  GodotConfig.canvas_resize_policy = p_opts["canvasResizePolicy"];
-  GodotConfig.canvas = p_opts["canvas"];
-  GodotConfig.locale = p_opts["locale"] || GodotConfig.locale;
-  GodotConfig.virtual_keyboard = p_opts["virtualKeyboard"];
-  GodotConfig.persistent_drops = !!p_opts["persistentDrops"];
-  GodotConfig.on_execute = p_opts["onExecute"];
-  GodotConfig.on_exit = p_opts["onExit"];
-  if (p_opts["focusCanvas"]) {
-   GodotConfig.canvas.focus();
-  }
- },
- locate_file: function(file) {
-  return Module["locateFile"](file);
- },
- clear: function() {
-  GodotConfig.canvas = null;
-  GodotConfig.locale = "en";
-  GodotConfig.canvas_resize_policy = 2;
-  GodotConfig.virtual_keyboard = false;
-  GodotConfig.persistent_drops = false;
-  GodotConfig.on_execute = null;
-  GodotConfig.on_exit = null;
- }
-};
-
-var GodotFS = {
- ENOENT: 44,
- _idbfs: false,
- _syncing: false,
- _mount_points: [],
- is_persistent: function() {
-  return GodotFS._idbfs ? 1 : 0;
- },
- init: function(persistentPaths) {
-  GodotFS._idbfs = false;
-  if (!Array.isArray(persistentPaths)) {
-   return Promise.reject(new Error("Persistent paths must be an array"));
-  }
-  if (!persistentPaths.length) {
-   return Promise.resolve();
-  }
-  GodotFS._mount_points = persistentPaths.slice();
-  function createRecursive(dir) {
-   try {
-    FS.stat(dir);
-   } catch (e) {
-    if (e.errno !== GodotFS.ENOENT) {
-     GodotRuntime.error(e);
-    }
-    FS.mkdirTree(dir);
-   }
-  }
-  GodotFS._mount_points.forEach(function(path) {
-   createRecursive(path);
-   FS.mount(IDBFS, {}, path);
-  });
-  return new Promise(function(resolve, reject) {
-   FS.syncfs(true, function(err) {
-    if (err) {
-     GodotFS._mount_points = [];
-     GodotFS._idbfs = false;
-     GodotRuntime.print(`IndexedDB not available: ${err.message}`);
-    } else {
-     GodotFS._idbfs = true;
-    }
-    resolve(err);
-   });
-  });
- },
- deinit: function() {
-  GodotFS._mount_points.forEach(function(path) {
-   try {
-    FS.unmount(path);
-   } catch (e) {
-    GodotRuntime.print("Already unmounted", e);
-   }
-   if (GodotFS._idbfs && IDBFS.dbs[path]) {
-    IDBFS.dbs[path].close();
-    delete IDBFS.dbs[path];
-   }
-  });
-  GodotFS._mount_points = [];
-  GodotFS._idbfs = false;
-  GodotFS._syncing = false;
- },
- sync: function() {
-  if (GodotFS._syncing) {
-   GodotRuntime.error("Already syncing!");
-   return Promise.resolve();
-  }
-  GodotFS._syncing = true;
-  return new Promise(function(resolve, reject) {
-   FS.syncfs(false, function(error) {
-    if (error) {
-     GodotRuntime.error(`Failed to save IDB file system: ${error.message}`);
-    }
-    GodotFS._syncing = false;
-    resolve(error);
-   });
-  });
- },
- copy_to_fs: function(path, buffer) {
-  const idx = path.lastIndexOf("/");
-  let dir = "/";
-  if (idx > 0) {
-   dir = path.slice(0, idx);
-  }
-  try {
-   FS.stat(dir);
-  } catch (e) {
-   if (e.errno !== GodotFS.ENOENT) {
-    GodotRuntime.error(e);
-   }
-   FS.mkdirTree(dir);
-  }
-  FS.writeFile(path, new Uint8Array(buffer));
- }
-};
-
-var GodotOS = {
- request_quit: function() {},
- _async_cbs: [],
- _fs_sync_promise: null,
- atexit: function(p_promise_cb) {
-  GodotOS._async_cbs.push(p_promise_cb);
- },
- cleanup: function(exit_code) {
-  const cb = GodotConfig.on_exit;
-  GodotFS.deinit();
-  GodotConfig.clear();
-  if (cb) {
-   cb(exit_code);
-  }
- },
- finish_async: function(callback) {
-  GodotOS._fs_sync_promise.then(function(err) {
-   const promises = [];
-   GodotOS._async_cbs.forEach(function(cb) {
-    promises.push(new Promise(cb));
-   });
-   return Promise.all(promises);
-  }).then(function() {
-   return GodotFS.sync();
-  }).then(function(err) {
-   setTimeout(function() {
-    callback();
-   }, 0);
-  });
- }
-};
-
-var GodotAudio = {
- ctx: null,
- input: null,
- driver: null,
- interval: 0,
- init: function(mix_rate, latency, onstatechange, onlatencyupdate) {
-  const opts = {};
-  if (mix_rate) {
-   opts["sampleRate"] = mix_rate;
-  }
-  const ctx = new (window.AudioContext || window.webkitAudioContext)(opts);
-  GodotAudio.ctx = ctx;
-  ctx.onstatechange = function() {
-   let state = 0;
-   switch (ctx.state) {
-   case "suspended":
-    state = 0;
-    break;
-
-   case "running":
-    state = 1;
-    break;
-
-   case "closed":
-    state = 2;
-    break;
-   }
-   onstatechange(state);
-  };
-  ctx.onstatechange();
-  GodotAudio.interval = setInterval(function() {
-   let computed_latency = 0;
-   if (ctx.baseLatency) {
-    computed_latency += GodotAudio.ctx.baseLatency;
-   }
-   if (ctx.outputLatency) {
-    computed_latency += GodotAudio.ctx.outputLatency;
-   }
-   onlatencyupdate(computed_latency);
-  }, 1e3);
-  GodotOS.atexit(GodotAudio.close_async);
-  return ctx.destination.channelCount;
- },
- create_input: function(callback) {
-  if (GodotAudio.input) {
-   return 0;
-  }
-  function gotMediaInput(stream) {
-   try {
-    GodotAudio.input = GodotAudio.ctx.createMediaStreamSource(stream);
-    callback(GodotAudio.input);
-   } catch (e) {
-    GodotRuntime.error("Failed creaating input.", e);
-   }
-  }
-  if (navigator.mediaDevices && navigator.mediaDevices.getUserMedia) {
-   navigator.mediaDevices.getUserMedia({
-    "audio": true
-   }).then(gotMediaInput, function(e) {
-    GodotRuntime.error("Error getting user media.", e);
-   });
-  } else {
-   if (!navigator.getUserMedia) {
-    navigator.getUserMedia = navigator.webkitGetUserMedia || navigator.mozGetUserMedia;
-   }
-   if (!navigator.getUserMedia) {
-    GodotRuntime.error("getUserMedia not available.");
-    return 1;
-   }
-   navigator.getUserMedia({
-    "audio": true
-   }, gotMediaInput, function(e) {
-    GodotRuntime.print(e);
-   });
-  }
-  return 0;
- },
- close_async: function(resolve, reject) {
-  const ctx = GodotAudio.ctx;
-  GodotAudio.ctx = null;
-  if (!ctx) {
-   resolve();
-   return;
-  }
-  if (GodotAudio.interval) {
-   clearInterval(GodotAudio.interval);
-   GodotAudio.interval = 0;
-  }
-  if (GodotAudio.input) {
-   GodotAudio.input.disconnect();
-   GodotAudio.input = null;
-  }
-  let closed = Promise.resolve();
-  if (GodotAudio.driver) {
-   closed = GodotAudio.driver.close();
-  }
-  closed.then(function() {
-   return ctx.close();
-  }).then(function() {
-   ctx.onstatechange = null;
-   resolve();
-  }).catch(function(e) {
-   ctx.onstatechange = null;
-   GodotRuntime.error("Error closing AudioContext", e);
-   resolve();
-  });
- }
-};
-
-function _godot_audio_has_worklet() {
- return GodotAudio.ctx && GodotAudio.ctx.audioWorklet ? 1 : 0;
-}
-
-function _godot_audio_init(p_mix_rate, p_latency, p_state_change, p_latency_update) {
- const statechange = GodotRuntime.get_func(p_state_change);
- const latencyupdate = GodotRuntime.get_func(p_latency_update);
- const mix_rate = GodotRuntime.getHeapValue(p_mix_rate, "i32");
- const channels = GodotAudio.init(mix_rate, p_latency, statechange, latencyupdate);
- GodotRuntime.setHeapValue(p_mix_rate, GodotAudio.ctx.sampleRate, "i32");
- return channels;
-}
-
-function _godot_audio_input_start() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(49, 1);
- return GodotAudio.create_input(function(input) {
-  input.connect(GodotAudio.driver.get_node());
- });
-}
-
-function _godot_audio_input_stop() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(50, 1);
- if (GodotAudio.input) {
-  const tracks = GodotAudio.input["mediaStream"]["getTracks"]();
-  for (let i = 0; i < tracks.length; i++) {
-   tracks[i]["stop"]();
-  }
-  GodotAudio.input.disconnect();
-  GodotAudio.input = null;
- }
-}
-
-function _godot_audio_is_available() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(51, 1);
- if (!(window.AudioContext || window.webkitAudioContext)) {
-  return 0;
- }
- return 1;
-}
-
-function _godot_audio_resume() {
- if (GodotAudio.ctx && GodotAudio.ctx.state !== "running") {
-  GodotAudio.ctx.resume();
- }
-}
-
-var GodotAudioWorklet = {
- promise: null,
- worklet: null,
- ring_buffer: null,
- create: function(channels) {
-  const path = GodotConfig.locate_file("godot.audio.worklet.js");
-  GodotAudioWorklet.promise = GodotAudio.ctx.audioWorklet.addModule(path).then(function() {
-   GodotAudioWorklet.worklet = new AudioWorkletNode(GodotAudio.ctx, "godot-processor", {
-    "outputChannelCount": [ channels ]
-   });
-   return Promise.resolve();
-  });
-  GodotAudio.driver = GodotAudioWorklet;
- },
- start: function(in_buf, out_buf, state) {
-  GodotAudioWorklet.promise.then(function() {
-   const node = GodotAudioWorklet.worklet;
-   node.connect(GodotAudio.ctx.destination);
-   node.port.postMessage({
-    "cmd": "start",
-    "data": [ state, in_buf, out_buf ]
-   });
-   node.port.onmessage = function(event) {
-    GodotRuntime.error(event.data);
-   };
-  });
- },
- start_no_threads: function(p_out_buf, p_out_size, out_callback, p_in_buf, p_in_size, in_callback) {
-  function RingBuffer() {
-   let wpos = 0;
-   let rpos = 0;
-   let pending_samples = 0;
-   const wbuf = new Float32Array(p_out_size);
-   function send(port) {
-    if (pending_samples === 0) {
-     return;
-    }
-    const buffer = GodotRuntime.heapSub(GROWABLE_HEAP_F32(), p_out_buf, p_out_size);
-    const size = buffer.length;
-    const tot_sent = pending_samples;
-    out_callback(wpos, pending_samples);
-    if (wpos + pending_samples >= size) {
-     const high = size - wpos;
-     wbuf.set(buffer.subarray(wpos, size));
-     pending_samples -= high;
-     wpos = 0;
-    }
-    if (pending_samples > 0) {
-     wbuf.set(buffer.subarray(wpos, wpos + pending_samples), tot_sent - pending_samples);
-    }
-    port.postMessage({
-     "cmd": "chunk",
-     "data": wbuf.subarray(0, tot_sent)
-    });
-    wpos += pending_samples;
-    pending_samples = 0;
-   }
-   this.receive = function(recv_buf) {
-    const buffer = GodotRuntime.heapSub(GROWABLE_HEAP_F32(), p_in_buf, p_in_size);
-    const from = rpos;
-    let to_write = recv_buf.length;
-    let high = 0;
-    if (rpos + to_write >= p_in_size) {
-     high = p_in_size - rpos;
-     buffer.set(recv_buf.subarray(0, high), rpos);
-     to_write -= high;
-     rpos = 0;
-    }
-    if (to_write) {
-     buffer.set(recv_buf.subarray(high, to_write), rpos);
-    }
-    in_callback(from, recv_buf.length);
-    rpos += to_write;
-   };
-   this.consumed = function(size, port) {
-    pending_samples += size;
-    send(port);
-   };
-  }
-  GodotAudioWorklet.ring_buffer = new RingBuffer();
-  GodotAudioWorklet.promise.then(function() {
-   const node = GodotAudioWorklet.worklet;
-   const buffer = GodotRuntime.heapSlice(GROWABLE_HEAP_F32(), p_out_buf, p_out_size);
-   node.connect(GodotAudio.ctx.destination);
-   node.port.postMessage({
-    "cmd": "start_nothreads",
-    "data": [ buffer, p_in_size ]
-   });
-   node.port.onmessage = function(event) {
-    if (!GodotAudioWorklet.worklet) {
-     return;
-    }
-    if (event.data["cmd"] === "read") {
-     const read = event.data["data"];
-     GodotAudioWorklet.ring_buffer.consumed(read, GodotAudioWorklet.worklet.port);
-    } else if (event.data["cmd"] === "input") {
-     const buf = event.data["data"];
-     if (buf.length > p_in_size) {
-      GodotRuntime.error("Input chunk is too big");
-      return;
-     }
-     GodotAudioWorklet.ring_buffer.receive(buf);
-    } else {
-     GodotRuntime.error(event.data);
-    }
-   };
-  });
- },
- get_node: function() {
-  return GodotAudioWorklet.worklet;
- },
- close: function() {
-  return new Promise(function(resolve, reject) {
-   if (GodotAudioWorklet.promise === null) {
-    return;
-   }
-   const p = GodotAudioWorklet.promise;
-   p.then(function() {
-    GodotAudioWorklet.worklet.port.postMessage({
-     "cmd": "stop",
-     "data": null
-    });
-    GodotAudioWorklet.worklet.disconnect();
-    GodotAudioWorklet.worklet.port.onmessage = null;
-    GodotAudioWorklet.worklet = null;
-    GodotAudioWorklet.promise = null;
-    resolve();
-   }).catch(function(err) {
-    GodotRuntime.error(err);
-   });
-  });
- }
-};
-
-function _godot_audio_worklet_create(channels) {
- try {
-  GodotAudioWorklet.create(channels);
- } catch (e) {
-  GodotRuntime.error("Error starting AudioDriverWorklet", e);
-  return 1;
- }
- return 0;
-}
-
-function _godot_audio_worklet_start(p_in_buf, p_in_size, p_out_buf, p_out_size, p_state) {
- const out_buffer = GodotRuntime.heapSub(GROWABLE_HEAP_F32(), p_out_buf, p_out_size);
- const in_buffer = GodotRuntime.heapSub(GROWABLE_HEAP_F32(), p_in_buf, p_in_size);
- const state = GodotRuntime.heapSub(GROWABLE_HEAP_I32(), p_state, 4);
- GodotAudioWorklet.start(in_buffer, out_buffer, state);
-}
-
-function _godot_audio_worklet_state_add(p_state, p_idx, p_value) {
- return Atomics.add(GROWABLE_HEAP_I32(), (p_state >> 2) + p_idx, p_value);
-}
-
-function _godot_audio_worklet_state_get(p_state, p_idx) {
- return Atomics.load(GROWABLE_HEAP_I32(), (p_state >> 2) + p_idx);
-}
-
-function _godot_audio_worklet_state_wait(p_state, p_idx, p_expected, p_timeout) {
- Atomics.wait(GROWABLE_HEAP_I32(), (p_state >> 2) + p_idx, p_expected, p_timeout);
- return Atomics.load(GROWABLE_HEAP_I32(), (p_state >> 2) + p_idx);
-}
-
-function _godot_js_config_canvas_id_get(p_ptr, p_ptr_max) {
- GodotRuntime.stringToHeap(`#${GodotConfig.canvas.id}`, p_ptr, p_ptr_max);
-}
-
-function _godot_js_config_locale_get(p_ptr, p_ptr_max) {
- GodotRuntime.stringToHeap(GodotConfig.locale, p_ptr, p_ptr_max);
-}
-
-var GodotDisplayCursor = {
- shape: "default",
- visible: true,
- cursors: {},
- set_style: function(style) {
-  GodotConfig.canvas.style.cursor = style;
- },
- set_shape: function(shape) {
-  GodotDisplayCursor.shape = shape;
-  let css = shape;
-  if (shape in GodotDisplayCursor.cursors) {
-   const c = GodotDisplayCursor.cursors[shape];
-   css = `url("${c.url}") ${c.x} ${c.y}, default`;
-  }
-  if (GodotDisplayCursor.visible) {
-   GodotDisplayCursor.set_style(css);
-  }
- },
- clear: function() {
-  GodotDisplayCursor.set_style("");
-  GodotDisplayCursor.shape = "default";
-  GodotDisplayCursor.visible = true;
-  Object.keys(GodotDisplayCursor.cursors).forEach(function(key) {
-   URL.revokeObjectURL(GodotDisplayCursor.cursors[key]);
-   delete GodotDisplayCursor.cursors[key];
-  });
- },
- lockPointer: function() {
-  const canvas = GodotConfig.canvas;
-  if (canvas.requestPointerLock) {
-   canvas.requestPointerLock();
-  }
- },
- releasePointer: function() {
-  if (document.exitPointerLock) {
-   document.exitPointerLock();
-  }
- },
- isPointerLocked: function() {
-  return document.pointerLockElement === GodotConfig.canvas;
- }
-};
-
-var GodotEventListeners = {
- handlers: [],
- has: function(target, event, method, capture) {
-  return GodotEventListeners.handlers.findIndex(function(e) {
-   return e.target === target && e.event === event && e.method === method && e.capture === capture;
-  }) !== -1;
- },
- add: function(target, event, method, capture) {
-  if (GodotEventListeners.has(target, event, method, capture)) {
-   return;
-  }
-  function Handler(p_target, p_event, p_method, p_capture) {
-   this.target = p_target;
-   this.event = p_event;
-   this.method = p_method;
-   this.capture = p_capture;
-  }
-  GodotEventListeners.handlers.push(new Handler(target, event, method, capture));
-  target.addEventListener(event, method, capture);
- },
- clear: function() {
-  GodotEventListeners.handlers.forEach(function(h) {
-   h.target.removeEventListener(h.event, h.method, h.capture);
-  });
-  GodotEventListeners.handlers.length = 0;
- }
-};
-
-var GodotDisplayScreen = {
- desired_size: [ 0, 0 ],
- hidpi: true,
- getPixelRatio: function() {
-  return GodotDisplayScreen.hidpi ? window.devicePixelRatio || 1 : 1;
- },
- isFullscreen: function() {
-  const elem = document.fullscreenElement || document.mozFullscreenElement || document.webkitFullscreenElement || document.msFullscreenElement;
-  if (elem) {
-   return elem === GodotConfig.canvas;
-  }
-  return document.fullscreen || document.mozFullScreen || document.webkitIsFullscreen;
- },
- hasFullscreen: function() {
-  return document.fullscreenEnabled || document.mozFullScreenEnabled || document.webkitFullscreenEnabled;
- },
- requestFullscreen: function() {
-  if (!GodotDisplayScreen.hasFullscreen()) {
-   return 1;
-  }
-  const canvas = GodotConfig.canvas;
-  try {
-   const promise = (canvas.requestFullscreen || canvas.msRequestFullscreen || canvas.mozRequestFullScreen || canvas.mozRequestFullscreen || canvas.webkitRequestFullscreen).call(canvas);
-   if (promise) {
-    promise.catch(function() {});
-   }
-  } catch (e) {
-   return 1;
-  }
-  return 0;
- },
- exitFullscreen: function() {
-  if (!GodotDisplayScreen.isFullscreen()) {
-   return 0;
-  }
-  try {
-   const promise = document.exitFullscreen();
-   if (promise) {
-    promise.catch(function() {});
-   }
-  } catch (e) {
-   return 1;
-  }
-  return 0;
- },
- _updateGL: function() {
-  const gl_context_handle = _emscripten_webgl_get_current_context();
-  const gl = GL.getContext(gl_context_handle);
-  if (gl) {
-   GL.resizeOffscreenFramebuffer(gl);
-  }
- },
- updateSize: function() {
-  const isFullscreen = GodotDisplayScreen.isFullscreen();
-  const wantsFullWindow = GodotConfig.canvas_resize_policy === 2;
-  const noResize = GodotConfig.canvas_resize_policy === 0;
-  const wwidth = GodotDisplayScreen.desired_size[0];
-  const wheight = GodotDisplayScreen.desired_size[1];
-  const canvas = GodotConfig.canvas;
-  let width = wwidth;
-  let height = wheight;
-  if (noResize) {
-   if (canvas.width !== width || canvas.height !== height) {
-    GodotDisplayScreen.desired_size = [ canvas.width, canvas.height ];
-    GodotDisplayScreen._updateGL();
-    return 1;
-   }
-   return 0;
-  }
-  const scale = GodotDisplayScreen.getPixelRatio();
-  if (isFullscreen || wantsFullWindow) {
-   width = window.innerWidth * scale;
-   height = window.innerHeight * scale;
-  }
-  const csw = `${width / scale}px`;
-  const csh = `${height / scale}px`;
-  if (canvas.style.width !== csw || canvas.style.height !== csh || canvas.width !== width || canvas.height !== height) {
-   canvas.width = width;
-   canvas.height = height;
-   canvas.style.width = csw;
-   canvas.style.height = csh;
-   GodotDisplayScreen._updateGL();
-   return 1;
-  }
-  return 0;
- }
-};
-
-var GodotDisplayVK = {
- textinput: null,
- textarea: null,
- available: function() {
-  return GodotConfig.virtual_keyboard && "ontouchstart" in window;
- },
- init: function(input_cb) {
-  function create(what) {
-   const elem = document.createElement(what);
-   elem.style.display = "none";
-   elem.style.position = "absolute";
-   elem.style.zIndex = "-1";
-   elem.style.background = "transparent";
-   elem.style.padding = "0px";
-   elem.style.margin = "0px";
-   elem.style.overflow = "hidden";
-   elem.style.width = "0px";
-   elem.style.height = "0px";
-   elem.style.border = "0px";
-   elem.style.outline = "none";
-   elem.readonly = true;
-   elem.disabled = true;
-   GodotEventListeners.add(elem, "input", function(evt) {
-    const c_str = GodotRuntime.allocString(elem.value);
-    input_cb(c_str, elem.selectionEnd);
-    GodotRuntime.free(c_str);
-   }, false);
-   GodotEventListeners.add(elem, "blur", function(evt) {
-    elem.style.display = "none";
-    elem.readonly = true;
-    elem.disabled = true;
-   }, false);
-   GodotConfig.canvas.insertAdjacentElement("beforebegin", elem);
-   return elem;
-  }
-  GodotDisplayVK.textinput = create("input");
-  GodotDisplayVK.textarea = create("textarea");
-  GodotDisplayVK.updateSize();
- },
- show: function(text, type, start, end) {
-  if (!GodotDisplayVK.textinput || !GodotDisplayVK.textarea) {
-   return;
-  }
-  if (GodotDisplayVK.textinput.style.display !== "" || GodotDisplayVK.textarea.style.display !== "") {
-   GodotDisplayVK.hide();
-  }
-  GodotDisplayVK.updateSize();
-  let elem = GodotDisplayVK.textinput;
-  switch (type) {
-  case 0:
-   elem.type = "text";
-   elem.inputmode = "";
-   break;
-
-  case 1:
-   elem = GodotDisplayVK.textarea;
-   break;
-
-  case 2:
-   elem.type = "text";
-   elem.inputmode = "numeric";
-   break;
-
-  case 3:
-   elem.type = "text";
-   elem.inputmode = "decimal";
-   break;
-
-  case 4:
-   elem.type = "tel";
-   elem.inputmode = "";
-   break;
-
-  case 5:
-   elem.type = "email";
-   elem.inputmode = "";
-   break;
-
-  case 6:
-   elem.type = "password";
-   elem.inputmode = "";
-   break;
-
-  case 7:
-   elem.type = "url";
-   elem.inputmode = "";
-   break;
-
-  default:
-   elem.type = "text";
-   elem.inputmode = "";
-   break;
-  }
-  elem.readonly = false;
-  elem.disabled = false;
-  elem.value = text;
-  elem.style.display = "block";
-  elem.focus();
-  elem.setSelectionRange(start, end);
- },
- hide: function() {
-  if (!GodotDisplayVK.textinput || !GodotDisplayVK.textarea) {
-   return;
-  }
-  [ GodotDisplayVK.textinput, GodotDisplayVK.textarea ].forEach(function(elem) {
-   elem.blur();
-   elem.style.display = "none";
-   elem.value = "";
-  });
- },
- updateSize: function() {
-  if (!GodotDisplayVK.textinput || !GodotDisplayVK.textarea) {
-   return;
-  }
-  const rect = GodotConfig.canvas.getBoundingClientRect();
-  function update(elem) {
-   elem.style.left = `${rect.left}px`;
-   elem.style.top = `${rect.top}px`;
-   elem.style.width = `${rect.width}px`;
-   elem.style.height = `${rect.height}px`;
-  }
-  update(GodotDisplayVK.textinput);
-  update(GodotDisplayVK.textarea);
- },
- clear: function() {
-  if (GodotDisplayVK.textinput) {
-   GodotDisplayVK.textinput.remove();
-   GodotDisplayVK.textinput = null;
-  }
-  if (GodotDisplayVK.textarea) {
-   GodotDisplayVK.textarea.remove();
-   GodotDisplayVK.textarea = null;
-  }
- }
-};
-
-var GodotDisplay = {
- window_icon: "",
- getDPI: function() {
-  const dpi = Math.round(window.devicePixelRatio * 96);
-  return dpi >= 96 ? dpi : 96;
- }
-};
-
-function _godot_js_display_alert(p_text) {
- window.alert(GodotRuntime.parseString(p_text));
-}
-
-function _godot_js_display_canvas_focus() {
- GodotConfig.canvas.focus();
-}
-
-function _godot_js_display_canvas_is_focused() {
- return document.activeElement === GodotConfig.canvas;
-}
-
-function _godot_js_display_clipboard_get(callback) {
- const func = GodotRuntime.get_func(callback);
- try {
-  navigator.clipboard.readText().then(function(result) {
-   const ptr = GodotRuntime.allocString(result);
-   func(ptr);
-   GodotRuntime.free(ptr);
-  }).catch(function(e) {});
- } catch (e) {}
-}
-
-function _godot_js_display_clipboard_set(p_text) {
- const text = GodotRuntime.parseString(p_text);
- if (!navigator.clipboard || !navigator.clipboard.writeText) {
-  return 1;
- }
- navigator.clipboard.writeText(text).catch(function(e) {
-  GodotRuntime.error("Setting OS clipboard is only possible from an input callback for the Web platform. Exception:", e);
- });
- return 0;
-}
-
-function _godot_js_display_cursor_is_hidden() {
- return !GodotDisplayCursor.visible;
-}
-
-function _godot_js_display_cursor_is_locked() {
- return GodotDisplayCursor.isPointerLocked() ? 1 : 0;
-}
-
-function _godot_js_display_cursor_lock_set(p_lock) {
- if (p_lock) {
-  GodotDisplayCursor.lockPointer();
- } else {
-  GodotDisplayCursor.releasePointer();
- }
-}
-
-function _godot_js_display_cursor_set_custom_shape(p_shape, p_ptr, p_len, p_hotspot_x, p_hotspot_y) {
- const shape = GodotRuntime.parseString(p_shape);
- const old_shape = GodotDisplayCursor.cursors[shape];
- if (p_len > 0) {
-  const png = new Blob([ GodotRuntime.heapSlice(GROWABLE_HEAP_U8(), p_ptr, p_len) ], {
-   type: "image/png"
-  });
-  const url = URL.createObjectURL(png);
-  GodotDisplayCursor.cursors[shape] = {
-   url: url,
-   x: p_hotspot_x,
-   y: p_hotspot_y
-  };
- } else {
-  delete GodotDisplayCursor.cursors[shape];
- }
- if (shape === GodotDisplayCursor.shape) {
-  GodotDisplayCursor.set_shape(GodotDisplayCursor.shape);
- }
- if (old_shape) {
-  URL.revokeObjectURL(old_shape.url);
- }
-}
-
-function _godot_js_display_cursor_set_shape(p_string) {
- GodotDisplayCursor.set_shape(GodotRuntime.parseString(p_string));
-}
-
-function _godot_js_display_cursor_set_visible(p_visible) {
- const visible = p_visible !== 0;
- if (visible === GodotDisplayCursor.visible) {
-  return;
- }
- GodotDisplayCursor.visible = visible;
- if (visible) {
-  GodotDisplayCursor.set_shape(GodotDisplayCursor.shape);
- } else {
-  GodotDisplayCursor.set_style("none");
- }
-}
-
-function _godot_js_display_desired_size_set(width, height) {
- GodotDisplayScreen.desired_size = [ width, height ];
- GodotDisplayScreen.updateSize();
-}
-
-function _godot_js_display_fullscreen_cb(callback) {
- const canvas = GodotConfig.canvas;
- const func = GodotRuntime.get_func(callback);
- function change_cb(evt) {
-  if (evt.target === canvas) {
-   func(GodotDisplayScreen.isFullscreen());
-  }
- }
- GodotEventListeners.add(document, "fullscreenchange", change_cb, false);
- GodotEventListeners.add(document, "mozfullscreenchange", change_cb, false);
- GodotEventListeners.add(document, "webkitfullscreenchange", change_cb, false);
-}
-
-function _godot_js_display_fullscreen_exit() {
- return GodotDisplayScreen.exitFullscreen();
-}
-
-function _godot_js_display_fullscreen_request() {
- return GodotDisplayScreen.requestFullscreen();
-}
-
-function _godot_js_display_has_webgl(p_version) {
- if (p_version !== 1 && p_version !== 2) {
-  return false;
- }
- try {
-  return !!document.createElement("canvas").getContext(p_version === 2 ? "webgl2" : "webgl");
- } catch (e) {}
- return false;
-}
-
-function _godot_js_display_is_swap_ok_cancel() {
- const win = [ "Windows", "Win64", "Win32", "WinCE" ];
- const plat = navigator.platform || "";
- if (win.indexOf(plat) !== -1) {
-  return 1;
- }
- return 0;
-}
-
-function _godot_js_display_notification_cb(callback, p_enter, p_exit, p_in, p_out) {
- const canvas = GodotConfig.canvas;
- const func = GodotRuntime.get_func(callback);
- const notif = [ p_enter, p_exit, p_in, p_out ];
- [ "mouseover", "mouseleave", "focus", "blur" ].forEach(function(evt_name, idx) {
-  GodotEventListeners.add(canvas, evt_name, function() {
-   func(notif[idx]);
-  }, true);
- });
-}
-
-function _godot_js_display_pixel_ratio_get() {
- return GodotDisplayScreen.getPixelRatio();
-}
-
-function _godot_js_display_screen_dpi_get() {
- return GodotDisplay.getDPI();
-}
-
-function _godot_js_display_screen_size_get(width, height) {
- const scale = GodotDisplayScreen.getPixelRatio();
- GodotRuntime.setHeapValue(width, window.screen.width * scale, "i32");
- GodotRuntime.setHeapValue(height, window.screen.height * scale, "i32");
-}
-
-function _godot_js_display_setup_canvas(p_width, p_height, p_fullscreen, p_hidpi) {
- const canvas = GodotConfig.canvas;
- GodotEventListeners.add(canvas, "contextmenu", function(ev) {
-  ev.preventDefault();
- }, false);
- GodotEventListeners.add(canvas, "webglcontextlost", function(ev) {
-  alert("WebGL context lost, please reload the page");
-  ev.preventDefault();
- }, false);
- GodotDisplayScreen.hidpi = !!p_hidpi;
- switch (GodotConfig.canvas_resize_policy) {
- case 0:
-  GodotDisplayScreen.desired_size = [ canvas.width, canvas.height ];
-  break;
-
- case 1:
-  GodotDisplayScreen.desired_size = [ p_width, p_height ];
-  break;
-
- default:
-  canvas.style.position = "absolute";
-  canvas.style.top = 0;
-  canvas.style.left = 0;
-  break;
- }
- GodotDisplayScreen.updateSize();
- if (p_fullscreen) {
-  GodotDisplayScreen.requestFullscreen();
- }
-}
-
-function _godot_js_display_size_update() {
- const updated = GodotDisplayScreen.updateSize();
- if (updated) {
-  GodotDisplayVK.updateSize();
- }
- return updated;
-}
-
-function _godot_js_display_touchscreen_is_available() {
- return "ontouchstart" in window;
-}
-
-function _godot_js_display_tts_available() {
- return "speechSynthesis" in window;
-}
-
-function _godot_js_display_vk_available() {
- return GodotDisplayVK.available();
-}
-
-function _godot_js_display_vk_cb(p_input_cb) {
- const input_cb = GodotRuntime.get_func(p_input_cb);
- if (GodotDisplayVK.available()) {
-  GodotDisplayVK.init(input_cb);
- }
-}
-
-function _godot_js_display_vk_hide() {
- GodotDisplayVK.hide();
-}
-
-function _godot_js_display_vk_show(p_text, p_type, p_start, p_end) {
- const text = GodotRuntime.parseString(p_text);
- const start = p_start > 0 ? p_start : 0;
- const end = p_end > 0 ? p_end : start;
- GodotDisplayVK.show(text, p_type, start, end);
-}
-
-function _godot_js_display_window_blur_cb(callback) {
- const func = GodotRuntime.get_func(callback);
- GodotEventListeners.add(window, "blur", function() {
-  func();
- }, false);
-}
-
-function _godot_js_display_window_icon_set(p_ptr, p_len) {
- let link = document.getElementById("-gd-engine-icon");
- if (link === null) {
-  link = document.createElement("link");
-  link.rel = "icon";
-  link.id = "-gd-engine-icon";
-  document.head.appendChild(link);
- }
- const old_icon = GodotDisplay.window_icon;
- const png = new Blob([ GodotRuntime.heapSlice(GROWABLE_HEAP_U8(), p_ptr, p_len) ], {
-  type: "image/png"
- });
- GodotDisplay.window_icon = URL.createObjectURL(png);
- link.href = GodotDisplay.window_icon;
- if (old_icon) {
-  URL.revokeObjectURL(old_icon);
- }
-}
-
-function _godot_js_display_window_size_get(p_width, p_height) {
- GodotRuntime.setHeapValue(p_width, GodotConfig.canvas.width, "i32");
- GodotRuntime.setHeapValue(p_height, GodotConfig.canvas.height, "i32");
-}
-
-function _godot_js_display_window_title_set(p_data) {
- document.title = GodotRuntime.parseString(p_data);
-}
-
-function _godot_js_eval(p_js, p_use_global_ctx, p_union_ptr, p_byte_arr, p_byte_arr_write, p_callback) {
- const js_code = GodotRuntime.parseString(p_js);
- let eval_ret = null;
- try {
-  if (p_use_global_ctx) {
-   const global_eval = eval;
-   eval_ret = global_eval(js_code);
-  } else {
-   eval_ret = eval(js_code);
-  }
- } catch (e) {
-  GodotRuntime.error(e);
- }
- switch (typeof eval_ret) {
- case "boolean":
-  GodotRuntime.setHeapValue(p_union_ptr, eval_ret, "i32");
-  return 1;
-
- case "number":
-  GodotRuntime.setHeapValue(p_union_ptr, eval_ret, "double");
-  return 3;
-
- case "string":
-  GodotRuntime.setHeapValue(p_union_ptr, GodotRuntime.allocString(eval_ret), "*");
-  return 4;
-
- case "object":
-  if (eval_ret === null) {
-   break;
-  }
-  if (ArrayBuffer.isView(eval_ret) && !(eval_ret instanceof Uint8Array)) {
-   eval_ret = new Uint8Array(eval_ret.buffer);
-  } else if (eval_ret instanceof ArrayBuffer) {
-   eval_ret = new Uint8Array(eval_ret);
-  }
-  if (eval_ret instanceof Uint8Array) {
-   const func = GodotRuntime.get_func(p_callback);
-   const bytes_ptr = func(p_byte_arr, p_byte_arr_write, eval_ret.length);
-   GROWABLE_HEAP_U8().set(eval_ret, bytes_ptr);
-   return 20;
-  }
-  break;
- }
- return 0;
-}
-
-var IDHandler = {
- _last_id: 0,
- _references: {},
- get: function(p_id) {
-  return IDHandler._references[p_id];
- },
- add: function(p_data) {
-  const id = ++IDHandler._last_id;
-  IDHandler._references[id] = p_data;
-  return id;
- },
- remove: function(p_id) {
-  delete IDHandler._references[p_id];
- }
-};
-
-var GodotFetch = {
- onread: function(id, result) {
-  const obj = IDHandler.get(id);
-  if (!obj) {
-   return;
-  }
-  if (result.value) {
-   obj.chunks.push(result.value);
-  }
-  obj.reading = false;
-  obj.done = result.done;
- },
- onresponse: function(id, response) {
-  const obj = IDHandler.get(id);
-  if (!obj) {
-   return;
-  }
-  let chunked = false;
-  let bodySize = -1;
-  response.headers.forEach(function(value, header) {
-   const v = value.toLowerCase().trim();
-   const h = header.toLowerCase().trim();
-   if (h === "transfer-encoding" && v === "chunked") {
-    chunked = true;
-   }
-   if (h === "content-length") {
-    bodySize = parseInt(v, 10);
-   }
-  });
-  obj.status = response.status;
-  obj.response = response;
-  obj.reader = response.body.getReader();
-  obj.chunked = chunked;
-  obj.bodySize = bodySize;
- },
- onerror: function(id, err) {
-  GodotRuntime.error(err);
-  const obj = IDHandler.get(id);
-  if (!obj) {
-   return;
-  }
-  obj.error = err;
- },
- create: function(method, url, headers, body) {
-  const obj = {
-   request: null,
-   response: null,
-   reader: null,
-   error: null,
-   done: false,
-   reading: false,
-   status: 0,
-   chunks: [],
-   bodySize: -1
-  };
-  const id = IDHandler.add(obj);
-  const init = {
-   method: method,
-   headers: headers,
-   body: body
-  };
-  obj.request = fetch(url, init);
-  obj.request.then(GodotFetch.onresponse.bind(null, id)).catch(GodotFetch.onerror.bind(null, id));
-  return id;
- },
- free: function(id) {
-  const obj = IDHandler.get(id);
-  if (!obj) {
-   return;
-  }
-  IDHandler.remove(id);
-  if (!obj.request) {
-   return;
-  }
-  obj.request.then(function(response) {
-   response.abort();
-  }).catch(function(e) {});
- },
- read: function(id) {
-  const obj = IDHandler.get(id);
-  if (!obj) {
-   return;
-  }
-  if (obj.reader && !obj.reading) {
-   if (obj.done) {
-    obj.reader = null;
-    return;
-   }
-   obj.reading = true;
-   obj.reader.read().then(GodotFetch.onread.bind(null, id)).catch(GodotFetch.onerror.bind(null, id));
-  }
- }
-};
-
-function _godot_js_fetch_body_length_get(p_id) {
- const obj = IDHandler.get(p_id);
- if (!obj || !obj.response) {
-  return -1;
- }
- return obj.bodySize;
-}
-
-function _godot_js_fetch_create(p_method, p_url, p_headers, p_headers_size, p_body, p_body_size) {
- const method = GodotRuntime.parseString(p_method);
- const url = GodotRuntime.parseString(p_url);
- const headers = GodotRuntime.parseStringArray(p_headers, p_headers_size);
- const body = p_body_size ? GodotRuntime.heapSlice(GROWABLE_HEAP_I8(), p_body, p_body_size) : null;
- return GodotFetch.create(method, url, headers.map(function(hv) {
-  const idx = hv.indexOf(":");
-  if (idx <= 0) {
-   return [];
-  }
-  return [ hv.slice(0, idx).trim(), hv.slice(idx + 1).trim() ];
- }).filter(function(v) {
-  return v.length === 2;
- }), body);
-}
-
-function _godot_js_fetch_free(id) {
- GodotFetch.free(id);
-}
-
-function _godot_js_fetch_http_status_get(p_id) {
- const obj = IDHandler.get(p_id);
- if (!obj || !obj.response) {
-  return 0;
- }
- return obj.status;
-}
-
-function _godot_js_fetch_is_chunked(p_id) {
- const obj = IDHandler.get(p_id);
- if (!obj || !obj.response) {
-  return -1;
- }
- return obj.chunked ? 1 : 0;
-}
-
-function _godot_js_fetch_read_chunk(p_id, p_buf, p_buf_size) {
- const obj = IDHandler.get(p_id);
- if (!obj || !obj.response) {
-  return 0;
- }
- let to_read = p_buf_size;
- const chunks = obj.chunks;
- while (to_read && chunks.length) {
-  const chunk = obj.chunks[0];
-  if (chunk.length > to_read) {
-   GodotRuntime.heapCopy(GROWABLE_HEAP_I8(), chunk.slice(0, to_read), p_buf);
-   chunks[0] = chunk.slice(to_read);
-   to_read = 0;
-  } else {
-   GodotRuntime.heapCopy(GROWABLE_HEAP_I8(), chunk, p_buf);
-   to_read -= chunk.length;
-   chunks.pop();
-  }
- }
- if (!chunks.length) {
-  GodotFetch.read(p_id);
- }
- return p_buf_size - to_read;
-}
-
-function _godot_js_fetch_read_headers(p_id, p_parse_cb, p_ref) {
- const obj = IDHandler.get(p_id);
- if (!obj || !obj.response) {
-  return 1;
- }
- const cb = GodotRuntime.get_func(p_parse_cb);
- const arr = [];
- obj.response.headers.forEach(function(v, h) {
-  arr.push(`${h}:${v}`);
- });
- const c_ptr = GodotRuntime.allocStringArray(arr);
- cb(arr.length, c_ptr, p_ref);
- GodotRuntime.freeStringArray(c_ptr, arr.length);
- return 0;
-}
-
-function _godot_js_fetch_state_get(p_id) {
- const obj = IDHandler.get(p_id);
- if (!obj) {
-  return -1;
- }
- if (obj.error) {
-  return -1;
- }
- if (!obj.response) {
-  return 0;
- }
- if (obj.reader) {
-  return 1;
- }
- if (obj.done) {
-  return 2;
- }
- return -1;
-}
-
-var GodotInputGamepads = {
- samples: [],
- get_pads: function() {
-  try {
-   const pads = navigator.getGamepads();
-   if (pads) {
-    return pads;
-   }
-   return [];
-  } catch (e) {
-   return [];
-  }
- },
- get_samples: function() {
-  return GodotInputGamepads.samples;
- },
- get_sample: function(index) {
-  const samples = GodotInputGamepads.samples;
-  return index < samples.length ? samples[index] : null;
- },
- sample: function() {
-  const pads = GodotInputGamepads.get_pads();
-  const samples = [];
-  for (let i = 0; i < pads.length; i++) {
-   const pad = pads[i];
-   if (!pad) {
-    samples.push(null);
-    continue;
-   }
-   const s = {
-    standard: pad.mapping === "standard",
-    buttons: [],
-    axes: [],
-    connected: pad.connected
-   };
-   for (let b = 0; b < pad.buttons.length; b++) {
-    s.buttons.push(pad.buttons[b].value);
-   }
-   for (let a = 0; a < pad.axes.length; a++) {
-    s.axes.push(pad.axes[a]);
-   }
-   samples.push(s);
-  }
-  GodotInputGamepads.samples = samples;
- },
- init: function(onchange) {
-  GodotInputGamepads.samples = [];
-  function add(pad) {
-   const guid = GodotInputGamepads.get_guid(pad);
-   const c_id = GodotRuntime.allocString(pad.id);
-   const c_guid = GodotRuntime.allocString(guid);
-   onchange(pad.index, 1, c_id, c_guid);
-   GodotRuntime.free(c_id);
-   GodotRuntime.free(c_guid);
-  }
-  const pads = GodotInputGamepads.get_pads();
-  for (let i = 0; i < pads.length; i++) {
-   if (pads[i]) {
-    add(pads[i]);
-   }
-  }
-  GodotEventListeners.add(window, "gamepadconnected", function(evt) {
-   if (evt.gamepad) {
-    add(evt.gamepad);
-   }
-  }, false);
-  GodotEventListeners.add(window, "gamepaddisconnected", function(evt) {
-   if (evt.gamepad) {
-    onchange(evt.gamepad.index, 0);
-   }
-  }, false);
- },
- get_guid: function(pad) {
-  if (pad.mapping) {
-   return pad.mapping;
-  }
-  const ua = navigator.userAgent;
-  let os = "Unknown";
-  if (ua.indexOf("Android") >= 0) {
-   os = "Android";
-  } else if (ua.indexOf("Linux") >= 0) {
-   os = "Linux";
-  } else if (ua.indexOf("iPhone") >= 0) {
-   os = "iOS";
-  } else if (ua.indexOf("Macintosh") >= 0) {
-   os = "MacOSX";
-  } else if (ua.indexOf("Windows") >= 0) {
-   os = "Windows";
-  }
-  const id = pad.id;
-  const exp1 = /vendor: ([0-9a-f]{4}) product: ([0-9a-f]{4})/i;
-  const exp2 = /^([0-9a-f]+)-([0-9a-f]+)-/i;
-  let vendor = "";
-  let product = "";
-  if (exp1.test(id)) {
-   const match = exp1.exec(id);
-   vendor = match[1].padStart(4, "0");
-   product = match[2].padStart(4, "0");
-  } else if (exp2.test(id)) {
-   const match = exp2.exec(id);
-   vendor = match[1].padStart(4, "0");
-   product = match[2].padStart(4, "0");
-  }
-  if (!vendor || !product) {
-   return `${os}Unknown`;
-  }
-  return os + vendor + product;
- }
-};
-
-var GodotInputDragDrop = {
- promises: [],
- pending_files: [],
- add_entry: function(entry) {
-  if (entry.isDirectory) {
-   GodotInputDragDrop.add_dir(entry);
-  } else if (entry.isFile) {
-   GodotInputDragDrop.add_file(entry);
-  } else {
-   GodotRuntime.error("Unrecognized entry...", entry);
-  }
- },
- add_dir: function(entry) {
-  GodotInputDragDrop.promises.push(new Promise(function(resolve, reject) {
-   const reader = entry.createReader();
-   reader.readEntries(function(entries) {
-    for (let i = 0; i < entries.length; i++) {
-     GodotInputDragDrop.add_entry(entries[i]);
-    }
-    resolve();
-   });
-  }));
- },
- add_file: function(entry) {
-  GodotInputDragDrop.promises.push(new Promise(function(resolve, reject) {
-   entry.file(function(file) {
-    const reader = new FileReader();
-    reader.onload = function() {
-     const f = {
-      "path": file.relativePath || file.webkitRelativePath,
-      "name": file.name,
-      "type": file.type,
-      "size": file.size,
-      "data": reader.result
-     };
-     if (!f["path"]) {
-      f["path"] = f["name"];
-     }
-     GodotInputDragDrop.pending_files.push(f);
-     resolve();
-    };
-    reader.onerror = function() {
-     GodotRuntime.print("Error reading file");
-     reject();
-    };
-    reader.readAsArrayBuffer(file);
-   }, function(err) {
-    GodotRuntime.print("Error!");
-    reject();
-   });
-  }));
- },
- process: function(resolve, reject) {
-  if (GodotInputDragDrop.promises.length === 0) {
-   resolve();
-   return;
-  }
-  GodotInputDragDrop.promises.pop().then(function() {
-   setTimeout(function() {
-    GodotInputDragDrop.process(resolve, reject);
-   }, 0);
-  });
- },
- _process_event: function(ev, callback) {
-  ev.preventDefault();
-  if (ev.dataTransfer.items) {
-   for (let i = 0; i < ev.dataTransfer.items.length; i++) {
-    const item = ev.dataTransfer.items[i];
-    let entry = null;
-    if ("getAsEntry" in item) {
-     entry = item.getAsEntry();
-    } else if ("webkitGetAsEntry" in item) {
-     entry = item.webkitGetAsEntry();
-    }
-    if (entry) {
-     GodotInputDragDrop.add_entry(entry);
-    }
-   }
-  } else {
-   GodotRuntime.error("File upload not supported");
-  }
-  new Promise(GodotInputDragDrop.process).then(function() {
-   const DROP = `/tmp/drop-${parseInt(Math.random() * (1 << 30), 10)}/`;
-   const drops = [];
-   const files = [];
-   FS.mkdir(DROP.slice(0, -1));
-   GodotInputDragDrop.pending_files.forEach(elem => {
-    const path = elem["path"];
-    GodotFS.copy_to_fs(DROP + path, elem["data"]);
-    let idx = path.indexOf("/");
-    if (idx === -1) {
-     drops.push(DROP + path);
-    } else {
-     const sub = path.substr(0, idx);
-     idx = sub.indexOf("/");
-     if (idx < 0 && drops.indexOf(DROP + sub) === -1) {
-      drops.push(DROP + sub);
-     }
-    }
-    files.push(DROP + path);
-   });
-   GodotInputDragDrop.promises = [];
-   GodotInputDragDrop.pending_files = [];
-   callback(drops);
-   if (GodotConfig.persistent_drops) {
-    GodotOS.atexit(function(resolve, reject) {
-     GodotInputDragDrop.remove_drop(files, DROP);
-     resolve();
-    });
-   } else {
-    GodotInputDragDrop.remove_drop(files, DROP);
-   }
-  });
- },
- remove_drop: function(files, drop_path) {
-  const dirs = [ drop_path.substr(0, drop_path.length - 1) ];
-  files.forEach(function(file) {
-   FS.unlink(file);
-   let dir = file.replace(drop_path, "");
-   let idx = dir.lastIndexOf("/");
-   while (idx > 0) {
-    dir = dir.substr(0, idx);
-    if (dirs.indexOf(drop_path + dir) === -1) {
-     dirs.push(drop_path + dir);
-    }
-    idx = dir.lastIndexOf("/");
-   }
-  });
-  dirs.sort(function(a, b) {
-   const al = (a.match(/\//g) || []).length;
-   const bl = (b.match(/\//g) || []).length;
-   if (al > bl) {
-    return -1;
-   } else if (al < bl) {
-    return 1;
-   }
-   return 0;
-  }).forEach(function(dir) {
-   FS.rmdir(dir);
-  });
- },
- handler: function(callback) {
-  return function(ev) {
-   GodotInputDragDrop._process_event(ev, callback);
-  };
- }
-};
-
-var GodotInput = {
- getModifiers: function(evt) {
-  return evt.shiftKey + 0 + (evt.altKey + 0 << 1) + (evt.ctrlKey + 0 << 2) + (evt.metaKey + 0 << 3);
- },
- computePosition: function(evt, rect) {
-  const canvas = GodotConfig.canvas;
-  const rw = canvas.width / rect.width;
-  const rh = canvas.height / rect.height;
-  const x = (evt.clientX - rect.x) * rw;
-  const y = (evt.clientY - rect.y) * rh;
-  return [ x, y ];
- }
-};
-
-function _godot_js_input_drop_files_cb(callback) {
- const func = GodotRuntime.get_func(callback);
- const dropFiles = function(files) {
-  const args = files || [];
-  if (!args.length) {
-   return;
-  }
-  const argc = args.length;
-  const argv = GodotRuntime.allocStringArray(args);
-  func(argv, argc);
-  GodotRuntime.freeStringArray(argv, argc);
- };
- const canvas = GodotConfig.canvas;
- GodotEventListeners.add(canvas, "dragover", function(ev) {
-  ev.preventDefault();
- }, false);
- GodotEventListeners.add(canvas, "drop", GodotInputDragDrop.handler(dropFiles));
-}
-
-function _godot_js_input_gamepad_cb(change_cb) {
- const onchange = GodotRuntime.get_func(change_cb);
- GodotInputGamepads.init(onchange);
-}
-
-function _godot_js_input_gamepad_sample() {
- GodotInputGamepads.sample();
- return 0;
-}
-
-function _godot_js_input_gamepad_sample_count() {
- return GodotInputGamepads.get_samples().length;
-}
-
-function _godot_js_input_gamepad_sample_get(p_index, r_btns, r_btns_num, r_axes, r_axes_num, r_standard) {
- const sample = GodotInputGamepads.get_sample(p_index);
- if (!sample || !sample.connected) {
-  return 1;
- }
- const btns = sample.buttons;
- const btns_len = btns.length < 16 ? btns.length : 16;
- for (let i = 0; i < btns_len; i++) {
-  GodotRuntime.setHeapValue(r_btns + (i << 2), btns[i], "float");
- }
- GodotRuntime.setHeapValue(r_btns_num, btns_len, "i32");
- const axes = sample.axes;
- const axes_len = axes.length < 10 ? axes.length : 10;
- for (let i = 0; i < axes_len; i++) {
-  GodotRuntime.setHeapValue(r_axes + (i << 2), axes[i], "float");
- }
- GodotRuntime.setHeapValue(r_axes_num, axes_len, "i32");
- const is_standard = sample.standard ? 1 : 0;
- GodotRuntime.setHeapValue(r_standard, is_standard, "i32");
- return 0;
-}
-
-function _godot_js_input_key_cb(callback, code, key) {
- const func = GodotRuntime.get_func(callback);
- function key_cb(pressed, evt) {
-  const modifiers = GodotInput.getModifiers(evt);
-  GodotRuntime.stringToHeap(evt.code, code, 32);
-  GodotRuntime.stringToHeap(evt.key, key, 32);
-  func(pressed, evt.repeat, modifiers);
-  evt.preventDefault();
- }
- GodotEventListeners.add(GodotConfig.canvas, "keydown", key_cb.bind(null, 1), false);
- GodotEventListeners.add(GodotConfig.canvas, "keyup", key_cb.bind(null, 0), false);
-}
-
-function _godot_js_input_mouse_button_cb(callback) {
- const func = GodotRuntime.get_func(callback);
- const canvas = GodotConfig.canvas;
- function button_cb(p_pressed, evt) {
-  const rect = canvas.getBoundingClientRect();
-  const pos = GodotInput.computePosition(evt, rect);
-  const modifiers = GodotInput.getModifiers(evt);
-  if (p_pressed) {
-   GodotConfig.canvas.focus();
-  }
-  if (func(p_pressed, evt.button, pos[0], pos[1], modifiers)) {
-   evt.preventDefault();
-  }
- }
- GodotEventListeners.add(canvas, "mousedown", button_cb.bind(null, 1), false);
- GodotEventListeners.add(window, "mouseup", button_cb.bind(null, 0), false);
-}
-
-function _godot_js_input_mouse_move_cb(callback) {
- const func = GodotRuntime.get_func(callback);
- const canvas = GodotConfig.canvas;
- function move_cb(evt) {
-  const rect = canvas.getBoundingClientRect();
-  const pos = GodotInput.computePosition(evt, rect);
-  const rw = canvas.width / rect.width;
-  const rh = canvas.height / rect.height;
-  const rel_pos_x = evt.movementX * rw;
-  const rel_pos_y = evt.movementY * rh;
-  const modifiers = GodotInput.getModifiers(evt);
-  func(pos[0], pos[1], rel_pos_x, rel_pos_y, modifiers);
- }
- GodotEventListeners.add(window, "mousemove", move_cb, false);
-}
-
-function _godot_js_input_mouse_wheel_cb(callback) {
- const func = GodotRuntime.get_func(callback);
- function wheel_cb(evt) {
-  if (func(evt["deltaX"] || 0, evt["deltaY"] || 0)) {
-   evt.preventDefault();
-  }
- }
- GodotEventListeners.add(GodotConfig.canvas, "wheel", wheel_cb, false);
-}
-
-function _godot_js_input_paste_cb(callback) {
- const func = GodotRuntime.get_func(callback);
- GodotEventListeners.add(window, "paste", function(evt) {
-  const text = evt.clipboardData.getData("text");
-  const ptr = GodotRuntime.allocString(text);
-  func(ptr);
-  GodotRuntime.free(ptr);
- }, false);
-}
-
-function _godot_js_input_touch_cb(callback, ids, coords) {
- const func = GodotRuntime.get_func(callback);
- const canvas = GodotConfig.canvas;
- function touch_cb(type, evt) {
-  if (type === 0) {
-   GodotConfig.canvas.focus();
-  }
-  const rect = canvas.getBoundingClientRect();
-  const touches = evt.changedTouches;
-  for (let i = 0; i < touches.length; i++) {
-   const touch = touches[i];
-   const pos = GodotInput.computePosition(touch, rect);
-   GodotRuntime.setHeapValue(coords + i * 2 * 8, pos[0], "double");
-   GodotRuntime.setHeapValue(coords + (i * 2 + 1) * 8, pos[1], "double");
-   GodotRuntime.setHeapValue(ids + i * 4, touch.identifier, "i32");
-  }
-  func(type, touches.length);
-  if (evt.cancelable) {
-   evt.preventDefault();
-  }
- }
- GodotEventListeners.add(canvas, "touchstart", touch_cb.bind(null, 0), false);
- GodotEventListeners.add(canvas, "touchend", touch_cb.bind(null, 1), false);
- GodotEventListeners.add(canvas, "touchcancel", touch_cb.bind(null, 1), false);
- GodotEventListeners.add(canvas, "touchmove", touch_cb.bind(null, 2), false);
-}
-
-function _godot_js_input_vibrate_handheld(p_duration_ms) {
- if (typeof navigator.vibrate !== "function") {
-  GodotRuntime.print("This browser does not support vibration.");
- } else {
-  navigator.vibrate(p_duration_ms);
- }
-}
-
-function _godot_js_os_download_buffer(p_ptr, p_size, p_name, p_mime) {
- const buf = GodotRuntime.heapSlice(GROWABLE_HEAP_I8(), p_ptr, p_size);
- const name = GodotRuntime.parseString(p_name);
- const mime = GodotRuntime.parseString(p_mime);
- const blob = new Blob([ buf ], {
-  type: mime
- });
- const url = window.URL.createObjectURL(blob);
- const a = document.createElement("a");
- a.href = url;
- a.download = name;
- a.style.display = "none";
- document.body.appendChild(a);
- a.click();
- a.remove();
- window.URL.revokeObjectURL(url);
-}
-
-function _godot_js_os_execute(p_json) {
- const json_args = GodotRuntime.parseString(p_json);
- const args = JSON.parse(json_args);
- if (GodotConfig.on_execute) {
-  GodotConfig.on_execute(args);
-  return 0;
- }
- return 1;
-}
-
-function _godot_js_os_finish_async(p_callback) {
- const func = GodotRuntime.get_func(p_callback);
- GodotOS.finish_async(func);
-}
-
-function _godot_js_os_fs_is_persistent() {
- return GodotFS.is_persistent();
-}
-
-function _godot_js_os_fs_sync(callback) {
- const func = GodotRuntime.get_func(callback);
- GodotOS._fs_sync_promise = GodotFS.sync();
- GodotOS._fs_sync_promise.then(function(err) {
-  func();
- });
-}
-
-function _godot_js_os_has_feature(p_ftr) {
- const ftr = GodotRuntime.parseString(p_ftr);
- const ua = navigator.userAgent;
- if (ftr === "web_macos") {
-  return ua.indexOf("Mac") !== -1 ? 1 : 0;
- }
- if (ftr === "web_windows") {
-  return ua.indexOf("Windows") !== -1 ? 1 : 0;
- }
- if (ftr === "web_android") {
-  return ua.indexOf("Android") !== -1 ? 1 : 0;
- }
- if (ftr === "web_ios") {
-  return ua.indexOf("iPhone") !== -1 || ua.indexOf("iPad") !== -1 || ua.indexOf("iPod") !== -1 ? 1 : 0;
- }
- if (ftr === "web_linuxbsd") {
-  return ua.indexOf("CrOS") !== -1 || ua.indexOf("BSD") !== -1 || ua.indexOf("Linux") !== -1 || ua.indexOf("X11") !== -1 ? 1 : 0;
- }
- return 0;
-}
-
-function _godot_js_os_hw_concurrency_get() {
- const concurrency = navigator.hardwareConcurrency || 1;
- return concurrency < 2 ? concurrency : 2;
-}
-
-function _godot_js_os_request_quit_cb(p_callback) {
- GodotOS.request_quit = GodotRuntime.get_func(p_callback);
-}
-
-function _godot_js_os_shell_open(p_uri) {
- window.open(GodotRuntime.parseString(p_uri), "_blank");
-}
-
-var GodotPWA = {
- hasUpdate: false,
- updateState: function(cb, reg) {
-  if (!reg) {
-   return;
-  }
-  if (!reg.active) {
-   return;
-  }
-  if (reg.waiting) {
-   GodotPWA.hasUpdate = true;
-   cb();
-  }
-  GodotEventListeners.add(reg, "updatefound", function() {
-   const installing = reg.installing;
-   GodotEventListeners.add(installing, "statechange", function() {
-    if (installing.state === "installed") {
-     GodotPWA.hasUpdate = true;
-     cb();
-    }
-   });
-  });
- }
-};
-
-function _godot_js_pwa_cb(p_update_cb) {
- if ("serviceWorker" in navigator) {
-  const cb = GodotRuntime.get_func(p_update_cb);
-  navigator.serviceWorker.getRegistration().then(GodotPWA.updateState.bind(null, cb));
- }
-}
-
-function _godot_js_pwa_update() {
- if ("serviceWorker" in navigator && GodotPWA.hasUpdate) {
-  navigator.serviceWorker.getRegistration().then(function(reg) {
-   if (!reg || !reg.waiting) {
-    return;
-   }
-   reg.waiting.postMessage("update");
-  });
-  return 0;
- }
- return 1;
-}
-
-var GodotRTCDataChannel = {
- connect: function(p_id, p_on_open, p_on_message, p_on_error, p_on_close) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  ref.binaryType = "arraybuffer";
-  ref.onopen = function(event) {
-   p_on_open();
-  };
-  ref.onclose = function(event) {
-   p_on_close();
-  };
-  ref.onerror = function(event) {
-   p_on_error();
-  };
-  ref.onmessage = function(event) {
-   let buffer;
-   let is_string = 0;
-   if (event.data instanceof ArrayBuffer) {
-    buffer = new Uint8Array(event.data);
-   } else if (event.data instanceof Blob) {
-    GodotRuntime.error("Blob type not supported");
-    return;
-   } else if (typeof event.data === "string") {
-    is_string = 1;
-    const enc = new TextEncoder("utf-8");
-    buffer = new Uint8Array(enc.encode(event.data));
-   } else {
-    GodotRuntime.error("Unknown message type");
-    return;
-   }
-   const len = buffer.length * buffer.BYTES_PER_ELEMENT;
-   const out = GodotRuntime.malloc(len);
-   GROWABLE_HEAP_U8().set(buffer, out);
-   p_on_message(out, len, is_string);
-   GodotRuntime.free(out);
-  };
- },
- close: function(p_id) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  ref.onopen = null;
-  ref.onmessage = null;
-  ref.onerror = null;
-  ref.onclose = null;
-  ref.close();
- },
- get_prop: function(p_id, p_prop, p_def) {
-  const ref = IDHandler.get(p_id);
-  return ref && ref[p_prop] !== undefined ? ref[p_prop] : p_def;
- }
-};
-
-function _godot_js_rtc_datachannel_close(p_id) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return;
- }
- GodotRTCDataChannel.close(p_id);
-}
-
-function _godot_js_rtc_datachannel_connect(p_id, p_ref, p_on_open, p_on_message, p_on_error, p_on_close) {
- const onopen = GodotRuntime.get_func(p_on_open).bind(null, p_ref);
- const onmessage = GodotRuntime.get_func(p_on_message).bind(null, p_ref);
- const onerror = GodotRuntime.get_func(p_on_error).bind(null, p_ref);
- const onclose = GodotRuntime.get_func(p_on_close).bind(null, p_ref);
- GodotRTCDataChannel.connect(p_id, onopen, onmessage, onerror, onclose);
-}
-
-function _godot_js_rtc_datachannel_destroy(p_id) {
- GodotRTCDataChannel.close(p_id);
- IDHandler.remove(p_id);
-}
-
-function _godot_js_rtc_datachannel_get_buffered_amount(p_id) {
- return GodotRTCDataChannel.get_prop(p_id, "bufferedAmount", 0);
-}
-
-function _godot_js_rtc_datachannel_id_get(p_id) {
- return GodotRTCDataChannel.get_prop(p_id, "id", 65535);
-}
-
-function _godot_js_rtc_datachannel_is_negotiated(p_id) {
- return GodotRTCDataChannel.get_prop(p_id, "negotiated", 65535);
-}
-
-function _godot_js_rtc_datachannel_is_ordered(p_id) {
- return GodotRTCDataChannel.get_prop(p_id, "ordered", true);
-}
-
-function _godot_js_rtc_datachannel_label_get(p_id) {
- const ref = IDHandler.get(p_id);
- if (!ref || !ref.label) {
-  return 0;
- }
- return GodotRuntime.allocString(ref.label);
-}
-
-function _godot_js_rtc_datachannel_max_packet_lifetime_get(p_id) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return 65535;
- }
- if (ref["maxPacketLifeTime"] !== undefined) {
-  return ref["maxPacketLifeTime"];
- } else if (ref["maxRetransmitTime"] !== undefined) {
-  return ref["maxRetransmitTime"];
- }
- return 65535;
-}
-
-function _godot_js_rtc_datachannel_max_retransmits_get(p_id) {
- return GodotRTCDataChannel.get_prop(p_id, "maxRetransmits", 65535);
-}
-
-function _godot_js_rtc_datachannel_protocol_get(p_id) {
- const ref = IDHandler.get(p_id);
- if (!ref || !ref.protocol) {
-  return 0;
- }
- return GodotRuntime.allocString(ref.protocol);
-}
-
-function _godot_js_rtc_datachannel_ready_state_get(p_id) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return 3;
- }
- switch (ref.readyState) {
- case "connecting":
-  return 0;
-
- case "open":
-  return 1;
-
- case "closing":
-  return 2;
-
- case "closed":
- default:
-  return 3;
- }
-}
-
-function _godot_js_rtc_datachannel_send(p_id, p_buffer, p_length, p_raw) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return 1;
- }
- const bytes_array = new Uint8Array(p_length);
- for (let i = 0; i < p_length; i++) {
-  bytes_array[i] = GodotRuntime.getHeapValue(p_buffer + i, "i8");
- }
- if (p_raw) {
-  ref.send(bytes_array.buffer);
- } else {
-  const string = new TextDecoder("utf-8").decode(bytes_array);
-  ref.send(string);
- }
- return 0;
-}
-
-var GodotRTCPeerConnection = {
- ConnectionState: {
-  new: 0,
-  connecting: 1,
-  connected: 2,
-  disconnected: 3,
-  failed: 4,
-  closed: 5
- },
- ConnectionStateCompat: {
-  new: 0,
-  checking: 1,
-  connected: 2,
-  completed: 2,
-  disconnected: 3,
-  failed: 4,
-  closed: 5
- },
- IceGatheringState: {
-  new: 0,
-  gathering: 1,
-  complete: 2
- },
- SignalingState: {
-  stable: 0,
-  "have-local-offer": 1,
-  "have-remote-offer": 2,
-  "have-local-pranswer": 3,
-  "have-remote-pranswer": 4,
-  closed: 5
- },
- create: function(config, onConnectionChange, onSignalingChange, onIceGatheringChange, onIceCandidate, onDataChannel) {
-  let conn = null;
-  try {
-   conn = new RTCPeerConnection(config);
-  } catch (e) {
-   GodotRuntime.error(e);
-   return 0;
-  }
-  const id = IDHandler.add(conn);
-  if ("connectionState" in conn && conn["connectionState"] !== undefined) {
-   conn.onconnectionstatechange = function(event) {
-    if (!IDHandler.get(id)) {
-     return;
-    }
-    onConnectionChange(GodotRTCPeerConnection.ConnectionState[conn.connectionState] || 0);
-   };
-  } else {
-   conn.oniceconnectionstatechange = function(event) {
-    if (!IDHandler.get(id)) {
-     return;
-    }
-    onConnectionChange(GodotRTCPeerConnection.ConnectionStateCompat[conn.iceConnectionState] || 0);
-   };
-  }
-  conn.onicegatheringstatechange = function(event) {
-   if (!IDHandler.get(id)) {
-    return;
-   }
-   onIceGatheringChange(GodotRTCPeerConnection.IceGatheringState[conn.iceGatheringState] || 0);
-  };
-  conn.onsignalingstatechange = function(event) {
-   if (!IDHandler.get(id)) {
-    return;
-   }
-   onSignalingChange(GodotRTCPeerConnection.SignalingState[conn.signalingState] || 0);
-  };
-  conn.onicecandidate = function(event) {
-   if (!IDHandler.get(id)) {
-    return;
-   }
-   const c = event.candidate;
-   if (!c || !c.candidate) {
-    return;
-   }
-   const candidate_str = GodotRuntime.allocString(c.candidate);
-   const mid_str = GodotRuntime.allocString(c.sdpMid);
-   onIceCandidate(mid_str, c.sdpMLineIndex, candidate_str);
-   GodotRuntime.free(candidate_str);
-   GodotRuntime.free(mid_str);
-  };
-  conn.ondatachannel = function(event) {
-   if (!IDHandler.get(id)) {
-    return;
-   }
-   const cid = IDHandler.add(event.channel);
-   onDataChannel(cid);
-  };
-  return id;
- },
- destroy: function(p_id) {
-  const conn = IDHandler.get(p_id);
-  if (!conn) {
-   return;
-  }
-  conn.onconnectionstatechange = null;
-  conn.oniceconnectionstatechange = null;
-  conn.onicegatheringstatechange = null;
-  conn.onsignalingstatechange = null;
-  conn.onicecandidate = null;
-  conn.ondatachannel = null;
-  IDHandler.remove(p_id);
- },
- onsession: function(p_id, callback, session) {
-  if (!IDHandler.get(p_id)) {
-   return;
-  }
-  const type_str = GodotRuntime.allocString(session.type);
-  const sdp_str = GodotRuntime.allocString(session.sdp);
-  callback(type_str, sdp_str);
-  GodotRuntime.free(type_str);
-  GodotRuntime.free(sdp_str);
- },
- onerror: function(p_id, callback, error) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  GodotRuntime.error(error);
-  callback();
- }
-};
-
-function _godot_js_rtc_pc_close(p_id) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return;
- }
- ref.close();
-}
-
-function _godot_js_rtc_pc_create(p_config, p_ref, p_on_connection_state_change, p_on_ice_gathering_state_change, p_on_signaling_state_change, p_on_ice_candidate, p_on_datachannel) {
- const wrap = function(p_func) {
-  return GodotRuntime.get_func(p_func).bind(null, p_ref);
- };
- return GodotRTCPeerConnection.create(JSON.parse(GodotRuntime.parseString(p_config)), wrap(p_on_connection_state_change), wrap(p_on_signaling_state_change), wrap(p_on_ice_gathering_state_change), wrap(p_on_ice_candidate), wrap(p_on_datachannel));
-}
-
-function _godot_js_rtc_pc_datachannel_create(p_id, p_label, p_config) {
- try {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return 0;
-  }
-  const label = GodotRuntime.parseString(p_label);
-  const config = JSON.parse(GodotRuntime.parseString(p_config));
-  const channel = ref.createDataChannel(label, config);
-  return IDHandler.add(channel);
- } catch (e) {
-  GodotRuntime.error(e);
-  return 0;
- }
-}
-
-function _godot_js_rtc_pc_destroy(p_id) {
- GodotRTCPeerConnection.destroy(p_id);
-}
-
-function _godot_js_rtc_pc_ice_candidate_add(p_id, p_mid_name, p_mline_idx, p_sdp) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return;
- }
- const sdpMidName = GodotRuntime.parseString(p_mid_name);
- const sdpName = GodotRuntime.parseString(p_sdp);
- ref.addIceCandidate(new RTCIceCandidate({
-  "candidate": sdpName,
-  "sdpMid": sdpMidName,
-  "sdpMlineIndex": p_mline_idx
- }));
-}
-
-function _godot_js_rtc_pc_local_description_set(p_id, p_type, p_sdp, p_obj, p_on_error) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return;
- }
- const type = GodotRuntime.parseString(p_type);
- const sdp = GodotRuntime.parseString(p_sdp);
- const onerror = GodotRuntime.get_func(p_on_error).bind(null, p_obj);
- ref.setLocalDescription({
-  "sdp": sdp,
-  "type": type
- }).catch(function(error) {
-  GodotRTCPeerConnection.onerror(p_id, onerror, error);
- });
-}
-
-function _godot_js_rtc_pc_offer_create(p_id, p_obj, p_on_session, p_on_error) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return;
- }
- const onsession = GodotRuntime.get_func(p_on_session).bind(null, p_obj);
- const onerror = GodotRuntime.get_func(p_on_error).bind(null, p_obj);
- ref.createOffer().then(function(session) {
-  GodotRTCPeerConnection.onsession(p_id, onsession, session);
- }).catch(function(error) {
-  GodotRTCPeerConnection.onerror(p_id, onerror, error);
- });
-}
-
-function _godot_js_rtc_pc_remote_description_set(p_id, p_type, p_sdp, p_obj, p_session_created, p_on_error) {
- const ref = IDHandler.get(p_id);
- if (!ref) {
-  return;
- }
- const type = GodotRuntime.parseString(p_type);
- const sdp = GodotRuntime.parseString(p_sdp);
- const onerror = GodotRuntime.get_func(p_on_error).bind(null, p_obj);
- const onsession = GodotRuntime.get_func(p_session_created).bind(null, p_obj);
- ref.setRemoteDescription({
-  "sdp": sdp,
-  "type": type
- }).then(function() {
-  if (type !== "offer") {
-   return Promise.resolve();
-  }
-  return ref.createAnswer().then(function(session) {
-   GodotRTCPeerConnection.onsession(p_id, onsession, session);
-  });
- }).catch(function(error) {
-  GodotRTCPeerConnection.onerror(p_id, onerror, error);
- });
-}
-
-function _godot_js_tts_get_voices(p_callback) {
- const func = GodotRuntime.get_func(p_callback);
- try {
-  const arr = [];
-  const voices = window.speechSynthesis.getVoices();
-  for (let i = 0; i < voices.length; i++) {
-   arr.push(`${voices[i].lang};${voices[i].name}`);
-  }
-  const c_ptr = GodotRuntime.allocStringArray(arr);
-  func(arr.length, c_ptr);
-  GodotRuntime.freeStringArray(c_ptr, arr.length);
- } catch (e) {}
-}
-
-function _godot_js_tts_is_paused() {
- return window.speechSynthesis.paused;
-}
-
-function _godot_js_tts_is_speaking() {
- return window.speechSynthesis.speaking;
-}
-
-function _godot_js_tts_pause() {
- window.speechSynthesis.pause();
-}
-
-function _godot_js_tts_resume() {
- window.speechSynthesis.resume();
-}
-
-function _godot_js_tts_speak(p_text, p_voice, p_volume, p_pitch, p_rate, p_utterance_id, p_callback) {
- const func = GodotRuntime.get_func(p_callback);
- function listener_end(evt) {
-  evt.currentTarget.cb(1, evt.currentTarget.id, 0);
- }
- function listener_start(evt) {
-  evt.currentTarget.cb(0, evt.currentTarget.id, 0);
- }
- function listener_error(evt) {
-  evt.currentTarget.cb(2, evt.currentTarget.id, 0);
- }
- function listener_bound(evt) {
-  evt.currentTarget.cb(3, evt.currentTarget.id, evt.charIndex);
- }
- const utterance = new SpeechSynthesisUtterance(GodotRuntime.parseString(p_text));
- utterance.rate = p_rate;
- utterance.pitch = p_pitch;
- utterance.volume = p_volume / 100;
- utterance.addEventListener("end", listener_end);
- utterance.addEventListener("start", listener_start);
- utterance.addEventListener("error", listener_error);
- utterance.addEventListener("boundary", listener_bound);
- utterance.id = p_utterance_id;
- utterance.cb = func;
- const voice = GodotRuntime.parseString(p_voice);
- const voices = window.speechSynthesis.getVoices();
- for (let i = 0; i < voices.length; i++) {
-  if (voices[i].name === voice) {
-   utterance.voice = voices[i];
-   break;
-  }
- }
- window.speechSynthesis.resume();
- window.speechSynthesis.speak(utterance);
-}
-
-function _godot_js_tts_stop() {
- window.speechSynthesis.cancel();
- window.speechSynthesis.resume();
-}
-
-var GodotWebSocket = {
- _onopen: function(p_id, callback, event) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  const c_str = GodotRuntime.allocString(ref.protocol);
-  callback(c_str);
-  GodotRuntime.free(c_str);
- },
- _onmessage: function(p_id, callback, event) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  let buffer;
-  let is_string = 0;
-  if (event.data instanceof ArrayBuffer) {
-   buffer = new Uint8Array(event.data);
-  } else if (event.data instanceof Blob) {
-   GodotRuntime.error("Blob type not supported");
-   return;
-  } else if (typeof event.data === "string") {
-   is_string = 1;
-   const enc = new TextEncoder("utf-8");
-   buffer = new Uint8Array(enc.encode(event.data));
-  } else {
-   GodotRuntime.error("Unknown message type");
-   return;
-  }
-  const len = buffer.length * buffer.BYTES_PER_ELEMENT;
-  const out = GodotRuntime.malloc(len);
-  GROWABLE_HEAP_U8().set(buffer, out);
-  callback(out, len, is_string);
-  GodotRuntime.free(out);
- },
- _onerror: function(p_id, callback, event) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  callback();
- },
- _onclose: function(p_id, callback, event) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  const c_str = GodotRuntime.allocString(event.reason);
-  callback(event.code, c_str, event.wasClean ? 1 : 0);
-  GodotRuntime.free(c_str);
- },
- send: function(p_id, p_data) {
-  const ref = IDHandler.get(p_id);
-  if (!ref || ref.readyState !== ref.OPEN) {
-   return 1;
-  }
-  ref.send(p_data);
-  return 0;
- },
- bufferedAmount: function(p_id) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return 0;
-  }
-  return ref.bufferedAmount;
- },
- create: function(socket, p_on_open, p_on_message, p_on_error, p_on_close) {
-  const id = IDHandler.add(socket);
-  socket.onopen = GodotWebSocket._onopen.bind(null, id, p_on_open);
-  socket.onmessage = GodotWebSocket._onmessage.bind(null, id, p_on_message);
-  socket.onerror = GodotWebSocket._onerror.bind(null, id, p_on_error);
-  socket.onclose = GodotWebSocket._onclose.bind(null, id, p_on_close);
-  return id;
- },
- close: function(p_id, p_code, p_reason) {
-  const ref = IDHandler.get(p_id);
-  if (ref && ref.readyState < ref.CLOSING) {
-   const code = p_code;
-   const reason = GodotRuntime.parseString(p_reason);
-   ref.close(code, reason);
-  }
- },
- destroy: function(p_id) {
-  const ref = IDHandler.get(p_id);
-  if (!ref) {
-   return;
-  }
-  GodotWebSocket.close(p_id, 3001, "destroyed");
-  IDHandler.remove(p_id);
-  ref.onopen = null;
-  ref.onmessage = null;
-  ref.onerror = null;
-  ref.onclose = null;
- }
-};
-
-function _godot_js_websocket_buffered_amount(p_id) {
- return GodotWebSocket.bufferedAmount(p_id);
-}
-
-function _godot_js_websocket_close(p_id, p_code, p_reason) {
- const code = p_code;
- const reason = GodotRuntime.parseString(p_reason);
- GodotWebSocket.close(p_id, code, reason);
-}
-
-function _godot_js_websocket_create(p_ref, p_url, p_proto, p_on_open, p_on_message, p_on_error, p_on_close) {
- const on_open = GodotRuntime.get_func(p_on_open).bind(null, p_ref);
- const on_message = GodotRuntime.get_func(p_on_message).bind(null, p_ref);
- const on_error = GodotRuntime.get_func(p_on_error).bind(null, p_ref);
- const on_close = GodotRuntime.get_func(p_on_close).bind(null, p_ref);
- const url = GodotRuntime.parseString(p_url);
- const protos = GodotRuntime.parseString(p_proto);
- let socket = null;
- try {
-  if (protos) {
-   socket = new WebSocket(url, protos.split(","));
-  } else {
-   socket = new WebSocket(url);
-  }
- } catch (e) {
-  return 0;
- }
- socket.binaryType = "arraybuffer";
- return GodotWebSocket.create(socket, on_open, on_message, on_error, on_close);
-}
-
-function _godot_js_websocket_destroy(p_id) {
- GodotWebSocket.destroy(p_id);
-}
-
-function _godot_js_websocket_send(p_id, p_buf, p_buf_len, p_raw) {
- const bytes_array = new Uint8Array(p_buf_len);
- let i = 0;
- for (i = 0; i < p_buf_len; i++) {
-  bytes_array[i] = GodotRuntime.getHeapValue(p_buf + i, "i8");
- }
- let out = bytes_array.buffer;
- if (!p_raw) {
-  out = new TextDecoder("utf-8").decode(bytes_array);
- }
- return GodotWebSocket.send(p_id, out);
-}
-
-var GodotJSWrapper = {
- proxies: null,
- cb_ret: null,
- MyProxy: function(val) {
-  const id = IDHandler.add(this);
-  GodotJSWrapper.proxies.set(val, id);
-  let refs = 1;
-  this.ref = function() {
-   refs++;
-  };
-  this.unref = function() {
-   refs--;
-   if (refs === 0) {
-    IDHandler.remove(id);
-    GodotJSWrapper.proxies.delete(val);
-   }
-  };
-  this.get_val = function() {
-   return val;
-  };
-  this.get_id = function() {
-   return id;
-  };
- },
- get_proxied: function(val) {
-  const id = GodotJSWrapper.proxies.get(val);
-  if (id === undefined) {
-   const proxy = new GodotJSWrapper.MyProxy(val);
-   return proxy.get_id();
-  }
-  IDHandler.get(id).ref();
-  return id;
- },
- get_proxied_value: function(id) {
-  const proxy = IDHandler.get(id);
-  if (proxy === undefined) {
-   return undefined;
-  }
-  return proxy.get_val();
- },
- variant2js: function(type, val) {
-  switch (type) {
-  case 0:
-   return null;
-
-  case 1:
-   return !!GodotRuntime.getHeapValue(val, "i64");
-
-  case 2:
-   return GodotRuntime.getHeapValue(val, "i64");
-
-  case 3:
-   return GodotRuntime.getHeapValue(val, "double");
-
-  case 4:
-   return GodotRuntime.parseString(GodotRuntime.getHeapValue(val, "*"));
-
-  case 24:
-   return GodotJSWrapper.get_proxied_value(GodotRuntime.getHeapValue(val, "i64"));
-
-  default:
-   return undefined;
-  }
- },
- js2variant: function(p_val, p_exchange) {
-  if (p_val === undefined || p_val === null) {
-   return 0;
-  }
-  const type = typeof p_val;
-  if (type === "boolean") {
-   GodotRuntime.setHeapValue(p_exchange, p_val, "i64");
-   return 1;
-  } else if (type === "number") {
-   if (Number.isInteger(p_val)) {
-    GodotRuntime.setHeapValue(p_exchange, p_val, "i64");
-    return 2;
-   }
-   GodotRuntime.setHeapValue(p_exchange, p_val, "double");
-   return 3;
-  } else if (type === "string") {
-   const c_str = GodotRuntime.allocString(p_val);
-   GodotRuntime.setHeapValue(p_exchange, c_str, "*");
-   return 4;
-  }
-  const id = GodotJSWrapper.get_proxied(p_val);
-  GodotRuntime.setHeapValue(p_exchange, id, "i64");
-  return 24;
- }
-};
-
-function _godot_js_wrapper_create_cb(p_ref, p_func) {
- const func = GodotRuntime.get_func(p_func);
- let id = 0;
- const cb = function() {
-  if (!GodotJSWrapper.get_proxied_value(id)) {
-   return undefined;
-  }
-  GodotJSWrapper.cb_ret = null;
-  const args = Array.from(arguments);
-  func(p_ref, GodotJSWrapper.get_proxied(args), args.length);
-  const ret = GodotJSWrapper.cb_ret;
-  GodotJSWrapper.cb_ret = null;
-  return ret;
- };
- id = GodotJSWrapper.get_proxied(cb);
- return id;
-}
-
-function _godot_js_wrapper_create_object(p_object, p_args, p_argc, p_convert_callback, p_exchange, p_lock, p_free_lock_callback) {
- const name = GodotRuntime.parseString(p_object);
- if (typeof window[name] === "undefined") {
-  return -1;
- }
- const convert = GodotRuntime.get_func(p_convert_callback);
- const freeLock = GodotRuntime.get_func(p_free_lock_callback);
- const args = new Array(p_argc);
- for (let i = 0; i < p_argc; i++) {
-  const type = convert(p_args, i, p_exchange, p_lock);
-  const lock = GodotRuntime.getHeapValue(p_lock, "*");
-  args[i] = GodotJSWrapper.variant2js(type, p_exchange);
-  if (lock) {
-   freeLock(p_lock, type);
-  }
- }
- try {
-  const res = new window[name](...args);
-  return GodotJSWrapper.js2variant(res, p_exchange);
- } catch (e) {
-  GodotRuntime.error(`Error calling constructor ${name} with args:`, args, "error:", e);
-  return -1;
- }
-}
-
-function _godot_js_wrapper_interface_get(p_name) {
- const name = GodotRuntime.parseString(p_name);
- if (typeof window[name] !== "undefined") {
-  return GodotJSWrapper.get_proxied(window[name]);
- }
- return 0;
-}
-
-function _godot_js_wrapper_object_call(p_id, p_method, p_args, p_argc, p_convert_callback, p_exchange, p_lock, p_free_lock_callback) {
- const obj = GodotJSWrapper.get_proxied_value(p_id);
- if (obj === undefined) {
-  return -1;
- }
- const method = GodotRuntime.parseString(p_method);
- const convert = GodotRuntime.get_func(p_convert_callback);
- const freeLock = GodotRuntime.get_func(p_free_lock_callback);
- const args = new Array(p_argc);
- for (let i = 0; i < p_argc; i++) {
-  const type = convert(p_args, i, p_exchange, p_lock);
-  const lock = GodotRuntime.getHeapValue(p_lock, "*");
-  args[i] = GodotJSWrapper.variant2js(type, p_exchange);
-  if (lock) {
-   freeLock(p_lock, type);
-  }
- }
- try {
-  const res = obj[method](...args);
-  return GodotJSWrapper.js2variant(res, p_exchange);
- } catch (e) {
-  GodotRuntime.error(`Error calling method ${method} on:`, obj, "error:", e);
-  return -1;
- }
-}
-
-function _godot_js_wrapper_object_get(p_id, p_exchange, p_prop) {
- const obj = GodotJSWrapper.get_proxied_value(p_id);
- if (obj === undefined) {
-  return 0;
- }
- if (p_prop) {
-  const prop = GodotRuntime.parseString(p_prop);
-  try {
-   return GodotJSWrapper.js2variant(obj[prop], p_exchange);
-  } catch (e) {
-   GodotRuntime.error(`Error getting variable ${prop} on object`, obj);
-   return 0;
-  }
- }
- return GodotJSWrapper.js2variant(obj, p_exchange);
-}
-
-function _godot_js_wrapper_object_getvar(p_id, p_type, p_exchange) {
- const obj = GodotJSWrapper.get_proxied_value(p_id);
- if (obj === undefined) {
-  return -1;
- }
- const prop = GodotJSWrapper.variant2js(p_type, p_exchange);
- if (prop === undefined || prop === null) {
-  return -1;
- }
- try {
-  return GodotJSWrapper.js2variant(obj[prop], p_exchange);
- } catch (e) {
-  GodotRuntime.error(`Error getting variable ${prop} on object`, obj, e);
-  return -1;
- }
-}
-
-function _godot_js_wrapper_object_set(p_id, p_name, p_type, p_exchange) {
- const obj = GodotJSWrapper.get_proxied_value(p_id);
- if (obj === undefined) {
-  return;
- }
- const name = GodotRuntime.parseString(p_name);
- try {
-  obj[name] = GodotJSWrapper.variant2js(p_type, p_exchange);
- } catch (e) {
-  GodotRuntime.error(`Error setting variable ${name} on object`, obj);
- }
-}
-
-function _godot_js_wrapper_object_set_cb_ret(p_val_type, p_val_ex) {
- GodotJSWrapper.cb_ret = GodotJSWrapper.variant2js(p_val_type, p_val_ex);
-}
-
-function _godot_js_wrapper_object_setvar(p_id, p_key_type, p_key_ex, p_val_type, p_val_ex) {
- const obj = GodotJSWrapper.get_proxied_value(p_id);
- if (obj === undefined) {
-  return -1;
- }
- const key = GodotJSWrapper.variant2js(p_key_type, p_key_ex);
- try {
-  obj[key] = GodotJSWrapper.variant2js(p_val_type, p_val_ex);
-  return 0;
- } catch (e) {
-  GodotRuntime.error(`Error setting variable ${key} on object`, obj);
-  return -1;
- }
-}
-
-function _godot_js_wrapper_object_unref(p_id) {
- const proxy = IDHandler.get(p_id);
- if (proxy !== undefined) {
-  proxy.unref();
- }
-}
-
-var GodotWebGL2 = {};
-
-function _godot_webgl2_glFramebufferTextureMultiviewOVR(target, attachment, texture, level, base_view_index, num_views) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(52, 1, target, attachment, texture, level, base_view_index, num_views);
- const context = GL.currentContext;
- if (typeof context.multiviewExt === "undefined") {
-  const ext = context.GLctx.getExtension("OVR_multiview2");
-  if (!ext) {
-   console.error("Trying to call glFramebufferTextureMultiviewOVR() without the OVR_multiview2 extension");
-   return;
-  }
-  context.multiviewExt = ext;
- }
- const ext = context.multiviewExt;
- ext.framebufferTextureMultiviewOVR(target, attachment, GL.textures[texture], level, base_view_index, num_views);
-}
-
-var GodotWebXR = {
- gl: null,
- session: null,
- gl_binding: null,
- layer: null,
- space: null,
- frame: null,
- pose: null,
- view_count: 1,
- input_sources: [ , , , , , , , , , , , , , , ,  ],
- touches: [ , , , ,  ],
- onsimpleevent: null,
- orig_requestAnimationFrame: null,
- requestAnimationFrame: callback => {
-  if (GodotWebXR.session && GodotWebXR.space) {
-   const onFrame = function(time, frame) {
-    GodotWebXR.frame = frame;
-    GodotWebXR.pose = frame.getViewerPose(GodotWebXR.space);
-    callback(time);
-    GodotWebXR.frame = null;
-    GodotWebXR.pose = null;
-   };
-   GodotWebXR.session.requestAnimationFrame(onFrame);
-  } else {
-   GodotWebXR.orig_requestAnimationFrame(callback);
-  }
- },
- monkeyPatchRequestAnimationFrame: enable => {
-  if (GodotWebXR.orig_requestAnimationFrame === null) {
-   GodotWebXR.orig_requestAnimationFrame = Browser.requestAnimationFrame;
-  }
-  Browser.requestAnimationFrame = enable ? GodotWebXR.requestAnimationFrame : GodotWebXR.orig_requestAnimationFrame;
- },
- pauseResumeMainLoop: () => {
-  Browser.mainLoop.pause();
-  runtimeKeepalivePush();
-  window.setTimeout(function() {
-   runtimeKeepalivePop();
-   Browser.mainLoop.resume();
-  }, 0);
- },
- getLayer: () => {
-  const new_view_count = GodotWebXR.pose ? GodotWebXR.pose.views.length : 1;
-  let layer = GodotWebXR.layer;
-  if (layer && GodotWebXR.view_count === new_view_count) {
-   return layer;
-  }
-  if (!GodotWebXR.session || !GodotWebXR.gl_binding) {
-   return null;
-  }
-  const gl = GodotWebXR.gl;
-  layer = GodotWebXR.gl_binding.createProjectionLayer({
-   textureType: new_view_count > 1 ? "texture-array" : "texture",
-   colorFormat: gl.RGBA8,
-   depthFormat: gl.DEPTH_COMPONENT24
-  });
-  GodotWebXR.session.updateRenderState({
-   layers: [ layer ]
-  });
-  GodotWebXR.layer = layer;
-  GodotWebXR.view_count = new_view_count;
-  return layer;
- },
- getSubImage: () => {
-  if (!GodotWebXR.pose) {
-   return null;
-  }
-  const layer = GodotWebXR.getLayer();
-  if (layer === null) {
-   return null;
-  }
-  return GodotWebXR.gl_binding.getViewSubImage(layer, GodotWebXR.pose.views[0]);
- },
- getTextureId: texture => {
-  if (texture.name !== undefined) {
-   return texture.name;
-  }
-  const id = GL.getNewId(GL.textures);
-  texture.name = id;
-  GL.textures[id] = texture;
-  return id;
- },
- addInputSource: input_source => {
-  let name = -1;
-  if (input_source.targetRayMode === "tracked-pointer" && input_source.handedness === "left") {
-   name = 0;
-  } else if (input_source.targetRayMode === "tracked-pointer" && input_source.handedness === "right") {
-   name = 1;
-  } else {
-   for (let i = 2; i < 16; i++) {
-    if (!GodotWebXR.input_sources[i]) {
-     name = i;
-     break;
-    }
-   }
-  }
-  if (name >= 0) {
-   GodotWebXR.input_sources[name] = input_source;
-   input_source.name = name;
-   if (input_source.targetRayMode === "screen") {
-    let touch_index = -1;
-    for (let i = 0; i < 5; i++) {
-     if (!GodotWebXR.touches[i]) {
-      touch_index = i;
-      break;
-     }
-    }
-    if (touch_index >= 0) {
-     GodotWebXR.touches[touch_index] = input_source;
-     input_source.touch_index = touch_index;
-    }
-   }
-  }
-  return name;
- },
- removeInputSource: input_source => {
-  if (input_source.name !== undefined) {
-   const name = input_source.name;
-   if (name >= 0 && name < 16) {
-    GodotWebXR.input_sources[name] = null;
-   }
-   if (input_source.touch_index !== undefined) {
-    const touch_index = input_source.touch_index;
-    if (touch_index >= 0 && touch_index < 5) {
-     GodotWebXR.touches[touch_index] = null;
-    }
-   }
-   return name;
-  }
-  return -1;
- },
- getInputSourceId: input_source => {
-  if (input_source !== undefined) {
-   return input_source.name;
-  }
-  return -1;
- },
- getTouchIndex: input_source => {
-  if (input_source.touch_index !== undefined) {
-   return input_source.touch_index;
-  }
-  return -1;
- }
-};
-
-function _godot_webxr_get_bounds_geometry(r_points) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(53, 1, r_points);
- if (!GodotWebXR.space || !GodotWebXR.space.boundsGeometry) {
-  return 0;
- }
- const point_count = GodotWebXR.space.boundsGeometry.length;
- if (point_count === 0) {
-  return 0;
- }
- const buf = GodotRuntime.malloc(point_count * 3 * 4);
- for (let i = 0; i < point_count; i++) {
-  const point = GodotWebXR.space.boundsGeometry[i];
-  GodotRuntime.setHeapValue(buf + (i * 3 + 0) * 4, point.x, "float");
-  GodotRuntime.setHeapValue(buf + (i * 3 + 1) * 4, point.y, "float");
-  GodotRuntime.setHeapValue(buf + (i * 3 + 2) * 4, point.z, "float");
- }
- GodotRuntime.setHeapValue(r_points, buf, "i32");
- return point_count;
-}
-
-function _godot_webxr_get_color_texture() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(54, 1);
- const subimage = GodotWebXR.getSubImage();
- if (subimage === null) {
-  return 0;
- }
- return GodotWebXR.getTextureId(subimage.colorTexture);
-}
-
-function _godot_webxr_get_depth_texture() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(55, 1);
- const subimage = GodotWebXR.getSubImage();
- if (subimage === null) {
-  return 0;
- }
- if (!subimage.depthStencilTexture) {
-  return 0;
- }
- return GodotWebXR.getTextureId(subimage.depthStencilTexture);
-}
-
-function _godot_webxr_get_frame_rate() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(56, 1);
- if (!GodotWebXR.session || GodotWebXR.session.frameRate === undefined) {
-  return 0;
- }
- return GodotWebXR.session.frameRate;
-}
-
-function _godot_webxr_get_projection_for_view(p_view, r_transform) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(57, 1, p_view, r_transform);
- if (!GodotWebXR.session || !GodotWebXR.pose) {
-  return false;
- }
- const matrix = GodotWebXR.pose.views[p_view].projectionMatrix;
- for (let i = 0; i < 16; i++) {
-  GodotRuntime.setHeapValue(r_transform + i * 4, matrix[i], "float");
- }
- return true;
-}
-
-function _godot_webxr_get_render_target_size(r_size) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(58, 1, r_size);
- const subimage = GodotWebXR.getSubImage();
- if (subimage === null) {
-  return false;
- }
- GodotRuntime.setHeapValue(r_size + 0, subimage.viewport.width, "i32");
- GodotRuntime.setHeapValue(r_size + 4, subimage.viewport.height, "i32");
- return true;
-}
-
-function _godot_webxr_get_supported_frame_rates(r_frame_rates) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(59, 1, r_frame_rates);
- if (!GodotWebXR.session || GodotWebXR.session.supportedFrameRates === undefined) {
-  return 0;
- }
- const frame_rate_count = GodotWebXR.session.supportedFrameRates.length;
- if (frame_rate_count === 0) {
-  return 0;
- }
- const buf = GodotRuntime.malloc(frame_rate_count * 4);
- for (let i = 0; i < frame_rate_count; i++) {
-  GodotRuntime.setHeapValue(buf + i * 4, GodotWebXR.session.supportedFrameRates[i], "float");
- }
- GodotRuntime.setHeapValue(r_frame_rates, buf, "i32");
- return frame_rate_count;
-}
-
-function _godot_webxr_get_transform_for_view(p_view, r_transform) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(60, 1, p_view, r_transform);
- if (!GodotWebXR.session || !GodotWebXR.pose) {
-  return false;
- }
- const views = GodotWebXR.pose.views;
- let matrix;
- if (p_view >= 0) {
-  matrix = views[p_view].transform.matrix;
- } else {
-  matrix = GodotWebXR.pose.transform.matrix;
- }
- for (let i = 0; i < 16; i++) {
-  GodotRuntime.setHeapValue(r_transform + i * 4, matrix[i], "float");
- }
- return true;
-}
-
-function _godot_webxr_get_velocity_texture() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(61, 1);
- const subimage = GodotWebXR.getSubImage();
- if (subimage === null) {
-  return 0;
- }
- if (!subimage.motionVectorTexture) {
-  return 0;
- }
- return GodotWebXR.getTextureId(subimage.motionVectorTexture);
-}
-
-function _godot_webxr_get_view_count() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(62, 1);
- if (!GodotWebXR.session || !GodotWebXR.pose) {
-  return 1;
- }
- const view_count = GodotWebXR.pose.views.length;
- return view_count > 0 ? view_count : 1;
-}
-
-function _godot_webxr_get_visibility_state() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(63, 1);
- if (!GodotWebXR.session || !GodotWebXR.session.visibilityState) {
-  return 0;
- }
- return GodotRuntime.allocString(GodotWebXR.session.visibilityState);
-}
-
-function _godot_webxr_initialize(p_session_mode, p_required_features, p_optional_features, p_requested_reference_spaces, p_on_session_started, p_on_session_ended, p_on_session_failed, p_on_input_event, p_on_simple_event) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(64, 1, p_session_mode, p_required_features, p_optional_features, p_requested_reference_spaces, p_on_session_started, p_on_session_ended, p_on_session_failed, p_on_input_event, p_on_simple_event);
- GodotWebXR.monkeyPatchRequestAnimationFrame(true);
- const session_mode = GodotRuntime.parseString(p_session_mode);
- const required_features = GodotRuntime.parseString(p_required_features).split(",").map(s => s.trim()).filter(s => s !== "");
- const optional_features = GodotRuntime.parseString(p_optional_features).split(",").map(s => s.trim()).filter(s => s !== "");
- const requested_reference_space_types = GodotRuntime.parseString(p_requested_reference_spaces).split(",").map(s => s.trim());
- const onstarted = GodotRuntime.get_func(p_on_session_started);
- const onended = GodotRuntime.get_func(p_on_session_ended);
- const onfailed = GodotRuntime.get_func(p_on_session_failed);
- const oninputevent = GodotRuntime.get_func(p_on_input_event);
- const onsimpleevent = GodotRuntime.get_func(p_on_simple_event);
- const session_init = {};
- if (required_features.length > 0) {
-  session_init["requiredFeatures"] = required_features;
- }
- if (optional_features.length > 0) {
-  session_init["optionalFeatures"] = optional_features;
- }
- navigator.xr.requestSession(session_mode, session_init).then(function(session) {
-  GodotWebXR.session = session;
-  session.addEventListener("end", function(evt) {
-   onended();
-  });
-  session.addEventListener("inputsourceschange", function(evt) {
-   evt.added.forEach(GodotWebXR.addInputSource);
-   evt.removed.forEach(GodotWebXR.removeInputSource);
-  });
-  [ "selectstart", "selectend", "squeezestart", "squeezeend" ].forEach((input_event, index) => {
-   session.addEventListener(input_event, function(evt) {
-    GodotWebXR.frame = evt.frame;
-    oninputevent(index, GodotWebXR.getInputSourceId(evt.inputSource));
-    GodotWebXR.frame = null;
-   });
-  });
-  session.addEventListener("visibilitychange", function(evt) {
-   const c_str = GodotRuntime.allocString("visibility_state_changed");
-   onsimpleevent(c_str);
-   GodotRuntime.free(c_str);
-  });
-  GodotWebXR.onsimpleevent = onsimpleevent;
-  const gl_context_handle = _emscripten_webgl_get_current_context();
-  const gl = GL.getContext(gl_context_handle).GLctx;
-  GodotWebXR.gl = gl;
-  gl.makeXRCompatible().then(function() {
-   GodotWebXR.gl_binding = new XRWebGLBinding(session, gl);
-   GodotWebXR.getLayer();
-   function onReferenceSpaceSuccess(reference_space, reference_space_type) {
-    GodotWebXR.space = reference_space;
-    reference_space.onreset = function(evt) {
-     const c_str = GodotRuntime.allocString("reference_space_reset");
-     onsimpleevent(c_str);
-     GodotRuntime.free(c_str);
-    };
-    GodotWebXR.pauseResumeMainLoop();
-    window.setTimeout(function() {
-     const c_str = GodotRuntime.allocString(reference_space_type);
-     onstarted(c_str);
-     GodotRuntime.free(c_str);
-    }, 0);
-   }
-   function requestReferenceSpace() {
-    const reference_space_type = requested_reference_space_types.shift();
-    session.requestReferenceSpace(reference_space_type).then(refSpace => {
-     onReferenceSpaceSuccess(refSpace, reference_space_type);
-    }).catch(() => {
-     if (requested_reference_space_types.length === 0) {
-      const c_str = GodotRuntime.allocString("Unable to get any of the requested reference space types");
-      onfailed(c_str);
-      GodotRuntime.free(c_str);
-     } else {
-      requestReferenceSpace();
-     }
-    });
-   }
-   requestReferenceSpace();
-  }).catch(function(error) {
-   const c_str = GodotRuntime.allocString(`Unable to make WebGL context compatible with WebXR: ${error}`);
-   onfailed(c_str);
-   GodotRuntime.free(c_str);
-  });
- }).catch(function(error) {
-  const c_str = GodotRuntime.allocString(`Unable to start session: ${error}`);
-  onfailed(c_str);
-  GodotRuntime.free(c_str);
- });
-}
-
-function _godot_webxr_is_session_supported(p_session_mode, p_callback) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(65, 1, p_session_mode, p_callback);
- const session_mode = GodotRuntime.parseString(p_session_mode);
- const cb = GodotRuntime.get_func(p_callback);
- if (navigator.xr) {
-  navigator.xr.isSessionSupported(session_mode).then(function(supported) {
-   const c_str = GodotRuntime.allocString(session_mode);
-   cb(c_str, supported ? 1 : 0);
-   GodotRuntime.free(c_str);
-  });
- } else {
-  const c_str = GodotRuntime.allocString(session_mode);
-  cb(c_str, 0);
-  GodotRuntime.free(c_str);
- }
-}
-
-function _godot_webxr_is_supported() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(66, 1);
- return !!navigator.xr;
-}
-
-function _godot_webxr_uninitialize() {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(67, 1);
- if (GodotWebXR.session) {
-  GodotWebXR.session.end().catch(e => {});
- }
- GodotWebXR.session = null;
- GodotWebXR.gl_binding = null;
- GodotWebXR.layer = null;
- GodotWebXR.space = null;
- GodotWebXR.frame = null;
- GodotWebXR.pose = null;
- GodotWebXR.view_count = 1;
- GodotWebXR.input_sources = new Array(16);
- GodotWebXR.touches = new Array(5);
- GodotWebXR.onsimpleevent = null;
- GodotWebXR.monkeyPatchRequestAnimationFrame(false);
- GodotWebXR.pauseResumeMainLoop();
-}
-
-function _godot_webxr_update_input_source(p_input_source_id, r_target_pose, r_target_ray_mode, r_touch_index, r_has_grip_pose, r_grip_pose, r_has_standard_mapping, r_button_count, r_buttons, r_axes_count, r_axes) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(68, 1, p_input_source_id, r_target_pose, r_target_ray_mode, r_touch_index, r_has_grip_pose, r_grip_pose, r_has_standard_mapping, r_button_count, r_buttons, r_axes_count, r_axes);
- if (!GodotWebXR.session || !GodotWebXR.frame) {
-  return 0;
- }
- if (p_input_source_id < 0 || p_input_source_id >= GodotWebXR.input_sources.length || !GodotWebXR.input_sources[p_input_source_id]) {
-  return false;
- }
- const input_source = GodotWebXR.input_sources[p_input_source_id];
- const frame = GodotWebXR.frame;
- const space = GodotWebXR.space;
- const target_pose = frame.getPose(input_source.targetRaySpace, space);
- if (!target_pose) {
-  return false;
- }
- const target_pose_matrix = target_pose.transform.matrix;
- for (let i = 0; i < 16; i++) {
-  GodotRuntime.setHeapValue(r_target_pose + i * 4, target_pose_matrix[i], "float");
- }
- let target_ray_mode = 0;
- switch (input_source.targetRayMode) {
- case "gaze":
-  target_ray_mode = 1;
-  break;
-
- case "tracked-pointer":
-  target_ray_mode = 2;
-  break;
-
- case "screen":
-  target_ray_mode = 3;
-  break;
-
- default:
- }
- GodotRuntime.setHeapValue(r_target_ray_mode, target_ray_mode, "i32");
- GodotRuntime.setHeapValue(r_touch_index, GodotWebXR.getTouchIndex(input_source), "i32");
- let has_grip_pose = false;
- if (input_source.gripSpace) {
-  const grip_pose = frame.getPose(input_source.gripSpace, space);
-  if (grip_pose) {
-   const grip_pose_matrix = grip_pose.transform.matrix;
-   for (let i = 0; i < 16; i++) {
-    GodotRuntime.setHeapValue(r_grip_pose + i * 4, grip_pose_matrix[i], "float");
-   }
-   has_grip_pose = true;
-  }
- }
- GodotRuntime.setHeapValue(r_has_grip_pose, has_grip_pose ? 1 : 0, "i32");
- let has_standard_mapping = false;
- let button_count = 0;
- let axes_count = 0;
- if (input_source.gamepad) {
-  if (input_source.gamepad.mapping === "xr-standard") {
-   has_standard_mapping = true;
-  }
-  button_count = Math.min(input_source.gamepad.buttons.length, 10);
-  for (let i = 0; i < button_count; i++) {
-   GodotRuntime.setHeapValue(r_buttons + i * 4, input_source.gamepad.buttons[i].value, "float");
-  }
-  axes_count = Math.min(input_source.gamepad.axes.length, 10);
-  for (let i = 0; i < axes_count; i++) {
-   GodotRuntime.setHeapValue(r_axes + i * 4, input_source.gamepad.axes[i], "float");
-  }
- }
- GodotRuntime.setHeapValue(r_has_standard_mapping, has_standard_mapping ? 1 : 0, "i32");
- GodotRuntime.setHeapValue(r_button_count, button_count, "i32");
- GodotRuntime.setHeapValue(r_axes_count, axes_count, "i32");
- return true;
-}
-
-function _godot_webxr_update_target_frame_rate(p_frame_rate) {
- if (ENVIRONMENT_IS_PTHREAD) return _emscripten_proxy_to_main_thread_js(69, 1, p_frame_rate);
- if (!GodotWebXR.session || GodotWebXR.session.updateTargetFrameRate === undefined) {
-  return;
- }
- GodotWebXR.session.updateTargetFrameRate(p_frame_rate).then(() => {
-  const c_str = GodotRuntime.allocString("display_refresh_rate_changed");
-  GodotWebXR.onsimpleevent(c_str);
-  GodotRuntime.free(c_str);
- });
-}
-
-function _setTempRet0(val) {
- setTempRet0(val);
-}
-
-function __isLeapYear(year) {
- return year % 4 === 0 && (year % 100 !== 0 || year % 400 === 0);
-}
-
-function __arraySum(array, index) {
- var sum = 0;
- for (var i = 0; i <= index; sum += array[i++]) {}
- return sum;
-}
-
-var __MONTH_DAYS_LEAP = [ 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 ];
-
-var __MONTH_DAYS_REGULAR = [ 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 ];
-
-function __addDays(date, days) {
- var newDate = new Date(date.getTime());
- while (days > 0) {
-  var leap = __isLeapYear(newDate.getFullYear());
-  var currentMonth = newDate.getMonth();
-  var daysInCurrentMonth = (leap ? __MONTH_DAYS_LEAP : __MONTH_DAYS_REGULAR)[currentMonth];
-  if (days > daysInCurrentMonth - newDate.getDate()) {
-   days -= daysInCurrentMonth - newDate.getDate() + 1;
-   newDate.setDate(1);
-   if (currentMonth < 11) {
-    newDate.setMonth(currentMonth + 1);
-   } else {
-    newDate.setMonth(0);
-    newDate.setFullYear(newDate.getFullYear() + 1);
-   }
-  } else {
-   newDate.setDate(newDate.getDate() + days);
-   return newDate;
-  }
- }
- return newDate;
-}
-
-function _strftime(s, maxsize, format, tm) {
- var tm_zone = GROWABLE_HEAP_I32()[tm + 40 >> 2];
- var date = {
-  tm_sec: GROWABLE_HEAP_I32()[tm >> 2],
-  tm_min: GROWABLE_HEAP_I32()[tm + 4 >> 2],
-  tm_hour: GROWABLE_HEAP_I32()[tm + 8 >> 2],
-  tm_mday: GROWABLE_HEAP_I32()[tm + 12 >> 2],
-  tm_mon: GROWABLE_HEAP_I32()[tm + 16 >> 2],
-  tm_year: GROWABLE_HEAP_I32()[tm + 20 >> 2],
-  tm_wday: GROWABLE_HEAP_I32()[tm + 24 >> 2],
-  tm_yday: GROWABLE_HEAP_I32()[tm + 28 >> 2],
-  tm_isdst: GROWABLE_HEAP_I32()[tm + 32 >> 2],
-  tm_gmtoff: GROWABLE_HEAP_I32()[tm + 36 >> 2],
-  tm_zone: tm_zone ? UTF8ToString(tm_zone) : ""
- };
- var pattern = UTF8ToString(format);
- var EXPANSION_RULES_1 = {
-  "%c": "%a %b %d %H:%M:%S %Y",
-  "%D": "%m/%d/%y",
-  "%F": "%Y-%m-%d",
-  "%h": "%b",
-  "%r": "%I:%M:%S %p",
-  "%R": "%H:%M",
-  "%T": "%H:%M:%S",
-  "%x": "%m/%d/%y",
-  "%X": "%H:%M:%S",
-  "%Ec": "%c",
-  "%EC": "%C",
-  "%Ex": "%m/%d/%y",
-  "%EX": "%H:%M:%S",
-  "%Ey": "%y",
-  "%EY": "%Y",
-  "%Od": "%d",
-  "%Oe": "%e",
-  "%OH": "%H",
-  "%OI": "%I",
-  "%Om": "%m",
-  "%OM": "%M",
-  "%OS": "%S",
-  "%Ou": "%u",
-  "%OU": "%U",
-  "%OV": "%V",
-  "%Ow": "%w",
-  "%OW": "%W",
-  "%Oy": "%y"
- };
- for (var rule in EXPANSION_RULES_1) {
-  pattern = pattern.replace(new RegExp(rule, "g"), EXPANSION_RULES_1[rule]);
- }
- var WEEKDAYS = [ "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday" ];
- var MONTHS = [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ];
- function leadingSomething(value, digits, character) {
-  var str = typeof value == "number" ? value.toString() : value || "";
-  while (str.length < digits) {
-   str = character[0] + str;
-  }
-  return str;
- }
- function leadingNulls(value, digits) {
-  return leadingSomething(value, digits, "0");
- }
- function compareByDay(date1, date2) {
-  function sgn(value) {
-   return value < 0 ? -1 : value > 0 ? 1 : 0;
-  }
-  var compare;
-  if ((compare = sgn(date1.getFullYear() - date2.getFullYear())) === 0) {
-   if ((compare = sgn(date1.getMonth() - date2.getMonth())) === 0) {
-    compare = sgn(date1.getDate() - date2.getDate());
-   }
-  }
-  return compare;
- }
- function getFirstWeekStartDate(janFourth) {
-  switch (janFourth.getDay()) {
-  case 0:
-   return new Date(janFourth.getFullYear() - 1, 11, 29);
-
-  case 1:
-   return janFourth;
-
-  case 2:
-   return new Date(janFourth.getFullYear(), 0, 3);
-
-  case 3:
-   return new Date(janFourth.getFullYear(), 0, 2);
-
-  case 4:
-   return new Date(janFourth.getFullYear(), 0, 1);
-
-  case 5:
-   return new Date(janFourth.getFullYear() - 1, 11, 31);
-
-  case 6:
-   return new Date(janFourth.getFullYear() - 1, 11, 30);
-  }
- }
- function getWeekBasedYear(date) {
-  var thisDate = __addDays(new Date(date.tm_year + 1900, 0, 1), date.tm_yday);
-  var janFourthThisYear = new Date(thisDate.getFullYear(), 0, 4);
-  var janFourthNextYear = new Date(thisDate.getFullYear() + 1, 0, 4);
-  var firstWeekStartThisYear = getFirstWeekStartDate(janFourthThisYear);
-  var firstWeekStartNextYear = getFirstWeekStartDate(janFourthNextYear);
-  if (compareByDay(firstWeekStartThisYear, thisDate) <= 0) {
-   if (compareByDay(firstWeekStartNextYear, thisDate) <= 0) {
-    return thisDate.getFullYear() + 1;
-   }
-   return thisDate.getFullYear();
-  }
-  return thisDate.getFullYear() - 1;
- }
- var EXPANSION_RULES_2 = {
-  "%a": function(date) {
-   return WEEKDAYS[date.tm_wday].substring(0, 3);
-  },
-  "%A": function(date) {
-   return WEEKDAYS[date.tm_wday];
-  },
-  "%b": function(date) {
-   return MONTHS[date.tm_mon].substring(0, 3);
-  },
-  "%B": function(date) {
-   return MONTHS[date.tm_mon];
-  },
-  "%C": function(date) {
-   var year = date.tm_year + 1900;
-   return leadingNulls(year / 100 | 0, 2);
-  },
-  "%d": function(date) {
-   return leadingNulls(date.tm_mday, 2);
-  },
-  "%e": function(date) {
-   return leadingSomething(date.tm_mday, 2, " ");
-  },
-  "%g": function(date) {
-   return getWeekBasedYear(date).toString().substring(2);
-  },
-  "%G": function(date) {
-   return getWeekBasedYear(date);
-  },
-  "%H": function(date) {
-   return leadingNulls(date.tm_hour, 2);
-  },
-  "%I": function(date) {
-   var twelveHour = date.tm_hour;
-   if (twelveHour == 0) twelveHour = 12; else if (twelveHour > 12) twelveHour -= 12;
-   return leadingNulls(twelveHour, 2);
-  },
-  "%j": function(date) {
-   return leadingNulls(date.tm_mday + __arraySum(__isLeapYear(date.tm_year + 1900) ? __MONTH_DAYS_LEAP : __MONTH_DAYS_REGULAR, date.tm_mon - 1), 3);
-  },
-  "%m": function(date) {
-   return leadingNulls(date.tm_mon + 1, 2);
-  },
-  "%M": function(date) {
-   return leadingNulls(date.tm_min, 2);
-  },
-  "%n": function() {
-   return "\n";
-  },
-  "%p": function(date) {
-   if (date.tm_hour >= 0 && date.tm_hour < 12) {
-    return "AM";
-   }
-   return "PM";
-  },
-  "%S": function(date) {
-   return leadingNulls(date.tm_sec, 2);
-  },
-  "%t": function() {
-   return "\t";
-  },
-  "%u": function(date) {
-   return date.tm_wday || 7;
-  },
-  "%U": function(date) {
-   var days = date.tm_yday + 7 - date.tm_wday;
-   return leadingNulls(Math.floor(days / 7), 2);
-  },
-  "%V": function(date) {
-   var val = Math.floor((date.tm_yday + 7 - (date.tm_wday + 6) % 7) / 7);
-   if ((date.tm_wday + 371 - date.tm_yday - 2) % 7 <= 2) {
-    val++;
-   }
-   if (!val) {
-    val = 52;
-    var dec31 = (date.tm_wday + 7 - date.tm_yday - 1) % 7;
-    if (dec31 == 4 || dec31 == 5 && __isLeapYear(date.tm_year % 400 - 1)) {
-     val++;
-    }
-   } else if (val == 53) {
-    var jan1 = (date.tm_wday + 371 - date.tm_yday) % 7;
-    if (jan1 != 4 && (jan1 != 3 || !__isLeapYear(date.tm_year))) val = 1;
-   }
-   return leadingNulls(val, 2);
-  },
-  "%w": function(date) {
-   return date.tm_wday;
-  },
-  "%W": function(date) {
-   var days = date.tm_yday + 7 - (date.tm_wday + 6) % 7;
-   return leadingNulls(Math.floor(days / 7), 2);
-  },
-  "%y": function(date) {
-   return (date.tm_year + 1900).toString().substring(2);
-  },
-  "%Y": function(date) {
-   return date.tm_year + 1900;
-  },
-  "%z": function(date) {
-   var off = date.tm_gmtoff;
-   var ahead = off >= 0;
-   off = Math.abs(off) / 60;
-   off = off / 60 * 100 + off % 60;
-   return (ahead ? "+" : "-") + String("0000" + off).slice(-4);
-  },
-  "%Z": function(date) {
-   return date.tm_zone;
-  },
-  "%%": function() {
-   return "%";
-  }
- };
- pattern = pattern.replace(/%%/g, "\0\0");
- for (var rule in EXPANSION_RULES_2) {
-  if (pattern.includes(rule)) {
-   pattern = pattern.replace(new RegExp(rule, "g"), EXPANSION_RULES_2[rule](date));
-  }
- }
- pattern = pattern.replace(/\0\0/g, "%");
- var bytes = intArrayFromString(pattern, false);
- if (bytes.length > maxsize) {
-  return 0;
- }
- writeArrayToMemory(bytes, s);
- return bytes.length - 1;
-}
-
-function _strftime_l(s, maxsize, format, tm) {
- return _strftime(s, maxsize, format, tm);
-}
-
-function allocateUTF8OnStack(str) {
- var size = lengthBytesUTF8(str) + 1;
- var ret = stackAlloc(size);
- stringToUTF8Array(str, GROWABLE_HEAP_I8(), ret, size);
- return ret;
-}
-
-function uleb128Encode(n, target) {
- assert(n < 16384);
- if (n < 128) {
-  target.push(n);
- } else {
-  target.push(n % 128 | 128, n >> 7);
- }
-}
-
-function sigToWasmTypes(sig) {
- var typeNames = {
-  "i": "i32",
-  "j": "i64",
-  "f": "f32",
-  "d": "f64",
-  "p": "i32"
- };
- var type = {
-  parameters: [],
-  results: sig[0] == "v" ? [] : [ typeNames[sig[0]] ]
- };
- for (var i = 1; i < sig.length; ++i) {
-  assert(sig[i] in typeNames, "invalid signature char: " + sig[i]);
-  type.parameters.push(typeNames[sig[i]]);
- }
- return type;
-}
-
-function convertJsFunctionToWasm(func, sig) {
- if (typeof WebAssembly.Function == "function") {
-  return new WebAssembly.Function(sigToWasmTypes(sig), func);
- }
- var typeSectionBody = [ 1, 96 ];
- var sigRet = sig.slice(0, 1);
- var sigParam = sig.slice(1);
- var typeCodes = {
-  "i": 127,
-  "p": 127,
-  "j": 126,
-  "f": 125,
-  "d": 124
- };
- uleb128Encode(sigParam.length, typeSectionBody);
- for (var i = 0; i < sigParam.length; ++i) {
-  assert(sigParam[i] in typeCodes, "invalid signature char: " + sigParam[i]);
-  typeSectionBody.push(typeCodes[sigParam[i]]);
- }
- if (sigRet == "v") {
-  typeSectionBody.push(0);
- } else {
-  typeSectionBody.push(1, typeCodes[sigRet]);
- }
- var bytes = [ 0, 97, 115, 109, 1, 0, 0, 0, 1 ];
- uleb128Encode(typeSectionBody.length, bytes);
- bytes.push.apply(bytes, typeSectionBody);
- bytes.push(2, 7, 1, 1, 101, 1, 102, 0, 0, 7, 5, 1, 1, 102, 0, 0);
- var module = new WebAssembly.Module(new Uint8Array(bytes));
- var instance = new WebAssembly.Instance(module, {
-  "e": {
-   "f": func
-  }
- });
- var wrappedFunc = instance.exports["f"];
- return wrappedFunc;
-}
-
-function updateTableMap(offset, count) {
- if (functionsInTableMap) {
-  for (var i = offset; i < offset + count; i++) {
-   var item = getWasmTableEntry(i);
-   if (item) {
-    functionsInTableMap.set(item, i);
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-var functionsInTableMap = undefined;
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-var freeTableIndexes = [];
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-function getEmptyTableSlot() {
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-  wasmTable.grow(1);
- } catch (err) {
-  if (!(err instanceof RangeError)) {
-   throw err;
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-  throw "Unable to grow wasm table. Set ALLOW_TABLE_GROWTH.";
- }
- return wasmTable.length - 1;
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-
-function setWasmTableEntry(idx, func) {
- wasmTable.set(idx, func);
- wasmTableMirror[idx] = wasmTable.get(idx);
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-
-function addFunction(func, sig) {
- assert(typeof func != "undefined");
- if (!functionsInTableMap) {
-  functionsInTableMap = new WeakMap();
-  updateTableMap(0, wasmTable.length);
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-  return functionsInTableMap.get(func);
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- var ret = getEmptyTableSlot();
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-  setWasmTableEntry(ret, func);
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-  if (!(err instanceof TypeError)) {
-   throw err;
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-  assert(typeof sig != "undefined", "Missing signature argument to addFunction: " + func);
-  var wrapped = convertJsFunctionToWasm(func, sig);
-  setWasmTableEntry(ret, wrapped);
- }
- functionsInTableMap.set(func, ret);
- return ret;
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-
-function removeFunction(index) {
- functionsInTableMap.delete(getWasmTableEntry(index));
- freeTableIndexes.push(index);
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-
-var ALLOC_NORMAL = 0;
-
-var ALLOC_STACK = 1;
-
-function allocate(slab, allocator) {
- var ret;
- assert(typeof allocator == "number", "allocate no longer takes a type argument");
- assert(typeof slab != "number", "allocate no longer takes a number as arg0");
- if (allocator == ALLOC_STACK) {
-  ret = stackAlloc(slab.length);
- } else {
-  ret = _malloc(slab.length);
- }
- if (!slab.subarray && !slab.slice) {
-  slab = new Uint8Array(slab);
- }
- GROWABLE_HEAP_U8().set(slab, ret);
- return ret;
-}
-
-function AsciiToString(ptr) {
- var str = "";
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-  var ch = GROWABLE_HEAP_U8()[ptr++ >> 0];
-  if (!ch) return str;
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-function stringToAscii(str, outPtr) {
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-var UTF16Decoder = typeof TextDecoder != "undefined" ? new TextDecoder("utf-16le") : undefined;
-
-function UTF16ToString(ptr, maxBytesToRead) {
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-  return str;
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- return outPtr - startPtr;
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-
-function lengthBytesUTF16(str) {
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-  if (utf32 == 0) break;
-  ++i;
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-function intArrayToString(array) {
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- }
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- this.rdev = rdev;
-};
-
-var readMode = 292 | 73;
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-var writeMode = 146;
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-Object.defineProperties(FSNode.prototype, {
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-   return (this.mode & readMode) === readMode;
-  },
-  set: function(val) {
-   val ? this.mode |= readMode : this.mode &= ~readMode;
-  }
- },
- write: {
-  get: function() {
-   return (this.mode & writeMode) === writeMode;
-  },
-  set: function(val) {
-   val ? this.mode |= writeMode : this.mode &= ~writeMode;
-  }
- },
- isFolder: {
-  get: function() {
-   return FS.isDir(this.mode);
-  }
- },
- isDevice: {
-  get: function() {
-   return FS.isChrdev(this.mode);
-  }
- }
-});
-
-FS.FSNode = FSNode;
-
-FS.staticInit();
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-ERRNO_CODES = {
- "EPERM": 63,
- "ENOENT": 44,
- "ESRCH": 71,
- "EINTR": 27,
- "EIO": 29,
- "ENXIO": 60,
- "E2BIG": 1,
- "ENOEXEC": 45,
- "EBADF": 8,
- "ECHILD": 12,
- "EAGAIN": 6,
- "EWOULDBLOCK": 6,
- "ENOMEM": 48,
- "EACCES": 2,
- "EFAULT": 21,
- "ENOTBLK": 105,
- "EBUSY": 10,
- "EEXIST": 20,
- "EXDEV": 75,
- "ENODEV": 43,
- "ENOTDIR": 54,
- "EISDIR": 31,
- "EINVAL": 28,
- "ENFILE": 41,
- "EMFILE": 33,
- "ENOTTY": 59,
- "ETXTBSY": 74,
- "EFBIG": 22,
- "ENOSPC": 51,
- "ESPIPE": 70,
- "EROFS": 69,
- "EMLINK": 34,
- "EPIPE": 64,
- "EDOM": 18,
- "ERANGE": 68,
- "ENOMSG": 49,
- "EIDRM": 24,
- "ECHRNG": 106,
- "EL2NSYNC": 156,
- "EL3HLT": 107,
- "EL3RST": 108,
- "ELNRNG": 109,
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- "ENOCSI": 111,
- "EL2HLT": 112,
- "EDEADLK": 16,
- "ENOLCK": 46,
- "EBADE": 113,
- "EBADR": 114,
- "EXFULL": 115,
- "ENOANO": 104,
- "EBADRQC": 103,
- "EBADSLT": 102,
- "EDEADLOCK": 16,
- "EBFONT": 101,
- "ENOSTR": 100,
- "ENODATA": 116,
- "ETIME": 117,
- "ENOSR": 118,
- "ENONET": 119,
- "ENOPKG": 120,
- "EREMOTE": 121,
- "ENOLINK": 47,
- "EADV": 122,
- "ESRMNT": 123,
- "ECOMM": 124,
- "EPROTO": 65,
- "EMULTIHOP": 36,
- "EDOTDOT": 125,
- "EBADMSG": 9,
- "ENOTUNIQ": 126,
- "EBADFD": 127,
- "EREMCHG": 128,
- "ELIBACC": 129,
- "ELIBBAD": 130,
- "ELIBSCN": 131,
- "ELIBMAX": 132,
- "ELIBEXEC": 133,
- "ENOSYS": 52,
- "ENOTEMPTY": 55,
- "ENAMETOOLONG": 37,
- "ELOOP": 32,
- "EOPNOTSUPP": 138,
- "EPFNOSUPPORT": 139,
- "ECONNRESET": 15,
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- "ENOTSOCK": 57,
- "ENOPROTOOPT": 50,
- "ESHUTDOWN": 140,
- "ECONNREFUSED": 14,
- "EADDRINUSE": 3,
- "ECONNABORTED": 13,
- "ENETUNREACH": 40,
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- "ETIMEDOUT": 73,
- "EHOSTDOWN": 142,
- "EHOSTUNREACH": 23,
- "EINPROGRESS": 26,
- "EALREADY": 7,
- "EDESTADDRREQ": 17,
- "EMSGSIZE": 35,
- "EPROTONOSUPPORT": 66,
- "ESOCKTNOSUPPORT": 137,
- "EADDRNOTAVAIL": 4,
- "ENETRESET": 39,
- "EISCONN": 30,
- "ENOTCONN": 53,
- "ETOOMANYREFS": 141,
- "EUSERS": 136,
- "EDQUOT": 19,
- "ESTALE": 72,
- "ENOTSUP": 138,
- "ENOMEDIUM": 148,
- "EILSEQ": 25,
- "EOVERFLOW": 61,
- "ECANCELED": 11,
- "ENOTRECOVERABLE": 56,
- "EOWNERDEAD": 62,
- "ESTRPIPE": 135
-};
-
-Module["requestFullscreen"] = function Module_requestFullscreen(lockPointer, resizeCanvas) {
- Browser.requestFullscreen(lockPointer, resizeCanvas);
-};
-
-Module["requestFullScreen"] = function Module_requestFullScreen() {
- Browser.requestFullScreen();
-};
-
-Module["requestAnimationFrame"] = function Module_requestAnimationFrame(func) {
- Browser.requestAnimationFrame(func);
-};
-
-Module["setCanvasSize"] = function Module_setCanvasSize(width, height, noUpdates) {
- Browser.setCanvasSize(width, height, noUpdates);
-};
-
-Module["pauseMainLoop"] = function Module_pauseMainLoop() {
- Browser.mainLoop.pause();
-};
-
-Module["resumeMainLoop"] = function Module_resumeMainLoop() {
- Browser.mainLoop.resume();
-};
-
-Module["getUserMedia"] = function Module_getUserMedia() {
- Browser.getUserMedia();
-};
-
-Module["createContext"] = function Module_createContext(canvas, useWebGL, setInModule, webGLContextAttributes) {
- return Browser.createContext(canvas, useWebGL, setInModule, webGLContextAttributes);
-};
-
-var preloadedImages = {};
-
-var preloadedAudios = {};
-
-var GLctx;
-
-var __miniTempWebGLIntBuffersStorage = new Int32Array(288);
-
-for (var i = 0; i < 288; ++i) {
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-}
-
-var miniTempWebGLFloatBuffersStorage = new Float32Array(288);
-
-for (var i = 0; i < 288; ++i) {
- miniTempWebGLFloatBuffers[i] = miniTempWebGLFloatBuffersStorage.subarray(0, i + 1);
-}
-
-Module["request_quit"] = function() {
- GodotOS.request_quit();
-};
-
-Module["onExit"] = GodotOS.cleanup;
-
-GodotOS._fs_sync_promise = Promise.resolve();
-
-Module["initConfig"] = GodotConfig.init_config;
-
-Module["initFS"] = GodotFS.init;
-
-Module["copyToFS"] = GodotFS.copy_to_fs;
-
-GodotOS.atexit(function(resolve, reject) {
- GodotDisplayCursor.clear();
- resolve();
-});
-
-GodotOS.atexit(function(resolve, reject) {
- GodotEventListeners.clear();
- resolve();
-});
-
-GodotOS.atexit(function(resolve, reject) {
- GodotDisplayVK.clear();
- resolve();
-});
-
-GodotJSWrapper.proxies = new Map();
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-
-var ASSERTIONS = true;
-
-function checkIncomingModuleAPI() {
- ignoredModuleProp("fetchSettings");
-}
-
-var asmLibraryArg = {
- "__assert_fail": ___assert_fail,
- "__call_sighandler": ___call_sighandler,
- "__emscripten_init_main_thread_js": ___emscripten_init_main_thread_js,
- "__emscripten_thread_cleanup": ___emscripten_thread_cleanup,
- "__pthread_create_js": ___pthread_create_js,
- "__syscall__newselect": ___syscall__newselect,
- "__syscall_accept4": ___syscall_accept4,
- "__syscall_bind": ___syscall_bind,
- "__syscall_chdir": ___syscall_chdir,
- "__syscall_chmod": ___syscall_chmod,
- "__syscall_connect": ___syscall_connect,
- "__syscall_faccessat": ___syscall_faccessat,
- "__syscall_fchmod": ___syscall_fchmod,
- "__syscall_fcntl64": ___syscall_fcntl64,
- "__syscall_getcwd": ___syscall_getcwd,
- "__syscall_getdents64": ___syscall_getdents64,
- "__syscall_getsockname": ___syscall_getsockname,
- "__syscall_getsockopt": ___syscall_getsockopt,
- "__syscall_ioctl": ___syscall_ioctl,
- "__syscall_listen": ___syscall_listen,
- "__syscall_lstat64": ___syscall_lstat64,
- "__syscall_mkdirat": ___syscall_mkdirat,
- "__syscall_newfstatat": ___syscall_newfstatat,
- "__syscall_openat": ___syscall_openat,
- "__syscall_poll": ___syscall_poll,
- "__syscall_readlinkat": ___syscall_readlinkat,
- "__syscall_recvfrom": ___syscall_recvfrom,
- "__syscall_renameat": ___syscall_renameat,
- "__syscall_rmdir": ___syscall_rmdir,
- "__syscall_sendto": ___syscall_sendto,
- "__syscall_socket": ___syscall_socket,
- "__syscall_stat64": ___syscall_stat64,
- "__syscall_statfs64": ___syscall_statfs64,
- "__syscall_symlink": ___syscall_symlink,
- "__syscall_unlinkat": ___syscall_unlinkat,
- "_dlinit": __dlinit,
- "_dlopen_js": __dlopen_js,
- "_dlsym_js": __dlsym_js,
- "_emscripten_date_now": __emscripten_date_now,
- "_emscripten_default_pthread_stack_size": __emscripten_default_pthread_stack_size,
- "_emscripten_get_now_is_monotonic": __emscripten_get_now_is_monotonic,
- "_emscripten_notify_task_queue": __emscripten_notify_task_queue,
- "_emscripten_proxied_gl_context_activated_from_main_browser_thread": __emscripten_proxied_gl_context_activated_from_main_browser_thread,
- "_emscripten_set_offscreencanvas_size": __emscripten_set_offscreencanvas_size,
- "_emscripten_throw_longjmp": __emscripten_throw_longjmp,
- "_gmtime_js": __gmtime_js,
- "_localtime_js": __localtime_js,
- "_mmap_js": __mmap_js,
- "_munmap_js": __munmap_js,
- "_tzset_js": __tzset_js,
- "abort": _abort,
- "emscripten_cancel_main_loop": _emscripten_cancel_main_loop,
- "emscripten_check_blocking_allowed": _emscripten_check_blocking_allowed,
- "emscripten_console_error": _emscripten_console_error,
- "emscripten_force_exit": _emscripten_force_exit,
- "emscripten_get_now": _emscripten_get_now,
- "emscripten_glActiveTexture": _emscripten_glActiveTexture,
- "emscripten_glAttachShader": _emscripten_glAttachShader,
- "emscripten_glBeginTransformFeedback": _emscripten_glBeginTransformFeedback,
- "emscripten_glBindBuffer": _emscripten_glBindBuffer,
- "emscripten_glBindBufferBase": _emscripten_glBindBufferBase,
- "emscripten_glBindBufferRange": _emscripten_glBindBufferRange,
- "emscripten_glBindFramebuffer": _emscripten_glBindFramebuffer,
- "emscripten_glBindRenderbuffer": _emscripten_glBindRenderbuffer,
- "emscripten_glBindTexture": _emscripten_glBindTexture,
- "emscripten_glBindVertexArray": _emscripten_glBindVertexArray,
- "emscripten_glBlendColor": _emscripten_glBlendColor,
- "emscripten_glBlendEquation": _emscripten_glBlendEquation,
- "emscripten_glBlendFunc": _emscripten_glBlendFunc,
- "emscripten_glBlendFuncSeparate": _emscripten_glBlendFuncSeparate,
- "emscripten_glBlitFramebuffer": _emscripten_glBlitFramebuffer,
- "emscripten_glBufferData": _emscripten_glBufferData,
- "emscripten_glBufferSubData": _emscripten_glBufferSubData,
- "emscripten_glCheckFramebufferStatus": _emscripten_glCheckFramebufferStatus,
- "emscripten_glClear": _emscripten_glClear,
- "emscripten_glClearBufferfv": _emscripten_glClearBufferfv,
- "emscripten_glClearColor": _emscripten_glClearColor,
- "emscripten_glClearDepthf": _emscripten_glClearDepthf,
- "emscripten_glColorMask": _emscripten_glColorMask,
- "emscripten_glCompileShader": _emscripten_glCompileShader,
- "emscripten_glCompressedTexImage2D": _emscripten_glCompressedTexImage2D,
- "emscripten_glCopyBufferSubData": _emscripten_glCopyBufferSubData,
- "emscripten_glCreateProgram": _emscripten_glCreateProgram,
- "emscripten_glCreateShader": _emscripten_glCreateShader,
- "emscripten_glCullFace": _emscripten_glCullFace,
- "emscripten_glDeleteBuffers": _emscripten_glDeleteBuffers,
- "emscripten_glDeleteFramebuffers": _emscripten_glDeleteFramebuffers,
- "emscripten_glDeleteProgram": _emscripten_glDeleteProgram,
- "emscripten_glDeleteQueries": _emscripten_glDeleteQueries,
- "emscripten_glDeleteRenderbuffers": _emscripten_glDeleteRenderbuffers,
- "emscripten_glDeleteShader": _emscripten_glDeleteShader,
- "emscripten_glDeleteSync": _emscripten_glDeleteSync,
- "emscripten_glDeleteTextures": _emscripten_glDeleteTextures,
- "emscripten_glDeleteVertexArrays": _emscripten_glDeleteVertexArrays,
- "emscripten_glDepthFunc": _emscripten_glDepthFunc,
- "emscripten_glDepthMask": _emscripten_glDepthMask,
- "emscripten_glDisable": _emscripten_glDisable,
- "emscripten_glDisableVertexAttribArray": _emscripten_glDisableVertexAttribArray,
- "emscripten_glDrawArrays": _emscripten_glDrawArrays,
- "emscripten_glDrawArraysInstanced": _emscripten_glDrawArraysInstanced,
- "emscripten_glDrawElements": _emscripten_glDrawElements,
- "emscripten_glDrawElementsInstanced": _emscripten_glDrawElementsInstanced,
- "emscripten_glEnable": _emscripten_glEnable,
- "emscripten_glEnableVertexAttribArray": _emscripten_glEnableVertexAttribArray,
- "emscripten_glEndTransformFeedback": _emscripten_glEndTransformFeedback,
- "emscripten_glFenceSync": _emscripten_glFenceSync,
- "emscripten_glFinish": _emscripten_glFinish,
- "emscripten_glFramebufferRenderbuffer": _emscripten_glFramebufferRenderbuffer,
- "emscripten_glFramebufferTexture2D": _emscripten_glFramebufferTexture2D,
- "emscripten_glFramebufferTextureLayer": _emscripten_glFramebufferTextureLayer,
- "emscripten_glFrontFace": _emscripten_glFrontFace,
- "emscripten_glGenBuffers": _emscripten_glGenBuffers,
- "emscripten_glGenFramebuffers": _emscripten_glGenFramebuffers,
- "emscripten_glGenQueries": _emscripten_glGenQueries,
- "emscripten_glGenRenderbuffers": _emscripten_glGenRenderbuffers,
- "emscripten_glGenTextures": _emscripten_glGenTextures,
- "emscripten_glGenVertexArrays": _emscripten_glGenVertexArrays,
- "emscripten_glGenerateMipmap": _emscripten_glGenerateMipmap,
- "emscripten_glGetFloatv": _emscripten_glGetFloatv,
- "emscripten_glGetIntegerv": _emscripten_glGetIntegerv,
- "emscripten_glGetProgramInfoLog": _emscripten_glGetProgramInfoLog,
- "emscripten_glGetProgramiv": _emscripten_glGetProgramiv,
- "emscripten_glGetShaderInfoLog": _emscripten_glGetShaderInfoLog,
- "emscripten_glGetShaderiv": _emscripten_glGetShaderiv,
- "emscripten_glGetString": _emscripten_glGetString,
- "emscripten_glGetStringi": _emscripten_glGetStringi,
- "emscripten_glGetSynciv": _emscripten_glGetSynciv,
- "emscripten_glGetUniformBlockIndex": _emscripten_glGetUniformBlockIndex,
- "emscripten_glGetUniformLocation": _emscripten_glGetUniformLocation,
- "emscripten_glLinkProgram": _emscripten_glLinkProgram,
- "emscripten_glPixelStorei": _emscripten_glPixelStorei,
- "emscripten_glReadBuffer": _emscripten_glReadBuffer,
- "emscripten_glReadPixels": _emscripten_glReadPixels,
- "emscripten_glRenderbufferStorage": _emscripten_glRenderbufferStorage,
- "emscripten_glScissor": _emscripten_glScissor,
- "emscripten_glShaderSource": _emscripten_glShaderSource,
- "emscripten_glTexImage2D": _emscripten_glTexImage2D,
- "emscripten_glTexImage3D": _emscripten_glTexImage3D,
- "emscripten_glTexParameterf": _emscripten_glTexParameterf,
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- "emscripten_glTexStorage2D": _emscripten_glTexStorage2D,
- "emscripten_glTexSubImage3D": _emscripten_glTexSubImage3D,
- "emscripten_glTransformFeedbackVaryings": _emscripten_glTransformFeedbackVaryings,
- "emscripten_glUniform1f": _emscripten_glUniform1f,
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- "emscripten_glUniformMatrix4fv": _emscripten_glUniformMatrix4fv,
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- "emscripten_glVertexAttribDivisor": _emscripten_glVertexAttribDivisor,
- "emscripten_glVertexAttribIPointer": _emscripten_glVertexAttribIPointer,
- "emscripten_glVertexAttribPointer": _emscripten_glVertexAttribPointer,
- "emscripten_glViewport": _emscripten_glViewport,
- "emscripten_memcpy_big": _emscripten_memcpy_big,
- "emscripten_num_logical_cores": _emscripten_num_logical_cores,
- "emscripten_receive_on_main_thread_js": _emscripten_receive_on_main_thread_js,
- "emscripten_resize_heap": _emscripten_resize_heap,
- "emscripten_set_main_loop": _emscripten_set_main_loop,
- "emscripten_supports_offscreencanvas": _emscripten_supports_offscreencanvas,
- "emscripten_unwind_to_js_event_loop": _emscripten_unwind_to_js_event_loop,
- "emscripten_webgl_destroy_context": _emscripten_webgl_destroy_context,
- "emscripten_webgl_do_commit_frame": _emscripten_webgl_do_commit_frame,
- "emscripten_webgl_do_create_context": _emscripten_webgl_do_create_context,
- "emscripten_webgl_enable_extension": _emscripten_webgl_enable_extension,
- "emscripten_webgl_init_context_attributes": _emscripten_webgl_init_context_attributes,
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- "environ_sizes_get": _environ_sizes_get,
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- "fd_close": _fd_close,
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- "getnameinfo": _getnameinfo,
- "glGetBufferSubData": _glGetBufferSubData,
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- "godot_audio_init": _godot_audio_init,
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- "godot_audio_worklet_state_wait": _godot_audio_worklet_state_wait,
- "godot_js_config_canvas_id_get": _godot_js_config_canvas_id_get,
- "godot_js_config_locale_get": _godot_js_config_locale_get,
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- "godot_js_display_fullscreen_exit": _godot_js_display_fullscreen_exit,
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- "godot_js_display_is_swap_ok_cancel": _godot_js_display_is_swap_ok_cancel,
- "godot_js_display_notification_cb": _godot_js_display_notification_cb,
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- "godot_js_display_screen_size_get": _godot_js_display_screen_size_get,
- "godot_js_display_setup_canvas": _godot_js_display_setup_canvas,
- "godot_js_display_size_update": _godot_js_display_size_update,
- "godot_js_display_touchscreen_is_available": _godot_js_display_touchscreen_is_available,
- "godot_js_display_tts_available": _godot_js_display_tts_available,
- "godot_js_display_vk_available": _godot_js_display_vk_available,
- "godot_js_display_vk_cb": _godot_js_display_vk_cb,
- "godot_js_display_vk_hide": _godot_js_display_vk_hide,
- "godot_js_display_vk_show": _godot_js_display_vk_show,
- "godot_js_display_window_blur_cb": _godot_js_display_window_blur_cb,
- "godot_js_display_window_icon_set": _godot_js_display_window_icon_set,
- "godot_js_display_window_size_get": _godot_js_display_window_size_get,
- "godot_js_display_window_title_set": _godot_js_display_window_title_set,
- "godot_js_eval": _godot_js_eval,
- "godot_js_fetch_body_length_get": _godot_js_fetch_body_length_get,
- "godot_js_fetch_create": _godot_js_fetch_create,
- "godot_js_fetch_free": _godot_js_fetch_free,
- "godot_js_fetch_http_status_get": _godot_js_fetch_http_status_get,
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- "godot_js_rtc_datachannel_max_packet_lifetime_get": _godot_js_rtc_datachannel_max_packet_lifetime_get,
- "godot_js_rtc_datachannel_max_retransmits_get": _godot_js_rtc_datachannel_max_retransmits_get,
- "godot_js_rtc_datachannel_protocol_get": _godot_js_rtc_datachannel_protocol_get,
- "godot_js_rtc_datachannel_ready_state_get": _godot_js_rtc_datachannel_ready_state_get,
- "godot_js_rtc_datachannel_send": _godot_js_rtc_datachannel_send,
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- "godot_js_rtc_pc_ice_candidate_add": _godot_js_rtc_pc_ice_candidate_add,
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- "godot_js_rtc_pc_remote_description_set": _godot_js_rtc_pc_remote_description_set,
- "godot_js_tts_get_voices": _godot_js_tts_get_voices,
- "godot_js_tts_is_paused": _godot_js_tts_is_paused,
- "godot_js_tts_is_speaking": _godot_js_tts_is_speaking,
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- "godot_js_wrapper_object_unref": _godot_js_wrapper_object_unref,
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- "godot_webxr_get_bounds_geometry": _godot_webxr_get_bounds_geometry,
- "godot_webxr_get_color_texture": _godot_webxr_get_color_texture,
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- "godot_webxr_get_frame_rate": _godot_webxr_get_frame_rate,
- "godot_webxr_get_projection_for_view": _godot_webxr_get_projection_for_view,
- "godot_webxr_get_render_target_size": _godot_webxr_get_render_target_size,
- "godot_webxr_get_supported_frame_rates": _godot_webxr_get_supported_frame_rates,
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- "godot_webxr_uninitialize": _godot_webxr_uninitialize,
- "godot_webxr_update_input_source": _godot_webxr_update_input_source,
- "godot_webxr_update_target_frame_rate": _godot_webxr_update_target_frame_rate,
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- "invoke_iii": invoke_iii,
- "invoke_iiii": invoke_iiii,
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- "invoke_iiiiiii": invoke_iiiiiii,
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- "invoke_viii": invoke_viii,
- "invoke_viiii": invoke_viiii,
- "invoke_viiiiiii": invoke_viiiiiii,
- "memory": wasmMemory || Module["wasmMemory"],
- "setTempRet0": _setTempRet0,
- "strftime": _strftime,
- "strftime_l": _strftime_l
-};
-
-var asm = createWasm();
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-var _emscripten_webgl_make_context_current = Module["_emscripten_webgl_make_context_current"] = createExportWrapper("emscripten_webgl_make_context_current");
-
-var _emscripten_webgl_commit_frame = Module["_emscripten_webgl_commit_frame"] = createExportWrapper("emscripten_webgl_commit_frame");
-
-var _free = Module["_free"] = createExportWrapper("free");
-
-var __Z14godot_web_mainiPPc = Module["__Z14godot_web_mainiPPc"] = createExportWrapper("_Z14godot_web_mainiPPc");
-
-var _main = Module["_main"] = createExportWrapper("__main_argc_argv");
-
-var _malloc = Module["_malloc"] = createExportWrapper("malloc");
-
-var _htonl = Module["_htonl"] = createExportWrapper("htonl");
-
-var _htons = Module["_htons"] = createExportWrapper("htons");
-
-var _ntohs = Module["_ntohs"] = createExportWrapper("ntohs");
-
-var ___errno_location = Module["___errno_location"] = createExportWrapper("__errno_location");
-
-var _fflush = Module["_fflush"] = createExportWrapper("fflush");
-
-var __emwebxr_on_input_event = Module["__emwebxr_on_input_event"] = createExportWrapper("_emwebxr_on_input_event");
-
-var __emwebxr_on_simple_event = Module["__emwebxr_on_simple_event"] = createExportWrapper("_emwebxr_on_simple_event");
-
-var __emscripten_tls_init = Module["__emscripten_tls_init"] = createExportWrapper("_emscripten_tls_init");
-
-var _pthread_self = Module["_pthread_self"] = createExportWrapper("pthread_self");
-
-var _emscripten_builtin_memalign = Module["_emscripten_builtin_memalign"] = createExportWrapper("emscripten_builtin_memalign");
-
-var _emscripten_webgl_get_current_context = Module["_emscripten_webgl_get_current_context"] = createExportWrapper("emscripten_webgl_get_current_context");
-
-var _emscripten_dispatch_to_thread_ = Module["_emscripten_dispatch_to_thread_"] = createExportWrapper("emscripten_dispatch_to_thread_");
-
-var ___funcs_on_exit = Module["___funcs_on_exit"] = createExportWrapper("__funcs_on_exit");
-
-var ___dl_seterr = Module["___dl_seterr"] = createExportWrapper("__dl_seterr");
-
-var __emscripten_thread_init = Module["__emscripten_thread_init"] = createExportWrapper("_emscripten_thread_init");
-
-var __emscripten_thread_crashed = Module["__emscripten_thread_crashed"] = createExportWrapper("_emscripten_thread_crashed");
-
-var _emscripten_main_thread_process_queued_calls = Module["_emscripten_main_thread_process_queued_calls"] = createExportWrapper("emscripten_main_thread_process_queued_calls");
-
-var _raise = Module["_raise"] = createExportWrapper("raise");
-
-var _emscripten_main_browser_thread_id = Module["_emscripten_main_browser_thread_id"] = createExportWrapper("emscripten_main_browser_thread_id");
-
-var _emscripten_run_in_main_runtime_thread_js = Module["_emscripten_run_in_main_runtime_thread_js"] = createExportWrapper("emscripten_run_in_main_runtime_thread_js");
-
-var _emscripten_stack_get_base = Module["_emscripten_stack_get_base"] = function() {
- return (_emscripten_stack_get_base = Module["_emscripten_stack_get_base"] = Module["asm"]["emscripten_stack_get_base"]).apply(null, arguments);
-};
-
-var _emscripten_stack_get_end = Module["_emscripten_stack_get_end"] = function() {
- return (_emscripten_stack_get_end = Module["_emscripten_stack_get_end"] = Module["asm"]["emscripten_stack_get_end"]).apply(null, arguments);
-};
-
-var __emscripten_proxy_execute_task_queue = Module["__emscripten_proxy_execute_task_queue"] = createExportWrapper("_emscripten_proxy_execute_task_queue");
-
-var __emscripten_thread_free_data = Module["__emscripten_thread_free_data"] = createExportWrapper("_emscripten_thread_free_data");
-
-var __emscripten_thread_exit = Module["__emscripten_thread_exit"] = createExportWrapper("_emscripten_thread_exit");
-
-var _setThrew = Module["_setThrew"] = createExportWrapper("setThrew");
-
-var _saveSetjmp = Module["_saveSetjmp"] = createExportWrapper("saveSetjmp");
-
-var _emscripten_stack_init = Module["_emscripten_stack_init"] = function() {
- return (_emscripten_stack_init = Module["_emscripten_stack_init"] = Module["asm"]["emscripten_stack_init"]).apply(null, arguments);
-};
-
-var _emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = function() {
- return (_emscripten_stack_set_limits = Module["_emscripten_stack_set_limits"] = Module["asm"]["emscripten_stack_set_limits"]).apply(null, arguments);
-};
-
-var _emscripten_stack_get_free = Module["_emscripten_stack_get_free"] = function() {
- return (_emscripten_stack_get_free = Module["_emscripten_stack_get_free"] = Module["asm"]["emscripten_stack_get_free"]).apply(null, arguments);
-};
-
-var stackSave = Module["stackSave"] = createExportWrapper("stackSave");
-
-var stackRestore = Module["stackRestore"] = createExportWrapper("stackRestore");
-
-var stackAlloc = Module["stackAlloc"] = createExportWrapper("stackAlloc");
-
-var dynCall_vjiii = Module["dynCall_vjiii"] = createExportWrapper("dynCall_vjiii");
-
-var dynCall_vij = Module["dynCall_vij"] = createExportWrapper("dynCall_vij");
-
-var dynCall_ji = Module["dynCall_ji"] = createExportWrapper("dynCall_ji");
-
-var dynCall_viiij = Module["dynCall_viiij"] = createExportWrapper("dynCall_viiij");
-
-var dynCall_jii = Module["dynCall_jii"] = createExportWrapper("dynCall_jii");
-
-var dynCall_viij = Module["dynCall_viij"] = createExportWrapper("dynCall_viij");
-
-var dynCall_jiji = Module["dynCall_jiji"] = createExportWrapper("dynCall_jiji");
-
-var dynCall_viiiiifiijii = Module["dynCall_viiiiifiijii"] = createExportWrapper("dynCall_viiiiifiijii");
-
-var dynCall_viiiiifiiijjii = Module["dynCall_viiiiifiiijjii"] = createExportWrapper("dynCall_viiiiifiiijjii");
-
-var dynCall_viiiiifiiijii = Module["dynCall_viiiiifiiijii"] = createExportWrapper("dynCall_viiiiifiiijii");
-
-var dynCall_viiiiifiiiijjii = Module["dynCall_viiiiifiiiijjii"] = createExportWrapper("dynCall_viiiiifiiiijjii");
-
-var dynCall_jiifff = Module["dynCall_jiifff"] = createExportWrapper("dynCall_jiifff");
-
-var dynCall_vijf = Module["dynCall_vijf"] = createExportWrapper("dynCall_vijf");
-
-var dynCall_iij = Module["dynCall_iij"] = createExportWrapper("dynCall_iij");
-
-var dynCall_vijiii = Module["dynCall_vijiii"] = createExportWrapper("dynCall_vijiii");
-
-var dynCall_vijiiii = Module["dynCall_vijiiii"] = createExportWrapper("dynCall_vijiiii");
-
-var dynCall_vijii = Module["dynCall_vijii"] = createExportWrapper("dynCall_vijii");
-
-var dynCall_viiiiij = Module["dynCall_viiiiij"] = createExportWrapper("dynCall_viiiiij");
-
-var dynCall_viiiiiji = Module["dynCall_viiiiiji"] = createExportWrapper("dynCall_viiiiiji");
-
-var dynCall_viiijii = Module["dynCall_viiijii"] = createExportWrapper("dynCall_viiijii");
-
-var dynCall_vijiiiiiidddd = Module["dynCall_vijiiiiiidddd"] = createExportWrapper("dynCall_vijiiiiiidddd");
-
-var dynCall_jij = Module["dynCall_jij"] = createExportWrapper("dynCall_jij");
-
-var dynCall_jiiii = Module["dynCall_jiiii"] = createExportWrapper("dynCall_jiiii");
-
-var dynCall_jiij = Module["dynCall_jiij"] = createExportWrapper("dynCall_jiij");
-
-var dynCall_jiijiiii = Module["dynCall_jiijiiii"] = createExportWrapper("dynCall_jiijiiii");
-
-var dynCall_jiii = Module["dynCall_jiii"] = createExportWrapper("dynCall_jiii");
-
-var dynCall_jiiji = Module["dynCall_jiiji"] = createExportWrapper("dynCall_jiiji");
-
-var dynCall_jiiiji = Module["dynCall_jiiiji"] = createExportWrapper("dynCall_jiiiji");
-
-var dynCall_jiijii = Module["dynCall_jiijii"] = createExportWrapper("dynCall_jiijii");
-
-var dynCall_iijiiij = Module["dynCall_iijiiij"] = createExportWrapper("dynCall_iijiiij");
-
-var dynCall_jijjjiiiiijii = Module["dynCall_jijjjiiiiijii"] = createExportWrapper("dynCall_jijjjiiiiijii");
-
-var dynCall_jijiiiiifiii = Module["dynCall_jijiiiiifiii"] = createExportWrapper("dynCall_jijiiiiifiii");
-
-var dynCall_viijiiiiiifiii = Module["dynCall_viijiiiiiifiii"] = createExportWrapper("dynCall_viijiiiiiifiii");
-
-var dynCall_jiiiiiii = Module["dynCall_jiiiiiii"] = createExportWrapper("dynCall_jiiiiiii");
-
-var dynCall_viji = Module["dynCall_viji"] = createExportWrapper("dynCall_viji");
-
-var dynCall_viiji = Module["dynCall_viiji"] = createExportWrapper("dynCall_viiji");
-
-var dynCall_viiiij = Module["dynCall_viiiij"] = createExportWrapper("dynCall_viiiij");
-
-var dynCall_vijj = Module["dynCall_vijj"] = createExportWrapper("dynCall_vijj");
-
-var dynCall_vijji = Module["dynCall_vijji"] = createExportWrapper("dynCall_vijji");
-
-var dynCall_vijjii = Module["dynCall_vijjii"] = createExportWrapper("dynCall_vijjii");
-
-var dynCall_fij = Module["dynCall_fij"] = createExportWrapper("dynCall_fij");
-
-var dynCall_vijiffifff = Module["dynCall_vijiffifff"] = createExportWrapper("dynCall_vijiffifff");
-
-var dynCall_vijff = Module["dynCall_vijff"] = createExportWrapper("dynCall_vijff");
-
-var dynCall_vijiffff = Module["dynCall_vijiffff"] = createExportWrapper("dynCall_vijiffff");
-
-var dynCall_vijjf = Module["dynCall_vijjf"] = createExportWrapper("dynCall_vijjf");
-
-var dynCall_vijij = Module["dynCall_vijij"] = createExportWrapper("dynCall_vijij");
-
-var dynCall_vijif = Module["dynCall_vijif"] = createExportWrapper("dynCall_vijif");
-
-var dynCall_vijiiifi = Module["dynCall_vijiiifi"] = createExportWrapper("dynCall_vijiiifi");
-
-var dynCall_vijiifi = Module["dynCall_vijiifi"] = createExportWrapper("dynCall_vijiifi");
-
-var dynCall_vijiif = Module["dynCall_vijiif"] = createExportWrapper("dynCall_vijiif");
-
-var dynCall_vijifi = Module["dynCall_vijifi"] = createExportWrapper("dynCall_vijifi");
-
-var dynCall_vijijiii = Module["dynCall_vijijiii"] = createExportWrapper("dynCall_vijijiii");
-
-var dynCall_vijijiiii = Module["dynCall_vijijiiii"] = createExportWrapper("dynCall_vijijiiii");
-
-var dynCall_vijijiiiff = Module["dynCall_vijijiiiff"] = createExportWrapper("dynCall_vijijiiiff");
-
-var dynCall_vijijii = Module["dynCall_vijijii"] = createExportWrapper("dynCall_vijijii");
-
-var dynCall_vijiijiiiiii = Module["dynCall_vijiijiiiiii"] = createExportWrapper("dynCall_vijiijiiiiii");
-
-var dynCall_vijiiij = Module["dynCall_vijiiij"] = createExportWrapper("dynCall_vijiiij");
-
-var dynCall_vijiiiiiiji = Module["dynCall_vijiiiiiiji"] = createExportWrapper("dynCall_vijiiiiiiji");
-
-var dynCall_vijjj = Module["dynCall_vijjj"] = createExportWrapper("dynCall_vijjj");
-
-var dynCall_vijdddd = Module["dynCall_vijdddd"] = createExportWrapper("dynCall_vijdddd");
-
-var dynCall_vijififi = Module["dynCall_vijififi"] = createExportWrapper("dynCall_vijififi");
-
-var dynCall_iiiij = Module["dynCall_iiiij"] = createExportWrapper("dynCall_iiiij");
-
-var dynCall_iijji = Module["dynCall_iijji"] = createExportWrapper("dynCall_iijji");
-
-var dynCall_viijj = Module["dynCall_viijj"] = createExportWrapper("dynCall_viijj");
-
-var dynCall_jiiiiii = Module["dynCall_jiiiiii"] = createExportWrapper("dynCall_jiiiiii");
-
-var dynCall_viiiiji = Module["dynCall_viiiiji"] = createExportWrapper("dynCall_viiiiji");
-
-var dynCall_dij = Module["dynCall_dij"] = createExportWrapper("dynCall_dij");
-
-var dynCall_vijd = Module["dynCall_vijd"] = createExportWrapper("dynCall_vijd");
-
-var dynCall_iiij = Module["dynCall_iiij"] = createExportWrapper("dynCall_iiij");
-
-var dynCall_jiiiii = Module["dynCall_jiiiii"] = createExportWrapper("dynCall_jiiiii");
-
-var dynCall_ij = Module["dynCall_ij"] = createExportWrapper("dynCall_ij");
-
-var dynCall_viijiiii = Module["dynCall_viijiiii"] = createExportWrapper("dynCall_viijiiii");
-
-var dynCall_viijiii = Module["dynCall_viijiii"] = createExportWrapper("dynCall_viijiii");
-
-var dynCall_iiji = Module["dynCall_iiji"] = createExportWrapper("dynCall_iiji");
-
-var dynCall_iiiijf = Module["dynCall_iiiijf"] = createExportWrapper("dynCall_iiiijf");
-
-var dynCall_vijiiiii = Module["dynCall_vijiiiii"] = createExportWrapper("dynCall_vijiiiii");
-
-var dynCall_vijjjii = Module["dynCall_vijjjii"] = createExportWrapper("dynCall_vijjjii");
-
-var dynCall_viijd = Module["dynCall_viijd"] = createExportWrapper("dynCall_viijd");
-
-var dynCall_diij = Module["dynCall_diij"] = createExportWrapper("dynCall_diij");
-
-var dynCall_viiiji = Module["dynCall_viiiji"] = createExportWrapper("dynCall_viiiji");
-
-var dynCall_viiijj = Module["dynCall_viiijj"] = createExportWrapper("dynCall_viiijj");
-
-var dynCall_viijji = Module["dynCall_viijji"] = createExportWrapper("dynCall_viijji");
-
-var dynCall_jiiij = Module["dynCall_jiiij"] = createExportWrapper("dynCall_jiiij");
-
-var dynCall_viijii = Module["dynCall_viijii"] = createExportWrapper("dynCall_viijii");
-
-var dynCall_jiijjj = Module["dynCall_jiijjj"] = createExportWrapper("dynCall_jiijjj");
-
-var dynCall_jiijj = Module["dynCall_jiijj"] = createExportWrapper("dynCall_jiijj");
-
-var dynCall_viiijiji = Module["dynCall_viiijiji"] = createExportWrapper("dynCall_viiijiji");
-
-var dynCall_viiijjiji = Module["dynCall_viiijjiji"] = createExportWrapper("dynCall_viiijjiji");
-
-var dynCall_viijiji = Module["dynCall_viijiji"] = createExportWrapper("dynCall_viijiji");
-
-var dynCall_iiiiijiii = Module["dynCall_iiiiijiii"] = createExportWrapper("dynCall_iiiiijiii");
-
-var dynCall_iiiiiijd = Module["dynCall_iiiiiijd"] = createExportWrapper("dynCall_iiiiiijd");
-
-var dynCall_diidj = Module["dynCall_diidj"] = createExportWrapper("dynCall_diidj");
-
-var dynCall_viiiijij = Module["dynCall_viiiijij"] = createExportWrapper("dynCall_viiiijij");
-
-var dynCall_viiidjj = Module["dynCall_viiidjj"] = createExportWrapper("dynCall_viiidjj");
-
-var dynCall_viidj = Module["dynCall_viidj"] = createExportWrapper("dynCall_viidj");
-
-var dynCall_iiijj = Module["dynCall_iiijj"] = createExportWrapper("dynCall_iiijj");
-
-var dynCall_jiid = Module["dynCall_jiid"] = createExportWrapper("dynCall_jiid");
-
-var dynCall_viiiiddji = Module["dynCall_viiiiddji"] = createExportWrapper("dynCall_viiiiddji");
-
-var dynCall_vijiiiiiiiii = Module["dynCall_vijiiiiiiiii"] = createExportWrapper("dynCall_vijiiiiiiiii");
-
-var dynCall_vijiiiffi = Module["dynCall_vijiiiffi"] = createExportWrapper("dynCall_vijiiiffi");
-
-var dynCall_vijiiifii = Module["dynCall_vijiiifii"] = createExportWrapper("dynCall_vijiiifii");
-
-var dynCall_viijfii = Module["dynCall_viijfii"] = createExportWrapper("dynCall_viijfii");
-
-var dynCall_viiiiiiiiiiijjjjjjifiiiiii = Module["dynCall_viiiiiiiiiiijjjjjjifiiiiii"] = createExportWrapper("dynCall_viiiiiiiiiiijjjjjjifiiiiii");
-
-var dynCall_vijifff = Module["dynCall_vijifff"] = createExportWrapper("dynCall_vijifff");
-
-var dynCall_fiji = Module["dynCall_fiji"] = createExportWrapper("dynCall_fiji");
-
-var dynCall_vijiiffifffi = Module["dynCall_vijiiffifffi"] = createExportWrapper("dynCall_vijiiffifffi");
-
-var dynCall_iijj = Module["dynCall_iijj"] = createExportWrapper("dynCall_iijj");
-
-var dynCall_iijjfj = Module["dynCall_iijjfj"] = createExportWrapper("dynCall_iijjfj");
-
-var dynCall_vijiji = Module["dynCall_vijiji"] = createExportWrapper("dynCall_vijiji");
-
-var dynCall_jijii = Module["dynCall_jijii"] = createExportWrapper("dynCall_jijii");
-
-var dynCall_vijid = Module["dynCall_vijid"] = createExportWrapper("dynCall_vijid");
-
-var dynCall_vijiiiffiiiii = Module["dynCall_vijiiiffiiiii"] = createExportWrapper("dynCall_vijiiiffiiiii");
-
-var dynCall_vijiiiiii = Module["dynCall_vijiiiiii"] = createExportWrapper("dynCall_vijiiiiii");
-
-var dynCall_vijiff = Module["dynCall_vijiff"] = createExportWrapper("dynCall_vijiff");
-
-var dynCall_vijjjj = Module["dynCall_vijjjj"] = createExportWrapper("dynCall_vijjjj");
-
-var dynCall_vijiiiiiii = Module["dynCall_vijiiiiiii"] = createExportWrapper("dynCall_vijiiiiiii");
-
-var dynCall_jiiifi = Module["dynCall_jiiifi"] = createExportWrapper("dynCall_jiiifi");
-
-var dynCall_viiiifijii = Module["dynCall_viiiifijii"] = createExportWrapper("dynCall_viiiifijii");
-
-var dynCall_viiiifiijjii = Module["dynCall_viiiifiijjii"] = createExportWrapper("dynCall_viiiifiijjii");
-
-var dynCall_vijiiifiijii = Module["dynCall_vijiiifiijii"] = createExportWrapper("dynCall_vijiiifiijii");
-
-var dynCall_vijiiifiiijjii = Module["dynCall_vijiiifiiijjii"] = createExportWrapper("dynCall_vijiiifiiijjii");
-
-var dynCall_vijiiifiiijii = Module["dynCall_vijiiifiiijii"] = createExportWrapper("dynCall_vijiiifiiijii");
-
-var dynCall_vijiiifiiiijjii = Module["dynCall_vijiiifiiiijjii"] = createExportWrapper("dynCall_vijiiifiiiijjii");
-
-var dynCall_fijiiii = Module["dynCall_fijiiii"] = createExportWrapper("dynCall_fijiiii");
-
-var dynCall_fijiiiii = Module["dynCall_fijiiiii"] = createExportWrapper("dynCall_fijiiiii");
-
-var dynCall_iijii = Module["dynCall_iijii"] = createExportWrapper("dynCall_iijii");
-
-var dynCall_iijiijiiiii = Module["dynCall_iijiijiiiii"] = createExportWrapper("dynCall_iijiijiiiii");
-
-var dynCall_iijijiiiii = Module["dynCall_iijijiiiii"] = createExportWrapper("dynCall_iijijiiiii");
-
-var dynCall_vijijj = Module["dynCall_vijijj"] = createExportWrapper("dynCall_vijijj");
-
-var dynCall_vijiiijj = Module["dynCall_vijiiijj"] = createExportWrapper("dynCall_vijiiijj");
-
-var dynCall_vijiijj = Module["dynCall_vijiijj"] = createExportWrapper("dynCall_vijiijj");
-
-var dynCall_vijjiji = Module["dynCall_vijjiji"] = createExportWrapper("dynCall_vijjiji");
-
-var dynCall_vijjiijii = Module["dynCall_vijjiijii"] = createExportWrapper("dynCall_vijjiijii");
-
-var dynCall_fijii = Module["dynCall_fijii"] = createExportWrapper("dynCall_fijii");
-
-var dynCall_vijiiiij = Module["dynCall_vijiiiij"] = createExportWrapper("dynCall_vijiiiij");
-
-var dynCall_jijj = Module["dynCall_jijj"] = createExportWrapper("dynCall_jijj");
-
-var dynCall_jiiif = Module["dynCall_jiiif"] = createExportWrapper("dynCall_jiiif");
-
-var dynCall_vijfff = Module["dynCall_vijfff"] = createExportWrapper("dynCall_vijfff");
-
-var dynCall_vijfiff = Module["dynCall_vijfiff"] = createExportWrapper("dynCall_vijfiff");
-
-var dynCall_vijfi = Module["dynCall_vijfi"] = createExportWrapper("dynCall_vijfi");
-
-var dynCall_vijffffi = Module["dynCall_vijffffi"] = createExportWrapper("dynCall_vijffffi");
-
-var dynCall_vijiiffi = Module["dynCall_vijiiffi"] = createExportWrapper("dynCall_vijiiffi");
-
-var dynCall_vijiifffffff = Module["dynCall_vijiifffffff"] = createExportWrapper("dynCall_vijiifffffff");
-
-var dynCall_vijifiifffffifff = Module["dynCall_vijifiifffffifff"] = createExportWrapper("dynCall_vijifiifffffifff");
-
-var dynCall_vijiiffffiffffj = Module["dynCall_vijiiffffiffffj"] = createExportWrapper("dynCall_vijiiffffiffffj");
-
-var dynCall_vijiifff = Module["dynCall_vijiifff"] = createExportWrapper("dynCall_vijiifff");
-
-var dynCall_vijiffffffff = Module["dynCall_vijiffffffff"] = createExportWrapper("dynCall_vijiffffffff");
-
-var dynCall_vijiifiififff = Module["dynCall_vijiifiififff"] = createExportWrapper("dynCall_vijiifiififff");
-
-var dynCall_vijifffij = Module["dynCall_vijifffij"] = createExportWrapper("dynCall_vijifffij");
-
-var dynCall_viijjjiifjii = Module["dynCall_viijjjiifjii"] = createExportWrapper("dynCall_viijjjiifjii");
-
-var dynCall_fijj = Module["dynCall_fijj"] = createExportWrapper("dynCall_fijj");
-
-var dynCall_iijjiii = Module["dynCall_iijjiii"] = createExportWrapper("dynCall_iijjiii");
-
-var dynCall_iiiiij = Module["dynCall_iiiiij"] = createExportWrapper("dynCall_iiiiij");
-
-var dynCall_iiiiijj = Module["dynCall_iiiiijj"] = createExportWrapper("dynCall_iiiiijj");
-
-var dynCall_iiiiiijj = Module["dynCall_iiiiiijj"] = createExportWrapper("dynCall_iiiiiijj");
-
-function invoke_vii(index, a1, a2) {
- var sp = stackSave();
- try {
-  getWasmTableEntry(index)(a1, a2);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_vi(index, a1) {
- var sp = stackSave();
- try {
-  getWasmTableEntry(index)(a1);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_viii(index, a1, a2, a3) {
- var sp = stackSave();
- try {
-  getWasmTableEntry(index)(a1, a2, a3);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_ii(index, a1) {
- var sp = stackSave();
- try {
-  return getWasmTableEntry(index)(a1);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_viiii(index, a1, a2, a3, a4) {
- var sp = stackSave();
- try {
-  getWasmTableEntry(index)(a1, a2, a3, a4);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_iii(index, a1, a2) {
- var sp = stackSave();
- try {
-  return getWasmTableEntry(index)(a1, a2);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_iiii(index, a1, a2, a3) {
- var sp = stackSave();
- try {
-  return getWasmTableEntry(index)(a1, a2, a3);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_iiiii(index, a1, a2, a3, a4) {
- var sp = stackSave();
- try {
-  return getWasmTableEntry(index)(a1, a2, a3, a4);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_viiiiiii(index, a1, a2, a3, a4, a5, a6, a7) {
- var sp = stackSave();
- try {
-  getWasmTableEntry(index)(a1, a2, a3, a4, a5, a6, a7);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-function invoke_iiiiiii(index, a1, a2, a3, a4, a5, a6) {
- var sp = stackSave();
- try {
-  return getWasmTableEntry(index)(a1, a2, a3, a4, a5, a6);
- } catch (e) {
-  stackRestore(sp);
-  if (e !== e + 0) throw e;
-  _setThrew(1, 0);
- }
-}
-
-Module["callMain"] = callMain;
-
-Module["keepRuntimeAlive"] = keepRuntimeAlive;
-
-Module["wasmMemory"] = wasmMemory;
-
-Module["cwrap"] = cwrap;
-
-Module["ExitStatus"] = ExitStatus;
-
-Module["PThread"] = PThread;
-
-var unexportedRuntimeSymbols = [ "run", "UTF8ArrayToString", "UTF8ToString", "stringToUTF8Array", "stringToUTF8", "lengthBytesUTF8", "addOnPreRun", "addOnInit", "addOnPreMain", "addOnExit", "addOnPostRun", "addRunDependency", "removeRunDependency", "FS_createFolder", "FS_createPath", "FS_createDataFile", "FS_createPreloadedFile", "FS_createLazyFile", "FS_createLink", "FS_createDevice", "FS_unlink", "getLEB", "getFunctionTables", "alignFunctionTables", "registerFunctions", "prettyPrint", "getCompilerSetting", "print", "printErr", "getTempRet0", "setTempRet0", "abort", "stackSave", "stackRestore", "stackAlloc", "GROWABLE_HEAP_I8", "GROWABLE_HEAP_U8", "GROWABLE_HEAP_I16", "GROWABLE_HEAP_U16", "GROWABLE_HEAP_I32", "GROWABLE_HEAP_U32", "GROWABLE_HEAP_F32", "GROWABLE_HEAP_F64", "writeStackCookie", "checkStackCookie", "ptrToString", "zeroMemory", "stringToNewUTF8", "exitJS", "getHeapMax", "emscripten_realloc_buffer", "ENV", "ERRNO_CODES", "ERRNO_MESSAGES", "setErrNo", "inetPton4", "inetNtop4", "inetPton6", "inetNtop6", "readSockaddr", "writeSockaddr", "DNS", "getHostByName", "Protocols", "Sockets", "getRandomDevice", "warnOnce", "traverseStack", "UNWIND_CACHE", "convertPCtoSourceLocation", "readAsmConstArgsArray", "readAsmConstArgs", "mainThreadEM_ASM", "jstoi_q", "jstoi_s", "getExecutableName", "listenOnce", "autoResumeAudioContext", "dynCallLegacy", "getDynCaller", "dynCall", "handleException", "runtimeKeepalivePush", "runtimeKeepalivePop", "callUserCallback", "maybeExit", "safeSetTimeout", "asmjsMangle", "asyncLoad", "alignMemory", "mmapAlloc", "writeI53ToI64", "writeI53ToI64Clamped", "writeI53ToI64Signaling", "writeI53ToU64Clamped", "writeI53ToU64Signaling", "readI53FromI64", "readI53FromU64", "convertI32PairToI53", "convertI32PairToI53Checked", "convertU32PairToI53", "getCFunc", "ccall", "uleb128Encode", "sigToWasmTypes", "convertJsFunctionToWasm", "freeTableIndexes", "functionsInTableMap", "getEmptyTableSlot", "updateTableMap", "addFunction", "removeFunction", "reallyNegative", "unSign", "strLen", "reSign", "formatString", "setValue", "getValue", "PATH", "PATH_FS", "intArrayFromString", "intArrayToString", "AsciiToString", "stringToAscii", "UTF16Decoder", "UTF16ToString", "stringToUTF16", "lengthBytesUTF16", "UTF32ToString", "stringToUTF32", "lengthBytesUTF32", "allocateUTF8", "allocateUTF8OnStack", "writeStringToMemory", "writeArrayToMemory", "writeAsciiToMemory", "SYSCALLS", "getSocketFromFD", "getSocketAddress", "JSEvents", "registerKeyEventCallback", "specialHTMLTargets", "maybeCStringToJsString", "findEventTarget", "findCanvasEventTarget", "getBoundingClientRect", "fillMouseEventData", "registerMouseEventCallback", "registerWheelEventCallback", "registerUiEventCallback", "registerFocusEventCallback", "fillDeviceOrientationEventData", "registerDeviceOrientationEventCallback", "fillDeviceMotionEventData", "registerDeviceMotionEventCallback", "screenOrientation", "fillOrientationChangeEventData", "registerOrientationChangeEventCallback", "fillFullscreenChangeEventData", "registerFullscreenChangeEventCallback", "JSEvents_requestFullscreen", "JSEvents_resizeCanvasForFullscreen", "registerRestoreOldStyle", "hideEverythingExceptGivenElement", "restoreHiddenElements", "setLetterbox", "currentFullscreenStrategy", "restoreOldWindowedStyle", "softFullscreenResizeWebGLRenderTarget", "doRequestFullscreen", "fillPointerlockChangeEventData", "registerPointerlockChangeEventCallback", "registerPointerlockErrorEventCallback", "requestPointerLock", "fillVisibilityChangeEventData", "registerVisibilityChangeEventCallback", "registerTouchEventCallback", "fillGamepadEventData", "registerGamepadEventCallback", "registerBeforeUnloadEventCallback", "fillBatteryEventData", "battery", "registerBatteryEventCallback", "setCanvasElementSize", "getCanvasElementSize", "demangle", "demangleAll", "jsStackTrace", "stackTrace", "getEnvStrings", "checkWasiClock", "doReadv", "doWritev", "dlopenMissingError", "setImmediateWrapped", "clearImmediateWrapped", "polyfillSetImmediate", "uncaughtExceptionCount", "exceptionLast", "exceptionCaught", "ExceptionInfo", "exception_addRef", "exception_decRef", "Browser", "setMainLoop", "wget", "FS", "MEMFS", "TTY", "PIPEFS", "SOCKFS", "_setNetworkCallback", "tempFixedLengthArray", "miniTempWebGLFloatBuffers", "heapObjectForWebGLType", "heapAccessShiftForWebGLHeap", "GL", "emscriptenWebGLGet", "computeUnpackAlignedImageSize", "emscriptenWebGLGetTexPixelData", "emscriptenWebGLGetUniform", "webglGetUniformLocation", "webglPrepareUniformLocationsBeforeFirstUse", "webglGetLeftBracePos", "emscriptenWebGLGetVertexAttrib", "writeGLArray", "AL", "SDL_unicode", "SDL_ttfContext", "SDL_audio", "SDL", "SDL_gfx", "GLUT", "EGL", "GLFW_Window", "GLFW", "GLEW", "IDBStore", "runAndAbortIfError", "emscriptenWebGLGetIndexed", "ALLOC_NORMAL", "ALLOC_STACK", "allocate", "killThread", "cleanupThread", "registerTLSInit", "cancelThread", "spawnThread", "exitOnMainThread", "invokeEntryPoint", "executeNotifiedProxyingQueue", "GodotWebXR", "GodotWebSocket", "GodotRTCDataChannel", "GodotRTCPeerConnection", "GodotAudio", "GodotAudioWorklet", "GodotAudioScript", "GodotDisplayVK", "GodotDisplayCursor", "GodotDisplayScreen", "GodotDisplay", "GodotFetch", "IDHandler", "GodotConfig", "GodotFS", "GodotOS", "GodotEventListeners", "GodotPWA", "GodotRuntime", "GodotInputGamepads", "GodotInputDragDrop", "GodotInput", "GodotWebGL2", "GodotJSWrapper", "IDBFS" ];
-
-unexportedRuntimeSymbols.forEach(unexportedRuntimeSymbol);
-
-var missingLibrarySymbols = [ "getHostByName", "traverseStack", "convertPCtoSourceLocation", "readAsmConstArgs", "mainThreadEM_ASM", "jstoi_s", "listenOnce", "autoResumeAudioContext", "dynCallLegacy", "getDynCaller", "dynCall", "asmjsMangle", "writeI53ToI64Clamped", "writeI53ToI64Signaling", "writeI53ToU64Clamped", "writeI53ToU64Signaling", "convertI32PairToI53", "convertU32PairToI53", "reallyNegative", "unSign", "strLen", "reSign", "formatString", "registerKeyEventCallback", "getBoundingClientRect", "fillMouseEventData", "registerMouseEventCallback", "registerWheelEventCallback", "registerUiEventCallback", "registerFocusEventCallback", "fillDeviceOrientationEventData", "registerDeviceOrientationEventCallback", "fillDeviceMotionEventData", "registerDeviceMotionEventCallback", "screenOrientation", "fillOrientationChangeEventData", "registerOrientationChangeEventCallback", "fillFullscreenChangeEventData", "registerFullscreenChangeEventCallback", "JSEvents_requestFullscreen", "JSEvents_resizeCanvasForFullscreen", "registerRestoreOldStyle", "hideEverythingExceptGivenElement", "restoreHiddenElements", "setLetterbox", "softFullscreenResizeWebGLRenderTarget", "doRequestFullscreen", "fillPointerlockChangeEventData", "registerPointerlockChangeEventCallback", "registerPointerlockErrorEventCallback", "requestPointerLock", "fillVisibilityChangeEventData", "registerVisibilityChangeEventCallback", "registerTouchEventCallback", "fillGamepadEventData", "registerGamepadEventCallback", "registerBeforeUnloadEventCallback", "fillBatteryEventData", "battery", "registerBatteryEventCallback", "setCanvasElementSize", "getCanvasElementSize", "checkWasiClock", "setImmediateWrapped", "clearImmediateWrapped", "polyfillSetImmediate", "ExceptionInfo", "exception_addRef", "exception_decRef", "_setNetworkCallback", "emscriptenWebGLGetUniform", "emscriptenWebGLGetVertexAttrib", "writeGLArray", "SDL_unicode", "SDL_ttfContext", "SDL_audio", "GLFW_Window", "runAndAbortIfError", "emscriptenWebGLGetIndexed" ];
-
-missingLibrarySymbols.forEach(missingLibrarySymbol);
-
-var calledRun;
-
-dependenciesFulfilled = function runCaller() {
- if (!calledRun) run();
- if (!calledRun) dependenciesFulfilled = runCaller;
-};
-
-function callMain(args) {
- assert(runDependencies == 0, 'cannot call main when async dependencies remain! (listen on Module["onRuntimeInitialized"])');
- assert(__ATPRERUN__.length == 0, "cannot call main when preRun functions remain to be called");
- var entryFunction = Module["_main"];
- args = args || [];
- args.unshift(thisProgram);
- var argc = args.length;
- var argv = stackAlloc((argc + 1) * 4);
- var argv_ptr = argv >> 2;
- args.forEach(arg => {
-  GROWABLE_HEAP_I32()[argv_ptr++] = allocateUTF8OnStack(arg);
- });
- GROWABLE_HEAP_I32()[argv_ptr] = 0;
- try {
-  var ret = entryFunction(argc, argv);
-  exitJS(ret, true);
-  return ret;
- } catch (e) {
-  return handleException(e);
- }
-}
-
-function stackCheckInit() {
- assert(!ENVIRONMENT_IS_PTHREAD);
- _emscripten_stack_init();
- writeStackCookie();
-}
-
-function run(args) {
- args = args || arguments_;
- if (runDependencies > 0) {
-  return;
- }
- if (!ENVIRONMENT_IS_PTHREAD) stackCheckInit();
- if (ENVIRONMENT_IS_PTHREAD) {
-  readyPromiseResolve(Module);
-  initRuntime();
-  postMessage({
-   "cmd": "loaded"
-  });
-  return;
- }
- preRun();
- if (runDependencies > 0) {
-  return;
- }
- function doRun() {
-  if (calledRun) return;
-  calledRun = true;
-  Module["calledRun"] = true;
-  if (ABORT) return;
-  initRuntime();
-  preMain();
-  readyPromiseResolve(Module);
-  if (Module["onRuntimeInitialized"]) Module["onRuntimeInitialized"]();
-  if (shouldRunNow) callMain(args);
-  postRun();
- }
- if (Module["setStatus"]) {
-  Module["setStatus"]("Running...");
-  setTimeout(function() {
-   setTimeout(function() {
-    Module["setStatus"]("");
-   }, 1);
-   doRun();
-  }, 1);
- } else {
-  doRun();
- }
- checkStackCookie();
-}
-
-if (Module["preInit"]) {
- if (typeof Module["preInit"] == "function") Module["preInit"] = [ Module["preInit"] ];
- while (Module["preInit"].length > 0) {
-  Module["preInit"].pop()();
- }
-}
-
-var shouldRunNow = false;
-
-if (Module["noInitialRun"]) shouldRunNow = false;
-
-run();
-
-
-  return Godot.ready
-}
-);
-})();
-if (typeof exports === 'object' && typeof module === 'object')
-  module.exports = Godot;
-else if (typeof define === 'function' && define['amd'])
-  define([], function() { return Godot; });
-else if (typeof exports === 'object')
-  exports["Godot"] = Godot;
-
-const Features = { // eslint-disable-line no-unused-vars
-	/**
-	 * Check whether WebGL is available. Optionally, specify a particular version of WebGL to check for.
-	 *
-	 * @param {number=} [majorVersion=1] The major WebGL version to check for.
-	 * @returns {boolean} If the given major version of WebGL is available.
-	 * @function Engine.isWebGLAvailable
-	 */
-	isWebGLAvailable: function (majorVersion = 1) {
-		try {
-			return !!document.createElement('canvas').getContext(['webgl', 'webgl2'][majorVersion - 1]);
-		} catch (e) { /* Not available */ }
-		return false;
-	},
-
-	/**
-	 * Check whether the Fetch API available and supports streaming responses.
-	 *
-	 * @returns {boolean} If the Fetch API is available and supports streaming responses.
-	 * @function Engine.isFetchAvailable
-	 */
-	isFetchAvailable: function () {
-		return 'fetch' in window && 'Response' in window && 'body' in window.Response.prototype;
-	},
-
-	/**
-	 * Check whether the engine is running in a Secure Context.
-	 *
-	 * @returns {boolean} If the engine is running in a Secure Context.
-	 * @function Engine.isSecureContext
-	 */
-	isSecureContext: function () {
-		return window['isSecureContext'] === true;
-	},
-
-	/**
-	 * Check whether the engine is cross origin isolated.
-	 * This value is dependent on Cross-Origin-Opener-Policy and Cross-Origin-Embedder-Policy headers sent by the server.
-	 *
-	 * @returns {boolean} If the engine is running in a Secure Context.
-	 * @function Engine.isSecureContext
-	 */
-	isCrossOriginIsolated: function () {
-		return window['crossOriginIsolated'] === true;
-	},
-
-	/**
-	 * Check whether SharedBufferArray is available.
-	 *
-	 * Most browsers require the page to be running in a secure context, and the
-	 * the server to provide specific CORS headers for SharedArrayBuffer to be available.
-	 *
-	 * @returns {boolean} If SharedArrayBuffer is available.
-	 * @function Engine.isSharedArrayBufferAvailable
-	 */
-	isSharedArrayBufferAvailable: function () {
-		return 'SharedArrayBuffer' in window;
-	},
-
-	/**
-	 * Check whether the AudioContext supports AudioWorkletNodes.
-	 *
-	 * @returns {boolean} If AudioWorkletNode is available.
-	 * @function Engine.isAudioWorkletAvailable
-	 */
-	isAudioWorkletAvailable: function () {
-		return 'AudioContext' in window && 'audioWorklet' in AudioContext.prototype;
-	},
-
-	/**
-	 * Return an array of missing required features (as string).
-	 *
-	 * @returns {Array<string>} A list of human-readable missing features.
-	 * @function Engine.getMissingFeatures
-	 */
-	getMissingFeatures: function () {
-		const missing = [];
-		if (!Features.isWebGLAvailable(2)) {
-			missing.push('WebGL2 - Check web browser configuration and hardware support');
-		}
-		if (!Features.isFetchAvailable()) {
-			missing.push('Fetch - Check web browser version');
-		}
-		if (!Features.isSecureContext()) {
-			missing.push('Secure Context - Check web server configuration (use HTTPS)');
-		}
-		if (!Features.isCrossOriginIsolated()) {
-			missing.push('Cross Origin Isolation - Check web server configuration (send correct headers)');
-		}
-		if (!Features.isSharedArrayBufferAvailable()) {
-			missing.push('SharedArrayBuffer - Check web server configuration (send correct headers)');
-		}
-		// Audio is normally optional since we have a dummy fallback.
-		return missing;
-	},
-};
-
-const Preloader = /** @constructor */ function () { // eslint-disable-line no-unused-vars
-	function getTrackedResponse(response, load_status) {
-		function onloadprogress(reader, controller) {
-			return reader.read().then(function (result) {
-				if (load_status.done) {
-					return Promise.resolve();
-				}
-				if (result.value) {
-					controller.enqueue(result.value);
-					load_status.loaded += result.value.length;
-				}
-				if (!result.done) {
-					return onloadprogress(reader, controller);
-				}
-				load_status.done = true;
-				return Promise.resolve();
-			});
-		}
-		const reader = response.body.getReader();
-		return new Response(new ReadableStream({
-			start: function (controller) {
-				onloadprogress(reader, controller).then(function () {
-					controller.close();
-				});
-			},
-		}), { headers: response.headers });
-	}
-
-	function loadFetch(file, tracker, fileSize, raw) {
-		tracker[file] = {
-			total: fileSize || 0,
-			loaded: 0,
-			done: false,
-		};
-		return fetch(file).then(function (response) {
-			if (!response.ok) {
-				return Promise.reject(new Error(`Failed loading file '${file}'`));
-			}
-			const tr = getTrackedResponse(response, tracker[file]);
-			if (raw) {
-				return Promise.resolve(tr);
-			}
-			return tr.arrayBuffer();
-		});
-	}
-
-	function retry(func, attempts = 1) {
-		function onerror(err) {
-			if (attempts <= 1) {
-				return Promise.reject(err);
-			}
-			return new Promise(function (resolve, reject) {
-				setTimeout(function () {
-					retry(func, attempts - 1).then(resolve).catch(reject);
-				}, 1000);
-			});
-		}
-		return func().catch(onerror);
-	}
-
-	const DOWNLOAD_ATTEMPTS_MAX = 4;
-	const loadingFiles = {};
-	const lastProgress = { loaded: 0, total: 0 };
-	let progressFunc = null;
-
-	const animateProgress = function () {
-		let loaded = 0;
-		let total = 0;
-		let totalIsValid = true;
-		let progressIsFinal = true;
-
-		Object.keys(loadingFiles).forEach(function (file) {
-			const stat = loadingFiles[file];
-			if (!stat.done) {
-				progressIsFinal = false;
-			}
-			if (!totalIsValid || stat.total === 0) {
-				totalIsValid = false;
-				total = 0;
-			} else {
-				total += stat.total;
-			}
-			loaded += stat.loaded;
-		});
-		if (loaded !== lastProgress.loaded || total !== lastProgress.total) {
-			lastProgress.loaded = loaded;
-			lastProgress.total = total;
-			if (typeof progressFunc === 'function') {
-				progressFunc(loaded, total);
-			}
-		}
-		if (!progressIsFinal) {
-			requestAnimationFrame(animateProgress);
-		}
-	};
-
-	this.animateProgress = animateProgress;
-
-	this.setProgressFunc = function (callback) {
-		progressFunc = callback;
-	};
-
-	this.loadPromise = function (file, fileSize, raw = false) {
-		return retry(loadFetch.bind(null, file, loadingFiles, fileSize, raw), DOWNLOAD_ATTEMPTS_MAX);
-	};
-
-	this.preloadedFiles = [];
-	this.preload = function (pathOrBuffer, destPath, fileSize) {
-		let buffer = null;
-		if (typeof pathOrBuffer === 'string') {
-			const me = this;
-			return this.loadPromise(pathOrBuffer, fileSize).then(function (buf) {
-				me.preloadedFiles.push({
-					path: destPath || pathOrBuffer,
-					buffer: buf,
-				});
-				return Promise.resolve();
-			});
-		} else if (pathOrBuffer instanceof ArrayBuffer) {
-			buffer = new Uint8Array(pathOrBuffer);
-		} else if (ArrayBuffer.isView(pathOrBuffer)) {
-			buffer = new Uint8Array(pathOrBuffer.buffer);
-		}
-		if (buffer) {
-			this.preloadedFiles.push({
-				path: destPath,
-				buffer: pathOrBuffer,
-			});
-			return Promise.resolve();
-		}
-		return Promise.reject(new Error('Invalid object for preloading'));
-	};
-};
-
-/**
- * An object used to configure the Engine instance based on godot export options, and to override those in custom HTML
- * templates if needed.
- *
- * @header Engine configuration
- * @summary The Engine configuration object. This is just a typedef, create it like a regular object, e.g.:
- *
- * ``const MyConfig = { executable: 'godot', unloadAfterInit: false }``
- *
- * @typedef {Object} EngineConfig
- */
-const EngineConfig = {}; // eslint-disable-line no-unused-vars
-
-/**
- * @struct
- * @constructor
- * @ignore
- */
-const InternalConfig = function (initConfig) { // eslint-disable-line no-unused-vars
-	const cfg = /** @lends {InternalConfig.prototype} */ {
-		/**
-		 * Whether the unload the engine automatically after the instance is initialized.
-		 *
-		 * @memberof EngineConfig
-		 * @default
-		 * @type {boolean}
-		 */
-		unloadAfterInit: true,
-		/**
-		 * The HTML DOM Canvas object to use.
-		 *
-		 * By default, the first canvas element in the document will be used is none is specified.
-		 *
-		 * @memberof EngineConfig
-		 * @default
-		 * @type {?HTMLCanvasElement}
-		 */
-		canvas: null,
-		/**
-		 * The name of the WASM file without the extension. (Set by Godot Editor export process).
-		 *
-		 * @memberof EngineConfig
-		 * @default
-		 * @type {string}
-		 */
-		executable: '',
-		/**
-		 * An alternative name for the game pck to load. The executable name is used otherwise.
-		 *
-		 * @memberof EngineConfig
-		 * @default
-		 * @type {?string}
-		 */
-		mainPack: null,
-		/**
-		 * Specify a language code to select the proper localization for the game.
-		 *
-		 * The browser locale will be used if none is specified. See complete list of
-		 * :ref:`supported locales <doc_locales>`.
-		 *
-		 * @memberof EngineConfig
-		 * @type {?string}
-		 * @default
-		 */
-		locale: null,
-		/**
-		 * The canvas resize policy determines how the canvas should be resized by Godot.
-		 *
-		 * ``0`` means Godot won't do any resizing. This is useful if you want to control the canvas size from
-		 * javascript code in your template.
-		 *
-		 * ``1`` means Godot will resize the canvas on start, and when changing window size via engine functions.
-		 *
-		 * ``2`` means Godot will adapt the canvas size to match the whole browser window.
-		 *
-		 * @memberof EngineConfig
-		 * @type {number}
-		 * @default
-		 */
-		canvasResizePolicy: 2,
-		/**
-		 * The arguments to be passed as command line arguments on startup.
-		 *
-		 * See :ref:`command line tutorial <doc_command_line_tutorial>`.
-		 *
-		 * **Note**: :js:meth:`startGame <Engine.prototype.startGame>` will always add the ``--main-pack`` argument.
-		 *
-		 * @memberof EngineConfig
-		 * @type {Array<string>}
-		 * @default
-		 */
-		args: [],
-		/**
-		 * When enabled, the game canvas will automatically grab the focus when the engine starts.
-		 *
-		 * @memberof EngineConfig
-		 * @type {boolean}
-		 * @default
-		 */
-		focusCanvas: true,
-		/**
-		 * When enabled, this will turn on experimental virtual keyboard support on mobile.
-		 *
-		 * @memberof EngineConfig
-		 * @type {boolean}
-		 * @default
-		 */
-		experimentalVK: false,
-		/**
-		 * The progressive web app service worker to install.
-		 * @memberof EngineConfig
-		 * @default
-		 * @type {string}
-		 */
-		serviceWorker: '',
-		/**
-		 * @ignore
-		 * @type {Array.<string>}
-		 */
-		persistentPaths: ['/userfs'],
-		/**
-		 * @ignore
-		 * @type {boolean}
-		 */
-		persistentDrops: false,
-		/**
-		 * @ignore
-		 * @type {Array.<string>}
-		 */
-		gdextensionLibs: [],
-		/**
-		 * @ignore
-		 * @type {Array.<string>}
-		 */
-		fileSizes: [],
-		/**
-		 * A callback function for handling Godot's ``OS.execute`` calls.
-		 *
-		 * This is for example used in the Web Editor template to switch between project manager and editor, and for running the game.
-		 *
-		 * @callback EngineConfig.onExecute
-		 * @param {string} path The path that Godot's wants executed.
-		 * @param {Array.<string>} args The arguments of the "command" to execute.
-		 */
-		/**
-		 * @ignore
-		 * @type {?function(string, Array.<string>)}
-		 */
-		onExecute: null,
-		/**
-		 * A callback function for being notified when the Godot instance quits.
-		 *
-		 * **Note**: This function will not be called if the engine crashes or become unresponsive.
-		 *
-		 * @callback EngineConfig.onExit
-		 * @param {number} status_code The status code returned by Godot on exit.
-		 */
-		/**
-		 * @ignore
-		 * @type {?function(number)}
-		 */
-		onExit: null,
-		/**
-		 * A callback function for displaying download progress.
-		 *
-		 * The function is called once per frame while downloading files, so the usage of ``requestAnimationFrame()``
-		 * is not necessary.
-		 *
-		 * If the callback function receives a total amount of bytes as 0, this means that it is impossible to calculate.
-		 * Possible reasons include:
-		 *
-		 * -  Files are delivered with server-side chunked compression
-		 * -  Files are delivered with server-side compression on Chromium
-		 * -  Not all file downloads have started yet (usually on servers without multi-threading)
-		 *
-		 * @callback EngineConfig.onProgress
-		 * @param {number} current The current amount of downloaded bytes so far.
-		 * @param {number} total The total amount of bytes to be downloaded.
-		 */
-		/**
-		 * @ignore
-		 * @type {?function(number, number)}
-		 */
-		onProgress: null,
-		/**
-		 * A callback function for handling the standard output stream. This method should usually only be used in debug pages.
-		 *
-		 * By default, ``console.log()`` is used.
-		 *
-		 * @callback EngineConfig.onPrint
-		 * @param {...*} [var_args] A variadic number of arguments to be printed.
-		 */
-		/**
-		 * @ignore
-		 * @type {?function(...*)}
-		 */
-		onPrint: function () {
-			console.log.apply(console, Array.from(arguments)); // eslint-disable-line no-console
-		},
-		/**
-		 * A callback function for handling the standard error stream. This method should usually only be used in debug pages.
-		 *
-		 * By default, ``console.error()`` is used.
-		 *
-		 * @callback EngineConfig.onPrintError
-		 * @param {...*} [var_args] A variadic number of arguments to be printed as errors.
-		*/
-		/**
-		 * @ignore
-		 * @type {?function(...*)}
-		 */
-		onPrintError: function (var_args) {
-			console.error.apply(console, Array.from(arguments)); // eslint-disable-line no-console
-		},
-	};
-
-	/**
-	 * @ignore
-	 * @struct
-	 * @constructor
-	 * @param {EngineConfig} opts
-	 */
-	function Config(opts) {
-		this.update(opts);
-	}
-
-	Config.prototype = cfg;
-
-	/**
-	 * @ignore
-	 * @param {EngineConfig} opts
-	 */
-	Config.prototype.update = function (opts) {
-		const config = opts || {};
-		// NOTE: We must explicitly pass the default, accessing it via
-		// the key will fail due to closure compiler renames.
-		function parse(key, def) {
-			if (typeof (config[key]) === 'undefined') {
-				return def;
-			}
-			return config[key];
-		}
-		// Module config
-		this.unloadAfterInit = parse('unloadAfterInit', this.unloadAfterInit);
-		this.onPrintError = parse('onPrintError', this.onPrintError);
-		this.onPrint = parse('onPrint', this.onPrint);
-		this.onProgress = parse('onProgress', this.onProgress);
-
-		// Godot config
-		this.canvas = parse('canvas', this.canvas);
-		this.executable = parse('executable', this.executable);
-		this.mainPack = parse('mainPack', this.mainPack);
-		this.locale = parse('locale', this.locale);
-		this.canvasResizePolicy = parse('canvasResizePolicy', this.canvasResizePolicy);
-		this.persistentPaths = parse('persistentPaths', this.persistentPaths);
-		this.persistentDrops = parse('persistentDrops', this.persistentDrops);
-		this.experimentalVK = parse('experimentalVK', this.experimentalVK);
-		this.focusCanvas = parse('focusCanvas', this.focusCanvas);
-		this.serviceWorker = parse('serviceWorker', this.serviceWorker);
-		this.gdextensionLibs = parse('gdextensionLibs', this.gdextensionLibs);
-		this.fileSizes = parse('fileSizes', this.fileSizes);
-		this.args = parse('args', this.args);
-		this.onExecute = parse('onExecute', this.onExecute);
-		this.onExit = parse('onExit', this.onExit);
-	};
-
-	/**
-	 * @ignore
-	 * @param {string} loadPath
-	 * @param {Response} response
-	 */
-	Config.prototype.getModuleConfig = function (loadPath, response) {
-		let r = response;
-		return {
-			'print': this.onPrint,
-			'printErr': this.onPrintError,
-			'thisProgram': this.executable,
-			'noExitRuntime': false,
-			'dynamicLibraries': [`${loadPath}.side.wasm`],
-			'instantiateWasm': function (imports, onSuccess) {
-				function done(result) {
-					onSuccess(result['instance'], result['module']);
-				}
-				if (typeof (WebAssembly.instantiateStreaming) !== 'undefined') {
-					WebAssembly.instantiateStreaming(Promise.resolve(r), imports).then(done);
-				} else {
-					r.arrayBuffer().then(function (buffer) {
-						WebAssembly.instantiate(buffer, imports).then(done);
-					});
-				}
-				r = null;
-				return {};
-			},
-			'locateFile': function (path) {
-				if (path.endsWith('.worker.js')) {
-					return `${loadPath}.worker.js`;
-				} else if (path.endsWith('.audio.worklet.js')) {
-					return `${loadPath}.audio.worklet.js`;
-				} else if (path.endsWith('.js')) {
-					return `${loadPath}.js`;
-				} else if (path.endsWith('.side.wasm')) {
-					return `${loadPath}.side.wasm`;
-				} else if (path.endsWith('.wasm')) {
-					return `${loadPath}.wasm`;
-				}
-				return path;
-			},
-		};
-	};
-
-	/**
-	 * @ignore
-	 * @param {function()} cleanup
-	 */
-	Config.prototype.getGodotConfig = function (cleanup) {
-		// Try to find a canvas
-		if (!(this.canvas instanceof HTMLCanvasElement)) {
-			const nodes = document.getElementsByTagName('canvas');
-			if (nodes.length && nodes[0] instanceof HTMLCanvasElement) {
-				const first = nodes[0];
-				this.canvas = /** @type {!HTMLCanvasElement} */ (first);
-			}
-			if (!this.canvas) {
-				throw new Error('No canvas found in page');
-			}
-		}
-		// Canvas can grab focus on click, or key events won't work.
-		if (this.canvas.tabIndex < 0) {
-			this.canvas.tabIndex = 0;
-		}
-
-		// Browser locale, or custom one if defined.
-		let locale = this.locale;
-		if (!locale) {
-			locale = navigator.languages ? navigator.languages[0] : navigator.language;
-			locale = locale.split('.')[0];
-		}
-		locale = locale.replace('-', '_');
-		const onExit = this.onExit;
-
-		// Godot configuration.
-		return {
-			'canvas': this.canvas,
-			'canvasResizePolicy': this.canvasResizePolicy,
-			'locale': locale,
-			'persistentDrops': this.persistentDrops,
-			'virtualKeyboard': this.experimentalVK,
-			'focusCanvas': this.focusCanvas,
-			'onExecute': this.onExecute,
-			'onExit': function (p_code) {
-				cleanup(); // We always need to call the cleanup callback to free memory.
-				if (typeof (onExit) === 'function') {
-					onExit(p_code);
-				}
-			},
-		};
-	};
-	return new Config(initConfig);
-};
-
-/**
- * Projects exported for the Web expose the :js:class:`Engine` class to the JavaScript environment, that allows
- * fine control over the engine's start-up process.
- *
- * This API is built in an asynchronous manner and requires basic understanding
- * of `Promises <https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Using_promises>`__.
- *
- * @module Engine
- * @header Web export JavaScript reference
- */
-const Engine = (function () {
-	const preloader = new Preloader();
-
-	let loadPromise = null;
-	let loadPath = '';
-	let initPromise = null;
-
-	/**
-	 * @classdesc The ``Engine`` class provides methods for loading and starting exported projects on the Web. For default export
-	 * settings, this is already part of the exported HTML page. To understand practical use of the ``Engine`` class,
-	 * see :ref:`Custom HTML page for Web export <doc_customizing_html5_shell>`.
-	 *
-	 * @description Create a new Engine instance with the given configuration.
-	 *
-	 * @global
-	 * @constructor
-	 * @param {EngineConfig} initConfig The initial config for this instance.
-	 */
-	function Engine(initConfig) { // eslint-disable-line no-shadow
-		this.config = new InternalConfig(initConfig);
-		this.rtenv = null;
-	}
-
-	/**
-	 * Load the engine from the specified base path.
-	 *
-	 * @param {string} basePath Base path of the engine to load.
-	 * @param {number=} [size=0] The file size if known.
-	 * @returns {Promise} A Promise that resolves once the engine is loaded.
-	 *
-	 * @function Engine.load
-	 */
-	Engine.load = function (basePath, size) {
-		if (loadPromise == null) {
-			loadPath = basePath;
-			loadPromise = preloader.loadPromise(`${loadPath}.wasm`, size, true);
-			requestAnimationFrame(preloader.animateProgress);
-		}
-		return loadPromise;
-	};
-
-	/**
-	 * Unload the engine to free memory.
-	 *
-	 * This method will be called automatically depending on the configuration. See :js:attr:`unloadAfterInit`.
-	 *
-	 * @function Engine.unload
-	 */
-	Engine.unload = function () {
-		loadPromise = null;
-	};
-
-	/**
-	 * Safe Engine constructor, creates a new prototype for every new instance to avoid prototype pollution.
-	 * @ignore
-	 * @constructor
-	 */
-	function SafeEngine(initConfig) {
-		const proto = /** @lends Engine.prototype */ {
-			/**
-			 * Initialize the engine instance. Optionally, pass the base path to the engine to load it,
-			 * if it hasn't been loaded yet. See :js:meth:`Engine.load`.
-			 *
-			 * @param {string=} basePath Base path of the engine to load.
-			 * @return {Promise} A ``Promise`` that resolves once the engine is loaded and initialized.
-			 */
-			init: function (basePath) {
-				if (initPromise) {
-					return initPromise;
-				}
-				if (loadPromise == null) {
-					if (!basePath) {
-						initPromise = Promise.reject(new Error('A base path must be provided when calling `init` and the engine is not loaded.'));
-						return initPromise;
-					}
-					Engine.load(basePath, this.config.fileSizes[`${basePath}.wasm`]);
-				}
-				const me = this;
-				function doInit(promise) {
-					// Care! Promise chaining is bogus with old emscripten versions.
-					// This caused a regression with the Mono build (which uses an older emscripten version).
-					// Make sure to test that when refactoring.
-					return new Promise(function (resolve, reject) {
-						promise.then(function (response) {
-							const cloned = new Response(response.clone().body, { 'headers': [['content-type', 'application/wasm']] });
-							Godot(me.config.getModuleConfig(loadPath, cloned)).then(function (module) {
-								const paths = me.config.persistentPaths;
-								module['initFS'](paths).then(function (err) {
-									me.rtenv = module;
-									if (me.config.unloadAfterInit) {
-										Engine.unload();
-									}
-									resolve();
-								});
-							});
-						});
-					});
-				}
-				preloader.setProgressFunc(this.config.onProgress);
-				initPromise = doInit(loadPromise);
-				return initPromise;
-			},
-
-			/**
-			 * Load a file so it is available in the instance's file system once it runs. Must be called **before** starting the
-			 * instance.
-			 *
-			 * If not provided, the ``path`` is derived from the URL of the loaded file.
-			 *
-			 * @param {string|ArrayBuffer} file The file to preload.
-			 *
-			 * If a ``string`` the file will be loaded from that path.
-			 *
-			 * If an ``ArrayBuffer`` or a view on one, the buffer will used as the content of the file.
-			 *
-			 * @param {string=} path Path by which the file will be accessible. Required, if ``file`` is not a string.
-			 *
-			 * @returns {Promise} A Promise that resolves once the file is loaded.
-			 */
-			preloadFile: function (file, path) {
-				return preloader.preload(file, path, this.config.fileSizes[file]);
-			},
-
-			/**
-			 * Start the engine instance using the given override configuration (if any).
-			 * :js:meth:`startGame <Engine.prototype.startGame>` can be used in typical cases instead.
-			 *
-			 * This will initialize the instance if it is not initialized. For manual initialization, see :js:meth:`init <Engine.prototype.init>`.
-			 * The engine must be loaded beforehand.
-			 *
-			 * Fails if a canvas cannot be found on the page, or not specified in the configuration.
-			 *
-			 * @param {EngineConfig} override An optional configuration override.
-			 * @return {Promise} Promise that resolves once the engine started.
-			 */
-			start: function (override) {
-				this.config.update(override);
-				const me = this;
-				return me.init().then(function () {
-					if (!me.rtenv) {
-						return Promise.reject(new Error('The engine must be initialized before it can be started'));
-					}
-
-					let config = {};
-					try {
-						config = me.config.getGodotConfig(function () {
-							me.rtenv = null;
-						});
-					} catch (e) {
-						return Promise.reject(e);
-					}
-					// Godot configuration.
-					me.rtenv['initConfig'](config);
-
-					// Preload GDExtension libraries.
-					const libs = [];
-					me.config.gdextensionLibs.forEach(function (lib) {
-						libs.push(me.rtenv['loadDynamicLibrary'](lib, { 'loadAsync': true }));
-					});
-					return Promise.all(libs).then(function () {
-						return new Promise(function (resolve, reject) {
-							preloader.preloadedFiles.forEach(function (file) {
-								me.rtenv['copyToFS'](file.path, file.buffer);
-							});
-							preloader.preloadedFiles.length = 0; // Clear memory
-							me.rtenv['callMain'](me.config.args);
-							initPromise = null;
-							if (me.config.serviceWorker && 'serviceWorker' in navigator) {
-								navigator.serviceWorker.register(me.config.serviceWorker);
-							}
-							resolve();
-						});
-					});
-				});
-			},
-
-			/**
-			 * Start the game instance using the given configuration override (if any).
-			 *
-			 * This will initialize the instance if it is not initialized. For manual initialization, see :js:meth:`init <Engine.prototype.init>`.
-			 *
-			 * This will load the engine if it is not loaded, and preload the main pck.
-			 *
-			 * This method expects the initial config (or the override) to have both the :js:attr:`executable` and :js:attr:`mainPack`
-			 * properties set (normally done by the editor during export).
-			 *
-			 * @param {EngineConfig} override An optional configuration override.
-			 * @return {Promise} Promise that resolves once the game started.
-			 */
-			startGame: function (override) {
-				this.config.update(override);
-				// Add main-pack argument.
-				const exe = this.config.executable;
-				const pack = this.config.mainPack || `${exe}.pck`;
-				this.config.args = ['--main-pack', pack].concat(this.config.args);
-				// Start and init with execName as loadPath if not inited.
-				const me = this;
-				return Promise.all([
-					this.init(exe),
-					this.preloadFile(pack, pack),
-				]).then(function () {
-					return me.start.apply(me);
-				});
-			},
-
-			/**
-			 * Create a file at the specified ``path`` with the passed as ``buffer`` in the instance's file system.
-			 *
-			 * @param {string} path The location where the file will be created.
-			 * @param {ArrayBuffer} buffer The content of the file.
-			 */
-			copyToFS: function (path, buffer) {
-				if (this.rtenv == null) {
-					throw new Error('Engine must be inited before copying files');
-				}
-				this.rtenv['copyToFS'](path, buffer);
-			},
-
-			/**
-			 * Request that the current instance quit.
-			 *
-			 * This is akin the user pressing the close button in the window manager, and will
-			 * have no effect if the engine has crashed, or is stuck in a loop.
-			 *
-			 */
-			requestQuit: function () {
-				if (this.rtenv) {
-					this.rtenv['request_quit']();
-				}
-			},
-		};
-
-		Engine.prototype = proto;
-		// Closure compiler exported instance methods.
-		Engine.prototype['init'] = Engine.prototype.init;
-		Engine.prototype['preloadFile'] = Engine.prototype.preloadFile;
-		Engine.prototype['start'] = Engine.prototype.start;
-		Engine.prototype['startGame'] = Engine.prototype.startGame;
-		Engine.prototype['copyToFS'] = Engine.prototype.copyToFS;
-		Engine.prototype['requestQuit'] = Engine.prototype.requestQuit;
-		// Also expose static methods as instance methods
-		Engine.prototype['load'] = Engine.load;
-		Engine.prototype['unload'] = Engine.unload;
-		return new Engine(initConfig);
-	}
-
-	// Closure compiler exported static methods.
-	SafeEngine['load'] = Engine.load;
-	SafeEngine['unload'] = Engine.unload;
-
-	// Feature-detection utilities.
-	SafeEngine['isWebGLAvailable'] = Features.isWebGLAvailable;
-	SafeEngine['isFetchAvailable'] = Features.isFetchAvailable;
-	SafeEngine['isSecureContext'] = Features.isSecureContext;
-	SafeEngine['isCrossOriginIsolated'] = Features.isCrossOriginIsolated;
-	SafeEngine['isSharedArrayBufferAvailable'] = Features.isSharedArrayBufferAvailable;
-	SafeEngine['isAudioWorkletAvailable'] = Features.isAudioWorkletAvailable;
-	SafeEngine['getMissingFeatures'] = Features.getMissingFeatures;
-
-	return SafeEngine;
-}());
-if (typeof window !== 'undefined') {
-	window['Engine'] = Engine;
-}
diff --git a/spaces/ICML2022/OFA/fairseq/examples/hubert/update_ckpt.py b/spaces/ICML2022/OFA/fairseq/examples/hubert/update_ckpt.py
deleted file mode 100644
index 53c9e74ea613e30aa5c22614e658f2b7272bac0c..0000000000000000000000000000000000000000
--- a/spaces/ICML2022/OFA/fairseq/examples/hubert/update_ckpt.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import torch
-
-src_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2.pt"
-ref_ckpt = "/checkpoint/wnhsu/w2v/hubert_icassp_oss_v3/iter2_km100-400k-grp-L6/oss.km500_p0_1_s334.pmw1_0.puw0_0.grpnorm.ml10.mp0_8.untie.mxsz250000.ufreq1.maxtok1400000.MU100k.s1337.ngpu32/checkpoint_last.pt"
-new_ckpt = "/checkpoint/wnhsu/w2v/archived/hubert_base_ls960_it2_updated.pt"
-
-
-def update_state(state):
-    state["model"]["label_embs_concat"] = state["model"].pop("label_embs")
-    state["args"].task = "hubert_pretraining"
-    state["args"].labels = f"['{state['args'].labels}']"
-    return state
-
-
-src_state = torch.load(src_ckpt)
-src_state = update_state(src_state)
-torch.save(src_state, new_ckpt)
diff --git a/spaces/Ignahugging/Image_filtering/README.md b/spaces/Ignahugging/Image_filtering/README.md
deleted file mode 100644
index 5dd8e3c487d129dce3699ffbc30add2fae936bd8..0000000000000000000000000000000000000000
--- a/spaces/Ignahugging/Image_filtering/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
----
-title: Image_filtering
-emoji: 💩
-colorFrom: blue
-colorTo: red
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_
-Display title for the Space
-
-`emoji`: _string_
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_
-Can be either `gradio`, `streamlit`, or `static`
-
-`sdk_version` : _string_
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_
-Path to your main application file (which contains either `gradio` or `streamlit` Python code, or `static` html code).
-Path is relative to the root of the repository.
-
-`models`: _List[string]_
-HF model IDs (like "gpt2" or "deepset/roberta-base-squad2") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`datasets`: _List[string]_
-HF dataset IDs (like "common_voice" or "oscar-corpus/OSCAR-2109") used in the Space.
-Will be parsed automatically from your code if not specified here.
-
-`pinned`: _boolean_
-Whether the Space stays on top of your list.
diff --git a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/inference/infer_tool.py b/spaces/Ikaros521/so-vits-svc-4.0-ikaros/inference/infer_tool.py
deleted file mode 100644
index dbaff46f4f6eb792808e0a0cbb37fb86cb8372e2..0000000000000000000000000000000000000000
--- a/spaces/Ikaros521/so-vits-svc-4.0-ikaros/inference/infer_tool.py
+++ /dev/null
@@ -1,233 +0,0 @@
-import hashlib
-import io
-import json
-import logging
-import os
-import time
-from pathlib import Path
-from inference import slicer
-
-import librosa
-import numpy as np
-# import onnxruntime
-import parselmouth
-import soundfile
-import torch
-import torchaudio
-
-import cluster
-from hubert import hubert_model
-import utils
-from models import SynthesizerTrn
-
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-
-
-def read_temp(file_name):
-    if not os.path.exists(file_name):
-        with open(file_name, "w") as f:
-            f.write(json.dumps({"info": "temp_dict"}))
-        return {}
-    else:
-        try:
-            with open(file_name, "r") as f:
-                data = f.read()
-            data_dict = json.loads(data)
-            if os.path.getsize(file_name) > 50 * 1024 * 1024:
-                f_name = file_name.replace("\\", "/").split("/")[-1]
-                print(f"clean {f_name}")
-                for wav_hash in list(data_dict.keys()):
-                    if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
-                        del data_dict[wav_hash]
-        except Exception as e:
-            print(e)
-            print(f"{file_name} error,auto rebuild file")
-            data_dict = {"info": "temp_dict"}
-        return data_dict
-
-
-def write_temp(file_name, data):
-    with open(file_name, "w") as f:
-        f.write(json.dumps(data))
-
-
-def timeit(func):
-    def run(*args, **kwargs):
-        t = time.time()
-        res = func(*args, **kwargs)
-        print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
-        return res
-
-    return run
-
-
-def format_wav(audio_path):
-    if Path(audio_path).suffix == '.wav':
-        return
-    raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
-    soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
-
-
-def get_end_file(dir_path, end):
-    file_lists = []
-    for root, dirs, files in os.walk(dir_path):
-        files = [f for f in files if f[0] != '.']
-        dirs[:] = [d for d in dirs if d[0] != '.']
-        for f_file in files:
-            if f_file.endswith(end):
-                file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
-    return file_lists
-
-
-def get_md5(content):
-    return hashlib.new("md5", content).hexdigest()
-
-def fill_a_to_b(a, b):
-    if len(a) < len(b):
-        for _ in range(0, len(b) - len(a)):
-            a.append(a[0])
-
-def mkdir(paths: list):
-    for path in paths:
-        if not os.path.exists(path):
-            os.mkdir(path)
-
-
-class Svc(object):
-    def __init__(self, net_g_path, config_path,
-                 device=None,
-                 cluster_model_path="logs/44k/kmeans_10000.pt"):
-        self.net_g_path = net_g_path
-        if device is None:
-            self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        else:
-            self.dev = torch.device(device)
-        self.net_g_ms = None
-        self.hps_ms = utils.get_hparams_from_file(config_path)
-        self.target_sample = self.hps_ms.data.sampling_rate
-        self.hop_size = self.hps_ms.data.hop_length
-        self.spk2id = self.hps_ms.spk
-        # 加载hubert
-        self.hubert_model = utils.get_hubert_model().to(self.dev)
-        self.load_model()
-        if os.path.exists(cluster_model_path):
-            self.cluster_model = cluster.get_cluster_model(cluster_model_path)
-
-    def load_model(self):
-        # 获取模型配置
-        self.net_g_ms = SynthesizerTrn(
-            self.hps_ms.data.filter_length // 2 + 1,
-            self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
-            **self.hps_ms.model)
-        _ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            _ = self.net_g_ms.half().eval().to(self.dev)
-        else:
-            _ = self.net_g_ms.eval().to(self.dev)
-
-
-
-    def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker):
-
-        wav, sr = librosa.load(in_path, sr=self.target_sample)
-
-        f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
-        f0, uv = utils.interpolate_f0(f0)
-        f0 = torch.FloatTensor(f0)
-        uv = torch.FloatTensor(uv)
-        f0 = f0 * 2 ** (tran / 12)
-        f0 = f0.unsqueeze(0).to(self.dev)
-        uv = uv.unsqueeze(0).to(self.dev)
-
-        wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
-        wav16k = torch.from_numpy(wav16k).to(self.dev)
-        c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
-        c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
-
-        if cluster_infer_ratio !=0:
-            cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.numpy().T, speaker).T
-            cluster_c = torch.FloatTensor(cluster_c)
-            c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
-
-        c = c.unsqueeze(0)
-        return c, f0, uv
-
-    def infer(self, speaker, tran, raw_path,
-              cluster_infer_ratio=0,
-              auto_predict_f0=False,
-              noice_scale=0.4):
-        speaker_id = self.spk2id[speaker]
-        sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
-        c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker)
-        if "half" in self.net_g_path and torch.cuda.is_available():
-            c = c.half()
-        with torch.no_grad():
-            start = time.time()
-            audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
-            use_time = time.time() - start
-            print("vits use time:{}".format(use_time))
-        return audio, audio.shape[-1]
-
-    def slice_inference(self,raw_audio_path, spk, tran, slice_db,cluster_infer_ratio, auto_predict_f0,noice_scale, pad_seconds=0.5):
-        wav_path = raw_audio_path
-        chunks = slicer.cut(wav_path, db_thresh=slice_db)
-        audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
-
-        audio = []
-        for (slice_tag, data) in audio_data:
-            print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
-            # padd
-            pad_len = int(audio_sr * pad_seconds)
-            data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
-            length = int(np.ceil(len(data) / audio_sr * self.target_sample))
-            raw_path = io.BytesIO()
-            soundfile.write(raw_path, data, audio_sr, format="wav")
-            raw_path.seek(0)
-            if slice_tag:
-                print('jump empty segment')
-                _audio = np.zeros(length)
-            else:
-                out_audio, out_sr = self.infer(spk, tran, raw_path,
-                                                    cluster_infer_ratio=cluster_infer_ratio,
-                                                    auto_predict_f0=auto_predict_f0,
-                                                    noice_scale=noice_scale
-                                                    )
-                _audio = out_audio.cpu().numpy()
-
-            pad_len = int(self.target_sample * pad_seconds)
-            _audio = _audio[pad_len:-pad_len]
-            audio.extend(list(_audio))
-        return np.array(audio)
-
-
-class RealTimeVC:
-    def __init__(self):
-        self.last_chunk = None
-        self.last_o = None
-        self.chunk_len = 16000  # 区块长度
-        self.pre_len = 3840  # 交叉淡化长度，640的倍数
-
-    """输入输出都是1维numpy 音频波形数组"""
-
-    def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
-        import maad
-        audio, sr = torchaudio.load(input_wav_path)
-        audio = audio.cpu().numpy()[0]
-        temp_wav = io.BytesIO()
-        if self.last_chunk is None:
-            input_wav_path.seek(0)
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
-            audio = audio.cpu().numpy()
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return audio[-self.chunk_len:]
-        else:
-            audio = np.concatenate([self.last_chunk, audio])
-            soundfile.write(temp_wav, audio, sr, format="wav")
-            temp_wav.seek(0)
-            audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav)
-            audio = audio.cpu().numpy()
-            ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
-            self.last_chunk = audio[-self.pre_len:]
-            self.last_o = audio
-            return ret[self.chunk_len:2 * self.chunk_len]
diff --git a/spaces/ImagineAI-Real/ImagineAI-Image-Generator/README.md b/spaces/ImagineAI-Real/ImagineAI-Image-Generator/README.md
deleted file mode 100644
index 6b9ea450e927fdd4f3f9026d53bd846deae6e124..0000000000000000000000000000000000000000
--- a/spaces/ImagineAI-Real/ImagineAI-Image-Generator/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: ImagineAI Imagine Generator
-emoji: 💩
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.24.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Intoval/privateChatGPT/modules/models.py b/spaces/Intoval/privateChatGPT/modules/models.py
deleted file mode 100644
index 4617e3df52ce2e1e4ad743b577364a33b324f26c..0000000000000000000000000000000000000000
--- a/spaces/Intoval/privateChatGPT/modules/models.py
+++ /dev/null
@@ -1,578 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import platform
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
-
-from .presets import *
-from .llama_func import *
-from .utils import *
-from . import shared
-from .config import retrieve_proxy
-from modules import config
-from .base_model import BaseLLMModel, ModelType
-
-
-class OpenAIClient(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        api_key,
-        system_prompt=INITIAL_SYSTEM_PROMPT,
-        temperature=1.0,
-        top_p=1.0,
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-        )
-        self.api_key = api_key
-        self.need_api_key = True
-        self._refresh_header()
-
-    def get_answer_stream_iter(self):
-        response = self._get_response(stream=True)
-        if response is not None:
-            iter = self._decode_chat_response(response)
-            partial_text = ""
-            for i in iter:
-                partial_text += i
-                yield partial_text
-        else:
-            yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG
-
-    def get_answer_at_once(self):
-        response = self._get_response()
-        response = json.loads(response.text)
-        content = response["choices"][0]["message"]["content"]
-        total_token_count = response["usage"]["total_tokens"]
-        return content, total_token_count
-
-    def count_token(self, user_input):
-        input_token_count = count_token(construct_user(user_input))
-        if self.system_prompt is not None and len(self.all_token_counts) == 0:
-            system_prompt_token_count = count_token(
-                construct_system(self.system_prompt)
-            )
-            return input_token_count + system_prompt_token_count
-        return input_token_count
-
-    def billing_info(self):
-        try:
-            curr_time = datetime.datetime.now()
-            last_day_of_month = get_last_day_of_month(
-                curr_time).strftime("%Y-%m-%d")
-            first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d")
-            usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}"
-            try:
-                usage_data = self._get_billing_data(usage_url)
-            except Exception as e:
-                logging.error(f"获取API使用情况失败:" + str(e))
-                return i18n("**获取API使用情况失败**")
-            rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-        except requests.exceptions.ConnectTimeout:
-            status_text = (
-                STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            )
-            return status_text
-        except requests.exceptions.ReadTimeout:
-            status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG
-            return status_text
-        except Exception as e:
-            logging.error(i18n("获取API使用情况失败:") + str(e))
-            return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG
-
-    def set_token_upper_limit(self, new_upper_limit):
-        pass
-
-    @shared.state.switching_api_key  # 在不开启多账号模式的时候，这个装饰器不会起作用
-    def _get_response(self, stream=False):
-        openai_api_key = self.api_key
-        system_prompt = self.system_prompt
-        history = self.history
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {openai_api_key}",
-        }
-
-        if system_prompt is not None:
-            history = [construct_system(system_prompt), *history]
-
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "n": self.n_choices,
-            "stream": stream,
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-        }
-
-        if self.max_generation_token is not None:
-            payload["max_tokens"] = self.max_generation_token
-        if self.stop_sequence is not None:
-            payload["stop"] = self.stop_sequence
-        if self.logit_bias is not None:
-            payload["logit_bias"] = self.logit_bias
-        if self.user_identifier is not None:
-            payload["user"] = self.user_identifier
-
-        if stream:
-            timeout = TIMEOUT_STREAMING
-        else:
-            timeout = TIMEOUT_ALL
-
-        # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求
-        if shared.state.completion_url != COMPLETION_URL:
-            logging.info(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            try:
-                response = requests.post(
-                    shared.state.completion_url,
-                    headers=headers,
-                    json=payload,
-                    stream=stream,
-                    timeout=timeout,
-                )
-            except:
-                return None
-        return response
-
-    def _refresh_header(self):
-        self.headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-        }
-
-    def _get_billing_data(self, billing_url):
-        with retrieve_proxy():
-            response = requests.get(
-                billing_url,
-                headers=self.headers,
-                timeout=TIMEOUT_ALL,
-            )
-
-        if response.status_code == 200:
-            data = response.json()
-            return data
-        else:
-            raise Exception(
-                f"API request failed with status code {response.status_code}: {response.text}"
-            )
-
-    def _decode_chat_response(self, response):
-        error_msg = ""
-        for chunk in response.iter_lines():
-            if chunk:
-                chunk = chunk.decode()
-                chunk_length = len(chunk)
-                try:
-                    chunk = json.loads(chunk[6:])
-                except json.JSONDecodeError:
-                    print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}")
-                    error_msg += chunk
-                    continue
-                if chunk_length > 6 and "delta" in chunk["choices"][0]:
-                    if chunk["choices"][0]["finish_reason"] == "stop":
-                        break
-                    try:
-                        yield chunk["choices"][0]["delta"]["content"]
-                    except Exception as e:
-                        # logging.error(f"Error: {e}")
-                        continue
-        if error_msg:
-            raise Exception(error_msg)
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name) -> None:
-        super().__init__(model_name=model_name)
-        from transformers import AutoTokenizer, AutoModel
-        import torch
-        global CHATGLM_TOKENIZER, CHATGLM_MODEL
-        if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None:
-            system_name = platform.system()
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"THUDM/{model_name}"
-            CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained(
-                model_source, trust_remote_code=True
-            )
-            quantified = False
-            if "int4" in model_name:
-                quantified = True
-            model = AutoModel.from_pretrained(
-                    model_source, trust_remote_code=True
-                )
-            if torch.cuda.is_available():
-                # run on CUDA
-                logging.info("CUDA is available, using CUDA")
-                model = model.half().cuda()
-            # mps加速还存在一些问题，暂时不使用
-            elif system_name == "Darwin" and model_path is not None and not quantified:
-                logging.info("Running on macOS, using MPS")
-                # running on macOS and model already downloaded
-                model = model.half().to("mps")
-            else:
-                logging.info("GPU is not available, using CPU")
-                model = model.float()
-            model = model.eval()
-            CHATGLM_MODEL = model
-
-    def _get_glm_style_input(self):
-        history = [x["content"] for x in self.history]
-        query = history.pop()
-        logging.debug(colorama.Fore.YELLOW +
-                      f"{history}" + colorama.Fore.RESET)
-        assert (
-            len(history) % 2 == 0
-        ), f"History should be even length. current history is: {history}"
-        history = [[history[i], history[i + 1]]
-                   for i in range(0, len(history), 2)]
-        return history, query
-
-    def get_answer_at_once(self):
-        history, query = self._get_glm_style_input()
-        response, _ = CHATGLM_MODEL.chat(
-            CHATGLM_TOKENIZER, query, history=history)
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        history, query = self._get_glm_style_input()
-        for response, history in CHATGLM_MODEL.stream_chat(
-            CHATGLM_TOKENIZER,
-            query,
-            history,
-            max_length=self.token_upper_limit,
-            top_p=self.top_p,
-            temperature=self.temperature,
-        ):
-            yield response
-
-
-class LLaMA_Client(BaseLLMModel):
-    def __init__(
-        self,
-        model_name,
-        lora_path=None,
-    ) -> None:
-        super().__init__(model_name=model_name)
-        from lmflow.datasets.dataset import Dataset
-        from lmflow.pipeline.auto_pipeline import AutoPipeline
-        from lmflow.models.auto_model import AutoModel
-        from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments
-
-        self.max_generation_token = 1000
-        self.end_string = "\n\n"
-        # We don't need input data
-        data_args = DatasetArguments(dataset_path=None)
-        self.dataset = Dataset(data_args)
-        self.system_prompt = ""
-
-        global LLAMA_MODEL, LLAMA_INFERENCER
-        if LLAMA_MODEL is None or LLAMA_INFERENCER is None:
-            model_path = None
-            if os.path.exists("models"):
-                model_dirs = os.listdir("models")
-                if model_name in model_dirs:
-                    model_path = f"models/{model_name}"
-            if model_path is not None:
-                model_source = model_path
-            else:
-                model_source = f"decapoda-research/{model_name}"
-                # raise Exception(f"models目录下没有这个模型: {model_name}")
-            if lora_path is not None:
-                lora_path = f"lora/{lora_path}"
-            model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None,
-                                        use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True)
-            pipeline_args = InferencerArguments(
-                local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16')
-
-            with open(pipeline_args.deepspeed, "r") as f:
-                ds_config = json.load(f)
-            LLAMA_MODEL = AutoModel.get_model(
-                model_args,
-                tune_strategy="none",
-                ds_config=ds_config,
-            )
-            LLAMA_INFERENCER = AutoPipeline.get_pipeline(
-                pipeline_name="inferencer",
-                model_args=model_args,
-                data_args=data_args,
-                pipeline_args=pipeline_args,
-            )
-        # Chats
-        # model_name = model_args.model_name_or_path
-        # if model_args.lora_model_path is not None:
-        #     model_name += f" + {model_args.lora_model_path}"
-
-        # context = (
-        #     "You are a helpful assistant who follows the given instructions"
-        #     " unconditionally."
-        # )
-
-    def _get_llama_style_input(self):
-        history = []
-        instruction = ""
-        if self.system_prompt:
-            instruction = (f"Instruction: {self.system_prompt}\n")
-        for x in self.history:
-            if x["role"] == "user":
-                history.append(f"{instruction}Input: {x['content']}")
-            else:
-                history.append(f"Output: {x['content']}")
-        context = "\n\n".join(history)
-        context += "\n\nOutput: "
-        return context
-
-    def get_answer_at_once(self):
-        context = self._get_llama_style_input()
-
-        input_dataset = self.dataset.from_dict(
-            {"type": "text_only", "instances": [{"text": context}]}
-        )
-
-        output_dataset = LLAMA_INFERENCER.inference(
-            model=LLAMA_MODEL,
-            dataset=input_dataset,
-            max_new_tokens=self.max_generation_token,
-            temperature=self.temperature,
-        )
-
-        response = output_dataset.to_dict()["instances"][0]["text"]
-        return response, len(response)
-
-    def get_answer_stream_iter(self):
-        context = self._get_llama_style_input()
-        partial_text = ""
-        step = 1
-        for _ in range(0, self.max_generation_token, step):
-            input_dataset = self.dataset.from_dict(
-                {"type": "text_only", "instances": [
-                    {"text": context + partial_text}]}
-            )
-            output_dataset = LLAMA_INFERENCER.inference(
-                model=LLAMA_MODEL,
-                dataset=input_dataset,
-                max_new_tokens=step,
-                temperature=self.temperature,
-            )
-            response = output_dataset.to_dict()["instances"][0]["text"]
-            if response == "" or response == self.end_string:
-                break
-            partial_text += response
-            yield partial_text
-
-
-class XMBot_Client(BaseLLMModel):
-    def __init__(self, api_key):
-        super().__init__(model_name="xmchat")
-        self.api_key = api_key
-        self.session_id = None
-        self.reset()
-        self.image_bytes = None
-        self.image_path = None
-        self.xm_history = []
-        self.url = "https://xmbot.net/web"
-
-    def reset(self):
-        self.session_id = str(uuid.uuid4())
-        return [], "已重置"
-
-    def try_read_image(self, filepath):
-        import base64
-
-        def is_image_file(filepath):
-            # 判断文件是否为图片
-            valid_image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"]
-            file_extension = os.path.splitext(filepath)[1].lower()
-            return file_extension in valid_image_extensions
-
-        def read_image_as_bytes(filepath):
-            # 读取图片文件并返回比特流
-            with open(filepath, "rb") as f:
-                image_bytes = f.read()
-            return image_bytes
-
-        if is_image_file(filepath):
-            logging.info(f"读取图片文件: {filepath}")
-            image_bytes = read_image_as_bytes(filepath)
-            base64_encoded_image = base64.b64encode(image_bytes).decode()
-            self.image_bytes = base64_encoded_image
-            self.image_path = filepath
-        else:
-            self.image_bytes = None
-            self.image_path = None
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = real_inputs
-        display_append = ""
-        limited_context = False
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        if files:
-            for file in files:
-                if file.name:
-                    logging.info(f"尝试读取图像: {file.name}")
-                    self.try_read_image(file.name)
-            if self.image_path is not None:
-                chatbot = chatbot + [((self.image_path,), None)]
-            if self.image_bytes is not None:
-                logging.info("使用图片作为输入")
-                conv_id = str(uuid.uuid4())
-                data = {
-                    "user_id": self.api_key,
-                    "session_id": self.session_id,
-                    "uuid": conv_id,
-                    "data_type": "imgbase64",
-                    "data": self.image_bytes
-                }
-                response = requests.post(self.url, json=data)
-                response = json.loads(response.text)
-                logging.info(f"图片回复: {response['data']}")
-        return None, chatbot, None
-
-    def get_answer_at_once(self):
-        question = self.history[-1]["content"]
-        conv_id = str(uuid.uuid4())
-        data = {
-            "user_id": self.api_key,
-            "session_id": self.session_id,
-            "uuid": conv_id,
-            "data_type": "text",
-            "data": question
-        }
-        response = requests.post(self.url, json=data)
-        try:
-            response = json.loads(response.text)
-            return response["data"], len(response["data"])
-        except Exception as e:
-            return response.text, len(response.text)
-
-
-
-
-def get_model(
-    model_name,
-    lora_model_path=None,
-    access_key=None,
-    temperature=None,
-    top_p=None,
-    system_prompt=None,
-) -> BaseLLMModel:
-    msg = i18n("模型设置为了：") + f" {model_name}"
-    model_type = ModelType.get_type(model_name)
-    lora_selector_visibility = False
-    lora_choices = []
-    dont_change_lora_selector = False
-    if model_type != ModelType.OpenAI:
-        config.local_embedding = True
-    # del current_model.model
-    model = None
-    try:
-        if model_type == ModelType.OpenAI:
-            logging.info(f"正在加载OpenAI模型: {model_name}")
-            model = OpenAIClient(
-                model_name=model_name,
-                api_key=access_key,
-                system_prompt=system_prompt,
-                temperature=temperature,
-                top_p=top_p,
-            )
-        elif model_type == ModelType.ChatGLM:
-            logging.info(f"正在加载ChatGLM模型: {model_name}")
-            model = ChatGLM_Client(model_name)
-        elif model_type == ModelType.LLaMA and lora_model_path == "":
-            msg = f"现在请为 {model_name} 选择LoRA模型"
-            logging.info(msg)
-            lora_selector_visibility = True
-            if os.path.isdir("lora"):
-                lora_choices = get_file_names(
-                    "lora", plain=True, filetypes=[""])
-            lora_choices = ["No LoRA"] + lora_choices
-        elif model_type == ModelType.LLaMA and lora_model_path != "":
-            logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
-            dont_change_lora_selector = True
-            if lora_model_path == "No LoRA":
-                lora_model_path = None
-                msg += " + No LoRA"
-            else:
-                msg += f" + {lora_model_path}"
-            model = LLaMA_Client(model_name, lora_model_path)
-        elif model_type == ModelType.XMBot:
-            model = XMBot_Client(api_key=access_key)
-        elif model_type == ModelType.Unknown:
-            raise ValueError(f"未知模型: {model_name}")
-        logging.info(msg)
-    except Exception as e:
-        logging.error(e)
-        msg = f"{STANDARD_ERROR_MSG}: {e}"
-    if dont_change_lora_selector:
-        return model, msg
-    else:
-        return model, msg, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility)
-
-
-if __name__ == "__main__":
-    with open("config.json", "r") as f:
-        openai_api_key = cjson.load(f)["openai_api_key"]
-    # set logging level to debug
-    logging.basicConfig(level=logging.DEBUG)
-    # client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
-    client = get_model(model_name="chatglm-6b-int4")
-    chatbot = []
-    stream = False
-    # 测试账单功能
-    logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
-    logging.info(client.billing_info())
-    # 测试问答
-    logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
-    question = "巴黎是中国的首都吗？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试问答后history : {client.history}")
-    # 测试记忆力
-    logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
-    question = "我刚刚问了你什么问题？"
-    for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"测试记忆力后history : {client.history}")
-    # 测试重试功能
-    logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
-    for i in client.retry(chatbot=chatbot, stream=stream):
-        logging.info(i)
-    logging.info(f"重试后history : {client.history}")
-    # # 测试总结功能
-    # print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
-    # chatbot, msg = client.reduce_token_size(chatbot=chatbot)
-    # print(chatbot, msg)
-    # print(f"总结后history: {client.history}")
diff --git a/spaces/JUNGU/Image-to-Story-Ko/README.md b/spaces/JUNGU/Image-to-Story-Ko/README.md
deleted file mode 100644
index 26ca43dc72ad72a40617b89f3c829123e5ef5c5e..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/Image-to-Story-Ko/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Image To Story
-emoji: 👁
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-duplicated_from: wonwizard/Image-to-Story-Ko
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/JUNGU/VToonify/vtoonify/model/dualstylegan.py b/spaces/JUNGU/VToonify/vtoonify/model/dualstylegan.py
deleted file mode 100644
index 60d9850ad049a2751781871d6ae0c2779ecc863f..0000000000000000000000000000000000000000
--- a/spaces/JUNGU/VToonify/vtoonify/model/dualstylegan.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import random
-import torch
-from torch import nn
-from model.stylegan.model import ConvLayer, PixelNorm, EqualLinear, Generator
-
-class AdaptiveInstanceNorm(nn.Module):
-    def __init__(self, fin, style_dim=512):
-        super().__init__()
-
-        self.norm = nn.InstanceNorm2d(fin, affine=False)
-        self.style = nn.Linear(style_dim, fin * 2)
-
-        self.style.bias.data[:fin] = 1
-        self.style.bias.data[fin:] = 0
-
-    def forward(self, input, style):
-        style = self.style(style).unsqueeze(2).unsqueeze(3)
-        gamma, beta = style.chunk(2, 1)
-        out = self.norm(input)
-        out = gamma * out + beta
-        return out
-
-# modulative residual blocks (ModRes)
-class AdaResBlock(nn.Module):
-    def __init__(self, fin, style_dim=512, dilation=1): # modified
-        super().__init__()
-
-        self.conv = ConvLayer(fin, fin, 3, dilation=dilation) # modified
-        self.conv2 = ConvLayer(fin, fin, 3, dilation=dilation) # modified
-        self.norm = AdaptiveInstanceNorm(fin, style_dim)
-        self.norm2 = AdaptiveInstanceNorm(fin, style_dim)
-        
-        # model initialization
-        # the convolution filters are set to values close to 0 to produce negligible residual features
-        self.conv[0].weight.data *= 0.01
-        self.conv2[0].weight.data *= 0.01
-        
-    def forward(self, x, s, w=1):
-        skip = x
-        if w == 0:
-            return skip
-        out = self.conv(self.norm(x, s))
-        out = self.conv2(self.norm2(out, s))
-        out = out * w + skip
-        return out
-
-class DualStyleGAN(nn.Module):
-    def __init__(self, size, style_dim, n_mlp, channel_multiplier=2, twoRes=True, res_index=6):
-        super().__init__()
-        
-        layers = [PixelNorm()]
-        for i in range(n_mlp-6):
-            layers.append(EqualLinear(512, 512, lr_mul=0.01, activation="fused_lrelu"))
-        # color transform blocks T_c
-        self.style = nn.Sequential(*layers)
-        # StyleGAN2
-        self.generator = Generator(size, style_dim, n_mlp, channel_multiplier) 
-        # The extrinsic style path
-        self.res = nn.ModuleList()
-        self.res_index = res_index//2 * 2
-        self.res.append(AdaResBlock(self.generator.channels[2 ** 2])) # for conv1
-        for i in range(3, self.generator.log_size + 1):
-            out_channel = self.generator.channels[2 ** i]
-            if i < 3 + self.res_index//2:
-                # ModRes
-                self.res.append(AdaResBlock(out_channel))
-                self.res.append(AdaResBlock(out_channel))
-            else:
-                # structure transform block T_s
-                self.res.append(EqualLinear(512, 512))
-                # FC layer is initialized with identity matrices, meaning no changes to the input latent code
-                self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01
-                self.res.append(EqualLinear(512, 512)) 
-                self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01   
-        self.res.append(EqualLinear(512, 512)) # for to_rgb7
-        self.res[-1].weight.data = torch.eye(512) * 512.0**0.5 + torch.randn(512, 512) * 0.01             
-        self.size = self.generator.size
-        self.style_dim = self.generator.style_dim
-        self.log_size = self.generator.log_size
-        self.num_layers = self.generator.num_layers
-        self.n_latent = self.generator.n_latent
-        self.channels = self.generator.channels
-        
-    def forward(
-        self,
-        styles, # intrinsic style code
-        exstyles, # extrinsic style code
-        return_latents=False, 
-        return_feat=False,
-        inject_index=None,
-        truncation=1,
-        truncation_latent=None,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        z_plus_latent=False, # intrinsic style code is z+ or z
-        use_res=True,        # whether to use the extrinsic style path
-        fuse_index=18,       # layers > fuse_index do not use the extrinsic style path
-        interp_weights=[1]*18, # weight vector for style combination of two paths
-    ):
-
-        if not input_is_latent:
-            if not z_plus_latent:
-                styles = [self.generator.style(s) for s in styles]
-            else:
-                styles = [self.generator.style(s.reshape(s.shape[0]*s.shape[1], s.shape[2])).reshape(s.shape) for s in styles]
-
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.generator.num_layers
-            else:
-                noise = [
-                    getattr(self.generator.noises, f"noise_{i}") for i in range(self.generator.num_layers)
-                ]
-
-        if truncation < 1:
-            style_t = []
-
-            for style in styles:
-                style_t.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-
-            styles = style_t
-        
-        if len(styles) < 2:
-            inject_index = self.generator.n_latent
-
-            if styles[0].ndim < 3:
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-
-            else:
-                latent = styles[0]
-
-        else:
-            if inject_index is None:
-                inject_index = random.randint(1, self.generator.n_latent - 1)
-
-            if styles[0].ndim < 3:
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-                latent2 = styles[1].unsqueeze(1).repeat(1, self.generator.n_latent - inject_index, 1)
-
-                latent = torch.cat([latent, latent2], 1)
-            else:
-                latent = torch.cat([styles[0][:,0:inject_index], styles[1][:,inject_index:]], 1)
-            
-        if use_res:
-            if exstyles.ndim < 3:
-                resstyles = self.style(exstyles).unsqueeze(1).repeat(1, self.generator.n_latent, 1)
-                adastyles = exstyles.unsqueeze(1).repeat(1, self.generator.n_latent, 1)
-            else:
-                nB, nL, nD = exstyles.shape
-                resstyles = self.style(exstyles.reshape(nB*nL, nD)).reshape(nB, nL, nD)
-                adastyles = exstyles
-        
-        out = self.generator.input(latent)
-        out = self.generator.conv1(out, latent[:, 0], noise=noise[0])
-        if use_res and fuse_index > 0:
-            out = self.res[0](out, resstyles[:, 0], interp_weights[0])
-        
-        skip = self.generator.to_rgb1(out, latent[:, 1])
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.generator.convs[::2], self.generator.convs[1::2], noise[1::2], noise[2::2], self.generator.to_rgbs):
-            if use_res and fuse_index >= i and i > self.res_index:
-                out = conv1(out, interp_weights[i] * self.res[i](adastyles[:, i]) + 
-                            (1-interp_weights[i]) * latent[:, i], noise=noise1)
-            else:
-                out = conv1(out, latent[:, i], noise=noise1)
-            if use_res and fuse_index >= i and i <= self.res_index:
-                out = self.res[i](out, resstyles[:, i], interp_weights[i])
-            if use_res and fuse_index >= (i+1) and i > self.res_index:
-                out = conv2(out, interp_weights[i+1] * self.res[i+1](adastyles[:, i+1]) + 
-                            (1-interp_weights[i+1]) * latent[:, i+1], noise=noise2)
-            else:
-                out = conv2(out, latent[:, i + 1], noise=noise2)
-            if use_res and fuse_index >= (i+1) and i <= self.res_index:
-                out = self.res[i+1](out, resstyles[:, i+1], interp_weights[i+1])   
-            if use_res and fuse_index >= (i+2) and i >= self.res_index-1:
-                skip = to_rgb(out, interp_weights[i+2] * self.res[i+2](adastyles[:, i+2]) +
-                              (1-interp_weights[i+2]) * latent[:, i + 2], skip)
-            else:
-                skip = to_rgb(out, latent[:, i + 2], skip)
-            i += 2
-            if i > self.res_index and return_feat:
-                return out, skip
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-
-        else:
-            return image, None  
-        
-    def make_noise(self):
-        return self.generator.make_noise()
-
-    def mean_latent(self, n_latent):
-        return self.generator.mean_latent(n_latent)
-
-    def get_latent(self, input):
-        return self.generator.style(input)
\ No newline at end of file
diff --git a/spaces/Jarvis2301/Aku/transforms.py b/spaces/Jarvis2301/Aku/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/Jarvis2301/Aku/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/JayKen/Object-detection/app.py b/spaces/JayKen/Object-detection/app.py
deleted file mode 100644
index d9bc48e74005255f7cad1eeac9fb19f103bcda58..0000000000000000000000000000000000000000
--- a/spaces/JayKen/Object-detection/app.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import torch
-import cv2
-import gradio as gr
-import numpy as np
-from transformers import OwlViTProcessor, OwlViTForObjectDetection
-
-
-# Use GPU if available
-if torch.cuda.is_available():
-    device = torch.device("cuda")
-else:
-    device = torch.device("cpu")
-
-model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32").to(device)
-model.eval()
-processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
-
-
-def query_image(img, text_queries, score_threshold):
-    text_queries = text_queries
-    text_queries = text_queries.split(",")
-
-    target_sizes = torch.Tensor([img.shape[:2]])
-    inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device)
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-    
-    outputs.logits = outputs.logits.cpu()
-    outputs.pred_boxes = outputs.pred_boxes.cpu() 
-    results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
-    boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
-
-    font = cv2.FONT_HERSHEY_SIMPLEX
-
-    for box, score, label in zip(boxes, scores, labels):
-        box = [int(i) for i in box.tolist()]
-
-        if score >= score_threshold:
-            img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5)
-            if box[3] + 25 > 768:
-                y = box[3] - 10
-            else:
-                y = box[3] + 25
-                
-            img = cv2.putText(
-                img, text_queries[label], (box[0], y), font, 1, (255,0,0), 2, cv2.LINE_AA
-            )
-    return img
-
-
-
-demo = gr.Interface(
-    query_image, 
-    inputs=[gr.Image(), "text", gr.Slider(0, 1, value=0.1)], 
-    outputs="image",
-    title="Object detection",
-)
-demo.launch()
\ No newline at end of file
diff --git a/spaces/Josekutty/project_01/README.md b/spaces/Josekutty/project_01/README.md
deleted file mode 100644
index 27d68698e2b8b695a42378647a6989299a8684a2..0000000000000000000000000000000000000000
--- a/spaces/Josekutty/project_01/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Project 01
-emoji: 👁
-colorFrom: pink
-colorTo: gray
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/models_utils.py b/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/models_utils.py
deleted file mode 100644
index 749aff59b7116f944a9a250b8d3fcfd818f816f7..0000000000000000000000000000000000000000
--- a/spaces/KAIST-Geometric-AI-Lab/salad-demo/salad/spaghetti/models/models_utils.py
+++ /dev/null
@@ -1,138 +0,0 @@
-from ..custom_types import *
-from abc import ABC
-import math
-
-
-def torch_no_grad(func):
-    def wrapper(*args, **kwargs):
-        with torch.no_grad():
-            result = func(*args, **kwargs)
-        return result
-
-    return wrapper
-
-
-class Model(nn.Module, ABC):
-
-    def __init__(self):
-        super(Model, self).__init__()
-        self.save_model: Union[None, Callable[[nn.Module]]] = None
-
-    def save(self, **kwargs):
-        self.save_model(self, **kwargs)
-
-
-class Concatenate(nn.Module):
-    def __init__(self, dim):
-        super(Concatenate, self).__init__()
-        self.dim = dim
-
-    def forward(self, x):
-        return torch.cat(x, dim=self.dim)
-
-
-class View(nn.Module):
-
-    def __init__(self, *shape):
-        super(View, self).__init__()
-        self.shape = shape
-
-    def forward(self, x):
-        return x.view(*self.shape)
-
-
-class Transpose(nn.Module):
-
-    def __init__(self, dim0, dim1):
-        super(Transpose, self).__init__()
-        self.dim0, self.dim1 = dim0, dim1
-
-    def forward(self, x):
-        return x.transpose(self.dim0, self.dim1)
-
-
-class Dummy(nn.Module):
-
-    def __init__(self, *args):
-        super(Dummy, self).__init__()
-
-    def forward(self, *args):
-        return args[0]
-
-
-class SineLayer(nn.Module):
-    """
-    From the siren repository
-    https://colab.research.google.com/github/vsitzmann/siren/blob/master/explore_siren.ipynb
-    """
-    def __init__(self, in_features, out_features, bias=True,
-                 is_first=False, omega_0=30):
-        super().__init__()
-        self.omega_0 = omega_0
-        self.is_first = is_first
-
-        self.in_features = in_features
-        self.linear = nn.Linear(in_features, out_features, bias=bias)
-        self.output_channels = out_features
-        self.init_weights()
-
-    def init_weights(self):
-        with torch.no_grad():
-            if self.is_first:
-                self.linear.weight.uniform_(-1 / self.in_features,
-                                            1 / self.in_features)
-            else:
-                self.linear.weight.uniform_(-np.sqrt(6 / self.in_features) / self.omega_0,
-                                            np.sqrt(6 / self.in_features) / self.omega_0)
-
-    def forward(self, input):
-        return torch.sin(self.omega_0 * self.linear(input))
-
-
-class MLP(nn.Module):
-
-    def forward(self, x, *_):
-        return self.net(x)
-
-    def __init__(self, ch: Union[List[int], Tuple[int, ...]], act: nn.Module = nn.ReLU,
-                 weight_norm=False):
-        super(MLP, self).__init__()
-        layers = []
-        for i in range(len(ch) - 1):
-            layers.append(nn.Linear(ch[i], ch[i + 1]))
-            if weight_norm:
-                layers[-1] = nn.utils.weight_norm(layers[-1])
-            if i < len(ch) - 2:
-                layers.append(act(True))
-        self.net = nn.Sequential(*layers)
-
-
-class GMAttend(nn.Module):
-
-    def __init__(self, hidden_dim: int):
-        super(GMAttend, self).__init__()
-        self.key_dim = hidden_dim // 8
-        self.query_w = nn.Linear(hidden_dim, self.key_dim)
-        self.key_w = nn.Linear(hidden_dim, self.key_dim)
-        self.value_w = nn.Linear(hidden_dim, hidden_dim)
-        self.softmax = nn.Softmax(dim=3)
-        self.gamma = nn.Parameter(torch.zeros(1))
-        self.scale = 1 / torch.sqrt(torch.tensor(self.key_dim, dtype=torch.float32))
-
-    def forward(self, x):
-        queries = self.query_w(x)
-        keys = self.key_w(x)
-        vals = self.value_w(x)
-        attention = self.softmax(torch.einsum('bgqf,bgkf->bgqk', queries, keys))
-        out = torch.einsum('bgvf,bgqv->bgqf', vals, attention)
-        out = self.gamma * out + x
-        return out
-
-
-def recursive_to(item, device):
-    if type(item) is T:
-        return item.to(device)
-    elif type(item) is tuple or type(item) is list:
-        return [recursive_to(item[i], device) for i in range(len(item))]
-    return item
-
diff --git a/spaces/KOFTRFU204/AICoverGen/src/my_utils.py b/spaces/KOFTRFU204/AICoverGen/src/my_utils.py
deleted file mode 100644
index a5258394b8ae5385daa665ab6ba6380507d4798a..0000000000000000000000000000000000000000
--- a/spaces/KOFTRFU204/AICoverGen/src/my_utils.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import ffmpeg
-import numpy as np
-
-
-def load_audio(file, sr):
-    try:
-        # https://github.com/openai/whisper/blob/main/whisper/audio.py#L26
-        # This launches a subprocess to decode audio while down-mixing and resampling as necessary.
-        # Requires the ffmpeg CLI and `ffmpeg-python` package to be installed.
-        file = (
-            file.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
-        )  # 防止小白拷路径头尾带了空格和"和回车
-        out, _ = (
-            ffmpeg.input(file, threads=0)
-            .output("-", format="f32le", acodec="pcm_f32le", ac=1, ar=sr)
-            .run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
-        )
-    except Exception as e:
-        raise RuntimeError(f"Failed to load audio: {e}")
-
-    return np.frombuffer(out, np.float32).flatten()
diff --git a/spaces/KenjieDec/RemBG/rembg/sessions/u2net_cloth_seg.py b/spaces/KenjieDec/RemBG/rembg/sessions/u2net_cloth_seg.py
deleted file mode 100644
index 3e2ec65247d1f85f33036aa9e770456b72b202dc..0000000000000000000000000000000000000000
--- a/spaces/KenjieDec/RemBG/rembg/sessions/u2net_cloth_seg.py
+++ /dev/null
@@ -1,112 +0,0 @@
-import os
-from typing import List
-
-import numpy as np
-import pooch
-from PIL import Image
-from PIL.Image import Image as PILImage
-from scipy.special import log_softmax
-
-from .base import BaseSession
-
-pallete1 = [
-    0,
-    0,
-    0,
-    255,
-    255,
-    255,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-]
-
-pallete2 = [
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    255,
-    255,
-    255,
-    0,
-    0,
-    0,
-]
-
-pallete3 = [
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    255,
-    255,
-    255,
-]
-
-
-class Unet2ClothSession(BaseSession):
-    def predict(self, img: PILImage, *args, **kwargs) -> List[PILImage]:
-        ort_outs = self.inner_session.run(
-            None,
-            self.normalize(
-                img, (0.485, 0.456, 0.406), (0.229, 0.224, 0.225), (768, 768)
-            ),
-        )
-
-        pred = ort_outs
-        pred = log_softmax(pred[0], 1)
-        pred = np.argmax(pred, axis=1, keepdims=True)
-        pred = np.squeeze(pred, 0)
-        pred = np.squeeze(pred, 0)
-
-        mask = Image.fromarray(pred.astype("uint8"), mode="L")
-        mask = mask.resize(img.size, Image.LANCZOS)
-
-        masks = []
-
-        mask1 = mask.copy()
-        mask1.putpalette(pallete1)
-        mask1 = mask1.convert("RGB").convert("L")
-        masks.append(mask1)
-
-        mask2 = mask.copy()
-        mask2.putpalette(pallete2)
-        mask2 = mask2.convert("RGB").convert("L")
-        masks.append(mask2)
-
-        mask3 = mask.copy()
-        mask3.putpalette(pallete3)
-        mask3 = mask3.convert("RGB").convert("L")
-        masks.append(mask3)
-
-        return masks
-
-    @classmethod
-    def download_models(cls, *args, **kwargs):
-        fname = f"{cls.name()}.onnx"
-        pooch.retrieve(
-            "https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_cloth_seg.onnx",
-            None
-            if cls.checksum_disabled(*args, **kwargs)
-            else "md5:2434d1f3cb744e0e49386c906e5a08bb",
-            fname=fname,
-            path=cls.u2net_home(*args, **kwargs),
-            progressbar=True,
-        )
-
-        return os.path.join(cls.u2net_home(), fname)
-
-    @classmethod
-    def name(cls, *args, **kwargs):
-        return "u2net_cloth_seg"
diff --git a/spaces/Kevin676/AutoGPT/autogpt/speech/macos_tts.py b/spaces/Kevin676/AutoGPT/autogpt/speech/macos_tts.py
deleted file mode 100644
index 4c072ce256782e83a578b5181abf1a7b524c621b..0000000000000000000000000000000000000000
--- a/spaces/Kevin676/AutoGPT/autogpt/speech/macos_tts.py
+++ /dev/null
@@ -1,21 +0,0 @@
-""" MacOS TTS Voice. """
-import os
-
-from autogpt.speech.base import VoiceBase
-
-
-class MacOSTTS(VoiceBase):
-    """MacOS TTS Voice."""
-
-    def _setup(self) -> None:
-        pass
-
-    def _speech(self, text: str, voice_index: int = 0) -> bool:
-        """Play the given text."""
-        if voice_index == 0:
-            os.system(f'say "{text}"')
-        elif voice_index == 1:
-            os.system(f'say -v "Ava (Premium)" "{text}"')
-        else:
-            os.system(f'say -v Samantha "{text}"')
-        return True
diff --git a/spaces/Kimata/multimodal_deepfake_detection/models/image.py b/spaces/Kimata/multimodal_deepfake_detection/models/image.py
deleted file mode 100644
index a821f3d30e9971af89064ce48ed3985789a033f6..0000000000000000000000000000000000000000
--- a/spaces/Kimata/multimodal_deepfake_detection/models/image.py
+++ /dev/null
@@ -1,195 +0,0 @@
-import re
-import os
-import wget
-import torch
-import torchvision
-import torch.nn as nn
-import torch.nn.functional as F
-from models.rawnet import SincConv, Residual_block
-from models.classifiers import DeepFakeClassifier
-
-class ImageEncoder(nn.Module):
-    def __init__(self, args):
-        super(ImageEncoder, self).__init__()
-        self.device = args.device
-        self.args = args
-        self.flatten = nn.Flatten()
-        self.sigmoid = nn.Sigmoid()
-        # self.fc = nn.Linear(in_features=2560, out_features = 2)
-        self.pretrained_image_encoder = args.pretrained_image_encoder
-        self.freeze_image_encoder = args.freeze_image_encoder
-
-        if self.pretrained_image_encoder == False:
-            self.model = DeepFakeClassifier(encoder = "tf_efficientnet_b7_ns").to(self.device)
-
-        else:
-            self.pretrained_ckpt = torch.load('pretrained\\final_999_DeepFakeClassifier_tf_efficientnet_b7_ns_0_23', map_location = torch.device(self.args.device))
-            self.state_dict = self.pretrained_ckpt.get("state_dict", self.pretrained_ckpt)
-
-            self.model = DeepFakeClassifier(encoder = "tf_efficientnet_b7_ns").to(self.device)
-            print("Loading pretrained image encoder...")
-            self.model.load_state_dict({re.sub("^module.", "", k): v for k, v in self.state_dict.items()}, strict=True)
-            print("Loaded pretrained image encoder.")
-
-        if self.freeze_image_encoder == True:
-            for idx, param in self.model.named_parameters():
-                param.requires_grad = False
-
-        # self.model.fc = nn.Identity()
-
-    def forward(self, x):
-        x = self.model(x)
-        out = self.sigmoid(x)
-        # x = self.flatten(x)
-        # out = self.fc(x)
-        return out
-
-
-class RawNet(nn.Module):
-    def __init__(self, args):
-        super(RawNet, self).__init__()
-        
-        self.device=args.device
-        self.filts = [20, [20, 20], [20, 128], [128, 128]]
-
-        self.Sinc_conv=SincConv(device=self.device,
-			out_channels = self.filts[0],
-			kernel_size = 1024,
-            in_channels = args.in_channels)
-        
-        self.first_bn = nn.BatchNorm1d(num_features = self.filts[0])
-        self.selu = nn.SELU(inplace=True)
-        self.block0 = nn.Sequential(Residual_block(nb_filts = self.filts[1], first = True))
-        self.block1 = nn.Sequential(Residual_block(nb_filts = self.filts[1]))
-        self.block2 = nn.Sequential(Residual_block(nb_filts = self.filts[2]))
-        self.filts[2][0] = self.filts[2][1]
-        self.block3 = nn.Sequential(Residual_block(nb_filts = self.filts[2]))
-        self.block4 = nn.Sequential(Residual_block(nb_filts = self.filts[2]))
-        self.block5 = nn.Sequential(Residual_block(nb_filts = self.filts[2]))
-        self.avgpool = nn.AdaptiveAvgPool1d(1)
-
-        self.fc_attention0 = self._make_attention_fc(in_features = self.filts[1][-1],
-            l_out_features = self.filts[1][-1])
-        self.fc_attention1 = self._make_attention_fc(in_features = self.filts[1][-1],
-            l_out_features = self.filts[1][-1])
-        self.fc_attention2 = self._make_attention_fc(in_features = self.filts[2][-1],
-            l_out_features = self.filts[2][-1])
-        self.fc_attention3 = self._make_attention_fc(in_features = self.filts[2][-1],
-            l_out_features = self.filts[2][-1])
-        self.fc_attention4 = self._make_attention_fc(in_features = self.filts[2][-1],
-            l_out_features = self.filts[2][-1])
-        self.fc_attention5 = self._make_attention_fc(in_features = self.filts[2][-1],
-            l_out_features = self.filts[2][-1])
-
-        self.bn_before_gru = nn.BatchNorm1d(num_features = self.filts[2][-1])
-        self.gru = nn.GRU(input_size = self.filts[2][-1],
-			hidden_size = args.gru_node,
-			num_layers = args.nb_gru_layer,
-			batch_first = True)
-        
-        self.fc1_gru = nn.Linear(in_features = args.gru_node,
-			out_features = args.nb_fc_node)
-       
-        self.fc2_gru = nn.Linear(in_features = args.nb_fc_node,
-			out_features = args.nb_classes ,bias=True)
-       
-        self.sig = nn.Sigmoid()
-        self.logsoftmax = nn.LogSoftmax(dim=1)
-        self.pretrained_audio_encoder = args.pretrained_audio_encoder
-        self.freeze_audio_encoder = args.freeze_audio_encoder
-
-        if self.pretrained_audio_encoder == True:
-            print("Loading pretrained audio encoder")
-            ckpt = torch.load('pretrained\\RawNet.pth', map_location = torch.device(self.device))
-            print("Loaded pretrained audio encoder")
-            self.load_state_dict(ckpt, strict = True)
-        
-        if self.freeze_audio_encoder:
-                for param in self.parameters():
-                    param.requires_grad = False
-
-
-    def forward(self, x, y = None):
-
-        nb_samp = x.shape[0]
-        len_seq = x.shape[1]
-        x=x.view(nb_samp,1,len_seq)
-        
-        x = self.Sinc_conv(x)    
-        x = F.max_pool1d(torch.abs(x), 3)
-        x = self.first_bn(x)
-        x =  self.selu(x)
-        
-        x0 = self.block0(x)
-        y0 = self.avgpool(x0).view(x0.size(0), -1) # torch.Size([batch, filter])
-        y0 = self.fc_attention0(y0)
-        y0 = self.sig(y0).view(y0.size(0), y0.size(1), -1)  # torch.Size([batch, filter, 1])
-        x = x0 * y0 + y0  # (batch, filter, time) x (batch, filter, 1)
-        
-
-        x1 = self.block1(x)
-        y1 = self.avgpool(x1).view(x1.size(0), -1) # torch.Size([batch, filter])
-        y1 = self.fc_attention1(y1)
-        y1 = self.sig(y1).view(y1.size(0), y1.size(1), -1)  # torch.Size([batch, filter, 1])
-        x = x1 * y1 + y1 # (batch, filter, time) x (batch, filter, 1)
-
-        x2 = self.block2(x)
-        y2 = self.avgpool(x2).view(x2.size(0), -1) # torch.Size([batch, filter])
-        y2 = self.fc_attention2(y2)
-        y2 = self.sig(y2).view(y2.size(0), y2.size(1), -1)  # torch.Size([batch, filter, 1])
-        x = x2 * y2 + y2 # (batch, filter, time) x (batch, filter, 1)
-
-        x3 = self.block3(x)
-        y3 = self.avgpool(x3).view(x3.size(0), -1) # torch.Size([batch, filter])
-        y3 = self.fc_attention3(y3)
-        y3 = self.sig(y3).view(y3.size(0), y3.size(1), -1)  # torch.Size([batch, filter, 1])
-        x = x3 * y3 + y3 # (batch, filter, time) x (batch, filter, 1)
-
-        x4 = self.block4(x)
-        y4 = self.avgpool(x4).view(x4.size(0), -1) # torch.Size([batch, filter])
-        y4 = self.fc_attention4(y4)
-        y4 = self.sig(y4).view(y4.size(0), y4.size(1), -1)  # torch.Size([batch, filter, 1])
-        x = x4 * y4 + y4 # (batch, filter, time) x (batch, filter, 1)
-
-        x5 = self.block5(x)
-        y5 = self.avgpool(x5).view(x5.size(0), -1) # torch.Size([batch, filter])
-        y5 = self.fc_attention5(y5)
-        y5 = self.sig(y5).view(y5.size(0), y5.size(1), -1)  # torch.Size([batch, filter, 1])
-        x = x5 * y5 + y5 # (batch, filter, time) x (batch, filter, 1)
-
-        x = self.bn_before_gru(x)
-        x = self.selu(x)
-        x = x.permute(0, 2, 1)     #(batch, filt, time) >> (batch, time, filt)
-        self.gru.flatten_parameters()
-        x, _ = self.gru(x)
-        x = x[:,-1,:]
-        x = self.fc1_gru(x)
-        x = self.fc2_gru(x)
-        output=self.logsoftmax(x)
-      
-        return output
-        
-        
-
-    def _make_attention_fc(self, in_features, l_out_features):
-
-        l_fc = []
-        
-        l_fc.append(nn.Linear(in_features = in_features,
-			        out_features = l_out_features))
-
-        
-
-        return nn.Sequential(*l_fc)
-
-
-    def _make_layer(self, nb_blocks, nb_filts, first = False):
-        layers = []
-        #def __init__(self, nb_filts, first = False):
-        for i in range(nb_blocks):
-            first = first if i == 0 else False
-            layers.append(Residual_block(nb_filts = nb_filts,
-				first = first))
-            if i == 0: nb_filts[0] = nb_filts[1]
-            
-        return nn.Sequential(*layers)
\ No newline at end of file
diff --git a/spaces/KyanChen/FunSR/models/cnn_models/dcm.py b/spaces/KyanChen/FunSR/models/cnn_models/dcm.py
deleted file mode 100644
index b31372ef4a9fee7f4c343865f84eb497975fe60a..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/FunSR/models/cnn_models/dcm.py
+++ /dev/null
@@ -1,108 +0,0 @@
-
-from . import common
-
-from argparse import Namespace
-
-import torch
-import torch.nn as nn
-from models import register
-import torch.nn.functional as F
-
-def make_model(args, parent=False):
-    return DIM(args)
-
-@register('DCM')
-def DCM(scale_ratio, rgb_range=1):
-    args = Namespace()
-    args.scale = [scale_ratio]
-    args.n_colors = 3
-    args.rgb_range = rgb_range
-    return DIM(args)
-
-class DIM(nn.Module):
-    def __init__(self, args, conv=common.default_conv):
-        super(DIM, self).__init__()
-
-        self.scale = args.scale[0]
-
-        # feature extractor part
-        self.fe_conv1 = common.BasicBlock(conv, args.n_colors, 196, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv2 = common.BasicBlock(conv, 196, 166, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv3 = common.BasicBlock(conv, 166, 148, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv4 = common.BasicBlock(conv, 148, 133, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv5 = common.BasicBlock(conv, 133, 120, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv6 = common.BasicBlock(conv, 120, 108, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv7 = common.BasicBlock(conv, 108, 97, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv8 = common.BasicBlock(conv, 97, 86, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv9 = common.BasicBlock(conv, 86, 76, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv10 = common.BasicBlock(conv, 76, 66, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv11 = common.BasicBlock(conv, 66, 57, kernel_size=3, bias=True, act=nn.PReLU())
-        self.fe_conv12 = common.BasicBlock(conv, 57, 48, kernel_size=3, bias=True, act=nn.PReLU())
-
-        # reconstruction part
-        self.re_a = common.BasicBlock(conv, 196 + 48, 64, kernel_size=3, bias=True, act=nn.PReLU())
-        self.re_b1 = common.BasicBlock(conv, 196 + 48, 32, kernel_size=3, bias=True, act=nn.PReLU())
-        self.re_b2 = common.BasicBlock(conv, 32, 32, kernel_size=3, bias=True, act=nn.PReLU())
-        self.re_u = common.Upsampler(conv, self.scale, 96, act=False)
-        self.re_r = conv(96, args.n_colors, kernel_size=1)
-
-
-    def forward(self, x, out_size=None):
-
-        residual = F.interpolate(x, scale_factor=self.scale, mode='bicubic')
-
-        # feature extractor part
-        fe_conv1 = self.fe_conv1(x)
-        fe_conv2 = self.fe_conv2(fe_conv1)
-        fe_conv3 = self.fe_conv3(fe_conv2)
-        fe_conv4 = self.fe_conv4(fe_conv3)
-        fe_conv5 = self.fe_conv5(fe_conv4)
-        fe_conv6 = self.fe_conv6(fe_conv5)
-        fe_conv7 = self.fe_conv7(fe_conv6)
-        fe_conv8 = self.fe_conv8(fe_conv7)
-        fe_conv9 = self.fe_conv9(fe_conv8)
-        fe_conv10 = self.fe_conv10(fe_conv9)
-        fe_conv11 = self.fe_conv11(fe_conv10)
-        fe_conv12 = self.fe_conv12(fe_conv11)
-
-        # reconstruction part
-        feat = torch.cat((fe_conv1, fe_conv12), dim=1)
-        re_a = self.re_a(feat)
-        re_b1 = self.re_b1(feat)
-        re_b2 = self.re_b2(re_b1)
-        feat = torch.cat((re_a, re_b2), dim=1)
-        re_u = self.re_u(feat)
-        re_r = self.re_r(re_u)
-        out = re_r + residual
-
-        return out
-
-    def load_state_dict(self, state_dict, strict=False):
-        own_state = self.state_dict()
-        for name, param in state_dict.items():
-            if name in own_state:
-                if isinstance(param, nn.Parameter):
-                    param = param.data
-                try:
-                    own_state[name].copy_(param)
-                except Exception:
-                    if name.find('tail') >= 0:
-                        print('Replace pre-trained upsampler to new one...')
-                    else:
-                        raise RuntimeError('While copying the parameter named {}, '
-                                           'whose dimensions in the model are {} and '
-                                           'whose dimensions in the checkpoint are {}.'
-                                           .format(name, own_state[name].size(), param.size()))
-            elif strict:
-                if name.find('tail') == -1:
-                    raise KeyError('unexpected key "{}" in state_dict'
-                                   .format(name))
-
-        if strict:
-            missing = set(own_state.keys()) - set(state_dict.keys())
-            if len(missing) > 0:
-                raise KeyError('missing keys in state_dict: "{}"'.format(missing))
-
-
-
-
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/yolox_head.py b/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/yolox_head.py
deleted file mode 100644
index 00fe1e42766e4ca0052cf31d2e940dfab73fb200..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/dense_heads/yolox_head.py
+++ /dev/null
@@ -1,618 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import math
-from typing import List, Optional, Sequence, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
-from mmcv.ops.nms import batched_nms
-from mmengine.config import ConfigDict
-from mmengine.model import bias_init_with_prob
-from mmengine.structures import InstanceData
-from torch import Tensor
-
-from mmdet.registry import MODELS, TASK_UTILS
-from mmdet.structures.bbox import bbox_xyxy_to_cxcywh
-from mmdet.utils import (ConfigType, OptConfigType, OptInstanceList,
-                         OptMultiConfig, reduce_mean)
-from ..task_modules.prior_generators import MlvlPointGenerator
-from ..task_modules.samplers import PseudoSampler
-from ..utils import multi_apply
-from .base_dense_head import BaseDenseHead
-
-
-@MODELS.register_module()
-class YOLOXHead(BaseDenseHead):
-    """YOLOXHead head used in `YOLOX <https://arxiv.org/abs/2107.08430>`_.
-
-    Args:
-        num_classes (int): Number of categories excluding the background
-            category.
-        in_channels (int): Number of channels in the input feature map.
-        feat_channels (int): Number of hidden channels in stacking convs.
-            Defaults to 256
-        stacked_convs (int): Number of stacking convs of the head.
-            Defaults to (8, 16, 32).
-        strides (Sequence[int]): Downsample factor of each feature map.
-             Defaults to None.
-        use_depthwise (bool): Whether to depthwise separable convolution in
-            blocks. Defaults to False.
-        dcn_on_last_conv (bool): If true, use dcn in the last layer of
-            towers. Defaults to False.
-        conv_bias (bool or str): If specified as `auto`, it will be decided by
-            the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
-            None, otherwise False. Defaults to "auto".
-        conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for
-            convolution layer. Defaults to None.
-        norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization
-            layer. Defaults to dict(type='BN', momentum=0.03, eps=0.001).
-        act_cfg (:obj:`ConfigDict` or dict): Config dict for activation layer.
-            Defaults to None.
-        loss_cls (:obj:`ConfigDict` or dict): Config of classification loss.
-        loss_bbox (:obj:`ConfigDict` or dict): Config of localization loss.
-        loss_obj (:obj:`ConfigDict` or dict): Config of objectness loss.
-        loss_l1 (:obj:`ConfigDict` or dict): Config of L1 loss.
-        train_cfg (:obj:`ConfigDict` or dict, optional): Training config of
-            anchor head. Defaults to None.
-        test_cfg (:obj:`ConfigDict` or dict, optional): Testing config of
-            anchor head. Defaults to None.
-        init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
-            list[dict], optional): Initialization config dict.
-            Defaults to None.
-    """
-
-    def __init__(
-        self,
-        num_classes: int,
-        in_channels: int,
-        feat_channels: int = 256,
-        stacked_convs: int = 2,
-        strides: Sequence[int] = (8, 16, 32),
-        use_depthwise: bool = False,
-        dcn_on_last_conv: bool = False,
-        conv_bias: Union[bool, str] = 'auto',
-        conv_cfg: OptConfigType = None,
-        norm_cfg: ConfigType = dict(type='BN', momentum=0.03, eps=0.001),
-        act_cfg: ConfigType = dict(type='Swish'),
-        loss_cls: ConfigType = dict(
-            type='CrossEntropyLoss',
-            use_sigmoid=True,
-            reduction='sum',
-            loss_weight=1.0),
-        loss_bbox: ConfigType = dict(
-            type='IoULoss',
-            mode='square',
-            eps=1e-16,
-            reduction='sum',
-            loss_weight=5.0),
-        loss_obj: ConfigType = dict(
-            type='CrossEntropyLoss',
-            use_sigmoid=True,
-            reduction='sum',
-            loss_weight=1.0),
-        loss_l1: ConfigType = dict(
-            type='L1Loss', reduction='sum', loss_weight=1.0),
-        train_cfg: OptConfigType = None,
-        test_cfg: OptConfigType = None,
-        init_cfg: OptMultiConfig = dict(
-            type='Kaiming',
-            layer='Conv2d',
-            a=math.sqrt(5),
-            distribution='uniform',
-            mode='fan_in',
-            nonlinearity='leaky_relu')
-    ) -> None:
-
-        super().__init__(init_cfg=init_cfg)
-        self.num_classes = num_classes
-        self.cls_out_channels = num_classes
-        self.in_channels = in_channels
-        self.feat_channels = feat_channels
-        self.stacked_convs = stacked_convs
-        self.strides = strides
-        self.use_depthwise = use_depthwise
-        self.dcn_on_last_conv = dcn_on_last_conv
-        assert conv_bias == 'auto' or isinstance(conv_bias, bool)
-        self.conv_bias = conv_bias
-        self.use_sigmoid_cls = True
-
-        self.conv_cfg = conv_cfg
-        self.norm_cfg = norm_cfg
-        self.act_cfg = act_cfg
-
-        self.loss_cls: nn.Module = MODELS.build(loss_cls)
-        self.loss_bbox: nn.Module = MODELS.build(loss_bbox)
-        self.loss_obj: nn.Module = MODELS.build(loss_obj)
-
-        self.use_l1 = False  # This flag will be modified by hooks.
-        self.loss_l1: nn.Module = MODELS.build(loss_l1)
-
-        self.prior_generator = MlvlPointGenerator(strides, offset=0)
-
-        self.test_cfg = test_cfg
-        self.train_cfg = train_cfg
-
-        if self.train_cfg:
-            self.assigner = TASK_UTILS.build(self.train_cfg['assigner'])
-            # YOLOX does not support sampling
-            self.sampler = PseudoSampler()
-
-        self._init_layers()
-
-    def _init_layers(self) -> None:
-        """Initialize heads for all level feature maps."""
-        self.multi_level_cls_convs = nn.ModuleList()
-        self.multi_level_reg_convs = nn.ModuleList()
-        self.multi_level_conv_cls = nn.ModuleList()
-        self.multi_level_conv_reg = nn.ModuleList()
-        self.multi_level_conv_obj = nn.ModuleList()
-        for _ in self.strides:
-            self.multi_level_cls_convs.append(self._build_stacked_convs())
-            self.multi_level_reg_convs.append(self._build_stacked_convs())
-            conv_cls, conv_reg, conv_obj = self._build_predictor()
-            self.multi_level_conv_cls.append(conv_cls)
-            self.multi_level_conv_reg.append(conv_reg)
-            self.multi_level_conv_obj.append(conv_obj)
-
-    def _build_stacked_convs(self) -> nn.Sequential:
-        """Initialize conv layers of a single level head."""
-        conv = DepthwiseSeparableConvModule \
-            if self.use_depthwise else ConvModule
-        stacked_convs = []
-        for i in range(self.stacked_convs):
-            chn = self.in_channels if i == 0 else self.feat_channels
-            if self.dcn_on_last_conv and i == self.stacked_convs - 1:
-                conv_cfg = dict(type='DCNv2')
-            else:
-                conv_cfg = self.conv_cfg
-            stacked_convs.append(
-                conv(
-                    chn,
-                    self.feat_channels,
-                    3,
-                    stride=1,
-                    padding=1,
-                    conv_cfg=conv_cfg,
-                    norm_cfg=self.norm_cfg,
-                    act_cfg=self.act_cfg,
-                    bias=self.conv_bias))
-        return nn.Sequential(*stacked_convs)
-
-    def _build_predictor(self) -> Tuple[nn.Module, nn.Module, nn.Module]:
-        """Initialize predictor layers of a single level head."""
-        conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
-        conv_reg = nn.Conv2d(self.feat_channels, 4, 1)
-        conv_obj = nn.Conv2d(self.feat_channels, 1, 1)
-        return conv_cls, conv_reg, conv_obj
-
-    def init_weights(self) -> None:
-        """Initialize weights of the head."""
-        super(YOLOXHead, self).init_weights()
-        # Use prior in model initialization to improve stability
-        bias_init = bias_init_with_prob(0.01)
-        for conv_cls, conv_obj in zip(self.multi_level_conv_cls,
-                                      self.multi_level_conv_obj):
-            conv_cls.bias.data.fill_(bias_init)
-            conv_obj.bias.data.fill_(bias_init)
-
-    def forward_single(self, x: Tensor, cls_convs: nn.Module,
-                       reg_convs: nn.Module, conv_cls: nn.Module,
-                       conv_reg: nn.Module,
-                       conv_obj: nn.Module) -> Tuple[Tensor, Tensor, Tensor]:
-        """Forward feature of a single scale level."""
-
-        cls_feat = cls_convs(x)
-        reg_feat = reg_convs(x)
-
-        cls_score = conv_cls(cls_feat)
-        bbox_pred = conv_reg(reg_feat)
-        objectness = conv_obj(reg_feat)
-
-        return cls_score, bbox_pred, objectness
-
-    def forward(self, x: Tuple[Tensor]) -> Tuple[List]:
-        """Forward features from the upstream network.
-
-        Args:
-            x (Tuple[Tensor]): Features from the upstream network, each is
-                a 4D-tensor.
-        Returns:
-            Tuple[List]: A tuple of multi-level classification scores, bbox
-            predictions, and objectnesses.
-        """
-
-        return multi_apply(self.forward_single, x, self.multi_level_cls_convs,
-                           self.multi_level_reg_convs,
-                           self.multi_level_conv_cls,
-                           self.multi_level_conv_reg,
-                           self.multi_level_conv_obj)
-
-    def predict_by_feat(self,
-                        cls_scores: List[Tensor],
-                        bbox_preds: List[Tensor],
-                        objectnesses: Optional[List[Tensor]],
-                        batch_img_metas: Optional[List[dict]] = None,
-                        cfg: Optional[ConfigDict] = None,
-                        rescale: bool = False,
-                        with_nms: bool = True) -> List[InstanceData]:
-        """Transform a batch of output features extracted by the head into
-        bbox results.
-        Args:
-            cls_scores (list[Tensor]): Classification scores for all
-                scale levels, each is a 4D-tensor, has shape
-                (batch_size, num_priors * num_classes, H, W).
-            bbox_preds (list[Tensor]): Box energies / deltas for all
-                scale levels, each is a 4D-tensor, has shape
-                (batch_size, num_priors * 4, H, W).
-            objectnesses (list[Tensor], Optional): Score factor for
-                all scale level, each is a 4D-tensor, has shape
-                (batch_size, 1, H, W).
-            batch_img_metas (list[dict], Optional): Batch image meta info.
-                Defaults to None.
-            cfg (ConfigDict, optional): Test / postprocessing
-                configuration, if None, test_cfg would be used.
-                Defaults to None.
-            rescale (bool): If True, return boxes in original image space.
-                Defaults to False.
-            with_nms (bool): If True, do nms before return boxes.
-                Defaults to True.
-
-        Returns:
-            list[:obj:`InstanceData`]: Object detection results of each image
-            after the post process. Each item usually contains following keys.
-
-            - scores (Tensor): Classification scores, has a shape
-              (num_instance, )
-            - labels (Tensor): Labels of bboxes, has a shape
-              (num_instances, ).
-            - bboxes (Tensor): Has a shape (num_instances, 4),
-              the last dimension 4 arrange as (x1, y1, x2, y2).
-        """
-        assert len(cls_scores) == len(bbox_preds) == len(objectnesses)
-        cfg = self.test_cfg if cfg is None else cfg
-
-        num_imgs = len(batch_img_metas)
-        featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
-        mlvl_priors = self.prior_generator.grid_priors(
-            featmap_sizes,
-            dtype=cls_scores[0].dtype,
-            device=cls_scores[0].device,
-            with_stride=True)
-
-        # flatten cls_scores, bbox_preds and objectness
-        flatten_cls_scores = [
-            cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
-                                                  self.cls_out_channels)
-            for cls_score in cls_scores
-        ]
-        flatten_bbox_preds = [
-            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
-            for bbox_pred in bbox_preds
-        ]
-        flatten_objectness = [
-            objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
-            for objectness in objectnesses
-        ]
-
-        flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
-        flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
-        flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid()
-        flatten_priors = torch.cat(mlvl_priors)
-
-        flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds)
-
-        result_list = []
-        for img_id, img_meta in enumerate(batch_img_metas):
-            max_scores, labels = torch.max(flatten_cls_scores[img_id], 1)
-            valid_mask = flatten_objectness[
-                img_id] * max_scores >= cfg.score_thr
-            results = InstanceData(
-                bboxes=flatten_bboxes[img_id][valid_mask],
-                scores=max_scores[valid_mask] *
-                flatten_objectness[img_id][valid_mask],
-                labels=labels[valid_mask])
-
-            result_list.append(
-                self._bbox_post_process(
-                    results=results,
-                    cfg=cfg,
-                    rescale=rescale,
-                    with_nms=with_nms,
-                    img_meta=img_meta))
-
-        return result_list
-
-    def _bbox_decode(self, priors: Tensor, bbox_preds: Tensor) -> Tensor:
-        """Decode regression results (delta_x, delta_x, w, h) to bboxes (tl_x,
-        tl_y, br_x, br_y).
-
-        Args:
-            priors (Tensor): Center proiors of an image, has shape
-                (num_instances, 2).
-            bbox_preds (Tensor): Box energies / deltas for all instances,
-                has shape (batch_size, num_instances, 4).
-
-        Returns:
-            Tensor: Decoded bboxes in (tl_x, tl_y, br_x, br_y) format. Has
-            shape (batch_size, num_instances, 4).
-        """
-        xys = (bbox_preds[..., :2] * priors[:, 2:]) + priors[:, :2]
-        whs = bbox_preds[..., 2:].exp() * priors[:, 2:]
-
-        tl_x = (xys[..., 0] - whs[..., 0] / 2)
-        tl_y = (xys[..., 1] - whs[..., 1] / 2)
-        br_x = (xys[..., 0] + whs[..., 0] / 2)
-        br_y = (xys[..., 1] + whs[..., 1] / 2)
-
-        decoded_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1)
-        return decoded_bboxes
-
-    def _bbox_post_process(self,
-                           results: InstanceData,
-                           cfg: ConfigDict,
-                           rescale: bool = False,
-                           with_nms: bool = True,
-                           img_meta: Optional[dict] = None) -> InstanceData:
-        """bbox post-processing method.
-
-        The boxes would be rescaled to the original image scale and do
-        the nms operation. Usually `with_nms` is False is used for aug test.
-
-        Args:
-            results (:obj:`InstaceData`): Detection instance results,
-                each item has shape (num_bboxes, ).
-            cfg (mmengine.Config): Test / postprocessing configuration,
-                if None, test_cfg would be used.
-            rescale (bool): If True, return boxes in original image space.
-                Default to False.
-            with_nms (bool): If True, do nms before return boxes.
-                Default to True.
-            img_meta (dict, optional): Image meta info. Defaults to None.
-
-        Returns:
-            :obj:`InstanceData`: Detection results of each image
-            after the post process.
-            Each item usually contains following keys.
-
-            - scores (Tensor): Classification scores, has a shape
-              (num_instance, )
-            - labels (Tensor): Labels of bboxes, has a shape
-              (num_instances, ).
-            - bboxes (Tensor): Has a shape (num_instances, 4),
-              the last dimension 4 arrange as (x1, y1, x2, y2).
-        """
-
-        if rescale:
-            assert img_meta.get('scale_factor') is not None
-            results.bboxes /= results.bboxes.new_tensor(
-                img_meta['scale_factor']).repeat((1, 2))
-
-        if with_nms and results.bboxes.numel() > 0:
-            det_bboxes, keep_idxs = batched_nms(results.bboxes, results.scores,
-                                                results.labels, cfg.nms)
-            results = results[keep_idxs]
-            # some nms would reweight the score, such as softnms
-            results.scores = det_bboxes[:, -1]
-        return results
-
-    def loss_by_feat(
-            self,
-            cls_scores: Sequence[Tensor],
-            bbox_preds: Sequence[Tensor],
-            objectnesses: Sequence[Tensor],
-            batch_gt_instances: Sequence[InstanceData],
-            batch_img_metas: Sequence[dict],
-            batch_gt_instances_ignore: OptInstanceList = None) -> dict:
-        """Calculate the loss based on the features extracted by the detection
-        head.
-
-        Args:
-            cls_scores (Sequence[Tensor]): Box scores for each scale level,
-                each is a 4D-tensor, the channel number is
-                num_priors * num_classes.
-            bbox_preds (Sequence[Tensor]): Box energies / deltas for each scale
-                level, each is a 4D-tensor, the channel number is
-                num_priors * 4.
-            objectnesses (Sequence[Tensor]): Score factor for
-                all scale level, each is a 4D-tensor, has shape
-                (batch_size, 1, H, W).
-            batch_gt_instances (list[:obj:`InstanceData`]): Batch of
-                gt_instance. It usually includes ``bboxes`` and ``labels``
-                attributes.
-            batch_img_metas (list[dict]): Meta information of each image, e.g.,
-                image size, scaling factor, etc.
-            batch_gt_instances_ignore (list[:obj:`InstanceData`], optional):
-                Batch of gt_instances_ignore. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-        Returns:
-            dict[str, Tensor]: A dictionary of losses.
-        """
-        num_imgs = len(batch_img_metas)
-        if batch_gt_instances_ignore is None:
-            batch_gt_instances_ignore = [None] * num_imgs
-
-        featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
-        mlvl_priors = self.prior_generator.grid_priors(
-            featmap_sizes,
-            dtype=cls_scores[0].dtype,
-            device=cls_scores[0].device,
-            with_stride=True)
-
-        flatten_cls_preds = [
-            cls_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
-                                                 self.cls_out_channels)
-            for cls_pred in cls_scores
-        ]
-        flatten_bbox_preds = [
-            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
-            for bbox_pred in bbox_preds
-        ]
-        flatten_objectness = [
-            objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
-            for objectness in objectnesses
-        ]
-
-        flatten_cls_preds = torch.cat(flatten_cls_preds, dim=1)
-        flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
-        flatten_objectness = torch.cat(flatten_objectness, dim=1)
-        flatten_priors = torch.cat(mlvl_priors)
-        flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds)
-
-        (pos_masks, cls_targets, obj_targets, bbox_targets, l1_targets,
-         num_fg_imgs) = multi_apply(
-             self._get_targets_single,
-             flatten_priors.unsqueeze(0).repeat(num_imgs, 1, 1),
-             flatten_cls_preds.detach(), flatten_bboxes.detach(),
-             flatten_objectness.detach(), batch_gt_instances, batch_img_metas,
-             batch_gt_instances_ignore)
-
-        # The experimental results show that 'reduce_mean' can improve
-        # performance on the COCO dataset.
-        num_pos = torch.tensor(
-            sum(num_fg_imgs),
-            dtype=torch.float,
-            device=flatten_cls_preds.device)
-        num_total_samples = max(reduce_mean(num_pos), 1.0)
-
-        pos_masks = torch.cat(pos_masks, 0)
-        cls_targets = torch.cat(cls_targets, 0)
-        obj_targets = torch.cat(obj_targets, 0)
-        bbox_targets = torch.cat(bbox_targets, 0)
-        if self.use_l1:
-            l1_targets = torch.cat(l1_targets, 0)
-
-        loss_obj = self.loss_obj(flatten_objectness.view(-1, 1),
-                                 obj_targets) / num_total_samples
-        if num_pos > 0:
-            loss_cls = self.loss_cls(
-                flatten_cls_preds.view(-1, self.num_classes)[pos_masks],
-                cls_targets) / num_total_samples
-            loss_bbox = self.loss_bbox(
-                flatten_bboxes.view(-1, 4)[pos_masks],
-                bbox_targets) / num_total_samples
-        else:
-            # Avoid cls and reg branch not participating in the gradient
-            # propagation when there is no ground-truth in the images.
-            # For more details, please refer to
-            # https://github.com/open-mmlab/mmdetection/issues/7298
-            loss_cls = flatten_cls_preds.sum() * 0
-            loss_bbox = flatten_bboxes.sum() * 0
-
-        loss_dict = dict(
-            loss_cls=loss_cls, loss_bbox=loss_bbox, loss_obj=loss_obj)
-
-        if self.use_l1:
-            if num_pos > 0:
-                loss_l1 = self.loss_l1(
-                    flatten_bbox_preds.view(-1, 4)[pos_masks],
-                    l1_targets) / num_total_samples
-            else:
-                # Avoid cls and reg branch not participating in the gradient
-                # propagation when there is no ground-truth in the images.
-                # For more details, please refer to
-                # https://github.com/open-mmlab/mmdetection/issues/7298
-                loss_l1 = flatten_bbox_preds.sum() * 0
-            loss_dict.update(loss_l1=loss_l1)
-
-        return loss_dict
-
-    @torch.no_grad()
-    def _get_targets_single(
-            self,
-            priors: Tensor,
-            cls_preds: Tensor,
-            decoded_bboxes: Tensor,
-            objectness: Tensor,
-            gt_instances: InstanceData,
-            img_meta: dict,
-            gt_instances_ignore: Optional[InstanceData] = None) -> tuple:
-        """Compute classification, regression, and objectness targets for
-        priors in a single image.
-
-        Args:
-            priors (Tensor): All priors of one image, a 2D-Tensor with shape
-                [num_priors, 4] in [cx, xy, stride_w, stride_y] format.
-            cls_preds (Tensor): Classification predictions of one image,
-                a 2D-Tensor with shape [num_priors, num_classes]
-            decoded_bboxes (Tensor): Decoded bboxes predictions of one image,
-                a 2D-Tensor with shape [num_priors, 4] in [tl_x, tl_y,
-                br_x, br_y] format.
-            objectness (Tensor): Objectness predictions of one image,
-                a 1D-Tensor with shape [num_priors]
-            gt_instances (:obj:`InstanceData`): Ground truth of instance
-                annotations. It should includes ``bboxes`` and ``labels``
-                attributes.
-            img_meta (dict): Meta information for current image.
-            gt_instances_ignore (:obj:`InstanceData`, optional): Instances
-                to be ignored during training. It includes ``bboxes`` attribute
-                data that is ignored during training and testing.
-                Defaults to None.
-        Returns:
-            tuple:
-                foreground_mask (list[Tensor]): Binary mask of foreground
-                targets.
-                cls_target (list[Tensor]): Classification targets of an image.
-                obj_target (list[Tensor]): Objectness targets of an image.
-                bbox_target (list[Tensor]): BBox targets of an image.
-                l1_target (int): BBox L1 targets of an image.
-                num_pos_per_img (int): Number of positive samples in an image.
-        """
-
-        num_priors = priors.size(0)
-        num_gts = len(gt_instances)
-        # No target
-        if num_gts == 0:
-            cls_target = cls_preds.new_zeros((0, self.num_classes))
-            bbox_target = cls_preds.new_zeros((0, 4))
-            l1_target = cls_preds.new_zeros((0, 4))
-            obj_target = cls_preds.new_zeros((num_priors, 1))
-            foreground_mask = cls_preds.new_zeros(num_priors).bool()
-            return (foreground_mask, cls_target, obj_target, bbox_target,
-                    l1_target, 0)
-
-        # YOLOX uses center priors with 0.5 offset to assign targets,
-        # but use center priors without offset to regress bboxes.
-        offset_priors = torch.cat(
-            [priors[:, :2] + priors[:, 2:] * 0.5, priors[:, 2:]], dim=-1)
-
-        scores = cls_preds.sigmoid() * objectness.unsqueeze(1).sigmoid()
-        pred_instances = InstanceData(
-            bboxes=decoded_bboxes, scores=scores.sqrt_(), priors=offset_priors)
-        assign_result = self.assigner.assign(
-            pred_instances=pred_instances,
-            gt_instances=gt_instances,
-            gt_instances_ignore=gt_instances_ignore)
-
-        sampling_result = self.sampler.sample(assign_result, pred_instances,
-                                              gt_instances)
-        pos_inds = sampling_result.pos_inds
-        num_pos_per_img = pos_inds.size(0)
-
-        pos_ious = assign_result.max_overlaps[pos_inds]
-        # IOU aware classification score
-        cls_target = F.one_hot(sampling_result.pos_gt_labels,
-                               self.num_classes) * pos_ious.unsqueeze(-1)
-        obj_target = torch.zeros_like(objectness).unsqueeze(-1)
-        obj_target[pos_inds] = 1
-        bbox_target = sampling_result.pos_gt_bboxes
-        l1_target = cls_preds.new_zeros((num_pos_per_img, 4))
-        if self.use_l1:
-            l1_target = self._get_l1_target(l1_target, bbox_target,
-                                            priors[pos_inds])
-        foreground_mask = torch.zeros_like(objectness).to(torch.bool)
-        foreground_mask[pos_inds] = 1
-        return (foreground_mask, cls_target, obj_target, bbox_target,
-                l1_target, num_pos_per_img)
-
-    def _get_l1_target(self,
-                       l1_target: Tensor,
-                       gt_bboxes: Tensor,
-                       priors: Tensor,
-                       eps: float = 1e-8) -> Tensor:
-        """Convert gt bboxes to center offset and log width height."""
-        gt_cxcywh = bbox_xyxy_to_cxcywh(gt_bboxes)
-        l1_target[:, :2] = (gt_cxcywh[:, :2] - priors[:, :2]) / priors[:, 2:]
-        l1_target[:, 2:] = torch.log(gt_cxcywh[:, 2:] / priors[:, 2:] + eps)
-        return l1_target
diff --git a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/semi_base.py b/spaces/KyanChen/RSPrompter/mmdet/models/detectors/semi_base.py
deleted file mode 100644
index f3f0c8c030830e188bf3ad245d5b3cb471ecb04f..0000000000000000000000000000000000000000
--- a/spaces/KyanChen/RSPrompter/mmdet/models/detectors/semi_base.py
+++ /dev/null
@@ -1,266 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import copy
-from typing import Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-
-from mmdet.models.utils import (filter_gt_instances, rename_loss_dict,
-                                reweight_loss_dict)
-from mmdet.registry import MODELS
-from mmdet.structures import SampleList
-from mmdet.structures.bbox import bbox_project
-from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
-from .base import BaseDetector
-
-
-@MODELS.register_module()
-class SemiBaseDetector(BaseDetector):
-    """Base class for semi-supervised detectors.
-
-    Semi-supervised detectors typically consisting of a teacher model
-    updated by exponential moving average and a student model updated
-    by gradient descent.
-
-    Args:
-        detector (:obj:`ConfigDict` or dict): The detector config.
-        semi_train_cfg (:obj:`ConfigDict` or dict, optional):
-            The semi-supervised training config.
-        semi_test_cfg (:obj:`ConfigDict` or dict, optional):
-            The semi-supervised testing config.
-        data_preprocessor (:obj:`ConfigDict` or dict, optional): Config of
-            :class:`DetDataPreprocessor` to process the input data.
-            Defaults to None.
-        init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or
-            list[dict], optional): Initialization config dict.
-            Defaults to None.
-    """
-
-    def __init__(self,
-                 detector: ConfigType,
-                 semi_train_cfg: OptConfigType = None,
-                 semi_test_cfg: OptConfigType = None,
-                 data_preprocessor: OptConfigType = None,
-                 init_cfg: OptMultiConfig = None) -> None:
-        super().__init__(
-            data_preprocessor=data_preprocessor, init_cfg=init_cfg)
-        self.student = MODELS.build(detector)
-        self.teacher = MODELS.build(detector)
-        self.semi_train_cfg = semi_train_cfg
-        self.semi_test_cfg = semi_test_cfg
-        if self.semi_train_cfg.get('freeze_teacher', True) is True:
-            self.freeze(self.teacher)
-
-    @staticmethod
-    def freeze(model: nn.Module):
-        """Freeze the model."""
-        model.eval()
-        for param in model.parameters():
-            param.requires_grad = False
-
-    def loss(self, multi_batch_inputs: Dict[str, Tensor],
-             multi_batch_data_samples: Dict[str, SampleList]) -> dict:
-        """Calculate losses from multi-branch inputs and data samples.
-
-        Args:
-            multi_batch_inputs (Dict[str, Tensor]): The dict of multi-branch
-                input images, each value with shape (N, C, H, W).
-                Each value should usually be mean centered and std scaled.
-            multi_batch_data_samples (Dict[str, List[:obj:`DetDataSample`]]):
-                The dict of multi-branch data samples.
-
-        Returns:
-            dict: A dictionary of loss components
-        """
-        losses = dict()
-        losses.update(**self.loss_by_gt_instances(
-            multi_batch_inputs['sup'], multi_batch_data_samples['sup']))
-
-        origin_pseudo_data_samples, batch_info = self.get_pseudo_instances(
-            multi_batch_inputs['unsup_teacher'],
-            multi_batch_data_samples['unsup_teacher'])
-        multi_batch_data_samples[
-            'unsup_student'] = self.project_pseudo_instances(
-                origin_pseudo_data_samples,
-                multi_batch_data_samples['unsup_student'])
-        losses.update(**self.loss_by_pseudo_instances(
-            multi_batch_inputs['unsup_student'],
-            multi_batch_data_samples['unsup_student'], batch_info))
-        return losses
-
-    def loss_by_gt_instances(self, batch_inputs: Tensor,
-                             batch_data_samples: SampleList) -> dict:
-        """Calculate losses from a batch of inputs and ground-truth data
-        samples.
-
-        Args:
-            batch_inputs (Tensor): Input images of shape (N, C, H, W).
-                These should usually be mean centered and std scaled.
-            batch_data_samples (List[:obj:`DetDataSample`]): The batch
-                data samples. It usually includes information such
-                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`.
-
-        Returns:
-            dict: A dictionary of loss components
-        """
-
-        losses = self.student.loss(batch_inputs, batch_data_samples)
-        sup_weight = self.semi_train_cfg.get('sup_weight', 1.)
-        return rename_loss_dict('sup_', reweight_loss_dict(losses, sup_weight))
-
-    def loss_by_pseudo_instances(self,
-                                 batch_inputs: Tensor,
-                                 batch_data_samples: SampleList,
-                                 batch_info: Optional[dict] = None) -> dict:
-        """Calculate losses from a batch of inputs and pseudo data samples.
-
-        Args:
-            batch_inputs (Tensor): Input images of shape (N, C, H, W).
-                These should usually be mean centered and std scaled.
-            batch_data_samples (List[:obj:`DetDataSample`]): The batch
-                data samples. It usually includes information such
-                as `gt_instance` or `gt_panoptic_seg` or `gt_sem_seg`,
-                which are `pseudo_instance` or `pseudo_panoptic_seg`
-                or `pseudo_sem_seg` in fact.
-            batch_info (dict): Batch information of teacher model
-                forward propagation process. Defaults to None.
-
-        Returns:
-            dict: A dictionary of loss components
-        """
-        batch_data_samples = filter_gt_instances(
-            batch_data_samples, score_thr=self.semi_train_cfg.cls_pseudo_thr)
-        losses = self.student.loss(batch_inputs, batch_data_samples)
-        pseudo_instances_num = sum([
-            len(data_samples.gt_instances)
-            for data_samples in batch_data_samples
-        ])
-        unsup_weight = self.semi_train_cfg.get(
-            'unsup_weight', 1.) if pseudo_instances_num > 0 else 0.
-        return rename_loss_dict('unsup_',
-                                reweight_loss_dict(losses, unsup_weight))
-
-    @torch.no_grad()
-    def get_pseudo_instances(
-            self, batch_inputs: Tensor, batch_data_samples: SampleList
-    ) -> Tuple[SampleList, Optional[dict]]:
-        """Get pseudo instances from teacher model."""
-        self.teacher.eval()
-        results_list = self.teacher.predict(
-            batch_inputs, batch_data_samples, rescale=False)
-        batch_info = {}
-        for data_samples, results in zip(batch_data_samples, results_list):
-            data_samples.gt_instances = results.pred_instances
-            data_samples.gt_instances.bboxes = bbox_project(
-                data_samples.gt_instances.bboxes,
-                torch.from_numpy(data_samples.homography_matrix).inverse().to(
-                    self.data_preprocessor.device), data_samples.ori_shape)
-        return batch_data_samples, batch_info
-
-    def project_pseudo_instances(self, batch_pseudo_instances: SampleList,
-                                 batch_data_samples: SampleList) -> SampleList:
-        """Project pseudo instances."""
-        for pseudo_instances, data_samples in zip(batch_pseudo_instances,
-                                                  batch_data_samples):
-            data_samples.gt_instances = copy.deepcopy(
-                pseudo_instances.gt_instances)
-            data_samples.gt_instances.bboxes = bbox_project(
-                data_samples.gt_instances.bboxes,
-                torch.tensor(data_samples.homography_matrix).to(
-                    self.data_preprocessor.device), data_samples.img_shape)
-        wh_thr = self.semi_train_cfg.get('min_pseudo_bbox_wh', (1e-2, 1e-2))
-        return filter_gt_instances(batch_data_samples, wh_thr=wh_thr)
-
-    def predict(self, batch_inputs: Tensor,
-                batch_data_samples: SampleList) -> SampleList:
-        """Predict results from a batch of inputs and data samples with post-
-        processing.
-
-        Args:
-            batch_inputs (Tensor): Inputs with shape (N, C, H, W).
-            batch_data_samples (List[:obj:`DetDataSample`]): The Data
-                Samples. It usually includes information such as
-                `gt_instance`, `gt_panoptic_seg` and `gt_sem_seg`.
-            rescale (bool): Whether to rescale the results.
-                Defaults to True.
-
-        Returns:
-            list[:obj:`DetDataSample`]: Return the detection results of the
-            input images. The returns value is DetDataSample,
-            which usually contain 'pred_instances'. And the
-            ``pred_instances`` usually contains following keys.
-
-                - scores (Tensor): Classification scores, has a shape
-                    (num_instance, )
-                - labels (Tensor): Labels of bboxes, has a shape
-                    (num_instances, ).
-                - bboxes (Tensor): Has a shape (num_instances, 4),
-                    the last dimension 4 arrange as (x1, y1, x2, y2).
-                - masks (Tensor): Has a shape (num_instances, H, W).
-        """
-        if self.semi_test_cfg.get('predict_on', 'teacher') == 'teacher':
-            return self.teacher(
-                batch_inputs, batch_data_samples, mode='predict')
-        else:
-            return self.student(
-                batch_inputs, batch_data_samples, mode='predict')
-
-    def _forward(self, batch_inputs: Tensor,
-                 batch_data_samples: SampleList) -> SampleList:
-        """Network forward process. Usually includes backbone, neck and head
-        forward without any post-processing.
-
-        Args:
-            batch_inputs (Tensor): Inputs with shape (N, C, H, W).
-
-        Returns:
-            tuple: A tuple of features from ``rpn_head`` and ``roi_head``
-            forward.
-        """
-        if self.semi_test_cfg.get('forward_on', 'teacher') == 'teacher':
-            return self.teacher(
-                batch_inputs, batch_data_samples, mode='tensor')
-        else:
-            return self.student(
-                batch_inputs, batch_data_samples, mode='tensor')
-
-    def extract_feat(self, batch_inputs: Tensor) -> Tuple[Tensor]:
-        """Extract features.
-
-        Args:
-            batch_inputs (Tensor): Image tensor with shape (N, C, H ,W).
-
-        Returns:
-            tuple[Tensor]: Multi-level features that may have
-            different resolutions.
-        """
-        if self.semi_test_cfg.get('extract_feat_on', 'teacher') == 'teacher':
-            return self.teacher.extract_feat(batch_inputs)
-        else:
-            return self.student.extract_feat(batch_inputs)
-
-    def _load_from_state_dict(self, state_dict: dict, prefix: str,
-                              local_metadata: dict, strict: bool,
-                              missing_keys: Union[List[str], str],
-                              unexpected_keys: Union[List[str], str],
-                              error_msgs: Union[List[str], str]) -> None:
-        """Add teacher and student prefixes to model parameter names."""
-        if not any([
-                'student' in key or 'teacher' in key
-                for key in state_dict.keys()
-        ]):
-            keys = list(state_dict.keys())
-            state_dict.update({'teacher.' + k: state_dict[k] for k in keys})
-            state_dict.update({'student.' + k: state_dict[k] for k in keys})
-            for k in keys:
-                state_dict.pop(k)
-        return super()._load_from_state_dict(
-            state_dict,
-            prefix,
-            local_metadata,
-            strict,
-            missing_keys,
-            unexpected_keys,
-            error_msgs,
-        )
diff --git a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/train/preprocess.py b/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/train/preprocess.py
deleted file mode 100644
index d0ace7fd8e64d16a2062f14ce2cfabf9b8b60913..0000000000000000000000000000000000000000
--- a/spaces/LaynzKunz/Aesthetic_RVC_Inference_HF/lib/infer/modules/train/preprocess.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import os
-import sys
-
-from scipy import signal
-
-now_dir = os.getcwd()
-sys.path.append(now_dir)
-print(sys.argv)
-inp_root = sys.argv[1]
-sr = int(sys.argv[2])
-n_p = int(sys.argv[3])
-exp_dir = sys.argv[4]
-noparallel = sys.argv[5] == "True"
-per = float(sys.argv[6])
-import multiprocessing
-import os
-import traceback
-
-import librosa
-import numpy as np
-from scipy.io import wavfile
-
-from lib.infer.infer_libs.audio import load_audio
-from lib.infer.infer_libs.slicer2 import Slicer
-
-mutex = multiprocessing.Lock()
-f = open("%s/preprocess.log" % exp_dir, "a+")
-
-
-def println(strr):
-    mutex.acquire()
-    print(strr)
-    f.write("%s\n" % strr)
-    f.flush()
-    mutex.release()
-
-
-class PreProcess:
-    def __init__(self, sr, exp_dir, per=3.7):
-        self.slicer = Slicer(
-            sr=sr,
-            threshold=-42,
-            min_length=1500,
-            min_interval=400,
-            hop_size=15,
-            max_sil_kept=500,
-        )
-        self.sr = sr
-        self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
-        self.per = per
-        self.overlap = 0.3
-        self.tail = self.per + self.overlap
-        self.max = 0.9
-        self.alpha = 0.75
-        self.exp_dir = exp_dir
-        self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
-        self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
-        os.makedirs(self.exp_dir, exist_ok=True)
-        os.makedirs(self.gt_wavs_dir, exist_ok=True)
-        os.makedirs(self.wavs16k_dir, exist_ok=True)
-
-    def norm_write(self, tmp_audio, idx0, idx1):
-        tmp_max = np.abs(tmp_audio).max()
-        if tmp_max > 2.5:
-            print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max))
-            return
-        tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + (
-            1 - self.alpha
-        ) * tmp_audio
-        wavfile.write(
-            "%s/%s_%s.wav" % (self.gt_wavs_dir, idx0, idx1),
-            self.sr,
-            tmp_audio.astype(np.float32),
-        )
-        tmp_audio = librosa.resample(
-            tmp_audio, orig_sr=self.sr, target_sr=16000
-        )  # , res_type="soxr_vhq"
-        wavfile.write(
-            "%s/%s_%s.wav" % (self.wavs16k_dir, idx0, idx1),
-            16000,
-            tmp_audio.astype(np.float32),
-        )
-
-    def pipeline(self, path, idx0):
-        try:
-            audio = load_audio(path, self.sr)
-            # zero phased digital filter cause pre-ringing noise...
-            # audio = signal.filtfilt(self.bh, self.ah, audio)
-            audio = signal.lfilter(self.bh, self.ah, audio)
-
-            idx1 = 0
-            for audio in self.slicer.slice(audio):
-                i = 0
-                while 1:
-                    start = int(self.sr * (self.per - self.overlap) * i)
-                    i += 1
-                    if len(audio[start:]) > self.tail * self.sr:
-                        tmp_audio = audio[start : start + int(self.per * self.sr)]
-                        self.norm_write(tmp_audio, idx0, idx1)
-                        idx1 += 1
-                    else:
-                        tmp_audio = audio[start:]
-                        idx1 += 1
-                        break
-                self.norm_write(tmp_audio, idx0, idx1)
-            println("%s->Suc." % path)
-        except:
-            println("%s->%s" % (path, traceback.format_exc()))
-
-    def pipeline_mp(self, infos):
-        for path, idx0 in infos:
-            self.pipeline(path, idx0)
-
-    def pipeline_mp_inp_dir(self, inp_root, n_p):
-        try:
-            infos = [
-                ("%s/%s" % (inp_root, name), idx)
-                for idx, name in enumerate(sorted(list(os.listdir(inp_root))))
-            ]
-            if noparallel:
-                for i in range(n_p):
-                    self.pipeline_mp(infos[i::n_p])
-            else:
-                ps = []
-                for i in range(n_p):
-                    p = multiprocessing.Process(
-                        target=self.pipeline_mp, args=(infos[i::n_p],)
-                    )
-                    ps.append(p)
-                    p.start()
-                for i in range(n_p):
-                    ps[i].join()
-        except:
-            println("Fail. %s" % traceback.format_exc())
-
-
-def preprocess_trainset(inp_root, sr, n_p, exp_dir, per):
-    pp = PreProcess(sr, exp_dir, per)
-    os.system('cls' if os.name == 'nt' else 'clear')
-    println("Starting preprocessing...\n")
-    println(sys.argv)
-    pp.pipeline_mp_inp_dir(inp_root, n_p)
-    println("\nPreprocessing completed!\n\n")
-
-
-if __name__ == "__main__":
-    preprocess_trainset(inp_root, sr, n_p, exp_dir, per)
diff --git a/spaces/Lianjd/stock_dashboard/backtrader/commissions/__init__.py b/spaces/Lianjd/stock_dashboard/backtrader/commissions/__init__.py
deleted file mode 100644
index 16a82b0b42ba8d32760efe905c3b6b40b89a3747..0000000000000000000000000000000000000000
--- a/spaces/Lianjd/stock_dashboard/backtrader/commissions/__init__.py
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8; py-indent-offset:4 -*-
-###############################################################################
-#
-# Copyright (C) 2015-2020 Daniel Rodriguez
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
-#
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
-#
-###############################################################################
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-from ..comminfo import CommInfoBase
-
-
-class CommInfo(CommInfoBase):
-    pass  # clone of CommissionInfo but with xx% instead of 0.xx
-
-
-class CommInfo_Futures(CommInfoBase):
-    params = (
-        ('stocklike', False),
-    )
-
-
-class CommInfo_Futures_Perc(CommInfo_Futures):
-    params = (
-        ('commtype', CommInfoBase.COMM_PERC),
-    )
-
-
-class CommInfo_Futures_Fixed(CommInfo_Futures):
-    params = (
-        ('commtype', CommInfoBase.COMM_FIXED),
-    )
-
-
-class CommInfo_Stocks(CommInfoBase):
-    params = (
-        ('stocklike', True),
-    )
-
-
-class CommInfo_Stocks_Perc(CommInfo_Stocks):
-    params = (
-        ('commtype', CommInfoBase.COMM_PERC),
-    )
-
-
-class CommInfo_Stocks_Fixed(CommInfo_Stocks):
-    params = (
-        ('commtype', CommInfoBase.COMM_FIXED),
-    )
diff --git a/spaces/LucasCodeBreak/MusicGen/audiocraft/data/audio_dataset.py b/spaces/LucasCodeBreak/MusicGen/audiocraft/data/audio_dataset.py
deleted file mode 100644
index cf21422ea0059cb2d6553f93e608b8f9fa0d3a50..0000000000000000000000000000000000000000
--- a/spaces/LucasCodeBreak/MusicGen/audiocraft/data/audio_dataset.py
+++ /dev/null
@@ -1,525 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import copy
-from concurrent.futures import ThreadPoolExecutor, Future
-from dataclasses import dataclass, fields
-from contextlib import ExitStack
-import gzip
-import json
-import logging
-import os
-from pathlib import Path
-import random
-import sys
-import typing as tp
-
-import torch
-import torch.nn.functional as F
-
-from .audio import audio_read, audio_info
-from .audio_utils import convert_audio
-from .zip import PathInZip
-
-try:
-    import dora
-except ImportError:
-    dora = None  # type: ignore
-
-
-@dataclass(order=True)
-class BaseInfo:
-
-    @classmethod
-    def _dict2fields(cls, dictionary: dict):
-        return {
-            field.name: dictionary[field.name]
-            for field in fields(cls) if field.name in dictionary
-        }
-
-    @classmethod
-    def from_dict(cls, dictionary: dict):
-        _dictionary = cls._dict2fields(dictionary)
-        return cls(**_dictionary)
-
-    def to_dict(self):
-        return {
-            field.name: self.__getattribute__(field.name)
-            for field in fields(self)
-            }
-
-
-@dataclass(order=True)
-class AudioMeta(BaseInfo):
-    path: str
-    duration: float
-    sample_rate: int
-    amplitude: tp.Optional[float] = None
-    weight: tp.Optional[float] = None
-    # info_path is used to load additional information about the audio file that is stored in zip files.
-    info_path: tp.Optional[PathInZip] = None
-
-    @classmethod
-    def from_dict(cls, dictionary: dict):
-        base = cls._dict2fields(dictionary)
-        if 'info_path' in base and base['info_path'] is not None:
-            base['info_path'] = PathInZip(base['info_path'])
-        return cls(**base)
-
-    def to_dict(self):
-        d = super().to_dict()
-        if d['info_path'] is not None:
-            d['info_path'] = str(d['info_path'])
-        return d
-
-
-@dataclass(order=True)
-class SegmentInfo(BaseInfo):
-    meta: AudioMeta
-    seek_time: float
-    n_frames: int  # actual number of frames without padding
-    total_frames: int  # total number of frames, padding included
-    sample_rate: int  # actual sample rate
-
-
-DEFAULT_EXTS = ['.wav', '.mp3', '.flac', '.ogg', '.m4a']
-
-logger = logging.getLogger(__name__)
-
-
-def _get_audio_meta(file_path: str, minimal: bool = True) -> AudioMeta:
-    """AudioMeta from a path to an audio file.
-
-    Args:
-        file_path (str): Resolved path of valid audio file.
-        minimal (bool): Whether to only load the minimal set of metadata (takes longer if not).
-    Returns:
-        AudioMeta: Audio file path and its metadata.
-    """
-    info = audio_info(file_path)
-    amplitude: tp.Optional[float] = None
-    if not minimal:
-        wav, sr = audio_read(file_path)
-        amplitude = wav.abs().max().item()
-    return AudioMeta(file_path, info.duration, info.sample_rate, amplitude)
-
-
-def _resolve_audio_meta(m: AudioMeta, fast: bool = True) -> AudioMeta:
-    """If Dora is available as a dependency, try to resolve potential relative paths
-    in list of AudioMeta. This method is expected to be used when loading meta from file.
-
-    Args:
-        m (AudioMeta): Audio meta to resolve.
-        fast (bool): If True, uses a really fast check for determining if a file is already absolute or not.
-            Only valid on Linux/Mac.
-    Returns:
-        AudioMeta: Audio meta with resolved path.
-    """
-    def is_abs(m):
-        if fast:
-            return str(m)[0] == '/'
-        else:
-            os.path.isabs(str(m))
-
-    if not dora:
-        return m
-
-    if not is_abs(m.path):
-        m.path = dora.git_save.to_absolute_path(m.path)
-    if m.info_path is not None and not is_abs(m.info_path.zip_path):
-        m.info_path.zip_path = dora.git_save.to_absolute_path(m.path)
-    return m
-
-
-def find_audio_files(path: tp.Union[Path, str],
-                     exts: tp.List[str] = DEFAULT_EXTS,
-                     resolve: bool = True,
-                     minimal: bool = True,
-                     progress: bool = False,
-                     workers: int = 0) -> tp.List[AudioMeta]:
-    """Build a list of AudioMeta from a given path,
-    collecting relevant audio files and fetching meta info.
-
-    Args:
-        path (str or Path): Path to folder containing audio files.
-        exts (list of str): List of file extensions to consider for audio files.
-        minimal (bool): Whether to only load the minimal set of metadata (takes longer if not).
-        progress (bool): Whether to log progress on audio files collection.
-        workers (int): number of parallel workers, if 0, use only the current thread.
-    Returns:
-        List[AudioMeta]: List of audio file path and its metadata.
-    """
-    audio_files = []
-    futures: tp.List[Future] = []
-    pool: tp.Optional[ThreadPoolExecutor] = None
-    with ExitStack() as stack:
-        if workers > 0:
-            pool = ThreadPoolExecutor(workers)
-            stack.enter_context(pool)
-
-        if progress:
-            print("Finding audio files...")
-        for root, folders, files in os.walk(path, followlinks=True):
-            for file in files:
-                full_path = Path(root) / file
-                if full_path.suffix.lower() in exts:
-                    audio_files.append(full_path)
-                    if pool is not None:
-                        futures.append(pool.submit(_get_audio_meta, str(audio_files[-1]), minimal))
-                    if progress:
-                        print(format(len(audio_files), " 8d"), end='\r', file=sys.stderr)
-
-        if progress:
-            print("Getting audio metadata...")
-        meta: tp.List[AudioMeta] = []
-        for idx, file_path in enumerate(audio_files):
-            try:
-                if pool is None:
-                    m = _get_audio_meta(str(file_path), minimal)
-                else:
-                    m = futures[idx].result()
-                if resolve:
-                    m = _resolve_audio_meta(m)
-            except Exception as err:
-                print("Error with", str(file_path), err, file=sys.stderr)
-                continue
-            meta.append(m)
-            if progress:
-                print(format((1 + idx) / len(audio_files), " 3.1%"), end='\r', file=sys.stderr)
-    meta.sort()
-    return meta
-
-
-def load_audio_meta(path: tp.Union[str, Path],
-                    resolve: bool = True, fast: bool = True) -> tp.List[AudioMeta]:
-    """Load list of AudioMeta from an optionally compressed json file.
-
-    Args:
-        path (str or Path): Path to JSON file.
-        resolve (bool): Whether to resolve the path from AudioMeta (default=True).
-        fast (bool): activates some tricks to make things faster.
-    Returns:
-        List[AudioMeta]: List of audio file path and its total duration.
-    """
-    open_fn = gzip.open if str(path).lower().endswith('.gz') else open
-    with open_fn(path, 'rb') as fp:  # type: ignore
-        lines = fp.readlines()
-    meta = []
-    for line in lines:
-        d = json.loads(line)
-        m = AudioMeta.from_dict(d)
-        if resolve:
-            m = _resolve_audio_meta(m, fast=fast)
-        meta.append(m)
-    return meta
-
-
-def save_audio_meta(path: tp.Union[str, Path], meta: tp.List[AudioMeta]):
-    """Save the audio metadata to the file pointer as json.
-
-    Args:
-        path (str or Path): Path to JSON file.
-        metadata (list of BaseAudioMeta): List of audio meta to save.
-    """
-    Path(path).parent.mkdir(exist_ok=True, parents=True)
-    open_fn = gzip.open if str(path).lower().endswith('.gz') else open
-    with open_fn(path, 'wb') as fp:  # type: ignore
-        for m in meta:
-            json_str = json.dumps(m.to_dict()) + '\n'
-            json_bytes = json_str.encode('utf-8')
-            fp.write(json_bytes)
-
-
-class AudioDataset:
-    """Base audio dataset.
-
-    The dataset takes a list of AudioMeta and create a dataset composed of segments of audio
-    and potentially additional information, by creating random segments from the list of audio
-    files referenced in the metadata and applying minimal data pre-processing such as resampling,
-    mixing of channels, padding, etc.
-
-    If no segment_duration value is provided, the AudioDataset will return the full wav for each
-    audio file. Otherwise, it will randomly sample audio files and create a segment of the specified
-    duration, applying padding if required.
-
-    By default, only the torch Tensor corresponding to the waveform is returned. Setting return_info=True
-    allows to return a tuple containing the torch Tensor and additional metadata on the segment and the
-    original audio meta.
-
-    Args:
-        meta (tp.List[AudioMeta]): List of audio files metadata.
-        segment_duration (float): Optional segment duration of audio to load.
-            If not specified, the dataset will load the full audio segment from the file.
-        shuffle (bool): Set to `True` to have the data reshuffled at every epoch.
-        sample_rate (int): Target sample rate of the loaded audio samples.
-        channels (int): Target number of channels of the loaded audio samples.
-        sample_on_duration (bool): Set to `True` to sample segments with probability
-            dependent on audio file duration. This is only used if `segment_duration` is provided.
-        sample_on_weight (bool): Set to `True` to sample segments using the `weight` entry of
-            `AudioMeta`. If `sample_on_duration` is also True, the actual weight will be the product
-            of the file duration and file weight. This is only used if `segment_duration` is provided.
-        min_segment_ratio (float): Minimum segment ratio to use when the audio file
-            is shorter than the desired segment.
-        max_read_retry (int): Maximum number of retries to sample an audio segment from the dataset.
-        return_info (bool): Whether to return the wav only or return wav along with segment info and metadata.
-        min_audio_duration (tp.Optional[float], optional): Minimum audio file duration, in seconds, if provided
-            audio shorter than this will be filtered out.
-        max_audio_duration (tp.Optional[float], optional): Maximal audio file duration in seconds, if provided
-            audio longer than this will be filtered out.
-    """
-    def __init__(self,
-                 meta: tp.List[AudioMeta],
-                 segment_duration: tp.Optional[float] = None,
-                 shuffle: bool = True,
-                 num_samples: int = 10_000,
-                 sample_rate: int = 48_000,
-                 channels: int = 2,
-                 pad: bool = True,
-                 sample_on_duration: bool = True,
-                 sample_on_weight: bool = True,
-                 min_segment_ratio: float = 0.5,
-                 max_read_retry: int = 10,
-                 return_info: bool = False,
-                 min_audio_duration: tp.Optional[float] = None,
-                 max_audio_duration: tp.Optional[float] = None
-                 ):
-        assert len(meta) > 0, 'No audio meta provided to AudioDataset. Please check loading of audio meta.'
-        assert segment_duration is None or segment_duration > 0
-        assert segment_duration is None or min_segment_ratio >= 0
-        logging.debug(f'sample_on_duration: {sample_on_duration}')
-        logging.debug(f'sample_on_weight: {sample_on_weight}')
-        logging.debug(f'pad: {pad}')
-        logging.debug(f'min_segment_ratio: {min_segment_ratio}')
-
-        self.segment_duration = segment_duration
-        self.min_segment_ratio = min_segment_ratio
-        self.max_audio_duration = max_audio_duration
-        self.min_audio_duration = min_audio_duration
-        if self.min_audio_duration is not None and self.max_audio_duration is not None:
-            assert self.min_audio_duration <= self.max_audio_duration
-        self.meta: tp.List[AudioMeta] = self._filter_duration(meta)
-        assert len(self.meta)  # Fail fast if all data has been filtered.
-        self.total_duration = sum(d.duration for d in self.meta)
-
-        if segment_duration is None:
-            num_samples = len(self.meta)
-        self.num_samples = num_samples
-        self.shuffle = shuffle
-        self.sample_rate = sample_rate
-        self.channels = channels
-        self.pad = pad
-        self.sample_on_weight = sample_on_weight
-        self.sample_on_duration = sample_on_duration
-        self.sampling_probabilities = self._get_sampling_probabilities()
-        self.max_read_retry = max_read_retry
-        self.return_info = return_info
-
-    def __len__(self):
-        return self.num_samples
-
-    def _get_sampling_probabilities(self, normalized: bool = True):
-        """Return the sampling probabilities for each file inside `self.meta`.
-        """
-        scores: tp.List[float] = []
-        for file_meta in self.meta:
-            score = 1.
-            if self.sample_on_weight and file_meta.weight is not None:
-                score *= file_meta.weight
-            if self.sample_on_duration:
-                score *= file_meta.duration
-            scores.append(score)
-        probabilities = torch.tensor(scores)
-        if normalized:
-            probabilities /= probabilities.sum()
-        return probabilities
-
-    def sample_file(self, rng: torch.Generator) -> AudioMeta:
-        """Sample a given file from `self.meta`. Can be overriden in subclasses.
-        This is only called if `segment_duration` is not None.
-
-        You must use the provided random number generator `rng` for reproducibility.
-        """
-        if not self.sample_on_weight and not self.sample_on_duration:
-            file_index = int(torch.randint(len(self.sampling_probabilities), (1,), generator=rng).item())
-        else:
-            file_index = int(torch.multinomial(self.sampling_probabilities, 1, generator=rng).item())
-
-        return self.meta[file_index]
-
-    def __getitem__(self, index: int) -> tp.Union[torch.Tensor, tp.Tuple[torch.Tensor, SegmentInfo]]:
-        if self.segment_duration is None:
-            file_meta = self.meta[index]
-            out, sr = audio_read(file_meta.path)
-            out = convert_audio(out, sr, self.sample_rate, self.channels)
-            n_frames = out.shape[-1]
-            segment_info = SegmentInfo(file_meta, seek_time=0., n_frames=n_frames, total_frames=n_frames,
-                                       sample_rate=self.sample_rate)
-        else:
-            rng = torch.Generator()
-            if self.shuffle:
-                # We use index, plus extra randomness
-                rng.manual_seed(index + self.num_samples * random.randint(0, 2**24))
-            else:
-                # We only use index
-                rng.manual_seed(index)
-
-            for retry in range(self.max_read_retry):
-                file_meta = self.sample_file(rng)
-                # We add some variance in the file position even if audio file is smaller than segment
-                # without ending up with empty segments
-                max_seek = max(0, file_meta.duration - self.segment_duration * self.min_segment_ratio)
-                seek_time = torch.rand(1, generator=rng).item() * max_seek
-                try:
-                    out, sr = audio_read(file_meta.path, seek_time, self.segment_duration, pad=False)
-                    out = convert_audio(out, sr, self.sample_rate, self.channels)
-                    n_frames = out.shape[-1]
-                    target_frames = int(self.segment_duration * self.sample_rate)
-                    if self.pad:
-                        out = F.pad(out, (0, target_frames - n_frames))
-                    segment_info = SegmentInfo(file_meta, seek_time, n_frames=n_frames, total_frames=target_frames,
-                                               sample_rate=self.sample_rate)
-                except Exception as exc:
-                    logger.warning("Error opening file %s: %r", file_meta.path, exc)
-                    if retry == self.max_read_retry - 1:
-                        raise
-                else:
-                    break
-
-        if self.return_info:
-            # Returns the wav and additional information on the wave segment
-            return out, segment_info
-        else:
-            return out
-
-    def collater(self, samples):
-        """The collater function has to be provided to the dataloader
-        if AudioDataset has return_info=True in order to properly collate
-        the samples of a batch.
-        """
-        if self.segment_duration is None and len(samples) > 1:
-            assert self.pad, "Must allow padding when batching examples of different durations."
-
-        # In this case the audio reaching the collater is of variable length as segment_duration=None.
-        to_pad = self.segment_duration is None and self.pad
-        if to_pad:
-            max_len = max([wav.shape[-1] for wav, _ in samples])
-
-            def _pad_wav(wav):
-                return F.pad(wav, (0, max_len - wav.shape[-1]))
-
-        if self.return_info:
-            if len(samples) > 0:
-                assert len(samples[0]) == 2
-                assert isinstance(samples[0][0], torch.Tensor)
-                assert isinstance(samples[0][1], SegmentInfo)
-
-            wavs = [wav for wav, _ in samples]
-            segment_infos = [copy.deepcopy(info) for _, info in samples]
-
-            if to_pad:
-                # Each wav could be of a different duration as they are not segmented.
-                for i in range(len(samples)):
-                    # Determines the total legth of the signal with padding, so we update here as we pad.
-                    segment_infos[i].total_frames = max_len
-                    wavs[i] = _pad_wav(wavs[i])
-
-            wav = torch.stack(wavs)
-            return wav, segment_infos
-        else:
-            assert isinstance(samples[0], torch.Tensor)
-            if to_pad:
-                samples = [_pad_wav(s) for s in samples]
-            return torch.stack(samples)
-
-    def _filter_duration(self, meta: tp.List[AudioMeta]) -> tp.List[AudioMeta]:
-        """Filters out audio files with short durations.
-        Removes from meta files that have durations that will not allow to samples examples from them.
-        """
-        orig_len = len(meta)
-
-        # Filter data that is too short.
-        if self.min_audio_duration is not None:
-            meta = [m for m in meta if m.duration >= self.min_audio_duration]
-
-        # Filter data that is too long.
-        if self.max_audio_duration is not None:
-            meta = [m for m in meta if m.duration <= self.max_audio_duration]
-
-        filtered_len = len(meta)
-        removed_percentage = 100*(1-float(filtered_len)/orig_len)
-        msg = 'Removed %.2f percent of the data because it was too short or too long.' % removed_percentage
-        if removed_percentage < 10:
-            logging.debug(msg)
-        else:
-            logging.warning(msg)
-        return meta
-
-    @classmethod
-    def from_meta(cls, root: tp.Union[str, Path], **kwargs):
-        """Instantiate AudioDataset from a path to a directory containing a manifest as a jsonl file.
-
-        Args:
-            root (str or Path): Path to root folder containing audio files.
-            kwargs: Additional keyword arguments for the AudioDataset.
-        """
-        root = Path(root)
-        if root.is_dir():
-            if (root / 'data.jsonl').exists():
-                root = root / 'data.jsonl'
-            elif (root / 'data.jsonl.gz').exists():
-                root = root / 'data.jsonl.gz'
-            else:
-                raise ValueError("Don't know where to read metadata from in the dir. "
-                                 "Expecting either a data.jsonl or data.jsonl.gz file but none found.")
-        meta = load_audio_meta(root)
-        return cls(meta, **kwargs)
-
-    @classmethod
-    def from_path(cls, root: tp.Union[str, Path], minimal_meta: bool = True,
-                  exts: tp.List[str] = DEFAULT_EXTS, **kwargs):
-        """Instantiate AudioDataset from a path containing (possibly nested) audio files.
-
-        Args:
-            root (str or Path): Path to root folder containing audio files.
-            minimal_meta (bool): Whether to only load minimal metadata or not.
-            exts (list of str): Extensions for audio files.
-            kwargs: Additional keyword arguments for the AudioDataset.
-        """
-        root = Path(root)
-        if root.is_file():
-            meta = load_audio_meta(root, resolve=True)
-        else:
-            meta = find_audio_files(root, exts, minimal=minimal_meta, resolve=True)
-        return cls(meta, **kwargs)
-
-
-def main():
-    logging.basicConfig(stream=sys.stderr, level=logging.INFO)
-    parser = argparse.ArgumentParser(
-        prog='audio_dataset',
-        description='Generate .jsonl files by scanning a folder.')
-    parser.add_argument('root', help='Root folder with all the audio files')
-    parser.add_argument('output_meta_file',
-                        help='Output file to store the metadata, ')
-    parser.add_argument('--complete',
-                        action='store_false', dest='minimal', default=True,
-                        help='Retrieve all metadata, even the one that are expansive '
-                             'to compute (e.g. normalization).')
-    parser.add_argument('--resolve',
-                        action='store_true', default=False,
-                        help='Resolve the paths to be absolute and with no symlinks.')
-    parser.add_argument('--workers',
-                        default=10, type=int,
-                        help='Number of workers.')
-    args = parser.parse_args()
-    meta = find_audio_files(args.root, DEFAULT_EXTS, progress=True,
-                            resolve=args.resolve, minimal=args.minimal, workers=args.workers)
-    save_audio_meta(args.output_meta_file, meta)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/Lyra121/finetuned_diffusion/app.py b/spaces/Lyra121/finetuned_diffusion/app.py
deleted file mode 100644
index 1bf5690e32542558eca9316fa366597862c84b72..0000000000000000000000000000000000000000
--- a/spaces/Lyra121/finetuned_diffusion/app.py
+++ /dev/null
@@ -1,355 +0,0 @@
-from diffusers import AutoencoderKL, UNet2DConditionModel, StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler
-import gradio as gr
-import torch
-from PIL import Image
-import utils
-import datetime
-import time
-import psutil
-import random
-
-
-start_time = time.time()
-is_colab = utils.is_google_colab()
-state = None
-current_steps = 25
-
-class Model:
-    def __init__(self, name, path="", prefix=""):
-        self.name = name
-        self.path = path
-        self.prefix = prefix
-        self.pipe_t2i = None
-        self.pipe_i2i = None
-
-models = [
-     Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "),
-     Model("Dreamlike Diffusion 1.0", "dreamlike-art/dreamlike-diffusion-1.0", "dreamlikeart "),
-     Model("Archer", "nitrosocke/archer-diffusion", "archer style "),
-     Model("Anything V3 Better-Vae", "Linaqruf/anything-v3-better-vae", ""),
-     Model("Anything V4", "andite/anything-v4.0", ""),
-     Model("Anything midjourney", "Joeythemonster/anything-midjourney-v-4-1", ""),
-     Model("Anything V4.5", "ckpt/anything-v4.5", ""),
-     Model( "Pastel Mix","andite/pastel-mix", ""),
-     Model("Yohan","andite/yohan-diffusion", ""),
-     Model("cutesexyrobutts","andite/cutesexyrobutts-diffusion",""),
-     Model("Modern Disney", "nitrosocke/mo-di-diffusion", "modern disney style "),
-     Model("Classic Disney", "nitrosocke/classic-anim-diffusion", "classic disney style "),
-     Model("Loving Vincent (Van Gogh)", "dallinmackay/Van-Gogh-diffusion", "lvngvncnt "),
-     Model("Wavyfusion", "wavymulder/wavyfusion", "wa-vy style "),
-     Model("Analog Diffusion", "wavymulder/Analog-Diffusion", "analog style "),
-     Model("Redshift renderer (Cinema4D)", "nitrosocke/redshift-diffusion", "redshift style "),
-     Model("Midjourney v4 style", "prompthero/midjourney-v4-diffusion", "mdjrny-v4 style "),
-     Model("Waifu", "hakurei/waifu-diffusion"),
-     Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "),
-     Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "),
-     Model("TrinArt v2", "naclbit/trinart_stable_diffusion_v2"),
-     Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "),
-     Model("Balloon Art", "Fictiverse/Stable_Diffusion_BalloonArt_Model", "BalloonArt "),
-     Model("Tron Legacy", "dallinmackay/Tron-Legacy-diffusion", "trnlgcy "),
-     Model("Pokémon", "lambdalabs/sd-pokemon-diffusers"),
-     Model("Pony Diffusion", "AstraliteHeart/pony-diffusion"),
-     Model("Robo Diffusion", "nousr/robo-diffusion"),
-     Model("Epic Diffusion", "johnslegers/epic-diffusion")
-  ]
-
-custom_model = None
-if is_colab:
-  models.insert(0, Model("Custom model"))
-  custom_model = models[0]
-
-last_mode = "txt2img"
-current_model = models[1] if is_colab else models[0]
-current_model_path = current_model.path
-
-if is_colab:
-  pipe = StableDiffusionPipeline.from_pretrained(
-      current_model.path,
-      torch_dtype=torch.float16,
-      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
-      safety_checker=lambda images, clip_input: (images, False)
-      )
-
-else:
-  pipe = StableDiffusionPipeline.from_pretrained(
-      current_model.path,
-      torch_dtype=torch.float16,
-      scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
-      )
-    
-if torch.cuda.is_available():
-  pipe = pipe.to("cuda")
-  pipe.enable_xformers_memory_efficient_attention()
-
-device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
-
-def error_str(error, title="Error"):
-    return f"""#### {title}
-            {error}"""  if error else ""
-
-def update_state(new_state):
-  global state
-  state = new_state
-
-def update_state_info(old_state):
-  if state and state != old_state:
-    return gr.update(value=state)
-
-def custom_model_changed(path):
-  models[0].path = path
-  global current_model
-  current_model = models[0]
-
-def on_model_change(model_name):
-  
-  prefix = "Enter prompt. \"" + next((m.prefix for m in models if m.name == model_name), None) + "\" is prefixed automatically" if model_name != models[0].name else "Don't forget to use the custom model prefix in the prompt!"
-
-  return gr.update(visible = model_name == models[0].name), gr.update(placeholder=prefix)
-
-def on_steps_change(steps):
-  global current_steps
-  current_steps = steps
-
-def pipe_callback(step: int, timestep: int, latents: torch.FloatTensor):
-    update_state(f"{step}/{current_steps} steps")#\nTime left, sec: {timestep/100:.0f}")
-
-def inference(model_name, prompt, guidance, steps, n_images=1, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
-
-  update_state(" ")
-
-  print(psutil.virtual_memory()) # print memory usage
-
-  global current_model
-  for model in models:
-    if model.name == model_name:
-      current_model = model
-      model_path = current_model.path
-
-  # generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  if seed == 0:
-    seed = random.randint(0, 2147483647)
-
-  generator = torch.Generator('cuda').manual_seed(seed)
-
-  try:
-    if img is not None:
-      return img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
-    else:
-      return txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed), f"Done. Seed: {seed}"
-  except Exception as e:
-    return None, error_str(e)
-
-def txt_to_img(model_path, prompt, n_images, neg_prompt, guidance, steps, width, height, generator, seed):
-
-    print(f"{datetime.datetime.now()} txt_to_img, model: {current_model.name}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "txt2img":
-        current_model_path = model_path
-
-        update_state(f"Loading {current_model.name} text-to-image model...")
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torch.float16,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
-              safety_checker=lambda images, clip_input: (images, False)
-              )
-        else:
-          pipe = StableDiffusionPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torch.float16,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
-              )
-          # pipe = pipe.to("cpu")
-          # pipe = current_model.pipe_t2i
-
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-          pipe.enable_xformers_memory_efficient_attention()
-        last_mode = "txt2img"
-
-    prompt = current_model.prefix + prompt  
-    result = pipe(
-      prompt,
-      negative_prompt = neg_prompt,
-      num_images_per_prompt=n_images,
-      num_inference_steps = int(steps),
-      guidance_scale = guidance,
-      width = width,
-      height = height,
-      generator = generator,
-      callback=pipe_callback)
-
-    # update_state(f"Done. Seed: {seed}")
-    
-    return replace_nsfw_images(result)
-
-def img_to_img(model_path, prompt, n_images, neg_prompt, img, strength, guidance, steps, width, height, generator, seed):
-
-    print(f"{datetime.datetime.now()} img_to_img, model: {model_path}")
-
-    global last_mode
-    global pipe
-    global current_model_path
-    if model_path != current_model_path or last_mode != "img2img":
-        current_model_path = model_path
-
-        update_state(f"Loading {current_model.name} image-to-image model...")
-
-        if is_colab or current_model == custom_model:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torch.float16,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler"),
-              safety_checker=lambda images, clip_input: (images, False)
-              )
-        else:
-          pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-              current_model_path,
-              torch_dtype=torch.float16,
-              scheduler=DPMSolverMultistepScheduler.from_pretrained(current_model.path, subfolder="scheduler")
-              )
-          # pipe = pipe.to("cpu")
-          # pipe = current_model.pipe_i2i
-        
-        if torch.cuda.is_available():
-          pipe = pipe.to("cuda")
-          pipe.enable_xformers_memory_efficient_attention()
-        last_mode = "img2img"
-
-    prompt = current_model.prefix + prompt
-    ratio = min(height / img.height, width / img.width)
-    img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
-    result = pipe(
-        prompt,
-        negative_prompt = neg_prompt,
-        num_images_per_prompt=n_images,
-        image = img,
-        num_inference_steps = int(steps),
-        strength = strength,
-        guidance_scale = guidance,
-        # width = width,
-        # height = height,
-        generator = generator,
-        callback=pipe_callback)
-
-    # update_state(f"Done. Seed: {seed}")
-        
-    return replace_nsfw_images(result)
-
-def replace_nsfw_images(results):
-
-    if is_colab:
-      return results.images
-      
-    for i in range(len(results.images)):
-      if results.nsfw_content_detected[i]:
-        results.images[i] = Image.open("nsfw.png")
-    return results.images
-
-# css = """.finetuned-diffusion-div div{display:inline-flex;align-items:center;gap:.8rem;font-size:1.75rem}.finetuned-diffusion-div div h1{font-weight:900;margin-bottom:7px}.finetuned-diffusion-div p{margin-bottom:10px;font-size:94%}a{text-decoration:underline}.tabs{margin-top:0;margin-bottom:0}#gallery{min-height:20rem}
-# """
-with gr.Blocks(css="style.css") as demo:
-    gr.HTML(
-        f"""
-            <div class="finetuned-diffusion-div">
-              <div>
-                <h1>Finetuned Diffusion</h1>
-              </div>
-              <p>
-               Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br>
-               <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spider-Verse</a>, <a href="https://huggingface.co/nitrosocke/mo-di-diffusion">Modern Disney</a>, <a href="https://huggingface.co/nitrosocke/classic-anim-diffusion">Classic Disney</a>, <a href="https://huggingface.co/dallinmackay/Van-Gogh-diffusion">Loving Vincent (Van Gogh)</a>, <a href="https://huggingface.co/nitrosocke/redshift-diffusion">Redshift renderer (Cinema4D)</a>, <a href="https://huggingface.co/prompthero/midjourney-v4-diffusion">Midjourney v4 style</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokémon</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony Diffusion</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo Diffusion</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a>, <a href="https://huggingface.co/dallinmackay/Tron-Legacy-diffusion">Tron Legacy</a>, <a href="https://huggingface.co/Fictiverse/Stable_Diffusion_BalloonArt_Model">Balloon Art</a> + in colab notebook you can load any other Diffusers 🧨 SD model hosted on HuggingFace 🤗.
-              </p>
-              <p>You can skip the queue and load custom models in the colab: <a href="https://colab.research.google.com/gist/qunash/42112fb104509c24fd3aa6d1c11dd6e0/copy-of-fine-tuned-diffusion-gradio.ipynb"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
-               Running on <b>{device}</b>{(" in a <b>Google Colab</b>." if is_colab else "")}
-              </p>
-              <p>You can also duplicate this space and upgrade to gpu by going to settings:<br>
-              <a style="display:inline-block" href="https://huggingface.co/spaces/anzorq/finetuned_diffusion?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></p>
-            </div>
-        """
-    )
-    with gr.Row():
-        
-        with gr.Column(scale=55):
-          with gr.Group():
-              model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=current_model.name)
-              with gr.Box(visible=False) as custom_model_group:
-                custom_model_path = gr.Textbox(label="Custom model path", placeholder="Path to model, e.g. nitrosocke/Arcane-Diffusion", interactive=True)
-                gr.HTML("<div><font size='2'>Custom models have to be downloaded first, so give it some time.</font></div>")
-              
-              with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="Enter prompt. Style applied automatically").style(container=False)
-                generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
-
-
-              # image_out = gr.Image(height=512)
-              gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
-          
-          state_info = gr.Textbox(label="State", show_label=False, max_lines=2).style(container=False)
-          error_output = gr.Markdown()
-
-        with gr.Column(scale=45):
-          with gr.Tab("Options"):
-            with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-
-              n_images = gr.Slider(label="Images", value=1, minimum=1, maximum=4, step=1)
-
-              with gr.Row():
-                guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
-                steps = gr.Slider(label="Steps", value=current_steps, minimum=2, maximum=75, step=1)
-
-              with gr.Row():
-                width = gr.Slider(label="Width", value=512, minimum=64, maximum=1024, step=8)
-                height = gr.Slider(label="Height", value=512, minimum=64, maximum=1024, step=8)
-
-              seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1)
-
-          with gr.Tab("Image to image"):
-              with gr.Group():
-                image = gr.Image(label="Image", height=256, tool="editor", type="pil")
-                strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
-
-    if is_colab:
-        model_name.change(on_model_change, inputs=model_name, outputs=[custom_model_group, prompt], queue=False)
-        custom_model_path.change(custom_model_changed, inputs=custom_model_path, outputs=None)
-    # n_images.change(lambda n: gr.Gallery().style(grid=[2 if n > 1 else 1], height="auto"), inputs=n_images, outputs=gallery)
-    steps.change(on_steps_change, inputs=[steps], outputs=[], queue=False)
-
-    inputs = [model_name, prompt, guidance, steps, n_images, width, height, seed, image, strength, neg_prompt]
-    outputs = [gallery, error_output]
-    prompt.submit(inference, inputs=inputs, outputs=outputs)
-    generate.click(inference, inputs=inputs, outputs=outputs)
-
-    ex = gr.Examples([
-        [models[7].name, "tiny cute and adorable kitten adventurer dressed in a warm overcoat with survival gear on a winters day", 7.5, 25],
-        [models[4].name, "portrait of dwayne johnson", 7.0, 35],
-        [models[5].name, "portrait of a beautiful alyx vance half life", 10, 25],
-        [models[6].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 30],
-        [models[5].name, "fantasy portrait painting, digital art", 4.0, 20],
-    ], inputs=[model_name, prompt, guidance, steps], outputs=outputs, fn=inference, cache_examples=False)
-
-    gr.HTML("""
-    <div style="border-top: 1px solid #303030;">
-      <br>
-      <p>Models by <a href="https://huggingface.co/nitrosocke">@nitrosocke</a>, <a href="https://twitter.com/haruu1367">@haruu1367</a>, <a href="https://twitter.com/DGSpitzer">@Helixngc7293</a>, <a href="https://twitter.com/dal_mack">@dal_mack</a>, <a href="https://twitter.com/prompthero">@prompthero</a> and others. ❤️</p>
-      <p>This space uses the <a href="https://github.com/LuChengTHU/dpm-solver">DPM-Solver++</a> sampler by <a href="https://arxiv.org/abs/2206.00927">Cheng Lu, et al.</a>.</p>
-      <p>Space by:<br>
-      <a href="https://twitter.com/hahahahohohe"><img src="https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social" alt="Twitter Follow"></a><br>
-      <a href="https://github.com/qunash"><img alt="GitHub followers" src="https://img.shields.io/github/followers/qunash?style=social" alt="Github Follow"></a></p><br><br>
-      <a href="https://www.buymeacoffee.com/anzorq" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 45px !important;width: 162px !important;" ></a><br><br>
-      <p><img src="https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion" alt="visitors"></p>
-    </div>
-    """)
-
-    demo.load(update_state_info, inputs=state_info, outputs=state_info, every=0.5, show_progress=False)
-
-print(f"Space built in {time.time() - start_time:.2f} seconds")
-
-# if not is_colab:
-demo.queue(concurrency_count=1)
-demo.launch(debug=is_colab, share=is_colab)
diff --git a/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/sync_batchnorm/batchnorm.py b/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/sync_batchnorm/batchnorm.py
deleted file mode 100644
index bf8d7a7325b474771a11a137053971fd40426079..0000000000000000000000000000000000000000
--- a/spaces/MCkernick/Image_Restoration_Colorization/Face_Enhancement/models/networks/Synchronized-BatchNorm-PyTorch/sync_batchnorm/batchnorm.py
+++ /dev/null
@@ -1,412 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : batchnorm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-#
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import collections
-import contextlib
-
-import torch
-import torch.nn.functional as F
-
-from torch.nn.modules.batchnorm import _BatchNorm
-
-try:
-    from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast
-except ImportError:
-    ReduceAddCoalesced = Broadcast = None
-
-try:
-    from jactorch.parallel.comm import SyncMaster
-    from jactorch.parallel.data_parallel import JacDataParallel as DataParallelWithCallback
-except ImportError:
-    from .comm import SyncMaster
-    from .replicate import DataParallelWithCallback
-
-__all__ = [
-    'set_sbn_eps_mode',
-    'SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d',
-    'patch_sync_batchnorm', 'convert_model'
-]
-
-
-SBN_EPS_MODE = 'clamp'
-
-
-def set_sbn_eps_mode(mode):
-    global SBN_EPS_MODE
-    assert mode in ('clamp', 'plus')
-    SBN_EPS_MODE = mode
-
-
-def _sum_ft(tensor):
-    """sum over the first and last dimention"""
-    return tensor.sum(dim=0).sum(dim=-1)
-
-
-def _unsqueeze_ft(tensor):
-    """add new dimensions at the front and the tail"""
-    return tensor.unsqueeze(0).unsqueeze(-1)
-
-
-_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size'])
-_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std'])
-
-
-class _SynchronizedBatchNorm(_BatchNorm):
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True):
-        assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.'
-
-        super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine,
-                                                     track_running_stats=track_running_stats)
-
-        if not self.track_running_stats:
-            import warnings
-            warnings.warn('track_running_stats=False is not supported by the SynchronizedBatchNorm.')
-
-        self._sync_master = SyncMaster(self._data_parallel_master)
-
-        self._is_parallel = False
-        self._parallel_id = None
-        self._slave_pipe = None
-
-    def forward(self, input):
-        # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation.
-        if not (self._is_parallel and self.training):
-            return F.batch_norm(
-                input, self.running_mean, self.running_var, self.weight, self.bias,
-                self.training, self.momentum, self.eps)
-
-        # Resize the input to (B, C, -1).
-        input_shape = input.size()
-        assert input.size(1) == self.num_features, 'Channel size mismatch: got {}, expect {}.'.format(input.size(1), self.num_features)
-        input = input.view(input.size(0), self.num_features, -1)
-
-        # Compute the sum and square-sum.
-        sum_size = input.size(0) * input.size(2)
-        input_sum = _sum_ft(input)
-        input_ssum = _sum_ft(input ** 2)
-
-        # Reduce-and-broadcast the statistics.
-        if self._parallel_id == 0:
-            mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size))
-        else:
-            mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size))
-
-        # Compute the output.
-        if self.affine:
-            # MJY:: Fuse the multiplication for speed.
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias)
-        else:
-            output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std)
-
-        # Reshape it.
-        return output.view(input_shape)
-
-    def __data_parallel_replicate__(self, ctx, copy_id):
-        self._is_parallel = True
-        self._parallel_id = copy_id
-
-        # parallel_id == 0 means master device.
-        if self._parallel_id == 0:
-            ctx.sync_master = self._sync_master
-        else:
-            self._slave_pipe = ctx.sync_master.register_slave(copy_id)
-
-    def _data_parallel_master(self, intermediates):
-        """Reduce the sum and square-sum, compute the statistics, and broadcast it."""
-
-        # Always using same "device order" makes the ReduceAdd operation faster.
-        # Thanks to:: Tete Xiao (http://tetexiao.com/)
-        intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device())
-
-        to_reduce = [i[1][:2] for i in intermediates]
-        to_reduce = [j for i in to_reduce for j in i]  # flatten
-        target_gpus = [i[1].sum.get_device() for i in intermediates]
-
-        sum_size = sum([i[1].sum_size for i in intermediates])
-        sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce)
-        mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size)
-
-        broadcasted = Broadcast.apply(target_gpus, mean, inv_std)
-
-        outputs = []
-        for i, rec in enumerate(intermediates):
-            outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2])))
-
-        return outputs
-
-    def _compute_mean_std(self, sum_, ssum, size):
-        """Compute the mean and standard-deviation with sum and square-sum. This method
-        also maintains the moving average on the master device."""
-        assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.'
-        mean = sum_ / size
-        sumvar = ssum - sum_ * mean
-        unbias_var = sumvar / (size - 1)
-        bias_var = sumvar / size
-
-        if hasattr(torch, 'no_grad'):
-            with torch.no_grad():
-                self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
-                self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-        else:
-            self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data
-            self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data
-
-        if SBN_EPS_MODE == 'clamp':
-            return mean, bias_var.clamp(self.eps) ** -0.5
-        elif SBN_EPS_MODE == 'plus':
-            return mean, (bias_var + self.eps) ** -0.5
-        else:
-            raise ValueError('Unknown EPS mode: {}.'.format(SBN_EPS_MODE))
-
-
-class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
-    r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a
-    mini-batch.
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm1d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of size
-            `batch_size x num_features [x width]`
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape::
-        - Input: :math:`(N, C)` or :math:`(N, C, L)`
-        - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm1d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 2 and input.dim() != 3:
-            raise ValueError('expected 2D or 3D input (got {}D input)'
-                             .format(input.dim()))
-
-
-class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch
-    of 3d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm2d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape::
-        - Input: :math:`(N, C, H, W)`
-        - Output: :math:`(N, C, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm2d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 4:
-            raise ValueError('expected 4D input (got {}D input)'
-                             .format(input.dim()))
-
-
-class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
-    r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch
-    of 4d inputs
-
-    .. math::
-
-        y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta
-
-    This module differs from the built-in PyTorch BatchNorm3d as the mean and
-    standard-deviation are reduced across all devices during training.
-
-    For example, when one uses `nn.DataParallel` to wrap the network during
-    training, PyTorch's implementation normalize the tensor on each device using
-    the statistics only on that device, which accelerated the computation and
-    is also easy to implement, but the statistics might be inaccurate.
-    Instead, in this synchronized version, the statistics will be computed
-    over all training samples distributed on multiple devices.
-
-    Note that, for one-GPU or CPU-only case, this module behaves exactly same
-    as the built-in PyTorch implementation.
-
-    The mean and standard-deviation are calculated per-dimension over
-    the mini-batches and gamma and beta are learnable parameter vectors
-    of size C (where C is the input size).
-
-    During training, this layer keeps a running estimate of its computed mean
-    and variance. The running sum is kept with a default momentum of 0.1.
-
-    During evaluation, this running mean/variance is used for normalization.
-
-    Because the BatchNorm is done over the `C` dimension, computing statistics
-    on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm
-    or Spatio-temporal BatchNorm
-
-    Args:
-        num_features: num_features from an expected input of
-            size batch_size x num_features x depth x height x width
-        eps: a value added to the denominator for numerical stability.
-            Default: 1e-5
-        momentum: the value used for the running_mean and running_var
-            computation. Default: 0.1
-        affine: a boolean value that when set to ``True``, gives the layer learnable
-            affine parameters. Default: ``True``
-
-    Shape::
-        - Input: :math:`(N, C, D, H, W)`
-        - Output: :math:`(N, C, D, H, W)` (same shape as input)
-
-    Examples:
-        >>> # With Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100)
-        >>> # Without Learnable Parameters
-        >>> m = SynchronizedBatchNorm3d(100, affine=False)
-        >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10))
-        >>> output = m(input)
-    """
-
-    def _check_input_dim(self, input):
-        if input.dim() != 5:
-            raise ValueError('expected 5D input (got {}D input)'
-                             .format(input.dim()))
-
-
-@contextlib.contextmanager
-def patch_sync_batchnorm():
-    import torch.nn as nn
-
-    backup = nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d
-
-    nn.BatchNorm1d = SynchronizedBatchNorm1d
-    nn.BatchNorm2d = SynchronizedBatchNorm2d
-    nn.BatchNorm3d = SynchronizedBatchNorm3d
-
-    yield
-
-    nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d = backup
-
-
-def convert_model(module):
-    """Traverse the input module and its child recursively
-       and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d
-       to SynchronizedBatchNorm*N*d
-
-    Args:
-        module: the input module needs to be convert to SyncBN model
-
-    Examples:
-        >>> import torch.nn as nn
-        >>> import torchvision
-        >>> # m is a standard pytorch model
-        >>> m = torchvision.models.resnet18(True)
-        >>> m = nn.DataParallel(m)
-        >>> # after convert, m is using SyncBN
-        >>> m = convert_model(m)
-    """
-    if isinstance(module, torch.nn.DataParallel):
-        mod = module.module
-        mod = convert_model(mod)
-        mod = DataParallelWithCallback(mod, device_ids=module.device_ids)
-        return mod
-
-    mod = module
-    for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d,
-                                        torch.nn.modules.batchnorm.BatchNorm2d,
-                                        torch.nn.modules.batchnorm.BatchNorm3d],
-                                       [SynchronizedBatchNorm1d,
-                                        SynchronizedBatchNorm2d,
-                                        SynchronizedBatchNorm3d]):
-        if isinstance(module, pth_module):
-            mod = sync_module(module.num_features, module.eps, module.momentum, module.affine)
-            mod.running_mean = module.running_mean
-            mod.running_var = module.running_var
-            if module.affine:
-                mod.weight.data = module.weight.data.clone().detach()
-                mod.bias.data = module.bias.data.clone().detach()
-
-    for name, child in module.named_children():
-        mod.add_module(name, convert_model(child))
-
-    return mod
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h
deleted file mode 100644
index c7408eba007b424194618baa63726657e36875e3..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h
+++ /dev/null
@@ -1,64 +0,0 @@
-/*!
-**************************************************************************************************
-* Deformable DETR
-* Copyright (c) 2020 SenseTime. All Rights Reserved.
-* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
-**************************************************************************************************
-* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
-**************************************************************************************************
-*/
-
-#pragma once
-
-#include "ms_deform_attn_cpu.h"
-
-#ifdef WITH_CUDA
-#include "ms_deform_attn_cuda.h"
-#endif
-
-namespace groundingdino {
-
-at::Tensor
-ms_deform_attn_forward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_forward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-std::vector<at::Tensor>
-ms_deform_attn_backward(
-    const at::Tensor &value, 
-    const at::Tensor &spatial_shapes,
-    const at::Tensor &level_start_index,
-    const at::Tensor &sampling_loc,
-    const at::Tensor &attn_weight,
-    const at::Tensor &grad_output,
-    const int im2col_step)
-{
-    if (value.type().is_cuda())
-    {
-#ifdef WITH_CUDA
-        return ms_deform_attn_cuda_backward(
-            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
-#else
-        AT_ERROR("Not compiled with GPU support");
-#endif
-    }
-    AT_ERROR("Not implemented on the CPU");
-}
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp b/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
deleted file mode 100644
index c1f2c50c82909bbd5492c163d634af77a3ba1781..0000000000000000000000000000000000000000
--- a/spaces/Make-A-Protagonist/Make-A-Protagonist-inference/Make-A-Protagonist/experts/GroundedSAM/GroundingDINO/groundingdino/models/GroundingDINO/csrc/vision.cpp
+++ /dev/null
@@ -1,58 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
-
-#include "MsDeformAttn/ms_deform_attn.h"
-
-namespace groundingdino {
-
-#ifdef WITH_CUDA
-extern int get_cudart_version();
-#endif
-
-std::string get_cuda_version() {
-#ifdef WITH_CUDA
-  std::ostringstream oss;
-
-  // copied from
-  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
-  auto printCudaStyleVersion = [&](int v) {
-    oss << (v / 1000) << "." << (v / 10 % 100);
-    if (v % 10 != 0) {
-      oss << "." << (v % 10);
-    }
-  };
-  printCudaStyleVersion(get_cudart_version());
-  return oss.str();
-#else
-  return std::string("not available");
-#endif
-}
-
-// similar to
-// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
-std::string get_compiler_version() {
-  std::ostringstream ss;
-#if defined(__GNUC__)
-#ifndef __clang__
-  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
-#endif
-#endif
-
-#if defined(__clang_major__)
-  {
-    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
-       << __clang_patchlevel__;
-  }
-#endif
-
-#if defined(_MSC_VER)
-  { ss << "MSVC " << _MSC_FULL_VER; }
-#endif
-  return ss.str();
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
-  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
-}
-
-} // namespace groundingdino
\ No newline at end of file
diff --git a/spaces/Marne/MockingBird/mockingbirdforuse/encoder/inference.py b/spaces/Marne/MockingBird/mockingbirdforuse/encoder/inference.py
deleted file mode 100644
index 772619dcd8451674a05569f7fc2f73221c82c64a..0000000000000000000000000000000000000000
--- a/spaces/Marne/MockingBird/mockingbirdforuse/encoder/inference.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import torch
-import numpy as np
-from pathlib import Path
-
-from . import audio
-from .model import SpeakerEncoder
-from .audio import preprocess_wav  # We want to expose this function from here
-from .hparams import hparams as hp
-from ..log import logger
-
-
-class Encoder:
-    def __init__(self, model_path: Path):
-        self._device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        self._model = SpeakerEncoder(self._device, torch.device("cpu"))
-        checkpoint = torch.load(model_path, self._device)
-        self._model.load_state_dict(checkpoint["model_state"])
-        self._model.eval()
-        logger.info(
-            f"Loaded encoder {model_path.name} trained to step {checkpoint['step']}"
-        )
-
-    def embed_frames_batch(self, frames_batch):
-        """
-        Computes embeddings for a batch of mel spectrogram.
-
-        :param frames_batch: a batch mel of spectrogram as a numpy array of float32 of shape
-        (batch_size, n_frames, n_channels)
-        :return: the embeddings as a numpy array of float32 of shape (batch_size, model_embedding_size)
-        """
-
-        frames = torch.from_numpy(frames_batch).to(self._device)
-        embed = self._model.forward(frames).detach().cpu().numpy()
-        return embed
-
-    def compute_partial_slices(
-        self,
-        n_samples,
-        partial_utterance_n_frames=hp.partials_n_frames,
-        min_pad_coverage=0.75,
-        overlap=0.5,
-        rate=None,
-    ):
-        """
-        Computes where to split an utterance waveform and its corresponding mel spectrogram to obtain
-        partial utterances of <partial_utterance_n_frames> each. Both the waveform and the mel
-        spectrogram slices are returned, so as to make each partial utterance waveform correspond to
-        its spectrogram. This function assumes that the mel spectrogram parameters used are those
-        defined in params_data.py.
-
-        The returned ranges may be indexing further than the length of the waveform. It is
-        recommended that you pad the waveform with zeros up to wave_slices[-1].stop.
-
-        :param n_samples: the number of samples in the waveform
-        :param partial_utterance_n_frames: the number of mel spectrogram frames in each partial
-        utterance
-        :param min_pad_coverage: when reaching the last partial utterance, it may or may not have
-        enough frames. If at least <min_pad_coverage> of <partial_utterance_n_frames> are present,
-        then the last partial utterance will be considered, as if we padded the audio. Otherwise,
-        it will be discarded, as if we trimmed the audio. If there aren't enough frames for 1 partial
-        utterance, this parameter is ignored so that the function always returns at least 1 slice.
-        :param overlap: by how much the partial utterance should overlap. If set to 0, the partial
-        utterances are entirely disjoint.
-        :return: the waveform slices and mel spectrogram slices as lists of array slices. Index
-        respectively the waveform and the mel spectrogram with these slices to obtain the partial
-        utterances.
-        """
-        assert 0 <= overlap < 1
-        assert 0 < min_pad_coverage <= 1
-
-        if rate != None:
-            samples_per_frame = int((hp.sampling_rate * hp.mel_window_step / 1000))
-            n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
-            frame_step = int(np.round((hp.sampling_rate / rate) / samples_per_frame))
-        else:
-            samples_per_frame = int((hp.sampling_rate * hp.mel_window_step / 1000))
-            n_frames = int(np.ceil((n_samples + 1) / samples_per_frame))
-            frame_step = max(
-                int(np.round(partial_utterance_n_frames * (1 - overlap))), 1
-            )
-
-        assert 0 < frame_step, "The rate is too high"
-        assert (
-            frame_step <= hp.partials_n_frames
-        ), "The rate is too low, it should be %f at least" % (
-            hp.sampling_rate / (samples_per_frame * hp.partials_n_frames)
-        )
-
-        # Compute the slices
-        wav_slices, mel_slices = [], []
-        steps = max(1, n_frames - partial_utterance_n_frames + frame_step + 1)
-        for i in range(0, steps, frame_step):
-            mel_range = np.array([i, i + partial_utterance_n_frames])
-            wav_range = mel_range * samples_per_frame
-            mel_slices.append(slice(*mel_range))
-            wav_slices.append(slice(*wav_range))
-
-        # Evaluate whether extra padding is warranted or not
-        last_wav_range = wav_slices[-1]
-        coverage = (n_samples - last_wav_range.start) / (
-            last_wav_range.stop - last_wav_range.start
-        )
-        if coverage < min_pad_coverage and len(mel_slices) > 1:
-            mel_slices = mel_slices[:-1]
-            wav_slices = wav_slices[:-1]
-
-        return wav_slices, mel_slices
-
-    def embed_utterance(
-        self, wav, using_partials: bool = True, return_partials: bool = False, **kwargs
-    ):
-        """
-        Computes an embedding for a single utterance.
-
-        :param wav: a preprocessed (see audio.py) utterance waveform as a numpy array of float32
-        :param using_partials: if True, then the utterance is split in partial utterances of
-        <partial_utterance_n_frames> frames and the utterance embedding is computed from their
-        normalized average. If False, the utterance is instead computed from feeding the entire
-        spectogram to the network.
-        :param return_partials: if True, the partial embeddings will also be returned along with the
-        wav slices that correspond to the partial embeddings.
-        :param kwargs: additional arguments to compute_partial_splits()
-        :return: the embedding as a numpy array of float32 of shape (model_embedding_size,). If
-        <return_partials> is True, the partial utterances as a numpy array of float32 of shape
-        (n_partials, model_embedding_size) and the wav partials as a list of slices will also be
-        returned. If <using_partials> is simultaneously set to False, both these values will be None
-        instead.
-        """
-        # Process the entire utterance if not using partials
-        if not using_partials:
-            frames = audio.wav_to_mel_spectrogram(wav)
-            embed = self.embed_frames_batch(frames[None, ...])[0]
-            if return_partials:
-                return embed, None, None
-            return embed
-
-        # Compute where to split the utterance into partials and pad if necessary
-        wave_slices, mel_slices = self.compute_partial_slices(len(wav), **kwargs)
-        max_wave_length = wave_slices[-1].stop
-        if max_wave_length >= len(wav):
-            wav = np.pad(wav, (0, max_wave_length - len(wav)), "constant")
-
-        # Split the utterance into partials
-        frames = audio.wav_to_mel_spectrogram(wav)
-        frames_batch = np.array([frames[s] for s in mel_slices])
-        partial_embeds = self.embed_frames_batch(frames_batch)
-
-        # Compute the utterance embedding from the partial embeddings
-        raw_embed = np.mean(partial_embeds, axis=0)
-        embed = raw_embed / np.linalg.norm(raw_embed, 2)
-
-        if return_partials:
-            return embed, partial_embeds, wave_slices
-        return embed
diff --git a/spaces/Marshalls/testmtd/models/transflowpp_model.py b/spaces/Marshalls/testmtd/models/transflowpp_model.py
deleted file mode 100644
index 5ae59efd743b63b751773ee868839b7c9a19f39d..0000000000000000000000000000000000000000
--- a/spaces/Marshalls/testmtd/models/transflowpp_model.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import torch
-from torch import nn
-from .transformer import BasicTransformerModel
-from models import BaseModel
-from models.flowplusplus import FlowPlusPlus
-import ast
-from .util.generation import autoregressive_generation_multimodal
-
-class TransFlowppModel(BaseModel):
-    def __init__(self, opt):
-        super().__init__(opt)
-        input_mods = self.input_mods
-        output_mods = self.output_mods
-        dins = self.dins
-        douts = self.douts
-        input_lengths = self.input_lengths
-        output_lengths = self.output_lengths
-
-        self.input_mod_nets = []
-        self.output_mod_nets = []
-        self.output_mod_glows = []
-        self.module_names = []
-        for i, mod in enumerate(input_mods):
-            net = BasicTransformerModel(opt.dhid, dins[i], opt.nhead, opt.dhid, 2, opt.dropout, self.device, use_pos_emb=True, input_length=input_lengths[i]).to(self.device)
-            name = "_input_"+mod
-            setattr(self,"net"+name, net)
-            self.input_mod_nets.append(net)
-            self.module_names.append(name)
-        for i, mod in enumerate(output_mods):
-            net = BasicTransformerModel(opt.dhid, opt.dhid, opt.nhead, opt.dhid, opt.nlayers, opt.dropout, self.device, use_pos_emb=True, input_length=sum(input_lengths)).to(self.device)
-            name = "_output_"+mod
-            setattr(self, "net"+name, net)
-            self.output_mod_nets.append(net)
-            self.module_names.append(name)
-
-            # import pdb;pdb.set_trace()
-            glow = FlowPlusPlus(scales=ast.literal_eval(opt.scales),
-                                     in_shape=(douts[i], output_lengths[i], 1),
-                                     cond_dim=opt.dhid,
-                                     mid_channels=opt.dhid,
-                                     num_blocks=opt.num_glow_coupling_blocks,
-                                     num_components=opt.num_mixture_components,
-                                     use_attn=opt.glow_use_attn,
-                                     use_logmix=opt.num_mixture_components>0,
-                                     drop_prob=opt.dropout
-                                     )
-            name = "_output_glow_"+mod
-            setattr(self, "net"+name, glow)
-            self.output_mod_glows.append(glow)
-
-
-        # self.generate_full_masks()
-        self.inputs = []
-        self.targets = []
-        self.criterion = nn.MSELoss()
-
-    def name(self):
-        return "Transformerflow"
-
-    @staticmethod
-    def modify_commandline_options(parser, opt):
-        parser.add_argument('--dhid', type=int, default=512)
-        parser.add_argument('--nlayers', type=int, default=6)
-        parser.add_argument('--nhead', type=int, default=8)
-        parser.add_argument('--dropout', type=float, default=0.1)
-        parser.add_argument('--scales', type=str, default="[[10,0]]")
-        parser.add_argument('--num_glow_coupling_blocks', type=int, default=10)
-        parser.add_argument('--num_mixture_components', type=int, default=0)
-        parser.add_argument('--glow_use_attn', action='store_true', help="whether to use the internal attention for the FlowPlusPLus model")
-        return parser
-
-    # def generate_full_masks(self):
-    #     input_mods = self.input_mods
-    #     output_mods = self.output_mods
-    #     input_lengths = self.input_lengths
-    #     self.src_masks = []
-    #     for i, mod in enumerate(input_mods):
-    #         mask = torch.zeros(input_lengths[i],input_lengths[i])
-    #         self.register_buffer('src_mask_'+str(i), mask)
-    #         self.src_masks.append(mask)
-    #
-    #     self.output_masks = []
-    #     for i, mod in enumerate(output_mods):
-    #         mask = torch.zeros(sum(input_lengths),sum(input_lengths))
-    #         self.register_buffer('out_mask_'+str(i), mask)
-    #         self.output_masks.append(mask)
-
-    def forward(self, data):
-        # in lightning, forward defines the prediction/inference actions
-        latents = []
-        for i, mod in enumerate(self.input_mods):
-            # mask = getattr(self,"src_mask_"+str(i))
-            #mask = self.src_masks[i]
-            latents.append(self.input_mod_nets[i].forward(data[i]))
-        latent = torch.cat(latents)
-        outputs = []
-        for i, mod in enumerate(self.output_mods):
-            # mask = getattr(self,"out_mask_"+str(i))
-            #mask = self.output_masks[i]
-            trans_output = self.output_mod_nets[i].forward(latent)[:self.output_lengths[i]]
-            output, _ = self.output_mod_glows[i](x=None, cond=trans_output.permute(1,0,2), reverse=True)
-            outputs.append(output.permute(1,0,2))
-
-        # import pdb;pdb.set_trace()
-        #shape
-
-        return outputs
-
-    def training_step(self, batch, batch_idx):
-        self.set_inputs(batch)
-        latents = []
-        for i, mod in enumerate(self.input_mods):
-            # mask = getattr(self,"src_mask_"+str(i))
-            latents.append(self.input_mod_nets[i].forward(self.inputs[i]))
-
-        latent = torch.cat(latents)
-        loss = 0
-        for i, mod in enumerate(self.output_mods):
-            # mask = getattr(self,"out_mask_"+str(i))
-            output = self.output_mod_nets[i].forward(latent)[:self.output_lengths[i]]
-            glow = self.output_mod_glows[i]
-            # import pdb;pdb.set_trace()
-            z, sldj = glow(x=self.targets[i].permute(1,0,2), cond=output.permute(1,0,2)) #time, batch, features -> batch, time, features
-            loss += glow.loss_generative(z, sldj)
-        self.log('nll_loss', loss)
-        return loss
-
-    #def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx,
-    #                           optimizer_closure, on_tpu, using_native_amp, using_lbfgs):
-    #    optimizer.zero_grad()
diff --git a/spaces/MathysL/AutoGPT4/autogpt/commands/execute_code.py b/spaces/MathysL/AutoGPT4/autogpt/commands/execute_code.py
deleted file mode 100644
index 11266f852727f2f8aedbc995b1e504a17acbfb77..0000000000000000000000000000000000000000
--- a/spaces/MathysL/AutoGPT4/autogpt/commands/execute_code.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""Execute code in a Docker container"""
-import os
-import subprocess
-
-import docker
-from docker.errors import ImageNotFound
-
-from autogpt.workspace import WORKSPACE_PATH, path_in_workspace
-
-
-def execute_python_file(file: str) -> str:
-    """Execute a Python file in a Docker container and return the output
-
-    Args:
-        file (str): The name of the file to execute
-
-    Returns:
-        str: The output of the file
-    """
-
-    print(f"Executing file '{file}' in workspace '{WORKSPACE_PATH}'")
-
-    if not file.endswith(".py"):
-        return "Error: Invalid file type. Only .py files are allowed."
-
-    file_path = path_in_workspace(file)
-
-    if not os.path.isfile(file_path):
-        return f"Error: File '{file}' does not exist."
-
-    if we_are_running_in_a_docker_container():
-        result = subprocess.run(
-            f"python {file_path}", capture_output=True, encoding="utf8", shell=True
-        )
-        if result.returncode == 0:
-            return result.stdout
-        else:
-            return f"Error: {result.stderr}"
-
-    try:
-        client = docker.from_env()
-
-        # You can replace this with the desired Python image/version
-        # You can find available Python images on Docker Hub:
-        # https://hub.docker.com/_/python
-        image_name = "python:3-alpine"
-        try:
-            client.images.get(image_name)
-            print(f"Image '{image_name}' found locally")
-        except ImageNotFound:
-            print(f"Image '{image_name}' not found locally, pulling from Docker Hub")
-            # Use the low-level API to stream the pull response
-            low_level_client = docker.APIClient()
-            for line in low_level_client.pull(image_name, stream=True, decode=True):
-                # Print the status and progress, if available
-                status = line.get("status")
-                progress = line.get("progress")
-                if status and progress:
-                    print(f"{status}: {progress}")
-                elif status:
-                    print(status)
-
-        container = client.containers.run(
-            image_name,
-            f"python {file}",
-            volumes={
-                os.path.abspath(WORKSPACE_PATH): {
-                    "bind": "/workspace",
-                    "mode": "ro",
-                }
-            },
-            working_dir="/workspace",
-            stderr=True,
-            stdout=True,
-            detach=True,
-        )
-
-        container.wait()
-        logs = container.logs().decode("utf-8")
-        container.remove()
-
-        # print(f"Execution complete. Output: {output}")
-        # print(f"Logs: {logs}")
-
-        return logs
-
-    except docker.errors.DockerException as e:
-        print(
-            "Could not run the script in a container. If you haven't already, please install Docker https://docs.docker.com/get-docker/"
-        )
-        return f"Error: {str(e)}"
-
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-
-def execute_shell(command_line: str) -> str:
-    """Execute a shell command and return the output
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: The output of the command
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    result = subprocess.run(command_line, capture_output=True, shell=True)
-    output = f"STDOUT:\n{result.stdout}\nSTDERR:\n{result.stderr}"
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return output
-
-
-def execute_shell_popen(command_line) -> str:
-    """Execute a shell command with Popen and returns an english description
-    of the event and the process id
-
-    Args:
-        command_line (str): The command line to execute
-
-    Returns:
-        str: Description of the fact that the process started and its id
-    """
-    current_dir = os.getcwd()
-    # Change dir into workspace if necessary
-    if str(WORKSPACE_PATH) not in current_dir:
-        os.chdir(WORKSPACE_PATH)
-
-    print(f"Executing command '{command_line}' in working directory '{os.getcwd()}'")
-
-    do_not_show_output = subprocess.DEVNULL
-    process = subprocess.Popen(
-        command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output
-    )
-
-    # Change back to whatever the prior working dir was
-
-    os.chdir(current_dir)
-
-    return f"Subprocess started with PID:'{str(process.pid)}'"
-
-
-def we_are_running_in_a_docker_container() -> bool:
-    """Check if we are running in a Docker container
-
-    Returns:
-        bool: True if we are running in a Docker container, False otherwise
-    """
-    return os.path.exists("/.dockerenv")
diff --git a/spaces/Matthijs/image2reverb/image2reverb/model.py b/spaces/Matthijs/image2reverb/image2reverb/model.py
deleted file mode 100644
index 8e4d79317b734fb4764116164f362a2328bebfa8..0000000000000000000000000000000000000000
--- a/spaces/Matthijs/image2reverb/image2reverb/model.py
+++ /dev/null
@@ -1,207 +0,0 @@
-import os
-import json
-import numpy
-import torch
-from torch import nn
-import torch.nn.functional as F
-import pytorch_lightning as pl
-import torchvision
-import pyroomacoustics
-from .networks import Encoder, Generator, Discriminator
-from .stft import STFT
-from .mel import LogMel
-from .util import compare_t60
-
-
-# Hyperparameters
-G_LR = 4e-4
-D_LR = 2e-4
-ENC_LR = 1e-5
-ADAM_BETA = (0.0, 0.99)
-ADAM_EPS = 1e-8
-LAMBDA = 100
-
-
-class Image2Reverb(pl.LightningModule):
-    def __init__(self, encoder_path, depthmodel_path, latent_dimension=512, spec="stft", d_threshold=0.2, t60p=True, constant_depth = None, test_callback=None):
-        super().__init__()
-        self._latent_dimension = latent_dimension
-        self._d_threshold = d_threshold
-        self.constant_depth = constant_depth
-        self.t60p = t60p
-        self.confidence = {}
-        self.tau = 50
-        self.test_callback = test_callback
-        self._opt = (d_threshold != None) and (d_threshold > 0) and (d_threshold < 1)
-        self.enc = Encoder(encoder_path, depthmodel_path, constant_depth=self.constant_depth, device=self.device)
-        self.g = Generator(latent_dimension, spec == "mel")
-        self.d = Discriminator(365, spec == "mel")
-        self.validation_inputs = []
-        self.stft_type = spec
-
-    def forward(self, x):
-        f = self.enc.forward(x)[0]
-        z = torch.cat((f, torch.randn((f.shape[0], (self._latent_dimension - f.shape[1]) if f.shape[1] < self._latent_dimension else f.shape[1], f.shape[2], f.shape[3]), device=self.device)), 1)
-        return self.g(z)
-
-    def training_step(self, batch, batch_idx, optimizer_idx):
-        opts = None
-        if self._opt:
-            opts = self.optimizers()
-        
-        spec, label, p = batch
-        spec.requires_grad = True # For the backward pass, seems necessary for now
-        
-        # Forward passes through models
-        f = self.enc.forward(label)[0]
-        z = torch.cat((f, torch.randn((f.shape[0], (self._latent_dimension - f.shape[1]) if f.shape[1] < self._latent_dimension else f.shape[1], f.shape[2], f.shape[3]), device=self.device)), 1)
-        fake_spec = self.g(z)
-        d_fake = self.d(fake_spec.detach(), f)
-        d_real = self.d(spec, f)
-
-        # Train Generator or Encoder
-        if optimizer_idx == 0 or optimizer_idx == 1:
-            d_fake2 = self.d(fake_spec.detach(), f)
-            G_loss1 = F.mse_loss(d_fake2, torch.ones(d_fake2.shape, device=self.device))
-            G_loss2 = F.l1_loss(fake_spec, spec)
-            
-            
-            G_loss = G_loss1 + (LAMBDA * G_loss2)
-            if self.t60p:
-                t60_err = torch.Tensor([compare_t60(torch.exp(a).sum(-2).squeeze(), torch.exp(b).sum(-2).squeeze()) for a, b in zip(spec, fake_spec)]).to(self.device).mean()
-                G_loss += t60_err
-                self.log("t60", t60_err, on_step=True, on_epoch=True, prog_bar=True)
-
-            if self._opt:
-                self.manual_backward(G_loss, self.opts[optimizer_idx])
-                opts[optimizer_idx].step()
-                opts[optimizer_idx].zero_grad()
-
-            self.log("G", G_loss, on_step=True, on_epoch=True, prog_bar=True)
-
-            return G_loss
-        else: # Train Discriminator
-            l_fakeD = F.mse_loss(d_fake, torch.zeros(d_fake.shape, device=self.device))
-            l_realD = F.mse_loss(d_real, torch.ones(d_real.shape, device=self.device))
-            D_loss = (l_realD + l_fakeD)
-
-            if self._opt and (D_loss > self._d_threshold):
-                self.manual_backward(D_loss, self.opts[optimizer_idx])
-                opts[optimizer_idx].step()
-                opts[optimizer_idx].zero_grad()
-        
-            self.log("D", D_loss, on_step=True, on_epoch=True, prog_bar=True)
-
-            return D_loss
-
-    def configure_optimizers(self):
-        g_optim = torch.optim.Adam(self.g.parameters(), lr=G_LR, betas=ADAM_BETA, eps=ADAM_EPS)
-        d_optim = torch.optim.Adam(self.d.parameters(), lr=D_LR, betas=ADAM_BETA, eps=ADAM_EPS)
-        enc_optim = torch.optim.Adam(self.enc.parameters(), lr=ENC_LR, betas=ADAM_BETA, eps=ADAM_EPS)
-        return [enc_optim, g_optim, d_optim], []
-    
-    def validation_step(self, batch, batch_idx):
-        spec, label, paths = batch
-        examples = [os.path.basename(s[:s.rfind("_")]) for s, _ in zip(*paths)]
-        
-        # Forward passes through models
-        f = self.enc.forward(label)[0]
-        z = torch.cat((f, torch.randn((f.shape[0], (self._latent_dimension - f.shape[1]) if f.shape[1] < self._latent_dimension else f.shape[1], f.shape[2], f.shape[3]), device=self.device)), 1)
-        fake_spec = self.g(z)
-        
-        # Get audio
-        stft = LogMel() if self.stft_type == "mel" else STFT()
-        y_r = [stft.inverse(s.squeeze()) for s in spec]
-        y_f = [stft.inverse(s.squeeze()) for s in fake_spec]
-
-        # RT60 error (in percentages)
-        val_pct = 1
-        try:
-            f = lambda x : pyroomacoustics.experimental.rt60.measure_rt60(x, 22050)
-            t60_r = [f(y) for y in y_r if len(y)]
-            t60_f = [f(y) for y in y_f if len(y)]
-            val_pct = numpy.mean([((t_b - t_a)/t_a) for t_a, t_b in zip(t60_r, t60_f)])
-        except:
-            pass
-
-        return {"val_t60err": val_pct, "val_spec": fake_spec, "val_audio": torch.Tensor(y_f), "val_img": label, "val_examples": examples}
-    
-    def validation_epoch_end(self, outputs):
-        if not len(outputs):
-            return
-        # Log mean T60 errors (in percentages)
-        val_t60errmean = torch.Tensor(numpy.array([output["val_t60err"] for output in outputs])).mean()
-        self.log("val_t60err", val_t60errmean, on_epoch=True, prog_bar=True)
-
-        # Log generated spectrogram images
-        grid = torchvision.utils.make_grid([torch.flip(x, [0]) for y in [output["val_spec"] for output in outputs] for x in y])
-        self.logger.experiment.add_image("generated_spectrograms", grid, self.current_epoch)
-
-        # Log model input images
-        grid = torchvision.utils.make_grid([x for y in [output["val_img"] for output in outputs] for x in y])
-        self.logger.experiment.add_image("input_images_with_depthmaps", grid, self.current_epoch)
-        
-        # Log generated audio examples
-        for output in outputs:
-            for example, audio in zip(output["val_examples"], output["val_audio"]):
-                y = audio
-                self.logger.experiment.add_audio("generated_audio_%s" % example, y, self.current_epoch, sample_rate=22050)
-
-    def test_step(self, batch, batch_idx):
-        spec, label, paths = batch
-        examples = [os.path.basename(s[:s.rfind("_")]) for s, _ in zip(*paths)]
-        
-        # Forward passes through models
-        f, img = self.enc.forward(label)
-        img = (img + 1) * 0.5
-        z = torch.cat((f, torch.randn((f.shape[0], (self._latent_dimension - f.shape[1]) if f.shape[1] < self._latent_dimension else f.shape[1], f.shape[2], f.shape[3]), device=self.device)), 1)
-        fake_spec = self.g(z)
-        
-        # Get audio
-        stft = LogMel() if self.stft_type == "mel" else STFT()
-        y_r = [stft.inverse(s.squeeze()) for s in spec]
-        y_f = [stft.inverse(s.squeeze()) for s in fake_spec]
-
-        # RT60 error (in percentages)
-        val_pct = 1
-        f = lambda x : pyroomacoustics.experimental.rt60.measure_rt60(x, 22050)
-        val_pct = []
-        for y_real, y_fake in zip(y_r, y_f):
-            try:
-                t_a = f(y_real)
-                t_b = f(y_fake)
-                val_pct.append((t_b - t_a)/t_a)
-            except:
-                val_pct.append(numpy.nan)
-
-        return {"test_t60err": val_pct, "test_spec": fake_spec, "test_audio": y_f, "test_img": img, "test_examples": examples}
-    
-    def test_epoch_end(self, outputs):
-        if not self.test_callback:
-            return
-            
-        examples = []
-        t60 = []
-        spec_images = []
-        audio = []
-        input_images = []
-        input_depthmaps = []
-
-        for output in outputs:
-            for i in range(len(output["test_examples"])):
-                img = output["test_img"][i]
-                if img.shape[0] == 3:
-                    rgb = img
-                    img = torch.cat((rgb, torch.zeros((1, rgb.shape[1], rgb.shape[2]), device=self.device)), 0)
-                t60.append(output["test_t60err"][i])
-                spec_images.append(output["test_spec"][i].cpu().squeeze().detach().numpy())
-                audio.append(output["test_audio"][i])
-                input_images.append(img.cpu().squeeze().permute(1, 2, 0)[:,:,:-1].detach().numpy())
-                input_depthmaps.append(img.cpu().squeeze().permute(1, 2, 0)[:,:,-1].squeeze().detach().numpy())
-                examples.append(output["test_examples"][i])
-        
-        self.test_callback(examples, t60, spec_images, audio, input_images, input_depthmaps)
-    
-    @property
-    def automatic_optimization(self) -> bool:
-        return not self._opt
diff --git a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/utils.py b/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/utils.py
deleted file mode 100644
index ae3cf9420a33a4abae27c48ac4b90938c7d63cc3..0000000000000000000000000000000000000000
--- a/spaces/Mellow-ai/PhotoAI_Mellow/annotator/mlsd/utils.py
+++ /dev/null
@@ -1,580 +0,0 @@
-'''
-modified by  lihaoweicv
-pytorch version
-'''
-
-'''
-M-LSD
-Copyright 2021-present NAVER Corp.
-Apache License v2.0
-'''
-
-import os
-import numpy as np
-import cv2
-import torch
-from  torch.nn import  functional as F
-
-
-def deccode_output_score_and_ptss(tpMap, topk_n = 200, ksize = 5):
-    '''
-    tpMap:
-    center: tpMap[1, 0, :, :]
-    displacement: tpMap[1, 1:5, :, :]
-    '''
-    b, c, h, w = tpMap.shape
-    assert  b==1, 'only support bsize==1'
-    displacement = tpMap[:, 1:5, :, :][0]
-    center = tpMap[:, 0, :, :]
-    heat = torch.sigmoid(center)
-    hmax = F.max_pool2d( heat, (ksize, ksize), stride=1, padding=(ksize-1)//2)
-    keep = (hmax == heat).float()
-    heat = heat * keep
-    heat = heat.reshape(-1, )
-
-    scores, indices = torch.topk(heat, topk_n, dim=-1, largest=True)
-    yy = torch.floor_divide(indices, w).unsqueeze(-1)
-    xx = torch.fmod(indices, w).unsqueeze(-1)
-    ptss = torch.cat((yy, xx),dim=-1)
-
-    ptss   = ptss.detach().cpu().numpy()
-    scores = scores.detach().cpu().numpy()
-    displacement = displacement.detach().cpu().numpy()
-    displacement = displacement.transpose((1,2,0))
-    return  ptss, scores, displacement
-
-
-def pred_lines(image, model,
-               input_shape=[512, 512],
-               score_thr=0.10,
-               dist_thr=20.0):
-    h, w, _ = image.shape
-    h_ratio, w_ratio = [h / input_shape[0], w / input_shape[1]]
-
-    resized_image = np.concatenate([cv2.resize(image, (input_shape[1], input_shape[0]), interpolation=cv2.INTER_AREA),
-                                    np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
-
-    resized_image = resized_image.transpose((2,0,1))
-    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
-    batch_image = (batch_image / 127.5) - 1.0
-
-    batch_image = torch.from_numpy(batch_image).float().cuda()
-    outputs = model(batch_image)
-    pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3)
-    start = vmap[:, :, :2]
-    end = vmap[:, :, 2:]
-    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
-
-    segments_list = []
-    for center, score in zip(pts, pts_score):
-        y, x = center
-        distance = dist_map[y, x]
-        if score > score_thr and distance > dist_thr:
-            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
-            x_start = x + disp_x_start
-            y_start = y + disp_y_start
-            x_end = x + disp_x_end
-            y_end = y + disp_y_end
-            segments_list.append([x_start, y_start, x_end, y_end])
-
-    lines = 2 * np.array(segments_list)  # 256 > 512
-    lines[:, 0] = lines[:, 0] * w_ratio
-    lines[:, 1] = lines[:, 1] * h_ratio
-    lines[:, 2] = lines[:, 2] * w_ratio
-    lines[:, 3] = lines[:, 3] * h_ratio
-
-    return lines
-
-
-def pred_squares(image,
-                 model,
-                 input_shape=[512, 512],
-                 params={'score': 0.06,
-                         'outside_ratio': 0.28,
-                         'inside_ratio': 0.45,
-                         'w_overlap': 0.0,
-                         'w_degree': 1.95,
-                         'w_length': 0.0,
-                         'w_area': 1.86,
-                         'w_center': 0.14}):
-    '''
-    shape = [height, width]
-    '''
-    h, w, _ = image.shape
-    original_shape = [h, w]
-
-    resized_image = np.concatenate([cv2.resize(image, (input_shape[0], input_shape[1]), interpolation=cv2.INTER_AREA),
-                                    np.ones([input_shape[0], input_shape[1], 1])], axis=-1)
-    resized_image = resized_image.transpose((2, 0, 1))
-    batch_image = np.expand_dims(resized_image, axis=0).astype('float32')
-    batch_image = (batch_image / 127.5) - 1.0
-
-    batch_image = torch.from_numpy(batch_image).float().cuda()
-    outputs = model(batch_image)
-
-    pts, pts_score, vmap = deccode_output_score_and_ptss(outputs, 200, 3)
-    start = vmap[:, :, :2]  # (x, y)
-    end = vmap[:, :, 2:]  # (x, y)
-    dist_map = np.sqrt(np.sum((start - end) ** 2, axis=-1))
-
-    junc_list = []
-    segments_list = []
-    for junc, score in zip(pts, pts_score):
-        y, x = junc
-        distance = dist_map[y, x]
-        if score > params['score'] and distance > 20.0:
-            junc_list.append([x, y])
-            disp_x_start, disp_y_start, disp_x_end, disp_y_end = vmap[y, x, :]
-            d_arrow = 1.0
-            x_start = x + d_arrow * disp_x_start
-            y_start = y + d_arrow * disp_y_start
-            x_end = x + d_arrow * disp_x_end
-            y_end = y + d_arrow * disp_y_end
-            segments_list.append([x_start, y_start, x_end, y_end])
-
-    segments = np.array(segments_list)
-
-    ####### post processing for squares
-    # 1. get unique lines
-    point = np.array([[0, 0]])
-    point = point[0]
-    start = segments[:, :2]
-    end = segments[:, 2:]
-    diff = start - end
-    a = diff[:, 1]
-    b = -diff[:, 0]
-    c = a * start[:, 0] + b * start[:, 1]
-
-    d = np.abs(a * point[0] + b * point[1] - c) / np.sqrt(a ** 2 + b ** 2 + 1e-10)
-    theta = np.arctan2(diff[:, 0], diff[:, 1]) * 180 / np.pi
-    theta[theta < 0.0] += 180
-    hough = np.concatenate([d[:, None], theta[:, None]], axis=-1)
-
-    d_quant = 1
-    theta_quant = 2
-    hough[:, 0] //= d_quant
-    hough[:, 1] //= theta_quant
-    _, indices, counts = np.unique(hough, axis=0, return_index=True, return_counts=True)
-
-    acc_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='float32')
-    idx_map = np.zeros([512 // d_quant + 1, 360 // theta_quant + 1], dtype='int32') - 1
-    yx_indices = hough[indices, :].astype('int32')
-    acc_map[yx_indices[:, 0], yx_indices[:, 1]] = counts
-    idx_map[yx_indices[:, 0], yx_indices[:, 1]] = indices
-
-    acc_map_np = acc_map
-    # acc_map = acc_map[None, :, :, None]
-    #
-    # ### fast suppression using tensorflow op
-    # acc_map = tf.constant(acc_map, dtype=tf.float32)
-    # max_acc_map = tf.keras.layers.MaxPool2D(pool_size=(5, 5), strides=1, padding='same')(acc_map)
-    # acc_map = acc_map * tf.cast(tf.math.equal(acc_map, max_acc_map), tf.float32)
-    # flatten_acc_map = tf.reshape(acc_map, [1, -1])
-    # topk_values, topk_indices = tf.math.top_k(flatten_acc_map, k=len(pts))
-    # _, h, w, _ = acc_map.shape
-    # y = tf.expand_dims(topk_indices // w, axis=-1)
-    # x = tf.expand_dims(topk_indices % w, axis=-1)
-    # yx = tf.concat([y, x], axis=-1)
-
-    ### fast suppression using pytorch op
-    acc_map = torch.from_numpy(acc_map_np).unsqueeze(0).unsqueeze(0)
-    _,_, h, w = acc_map.shape
-    max_acc_map = F.max_pool2d(acc_map,kernel_size=5, stride=1, padding=2)
-    acc_map = acc_map * ( (acc_map == max_acc_map).float() )
-    flatten_acc_map = acc_map.reshape([-1, ])
-
-    scores, indices = torch.topk(flatten_acc_map, len(pts), dim=-1, largest=True)
-    yy = torch.div(indices, w, rounding_mode='floor').unsqueeze(-1)
-    xx = torch.fmod(indices, w).unsqueeze(-1)
-    yx = torch.cat((yy, xx), dim=-1)
-
-    yx = yx.detach().cpu().numpy()
-
-    topk_values = scores.detach().cpu().numpy()
-    indices = idx_map[yx[:, 0], yx[:, 1]]
-    basis = 5 // 2
-
-    merged_segments = []
-    for yx_pt, max_indice, value in zip(yx, indices, topk_values):
-        y, x = yx_pt
-        if max_indice == -1 or value == 0:
-            continue
-        segment_list = []
-        for y_offset in range(-basis, basis + 1):
-            for x_offset in range(-basis, basis + 1):
-                indice = idx_map[y + y_offset, x + x_offset]
-                cnt = int(acc_map_np[y + y_offset, x + x_offset])
-                if indice != -1:
-                    segment_list.append(segments[indice])
-                if cnt > 1:
-                    check_cnt = 1
-                    current_hough = hough[indice]
-                    for new_indice, new_hough in enumerate(hough):
-                        if (current_hough == new_hough).all() and indice != new_indice:
-                            segment_list.append(segments[new_indice])
-                            check_cnt += 1
-                        if check_cnt == cnt:
-                            break
-        group_segments = np.array(segment_list).reshape([-1, 2])
-        sorted_group_segments = np.sort(group_segments, axis=0)
-        x_min, y_min = sorted_group_segments[0, :]
-        x_max, y_max = sorted_group_segments[-1, :]
-
-        deg = theta[max_indice]
-        if deg >= 90:
-            merged_segments.append([x_min, y_max, x_max, y_min])
-        else:
-            merged_segments.append([x_min, y_min, x_max, y_max])
-
-    # 2. get intersections
-    new_segments = np.array(merged_segments)  # (x1, y1, x2, y2)
-    start = new_segments[:, :2]  # (x1, y1)
-    end = new_segments[:, 2:]  # (x2, y2)
-    new_centers = (start + end) / 2.0
-    diff = start - end
-    dist_segments = np.sqrt(np.sum(diff ** 2, axis=-1))
-
-    # ax + by = c
-    a = diff[:, 1]
-    b = -diff[:, 0]
-    c = a * start[:, 0] + b * start[:, 1]
-    pre_det = a[:, None] * b[None, :]
-    det = pre_det - np.transpose(pre_det)
-
-    pre_inter_y = a[:, None] * c[None, :]
-    inter_y = (pre_inter_y - np.transpose(pre_inter_y)) / (det + 1e-10)
-    pre_inter_x = c[:, None] * b[None, :]
-    inter_x = (pre_inter_x - np.transpose(pre_inter_x)) / (det + 1e-10)
-    inter_pts = np.concatenate([inter_x[:, :, None], inter_y[:, :, None]], axis=-1).astype('int32')
-
-    # 3. get corner information
-    # 3.1 get distance
-    '''
-    dist_segments:
-        | dist(0), dist(1), dist(2), ...|
-    dist_inter_to_segment1:
-        | dist(inter,0), dist(inter,0), dist(inter,0), ... |
-        | dist(inter,1), dist(inter,1), dist(inter,1), ... |
-        ...
-    dist_inter_to_semgnet2:
-        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
-        | dist(inter,0), dist(inter,1), dist(inter,2), ... |
-        ...
-    '''
-
-    dist_inter_to_segment1_start = np.sqrt(
-        np.sum(((inter_pts - start[:, None, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
-    dist_inter_to_segment1_end = np.sqrt(
-        np.sum(((inter_pts - end[:, None, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
-    dist_inter_to_segment2_start = np.sqrt(
-        np.sum(((inter_pts - start[None, :, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
-    dist_inter_to_segment2_end = np.sqrt(
-        np.sum(((inter_pts - end[None, :, :]) ** 2), axis=-1, keepdims=True))  # [n_batch, n_batch, 1]
-
-    # sort ascending
-    dist_inter_to_segment1 = np.sort(
-        np.concatenate([dist_inter_to_segment1_start, dist_inter_to_segment1_end], axis=-1),
-        axis=-1)  # [n_batch, n_batch, 2]
-    dist_inter_to_segment2 = np.sort(
-        np.concatenate([dist_inter_to_segment2_start, dist_inter_to_segment2_end], axis=-1),
-        axis=-1)  # [n_batch, n_batch, 2]
-
-    # 3.2 get degree
-    inter_to_start = new_centers[:, None, :] - inter_pts
-    deg_inter_to_start = np.arctan2(inter_to_start[:, :, 1], inter_to_start[:, :, 0]) * 180 / np.pi
-    deg_inter_to_start[deg_inter_to_start < 0.0] += 360
-    inter_to_end = new_centers[None, :, :] - inter_pts
-    deg_inter_to_end = np.arctan2(inter_to_end[:, :, 1], inter_to_end[:, :, 0]) * 180 / np.pi
-    deg_inter_to_end[deg_inter_to_end < 0.0] += 360
-
-    '''
-    B -- G
-    |    |
-    C -- R
-    B : blue / G: green / C: cyan / R: red
-
-    0 -- 1
-    |    |
-    3 -- 2
-    '''
-    # rename variables
-    deg1_map, deg2_map = deg_inter_to_start, deg_inter_to_end
-    # sort deg ascending
-    deg_sort = np.sort(np.concatenate([deg1_map[:, :, None], deg2_map[:, :, None]], axis=-1), axis=-1)
-
-    deg_diff_map = np.abs(deg1_map - deg2_map)
-    # we only consider the smallest degree of intersect
-    deg_diff_map[deg_diff_map > 180] = 360 - deg_diff_map[deg_diff_map > 180]
-
-    # define available degree range
-    deg_range = [60, 120]
-
-    corner_dict = {corner_info: [] for corner_info in range(4)}
-    inter_points = []
-    for i in range(inter_pts.shape[0]):
-        for j in range(i + 1, inter_pts.shape[1]):
-            # i, j > line index, always i < j
-            x, y = inter_pts[i, j, :]
-            deg1, deg2 = deg_sort[i, j, :]
-            deg_diff = deg_diff_map[i, j]
-
-            check_degree = deg_diff > deg_range[0] and deg_diff < deg_range[1]
-
-            outside_ratio = params['outside_ratio']  # over ratio >>> drop it!
-            inside_ratio = params['inside_ratio']  # over ratio >>> drop it!
-            check_distance = ((dist_inter_to_segment1[i, j, 1] >= dist_segments[i] and \
-                               dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * outside_ratio) or \
-                              (dist_inter_to_segment1[i, j, 1] <= dist_segments[i] and \
-                               dist_inter_to_segment1[i, j, 0] <= dist_segments[i] * inside_ratio)) and \
-                             ((dist_inter_to_segment2[i, j, 1] >= dist_segments[j] and \
-                               dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * outside_ratio) or \
-                              (dist_inter_to_segment2[i, j, 1] <= dist_segments[j] and \
-                               dist_inter_to_segment2[i, j, 0] <= dist_segments[j] * inside_ratio))
-
-            if check_degree and check_distance:
-                corner_info = None
-
-                if (deg1 >= 0 and deg1 <= 45 and deg2 >= 45 and deg2 <= 120) or \
-                        (deg2 >= 315 and deg1 >= 45 and deg1 <= 120):
-                    corner_info, color_info = 0, 'blue'
-                elif (deg1 >= 45 and deg1 <= 125 and deg2 >= 125 and deg2 <= 225):
-                    corner_info, color_info = 1, 'green'
-                elif (deg1 >= 125 and deg1 <= 225 and deg2 >= 225 and deg2 <= 315):
-                    corner_info, color_info = 2, 'black'
-                elif (deg1 >= 0 and deg1 <= 45 and deg2 >= 225 and deg2 <= 315) or \
-                        (deg2 >= 315 and deg1 >= 225 and deg1 <= 315):
-                    corner_info, color_info = 3, 'cyan'
-                else:
-                    corner_info, color_info = 4, 'red'  # we don't use it
-                    continue
-
-                corner_dict[corner_info].append([x, y, i, j])
-                inter_points.append([x, y])
-
-    square_list = []
-    connect_list = []
-    segments_list = []
-    for corner0 in corner_dict[0]:
-        for corner1 in corner_dict[1]:
-            connect01 = False
-            for corner0_line in corner0[2:]:
-                if corner0_line in corner1[2:]:
-                    connect01 = True
-                    break
-            if connect01:
-                for corner2 in corner_dict[2]:
-                    connect12 = False
-                    for corner1_line in corner1[2:]:
-                        if corner1_line in corner2[2:]:
-                            connect12 = True
-                            break
-                    if connect12:
-                        for corner3 in corner_dict[3]:
-                            connect23 = False
-                            for corner2_line in corner2[2:]:
-                                if corner2_line in corner3[2:]:
-                                    connect23 = True
-                                    break
-                            if connect23:
-                                for corner3_line in corner3[2:]:
-                                    if corner3_line in corner0[2:]:
-                                        # SQUARE!!!
-                                        '''
-                                        0 -- 1
-                                        |    |
-                                        3 -- 2
-                                        square_list:
-                                            order: 0 > 1 > 2 > 3
-                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
-                                            | x0, y0, x1, y1, x2, y2, x3, y3 |
-                                            ...
-                                        connect_list:
-                                            order: 01 > 12 > 23 > 30
-                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
-                                            | line_idx01, line_idx12, line_idx23, line_idx30 |
-                                            ...
-                                        segments_list:
-                                            order: 0 > 1 > 2 > 3
-                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
-                                            | line_idx0_i, line_idx0_j, line_idx1_i, line_idx1_j, line_idx2_i, line_idx2_j, line_idx3_i, line_idx3_j |
-                                            ...
-                                        '''
-                                        square_list.append(corner0[:2] + corner1[:2] + corner2[:2] + corner3[:2])
-                                        connect_list.append([corner0_line, corner1_line, corner2_line, corner3_line])
-                                        segments_list.append(corner0[2:] + corner1[2:] + corner2[2:] + corner3[2:])
-
-    def check_outside_inside(segments_info, connect_idx):
-        # return 'outside or inside', min distance, cover_param, peri_param
-        if connect_idx == segments_info[0]:
-            check_dist_mat = dist_inter_to_segment1
-        else:
-            check_dist_mat = dist_inter_to_segment2
-
-        i, j = segments_info
-        min_dist, max_dist = check_dist_mat[i, j, :]
-        connect_dist = dist_segments[connect_idx]
-        if max_dist > connect_dist:
-            return 'outside', min_dist, 0, 1
-        else:
-            return 'inside', min_dist, -1, -1
-
-    top_square = None
-
-    try:
-        map_size = input_shape[0] / 2
-        squares = np.array(square_list).reshape([-1, 4, 2])
-        score_array = []
-        connect_array = np.array(connect_list)
-        segments_array = np.array(segments_list).reshape([-1, 4, 2])
-
-        # get degree of corners:
-        squares_rollup = np.roll(squares, 1, axis=1)
-        squares_rolldown = np.roll(squares, -1, axis=1)
-        vec1 = squares_rollup - squares
-        normalized_vec1 = vec1 / (np.linalg.norm(vec1, axis=-1, keepdims=True) + 1e-10)
-        vec2 = squares_rolldown - squares
-        normalized_vec2 = vec2 / (np.linalg.norm(vec2, axis=-1, keepdims=True) + 1e-10)
-        inner_products = np.sum(normalized_vec1 * normalized_vec2, axis=-1)  # [n_squares, 4]
-        squares_degree = np.arccos(inner_products) * 180 / np.pi  # [n_squares, 4]
-
-        # get square score
-        overlap_scores = []
-        degree_scores = []
-        length_scores = []
-
-        for connects, segments, square, degree in zip(connect_array, segments_array, squares, squares_degree):
-            '''
-            0 -- 1
-            |    |
-            3 -- 2
-
-            # segments: [4, 2]
-            # connects: [4]
-            '''
-
-            ###################################### OVERLAP SCORES
-            cover = 0
-            perimeter = 0
-            # check 0 > 1 > 2 > 3
-            square_length = []
-
-            for start_idx in range(4):
-                end_idx = (start_idx + 1) % 4
-
-                connect_idx = connects[start_idx]  # segment idx of segment01
-                start_segments = segments[start_idx]
-                end_segments = segments[end_idx]
-
-                start_point = square[start_idx]
-                end_point = square[end_idx]
-
-                # check whether outside or inside
-                start_position, start_min, start_cover_param, start_peri_param = check_outside_inside(start_segments,
-                                                                                                      connect_idx)
-                end_position, end_min, end_cover_param, end_peri_param = check_outside_inside(end_segments, connect_idx)
-
-                cover += dist_segments[connect_idx] + start_cover_param * start_min + end_cover_param * end_min
-                perimeter += dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min
-
-                square_length.append(
-                    dist_segments[connect_idx] + start_peri_param * start_min + end_peri_param * end_min)
-
-            overlap_scores.append(cover / perimeter)
-            ######################################
-            ###################################### DEGREE SCORES
-            '''
-            deg0 vs deg2
-            deg1 vs deg3
-            '''
-            deg0, deg1, deg2, deg3 = degree
-            deg_ratio1 = deg0 / deg2
-            if deg_ratio1 > 1.0:
-                deg_ratio1 = 1 / deg_ratio1
-            deg_ratio2 = deg1 / deg3
-            if deg_ratio2 > 1.0:
-                deg_ratio2 = 1 / deg_ratio2
-            degree_scores.append((deg_ratio1 + deg_ratio2) / 2)
-            ######################################
-            ###################################### LENGTH SCORES
-            '''
-            len0 vs len2
-            len1 vs len3
-            '''
-            len0, len1, len2, len3 = square_length
-            len_ratio1 = len0 / len2 if len2 > len0 else len2 / len0
-            len_ratio2 = len1 / len3 if len3 > len1 else len3 / len1
-            length_scores.append((len_ratio1 + len_ratio2) / 2)
-
-            ######################################
-
-        overlap_scores = np.array(overlap_scores)
-        overlap_scores /= np.max(overlap_scores)
-
-        degree_scores = np.array(degree_scores)
-        # degree_scores /= np.max(degree_scores)
-
-        length_scores = np.array(length_scores)
-
-        ###################################### AREA SCORES
-        area_scores = np.reshape(squares, [-1, 4, 2])
-        area_x = area_scores[:, :, 0]
-        area_y = area_scores[:, :, 1]
-        correction = area_x[:, -1] * area_y[:, 0] - area_y[:, -1] * area_x[:, 0]
-        area_scores = np.sum(area_x[:, :-1] * area_y[:, 1:], axis=-1) - np.sum(area_y[:, :-1] * area_x[:, 1:], axis=-1)
-        area_scores = 0.5 * np.abs(area_scores + correction)
-        area_scores /= (map_size * map_size)  # np.max(area_scores)
-        ######################################
-
-        ###################################### CENTER SCORES
-        centers = np.array([[256 // 2, 256 // 2]], dtype='float32')  # [1, 2]
-        # squares: [n, 4, 2]
-        square_centers = np.mean(squares, axis=1)  # [n, 2]
-        center2center = np.sqrt(np.sum((centers - square_centers) ** 2))
-        center_scores = center2center / (map_size / np.sqrt(2.0))
-
-        '''
-        score_w = [overlap, degree, area, center, length]
-        '''
-        score_w = [0.0, 1.0, 10.0, 0.5, 1.0]
-        score_array = params['w_overlap'] * overlap_scores \
-                      + params['w_degree'] * degree_scores \
-                      + params['w_area'] * area_scores \
-                      - params['w_center'] * center_scores \
-                      + params['w_length'] * length_scores
-
-        best_square = []
-
-        sorted_idx = np.argsort(score_array)[::-1]
-        score_array = score_array[sorted_idx]
-        squares = squares[sorted_idx]
-
-    except Exception as e:
-        pass
-
-    '''return list
-    merged_lines, squares, scores
-    '''
-
-    try:
-        new_segments[:, 0] = new_segments[:, 0] * 2 / input_shape[1] * original_shape[1]
-        new_segments[:, 1] = new_segments[:, 1] * 2 / input_shape[0] * original_shape[0]
-        new_segments[:, 2] = new_segments[:, 2] * 2 / input_shape[1] * original_shape[1]
-        new_segments[:, 3] = new_segments[:, 3] * 2 / input_shape[0] * original_shape[0]
-    except:
-        new_segments = []
-
-    try:
-        squares[:, :, 0] = squares[:, :, 0] * 2 / input_shape[1] * original_shape[1]
-        squares[:, :, 1] = squares[:, :, 1] * 2 / input_shape[0] * original_shape[0]
-    except:
-        squares = []
-        score_array = []
-
-    try:
-        inter_points = np.array(inter_points)
-        inter_points[:, 0] = inter_points[:, 0] * 2 / input_shape[1] * original_shape[1]
-        inter_points[:, 1] = inter_points[:, 1] * 2 / input_shape[0] * original_shape[0]
-    except:
-        inter_points = []
-
-    return new_segments, squares, score_array, inter_points
diff --git a/spaces/Mileena/WebUIDx/app.py b/spaces/Mileena/WebUIDx/app.py
deleted file mode 100644
index 9aaf59d293dce427101bb90b54274afd589cedfd..0000000000000000000000000000000000000000
--- a/spaces/Mileena/WebUIDx/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-
-os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-#os.system(f"sed -i -e 's/inputs=\[component\],/&\\n                queue=False,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-#os.system(f"sed -i -e 's/outputs=\[token_counter\]/outputs=[token_counter], queue=False/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/outputs=\[/queue=False, &/g' home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-#os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
-#os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-    os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-    
-    # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
-    #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
-    #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
-    #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
-    #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
-    #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
-    #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-    os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test --disable-safe-unpickle")
diff --git a/spaces/MultiTransformer/vision-agent-with-llava/app.py b/spaces/MultiTransformer/vision-agent-with-llava/app.py
deleted file mode 100644
index 43c0431317563cb93f35ef985472bf0f322cc2a1..0000000000000000000000000000000000000000
--- a/spaces/MultiTransformer/vision-agent-with-llava/app.py
+++ /dev/null
@@ -1,169 +0,0 @@
-from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
-import autogen
-import replicate
-import requests
-from datetime import datetime
-import http.client
-import json
-import base64
-
-config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
-llm_config = {"config_list": config_list, "request_timeout": 120}
-
-# function to use llava model to review image
-
-
-def img_review(image_url, prompt):
-    data = {
-        "data": [
-            {
-                "image": "https://picsum.photos/200",
-                "features": [],
-            },
-        ]}
-
-    headers = {
-        "x-api-key": "token 8uOw4ntevc8JKo0Q3tQq:2975e2827ebeb4e103f7b58c1410ba58fa47bc27b1302de614a000bf51bd2114",
-        "content-type": "application/json",
-    }
-
-    connection = http.client.HTTPSConnection("api.scenex.jina.ai")
-    connection.request("POST", "/v1/describe", json.dumps(data), headers)
-    response = connection.getresponse()
-
-    print(response.status, response.reason)
-    response_data = response.read().decode("utf-8")
-    print(response_data)
-
-    connection.close()
-
-    return response_data
-
-
-result = img_review(
-    "https://cdn.discordapp.com/attachments/1083723388712919182/1089909178266558554/HannaD_A_captivating_digital_artwork_features_a_red-haired_girl_664d73dc-b537-490e-b044-4fbf22733559.png", "a llama driving a car")
-print(result)
-
-# def img_review(image_path, prompt):
-#     output = replicate.run(
-#         "yorickvp/llava-13b:6bc1c7bb0d2a34e413301fee8f7cc728d2d4e75bfab186aa995f63292bda92fc",
-#         input={
-#             "image": open(image_path, "rb"),
-#             "prompt": f"What is happening in the image? From scale 1 to 10, decide how similar the image is to the text prompt {prompt}?",
-#         }
-#     )
-
-#     result = ""
-#     for item in output:
-#         result += item
-
-#     return result
-
-
-# function to use stability-ai model to generate image
-def text_to_image_generation(prompt):
-    output = replicate.run(
-        "stability-ai/sdxl:c221b2b8ef527988fb59bf24a8b97c4561f1c671f73bd389f866bfb27c061316",
-        input={
-            "prompt": prompt
-        }
-    )
-
-    if output and len(output) > 0:
-        # Get the image URL from the output
-        image_url = output[0]
-        print(f"generated image for {prompt}: {image_url}")
-
-        # Download the image and save it with a filename based on the prompt and current time
-        current_time = datetime.now().strftime("%Y%m%d%H%M%S")
-        shortened_prompt = prompt[:50]
-        filename = f"imgs/{shortened_prompt}_{current_time}.png"
-
-        response = requests.get(image_url)
-        if response.status_code == 200:
-            with open(filename, "wb") as file:
-                file.write(response.content)
-            return f"Image saved as '{filename}'"
-        else:
-            return "Failed to download and save the image."
-    else:
-        return "Failed to generate the image."
-
-
-# Create llm config
-llm_config_assistants = {
-    "functions": [
-        {
-            "name": "text_to_image_generation",
-            "description": "use latest AI model to generate image based on a prompt, return the file path of image generated",
-            "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "prompt": {
-                            "type": "string",
-                            "description": "a great text to image prompt that describe the image",
-                        }
-                    },
-                "required": ["prompt"],
-            },
-        },
-        {
-            "name": "image_review",
-            "description": "review & critique the AI generated image based on original prompt, decide how can images & prompt can be improved",
-            "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "prompt": {
-                            "type": "string",
-                            "description": "the original prompt used to generate the image",
-                        },
-                        "image_path": {
-                            "type": "string",
-                            "description": "the image file path, make sure including the full file path & file extension",
-                        }
-                    },
-                "required": ["prompt", "image_path"],
-            },
-        },
-    ],
-    "config_list": config_list,
-    "request_timeout": 120}
-
-# Create assistant agent
-img_gen_assistant = AssistantAgent(
-    name="text_to_img_prompt_expert",
-    system_message="You are a text to image AI model expert, you will use text_to_image_generation function to generate image with prompt provided, and also improve prompt based on feedback provided until it is 10/10.",
-    llm_config=llm_config_assistants,
-    function_map={
-        "image_review": img_review,
-        "text_to_image_generation": text_to_image_generation
-    }
-)
-
-img_critic_assistant = AssistantAgent(
-    name="img_critic",
-    system_message="You are an AI image critique, you will use img_review function to review the image generated by the text_to_img_prompt_expert against the original prompt, and provide feedback on how to improve the prompt.",
-    llm_config=llm_config_assistants,
-    function_map={
-        "image_review": img_review,
-        "text_to_image_generation": text_to_image_generation
-    }
-)
-
-# Create user proxy agent
-user_proxy = UserProxyAgent(
-    name="user_proxy",
-    human_input_mode="ALWAYS",
-)
-
-# Create groupchat
-groupchat = autogen.GroupChat(
-    agents=[user_proxy, img_gen_assistant, img_critic_assistant], messages=[], max_round=50)
-
-manager = autogen.GroupChatManager(
-    groupchat=groupchat,
-    llm_config=llm_config)
-
-# # Start the conversation
-# user_proxy.initiate_chat(
-#     manager, message="Generate a photo realistic image of llama driving a car")
\ No newline at end of file
diff --git a/spaces/Munderstand/CLIP-Interrogator-3/README.md b/spaces/Munderstand/CLIP-Interrogator-3/README.md
deleted file mode 100644
index aa14799d7e724e5d8b7bede9c8e9c9d5e00eb8ba..0000000000000000000000000000000000000000
--- a/spaces/Munderstand/CLIP-Interrogator-3/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: CLIP Interrogator 2
-emoji: 🕵️‍♂️🕵️‍♂️
-colorFrom: green
-colorTo: purple
-sdk: gradio
-sdk_version: 3.11.0
-app_file: app.py
-pinned: false
-duplicated_from: fffiloni/CLIP-Interrogator-2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Mycroft756/artificialguybr-StickersRedmond/app.py b/spaces/Mycroft756/artificialguybr-StickersRedmond/app.py
deleted file mode 100644
index ded39a06ef92d701958156d909c0dd0a0965a4e5..0000000000000000000000000000000000000000
--- a/spaces/Mycroft756/artificialguybr-StickersRedmond/app.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import gradio as gr
-
-gr.Interface.load("models/artificialguybr/StickersRedmond").launch()
\ No newline at end of file
diff --git a/spaces/NCTCMumbai/NCTC/models/official/modeling/performance.py b/spaces/NCTCMumbai/NCTC/models/official/modeling/performance.py
deleted file mode 100644
index 4b264f53256db66326ee4e51c5a29676e273eca9..0000000000000000000000000000000000000000
--- a/spaces/NCTCMumbai/NCTC/models/official/modeling/performance.py
+++ /dev/null
@@ -1,56 +0,0 @@
-# Lint as: python3
-# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-"""Functions and classes related to training performance."""
-
-import tensorflow as tf
-
-
-def configure_optimizer(optimizer,
-                        use_float16=False,
-                        use_graph_rewrite=False,
-                        loss_scale="dynamic"):
-  """Configures optimizer object with performance options."""
-  if use_float16:
-    # Wraps optimizer with a LossScaleOptimizer. This is done automatically
-    # in compile() with the "mixed_float16" policy, but since we do not call
-    # compile(), we must wrap the optimizer manually.
-    optimizer = (
-        tf.keras.mixed_precision.experimental.LossScaleOptimizer(
-            optimizer, loss_scale=loss_scale))
-  if use_graph_rewrite:
-    # Note: the model dtype must be 'float32', which will ensure
-    # tf.ckeras.mixed_precision and
-    # tf.train.experimental.enable_mixed_precision_graph_rewrite do not double
-    # up.
-    optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
-        optimizer)
-  return optimizer
-
-
-def set_mixed_precision_policy(dtype, loss_scale=None):
-  """Sets mix precision policy."""
-  if dtype == tf.float16:
-    policy = tf.keras.mixed_precision.experimental.Policy(
-        'mixed_float16', loss_scale=loss_scale)
-    tf.keras.mixed_precision.experimental.set_policy(policy)
-  elif dtype == tf.bfloat16:
-    policy = tf.keras.mixed_precision.experimental.Policy(
-        'mixed_bfloat16')
-    tf.keras.mixed_precision.experimental.set_policy(policy)
-  elif dtype == tf.float32:
-    tf.keras.mixed_precision.experimental.set_policy('float32')
-  else:
-    raise ValueError("Unexpected dtype: %s" % dtype)
diff --git a/spaces/NSect/VALL-E-X/data/tokenizer.py b/spaces/NSect/VALL-E-X/data/tokenizer.py
deleted file mode 100644
index 9b8e889641b6715b9b4fa3cffd3dd7bef06ad7e9..0000000000000000000000000000000000000000
--- a/spaces/NSect/VALL-E-X/data/tokenizer.py
+++ /dev/null
@@ -1,117 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2023                            (authors: Feiteng Li)
-#
-# 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.
-
-import re
-from dataclasses import asdict, dataclass
-from typing import Any, Dict, List, Optional, Pattern, Union
-
-import numpy as np
-import torch
-import torchaudio
-from encodec import EncodecModel
-from encodec.utils import convert_audio
-
-def remove_encodec_weight_norm(model):
-    from encodec.modules import SConv1d
-    from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock
-    from torch.nn.utils import remove_weight_norm
-
-    encoder = model.encoder.model
-    for key in encoder._modules:
-        if isinstance(encoder._modules[key], SEANetResnetBlock):
-            remove_weight_norm(encoder._modules[key].shortcut.conv.conv)
-            block_modules = encoder._modules[key].block._modules
-            for skey in block_modules:
-                if isinstance(block_modules[skey], SConv1d):
-                    remove_weight_norm(block_modules[skey].conv.conv)
-        elif isinstance(encoder._modules[key], SConv1d):
-            remove_weight_norm(encoder._modules[key].conv.conv)
-
-    decoder = model.decoder.model
-    for key in decoder._modules:
-        if isinstance(decoder._modules[key], SEANetResnetBlock):
-            remove_weight_norm(decoder._modules[key].shortcut.conv.conv)
-            block_modules = decoder._modules[key].block._modules
-            for skey in block_modules:
-                if isinstance(block_modules[skey], SConv1d):
-                    remove_weight_norm(block_modules[skey].conv.conv)
-        elif isinstance(decoder._modules[key], SConvTranspose1d):
-            remove_weight_norm(decoder._modules[key].convtr.convtr)
-        elif isinstance(decoder._modules[key], SConv1d):
-            remove_weight_norm(decoder._modules[key].conv.conv)
-
-
-class AudioTokenizer:
-    """EnCodec audio."""
-
-    def __init__(
-        self,
-        device: Any = None,
-    ) -> None:
-        # Instantiate a pretrained EnCodec model
-        model = EncodecModel.encodec_model_24khz()
-        model.set_target_bandwidth(6.0)
-        remove_encodec_weight_norm(model)
-
-        if not device:
-            device = torch.device("cpu")
-            if torch.cuda.is_available():
-                device = torch.device("cuda:0")
-
-        self._device = device
-
-        self.codec = model.to(device)
-        self.sample_rate = model.sample_rate
-        self.channels = model.channels
-
-    @property
-    def device(self):
-        return self._device
-
-    def encode(self, wav: torch.Tensor) -> torch.Tensor:
-        return self.codec.encode(wav.to(self.device))
-
-    def decode(self, frames: torch.Tensor) -> torch.Tensor:
-        return self.codec.decode(frames)
-
-
-def tokenize_audio(tokenizer: AudioTokenizer, audio):
-    # Load and pre-process the audio waveform
-    if isinstance(audio, str):
-        wav, sr = torchaudio.load(audio)
-    else:
-        wav, sr = audio
-    wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
-    wav = wav.unsqueeze(0)
-
-    # Extract discrete codes from EnCodec
-    with torch.no_grad():
-        encoded_frames = tokenizer.encode(wav)
-    return encoded_frames
-
-
-if __name__ == "__main__":
-    model = EncodecModel.encodec_model_24khz()
-    model.set_target_bandwidth(6.0)
-
-    samples = torch.from_numpy(np.random.random([4, 1, 1600])).type(
-        torch.float32
-    )
-    codes_raw = model.encode(samples)
-
-    remove_encodec_weight_norm(model)
-    codes_norm = model.encode(samples)
-
-    assert torch.allclose(codes_raw[0][0], codes_norm[0][0])
diff --git a/spaces/NSect/voice_conversion_service/README.md b/spaces/NSect/voice_conversion_service/README.md
deleted file mode 100644
index bae294da88a7f8d2e7a04eaac0b4e1491165c920..0000000000000000000000000000000000000000
--- a/spaces/NSect/voice_conversion_service/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Voice Conversion Service
-emoji: 💬
-colorFrom: blue
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.32.0
-app_file: app.py
-pinned: false
-duplicated_from: balacoon/voice_conversion_service
----
-
-Interactive demo for Voice Conversion service by Balacoon.
diff --git a/spaces/Nephele/bert-vits2-multi-voice/monotonic_align/core.py b/spaces/Nephele/bert-vits2-multi-voice/monotonic_align/core.py
deleted file mode 100644
index 5ff728cd74c9228346a82ec64a9829cb98ad315e..0000000000000000000000000000000000000000
--- a/spaces/Nephele/bert-vits2-multi-voice/monotonic_align/core.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import numba
-
-
-@numba.jit(numba.void(numba.int32[:, :, ::1], numba.float32[:, :, ::1], numba.int32[::1], numba.int32[::1]),
-           nopython=True, nogil=True)
-def maximum_path_jit(paths, values, t_ys, t_xs):
-    b = paths.shape[0]
-    max_neg_val = -1e9
-    for i in range(int(b)):
-        path = paths[i]
-        value = values[i]
-        t_y = t_ys[i]
-        t_x = t_xs[i]
-
-        v_prev = v_cur = 0.0
-        index = t_x - 1
-
-        for y in range(t_y):
-            for x in range(max(0, t_x + y - t_y), min(t_x, y + 1)):
-                if x == y:
-                    v_cur = max_neg_val
-                else:
-                    v_cur = value[y - 1, x]
-                if x == 0:
-                    if y == 0:
-                        v_prev = 0.
-                    else:
-                        v_prev = max_neg_val
-                else:
-                    v_prev = value[y - 1, x - 1]
-                value[y, x] += max(v_prev, v_cur)
-
-        for y in range(t_y - 1, -1, -1):
-            path[y, index] = 1
-            if index != 0 and (index == y or value[y - 1, index] < value[y - 1, index - 1]):
-                index = index - 1
\ No newline at end of file
diff --git a/spaces/OAOA/DifFace/facelib/detection/yolov5face/__init__.py b/spaces/OAOA/DifFace/facelib/detection/yolov5face/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/OAOA/DifFace/facelib/parsing/bisenet.py b/spaces/OAOA/DifFace/facelib/parsing/bisenet.py
deleted file mode 100644
index 3898cab76ae5876459cd4899c54cafa14234971d..0000000000000000000000000000000000000000
--- a/spaces/OAOA/DifFace/facelib/parsing/bisenet.py
+++ /dev/null
@@ -1,140 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .resnet import ResNet18
-
-
-class ConvBNReLU(nn.Module):
-
-    def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1):
-        super(ConvBNReLU, self).__init__()
-        self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False)
-        self.bn = nn.BatchNorm2d(out_chan)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = F.relu(self.bn(x))
-        return x
-
-
-class BiSeNetOutput(nn.Module):
-
-    def __init__(self, in_chan, mid_chan, num_class):
-        super(BiSeNetOutput, self).__init__()
-        self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
-        self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False)
-
-    def forward(self, x):
-        feat = self.conv(x)
-        out = self.conv_out(feat)
-        return out, feat
-
-
-class AttentionRefinementModule(nn.Module):
-
-    def __init__(self, in_chan, out_chan):
-        super(AttentionRefinementModule, self).__init__()
-        self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
-        self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
-        self.bn_atten = nn.BatchNorm2d(out_chan)
-        self.sigmoid_atten = nn.Sigmoid()
-
-    def forward(self, x):
-        feat = self.conv(x)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv_atten(atten)
-        atten = self.bn_atten(atten)
-        atten = self.sigmoid_atten(atten)
-        out = torch.mul(feat, atten)
-        return out
-
-
-class ContextPath(nn.Module):
-
-    def __init__(self):
-        super(ContextPath, self).__init__()
-        self.resnet = ResNet18()
-        self.arm16 = AttentionRefinementModule(256, 128)
-        self.arm32 = AttentionRefinementModule(512, 128)
-        self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
-        self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
-
-    def forward(self, x):
-        feat8, feat16, feat32 = self.resnet(x)
-        h8, w8 = feat8.size()[2:]
-        h16, w16 = feat16.size()[2:]
-        h32, w32 = feat32.size()[2:]
-
-        avg = F.avg_pool2d(feat32, feat32.size()[2:])
-        avg = self.conv_avg(avg)
-        avg_up = F.interpolate(avg, (h32, w32), mode='nearest')
-
-        feat32_arm = self.arm32(feat32)
-        feat32_sum = feat32_arm + avg_up
-        feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest')
-        feat32_up = self.conv_head32(feat32_up)
-
-        feat16_arm = self.arm16(feat16)
-        feat16_sum = feat16_arm + feat32_up
-        feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest')
-        feat16_up = self.conv_head16(feat16_up)
-
-        return feat8, feat16_up, feat32_up  # x8, x8, x16
-
-
-class FeatureFusionModule(nn.Module):
-
-    def __init__(self, in_chan, out_chan):
-        super(FeatureFusionModule, self).__init__()
-        self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
-        self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False)
-        self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False)
-        self.relu = nn.ReLU(inplace=True)
-        self.sigmoid = nn.Sigmoid()
-
-    def forward(self, fsp, fcp):
-        fcat = torch.cat([fsp, fcp], dim=1)
-        feat = self.convblk(fcat)
-        atten = F.avg_pool2d(feat, feat.size()[2:])
-        atten = self.conv1(atten)
-        atten = self.relu(atten)
-        atten = self.conv2(atten)
-        atten = self.sigmoid(atten)
-        feat_atten = torch.mul(feat, atten)
-        feat_out = feat_atten + feat
-        return feat_out
-
-
-class BiSeNet(nn.Module):
-
-    def __init__(self, num_class):
-        super(BiSeNet, self).__init__()
-        self.cp = ContextPath()
-        self.ffm = FeatureFusionModule(256, 256)
-        self.conv_out = BiSeNetOutput(256, 256, num_class)
-        self.conv_out16 = BiSeNetOutput(128, 64, num_class)
-        self.conv_out32 = BiSeNetOutput(128, 64, num_class)
-
-    def forward(self, x, return_feat=False):
-        h, w = x.size()[2:]
-        feat_res8, feat_cp8, feat_cp16 = self.cp(x)  # return res3b1 feature
-        feat_sp = feat_res8  # replace spatial path feature with res3b1 feature
-        feat_fuse = self.ffm(feat_sp, feat_cp8)
-
-        out, feat = self.conv_out(feat_fuse)
-        out16, feat16 = self.conv_out16(feat_cp8)
-        out32, feat32 = self.conv_out32(feat_cp16)
-
-        out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True)
-        out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True)
-        out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True)
-
-        if return_feat:
-            feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True)
-            feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True)
-            feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True)
-            return out, out16, out32, feat, feat16, feat32
-        else:
-            return out, out16, out32
diff --git a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/dataclass/constants.py b/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/dataclass/constants.py
deleted file mode 100644
index 4f159cfe9ac72b0524228fe290181c6898787265..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Generic_Interface/fairseq/fairseq/dataclass/constants.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from enum import Enum, EnumMeta
-from typing import List
-
-
-class StrEnumMeta(EnumMeta):
-    # this is workaround for submitit pickling leading to instance checks failing in hydra for StrEnum, see
-    # https://github.com/facebookresearch/hydra/issues/1156
-    @classmethod
-    def __instancecheck__(cls, other):
-        return "enum" in str(type(other))
-
-
-class StrEnum(Enum, metaclass=StrEnumMeta):
-    def __str__(self):
-        return self.value
-
-    def __eq__(self, other: str):
-        return self.value == other
-
-    def __repr__(self):
-        return self.value
-
-    def __hash__(self):
-        return hash(str(self))
-
-
-def ChoiceEnum(choices: List[str]):
-    """return the Enum class used to enforce list of choices"""
-    return StrEnum("Choices", {k: k for k in choices})
-
-
-LOG_FORMAT_CHOICES = ChoiceEnum(["json", "none", "simple", "tqdm"])
-DDP_BACKEND_CHOICES = ChoiceEnum([
-    "c10d",  # alias for pytorch_ddp
-    "fully_sharded",  # FullyShardedDataParallel from fairscale
-    "legacy_ddp",
-    "no_c10d",  # alias for legacy_ddp
-    "pytorch_ddp",
-    "slow_mo",
-])
-DDP_COMM_HOOK_CHOICES = ChoiceEnum(["none", "fp16"])
-DATASET_IMPL_CHOICES = ChoiceEnum(["raw", "lazy", "cached", "mmap", "fasta", "huffman"])
-GENERATION_CONSTRAINTS_CHOICES = ChoiceEnum(["ordered", "unordered"])
-GENERATION_DECODING_FORMAT_CHOICES = ChoiceEnum(
-    ["unigram", "ensemble", "vote", "dp", "bs"]
-)
-ZERO_SHARDING_CHOICES = ChoiceEnum(["none", "os"])
-PIPELINE_CHECKPOINT_CHOICES = ChoiceEnum(["always", "never", "except_last"])
-PRINT_ALIGNMENT_CHOICES = ChoiceEnum(["hard", "soft"])
diff --git a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh b/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh
deleted file mode 100644
index cb5bbb7277bfb9f2d5440da0514bf7b16da8140d..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-Image_Caption/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh
+++ /dev/null
@@ -1,63 +0,0 @@
-#!/usr/bin/env bash
-# Copyright 2012  Johns Hopkins University (Author: Daniel Povey)
-#           2014  Guoguo Chen
-# Apache 2.0
-
-[ -f ./path.sh ] && . ./path.sh
-
-# begin configuration section.
-cmd=run.pl
-stage=0
-decode_mbr=true
-word_ins_penalty=0.0,0.5,1.0
-min_lmwt=7
-max_lmwt=17
-iter=final
-#end configuration section.
-
-[ -f ./path.sh ] && . ./path.sh
-. parse_options.sh || exit 1;
-
-if [ $# -ne 3 ]; then
-  echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)] <data-dir> <lang-dir|graph-dir> <decode-dir>"
-  echo " Options:"
-  echo "    --cmd (run.pl|queue.pl...)      # specify how to run the sub-processes."
-  echo "    --stage (0|1|2)                 # start scoring script from part-way through."
-  echo "    --decode_mbr (true/false)       # maximum bayes risk decoding (confusion network)."
-  echo "    --min_lmwt <int>                # minumum LM-weight for lattice rescoring "
-  echo "    --max_lmwt <int>                # maximum LM-weight for lattice rescoring "
-  exit 1;
-fi
-
-data=$1
-lang_or_graph=$2
-dir=$3
-
-symtab=$lang_or_graph/words.txt
-
-for f in $symtab $dir/lat.1.gz $data/text; do
-  [ ! -f $f ] && echo "score.sh: no such file $f" && exit 1;
-done
-
-mkdir -p $dir/scoring/log
-
-cat $data/text | sed 's:<NOISE>::g' | sed 's:<SPOKEN_NOISE>::g' > $dir/scoring/test_filt.txt
-
-for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do
-  $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/best_path.LMWT.$wip.log \
-    lattice-scale --inv-acoustic-scale=LMWT "ark:gunzip -c $dir/lat.*.gz|" ark:- \| \
-    lattice-add-penalty --word-ins-penalty=$wip ark:- ark:- \| \
-    lattice-best-path --word-symbol-table=$symtab \
-      ark:- ark,t:$dir/scoring/LMWT.$wip.tra || exit 1;
-done
-
-# Note: the double level of quoting for the sed command
-for wip in $(echo $word_ins_penalty | sed 's/,/ /g'); do
-  $cmd LMWT=$min_lmwt:$max_lmwt $dir/scoring/log/score.LMWT.$wip.log \
-    cat $dir/scoring/LMWT.$wip.tra \| \
-    utils/int2sym.pl -f 2- $symtab \| sed 's:\<UNK\>::g' \| \
-    compute-wer --text --mode=present \
-    ark:$dir/scoring/test_filt.txt  ark,p:- ">&" $dir/wer_LMWT_$wip || exit 1;
-done
-
-exit 0;
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py b/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py
deleted file mode 100644
index 7ec1fb7521b8a9b821d28bcaaaedb034f6e95e0b..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/examples/speech_synthesis/preprocessing/get_ljspeech_audio_manifest.py
+++ /dev/null
@@ -1,70 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import argparse
-import logging
-from pathlib import Path
-from collections import defaultdict
-
-import pandas as pd
-from torchaudio.datasets import LJSPEECH
-from tqdm import tqdm
-
-from examples.speech_to_text.data_utils import save_df_to_tsv
-
-
-log = logging.getLogger(__name__)
-
-SPLITS = ["train", "dev", "test"]
-
-
-def process(args):
-    out_root = Path(args.output_data_root).absolute()
-    out_root.mkdir(parents=True, exist_ok=True)
-
-    # Generate TSV manifest
-    print("Generating manifest...")
-    # following FastSpeech's splits
-    dataset = LJSPEECH(out_root.as_posix(), download=True)
-    id_to_split = {}
-    for x in dataset._flist:
-        id_ = x[0]
-        speaker = id_.split("-")[0]
-        id_to_split[id_] = {
-            "LJ001": "test", "LJ002": "test", "LJ003": "dev"
-        }.get(speaker, "train")
-    manifest_by_split = {split: defaultdict(list) for split in SPLITS}
-    progress = tqdm(enumerate(dataset), total=len(dataset))
-    for i, (waveform, _, utt, normalized_utt) in progress:
-        sample_id = dataset._flist[i][0]
-        split = id_to_split[sample_id]
-        manifest_by_split[split]["id"].append(sample_id)
-        audio_path = f"{dataset._path}/{sample_id}.wav"
-        manifest_by_split[split]["audio"].append(audio_path)
-        manifest_by_split[split]["n_frames"].append(len(waveform[0]))
-        manifest_by_split[split]["tgt_text"].append(normalized_utt)
-        manifest_by_split[split]["speaker"].append("ljspeech")
-        manifest_by_split[split]["src_text"].append(utt)
-
-    manifest_root = Path(args.output_manifest_root).absolute()
-    manifest_root.mkdir(parents=True, exist_ok=True)
-    for split in SPLITS:
-        save_df_to_tsv(
-            pd.DataFrame.from_dict(manifest_by_split[split]),
-            manifest_root / f"{split}.audio.tsv"
-        )
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--output-data-root", "-d", required=True, type=str)
-    parser.add_argument("--output-manifest-root", "-m", required=True, type=str)
-    args = parser.parse_args()
-
-    process(args)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/__init__.py b/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/__init__.py
deleted file mode 100644
index 1c5189c0f7fb4d66077d9d6498cb78cacff76de8..0000000000000000000000000000000000000000
--- a/spaces/OFA-Sys/OFA-vqa/fairseq/fairseq/models/speech_to_text/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-from .berard import *  # noqa
-from .convtransformer import *  # noqa
-from .s2t_transformer import *  # noqa
-from .xm_transformer import *  # noqa
diff --git a/spaces/Omnibus/game-test/game.html b/spaces/Omnibus/game-test/game.html
deleted file mode 100644
index a99445e89175cfbfe1a27c5a641eaed071b117b4..0000000000000000000000000000000000000000
--- a/spaces/Omnibus/game-test/game.html
+++ /dev/null
@@ -1,176 +0,0 @@
-<!doctype html> 
-<html lang="en"> 
-<head> 
-    <meta charset="UTF-8" />
-    <title>Game Test</title>
-    <script src="//cdn.jsdelivr.net/npm/phaser@3.11.0/dist/phaser.js"></script>
-    <style type="text/css">
-        body {
-            margin: 0;
-        }
-        canvas {
-          width:100%;
-          max-width:800px;
-        }
-    </style>
-</head>
-<body>
-
-<script type="text/javascript">
-var config = {
-    type: Phaser.AUTO,
-    width: 800,
-    height: 600,
-    physics: {
-        default: 'arcade',
-        arcade: {
-            gravity: { y: 300 },
-            debug: false
-        }
-    },
-    scene: {
-        preload: preload,
-        create: create,
-        update: update
-    }
-};
-var player;
-var stars;
-var bombs;
-var platforms;
-var cursors;
-var score = 0;
-var gameOver = false;
-var scoreText;
-var game = new Phaser.Game(config);
-const queryString = window.location.search;
-const urlParams = new URLSearchParams(queryString);
-const sky_img = urlParams.get("sky");
-const platform_img = urlParams.get("platform");
-const star_img = urlParams.get("star");
-const bomb_img = urlParams.get("bomb");
-const dude_img = urlParams.get("dude");
-  
-console.log(sky_img);
-console.log(platform_img);
-console.log(star_img);
-console.log(bomb_img);
-console.log(dude_img);
-  
-function preload ()
-{
-    this.load.image('sky', sky_img);
-    this.load.image('ground', platform_img);
-    this.load.image('star', star_img);
-    this.load.image('bomb', bomb_img);
-    this.load.spritesheet('dude', dude_img, {frameWidth: 32, frameHeight: 48 });
-}
-function create ()
-{
-    this.add.image(0, 0, 'sky').setOrigin(0,0);
-    platforms = this.physics.add.staticGroup();
-    platforms.create(400, 568, 'ground').setScale(2).refreshBody();
-    platforms.create(600, 400, 'ground');
-    platforms.create(50, 250, 'ground');
-    platforms.create(750, 220, 'ground');
-    player = this.physics.add.sprite(100, 450, 'dude');
-    player.setBounce(0.2);
-    player.setCollideWorldBounds(true);
-    this.anims.create({
-        key: 'left',
-        frames: this.anims.generateFrameNumbers('dude', { start: 0, end: 3 }),
-        frameRate: 10,
-        repeat: -1
-    });
-    this.anims.create({
-        key: 'turn',
-        frames: [ { key: 'dude', frame: 4 } ],
-        frameRate: 20
-    });
-    this.anims.create({
-        key: 'right',
-        frames: this.anims.generateFrameNumbers('dude', { start: 5, end: 8 }),
-        frameRate: 10,
-        repeat: -1
-    });
-    cursors = this.input.keyboard.createCursorKeys();
-    stars = this.physics.add.group({
-        key: 'star',
-        repeat: 11,
-        setXY: { x: 12, y: 0, stepX: 70 }
-    });
-    stars.children.iterate(function (child) {
-        child.setBounceY(Phaser.Math.FloatBetween(0.4, 0.8));
-    });
-    bombs = this.physics.add.group();
-    scoreText = this.add.text(16, 16, 'score: 0', { fontSize: '32px', fill: '#000' });
-    this.physics.add.collider(player, platforms);
-    this.physics.add.collider(stars, platforms);
-    this.physics.add.collider(bombs, platforms);
-    this.physics.add.overlap(player, stars, collectStar, null, this);
-    this.physics.add.collider(player, bombs, hitBomb, null, this);
-}
-function update ()
-{
-    if (gameOver)
-    {   
-        return;
-    }
-    if (cursors.left.isDown)
-    {
-        player.setVelocityX(-160);
-        player.anims.play('left', true);
-    }
-    else if (cursors.right.isDown)
-    {
-        player.setVelocityX(160);
-        player.anims.play('right', true);
-    }
-    else
-    {
-        player.setVelocityX(0);
-        player.anims.play('turn');
-    }
-    if (cursors.up.isDown && player.body.touching.down)
-    {
-        player.setVelocityY(-330);
-    }
-}
-function collectStar (player, star)
-{
-    star.disableBody(true, true);
-    //  Add and update the score
-    score += 10;
-    scoreText.setText('Score: ' + score);
-    document.getElementById('my_score').innerHTML = score;
-    if (stars.countActive(true) === 0)
-    {
-        //  A new batch of stars to collect
-        stars.children.iterate(function (child) {
-            child.enableBody(true, child.x, 0, true, true);
-        });
-        var x = (player.x < 400) ? Phaser.Math.Between(400, 800) : Phaser.Math.Between(0, 400);
-        var bomb = bombs.create(x, 16, 'bomb');
-        bomb.setBounce(1);
-        bomb.setCollideWorldBounds(true);
-        bomb.setVelocity(Phaser.Math.Between(-200, 200), 20);
-        bomb.allowGravity = false;
-    }
-}
-function hitBomb (player, bomb)
-{
-    this.physics.pause();
-    player.setTint(0xff0000);
-    player.anims.play('turn');
-    var mainCamera = this.cameras.main
-    mainCamera.shake(250);
-    mainCamera.fadeOut(3000);  
-   
-    gameOver = true;
-}
-</script>
-<div>
-<p hidden id="my_score"></p>
-</div>
-</body>
-</html>
\ No newline at end of file
diff --git a/spaces/OpenMotionLab/MotionGPT/mGPT/utils/temos_utils.py b/spaces/OpenMotionLab/MotionGPT/mGPT/utils/temos_utils.py
deleted file mode 100644
index 888fd69dbece5727f3955548af4faf6bb9d430bd..0000000000000000000000000000000000000000
--- a/spaces/OpenMotionLab/MotionGPT/mGPT/utils/temos_utils.py
+++ /dev/null
@@ -1,133 +0,0 @@
-from typing import Dict, List
-
-import numpy as np
-import torch
-from torch import Tensor
-
-import mGPT.utils.geometry_conver as geometry_conver
-
-
-def lengths_to_mask(lengths: List[int],
-                    device: torch.device,
-                    max_len: int = None) -> Tensor:
-    lengths = torch.tensor(lengths, device=device)
-    max_len = max_len if max_len else max(lengths)
-    mask = torch.arange(max_len, device=device).expand(
-        len(lengths), max_len) < lengths.unsqueeze(1)
-    return mask
-
-
-def detach_to_numpy(tensor):
-    return tensor.detach().cpu().numpy()
-
-
-def remove_padding(tensors, lengths):
-    return [
-        tensor[:tensor_length]
-        for tensor, tensor_length in zip(tensors, lengths)
-    ]
-
-
-def nfeats_of(rottype):
-    if rottype in ["rotvec", "axisangle"]:
-        return 3
-    elif rottype in ["rotquat", "quaternion"]:
-        return 4
-    elif rottype in ["rot6d", "6drot", "rotation6d"]:
-        return 6
-    elif rottype in ["rotmat"]:
-        return 9
-    else:
-        return TypeError("This rotation type doesn't have features.")
-
-
-def axis_angle_to(newtype, rotations):
-    if newtype in ["matrix"]:
-        rotations = geometry_conver.axis_angle_to_matrix(rotations)
-        return rotations
-    elif newtype in ["rotmat"]:
-        rotations = geometry_conver.axis_angle_to_matrix(rotations)
-        rotations = matrix_to("rotmat", rotations)
-        return rotations
-    elif newtype in ["rot6d", "6drot", "rotation6d"]:
-        rotations = geometry_conver.axis_angle_to_matrix(rotations)
-        rotations = matrix_to("rot6d", rotations)
-        return rotations
-    elif newtype in ["rotquat", "quaternion"]:
-        rotations = geometry_conver.axis_angle_to_quaternion(rotations)
-        return rotations
-    elif newtype in ["rotvec", "axisangle"]:
-        return rotations
-    else:
-        raise NotImplementedError
-
-
-def matrix_to(newtype, rotations):
-    if newtype in ["matrix"]:
-        return rotations
-    if newtype in ["rotmat"]:
-        rotations = rotations.reshape((*rotations.shape[:-2], 9))
-        return rotations
-    elif newtype in ["rot6d", "6drot", "rotation6d"]:
-        rotations = geometry_conver.matrix_to_rotation_6d(rotations)
-        return rotations
-    elif newtype in ["rotquat", "quaternion"]:
-        rotations = geometry_conver.matrix_to_quaternion(rotations)
-        return rotations
-    elif newtype in ["rotvec", "axisangle"]:
-        rotations = geometry_conver.matrix_to_axis_angle(rotations)
-        return rotations
-    else:
-        raise NotImplementedError
-
-
-def to_matrix(oldtype, rotations):
-    if oldtype in ["matrix"]:
-        return rotations
-    if oldtype in ["rotmat"]:
-        rotations = rotations.reshape((*rotations.shape[:-2], 3, 3))
-        return rotations
-    elif oldtype in ["rot6d", "6drot", "rotation6d"]:
-        rotations = geometry_conver.rotation_6d_to_matrix(rotations)
-        return rotations
-    elif oldtype in ["rotquat", "quaternion"]:
-        rotations = geometry_conver.quaternion_to_matrix(rotations)
-        return rotations
-    elif oldtype in ["rotvec", "axisangle"]:
-        rotations = geometry_conver.axis_angle_to_matrix(rotations)
-        return rotations
-    else:
-        raise NotImplementedError
-
-
-# TODO: use a real subsampler..
-def subsample(num_frames, last_framerate, new_framerate):
-    step = int(last_framerate / new_framerate)
-    assert step >= 1
-    frames = np.arange(0, num_frames, step)
-    return frames
-
-
-# TODO: use a real upsampler..
-def upsample(motion, last_framerate, new_framerate):
-    step = int(new_framerate / last_framerate)
-    assert step >= 1
-
-    # Alpha blending => interpolation
-    alpha = np.linspace(0, 1, step + 1)
-    last = np.einsum("l,...->l...", 1 - alpha, motion[:-1])
-    new = np.einsum("l,...->l...", alpha, motion[1:])
-
-    chuncks = (last + new)[:-1]
-    output = np.concatenate(chuncks.swapaxes(1, 0))
-    # Don't forget the last one
-    output = np.concatenate((output, motion[[-1]]))
-    return output
-
-
-if __name__ == "__main__":
-    motion = np.arange(105)
-    submotion = motion[subsample(len(motion), 100.0, 12.5)]
-    newmotion = upsample(submotion, 12.5, 100)
-
-    print(newmotion)
diff --git a/spaces/OptimalScale/Robin-33b/lmflow/models/interfaces/__init__.py b/spaces/OptimalScale/Robin-33b/lmflow/models/interfaces/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py b/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py
deleted file mode 100644
index 9b6d0aa078fba389ce2eafe1e43e5834df475ccb..0000000000000000000000000000000000000000
--- a/spaces/PAIR/PAIR-Diffusion/annotator/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py
+++ /dev/null
@@ -1,362 +0,0 @@
-# ------------------------------------------------------------------------------
-# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/msdeformattn.py
-# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
-# ------------------------------------------------------------------------------
-
-import logging
-import numpy as np
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import fvcore.nn.weight_init as weight_init
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
-from torch.cuda.amp import autocast
-
-from detectron2.config import configurable
-from detectron2.layers import Conv2d, ShapeSpec, get_norm
-from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
-
-from ..transformer_decoder.position_encoding import PositionEmbeddingSine
-from ..transformer_decoder.transformer import _get_clones, _get_activation_fn
-from .ops.modules import MSDeformAttn
-
-
-# MSDeformAttn Transformer encoder in deformable detr
-class MSDeformAttnTransformerEncoderOnly(nn.Module):
-    def __init__(self, d_model=256, nhead=8,
-                 num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
-                 activation="relu",
-                 num_feature_levels=4, enc_n_points=4,
-        ):
-        super().__init__()
-
-        self.d_model = d_model
-        self.nhead = nhead
-
-        encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,
-                                                            dropout, activation,
-                                                            num_feature_levels, nhead, enc_n_points)
-        self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)
-
-        self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
-
-        self._reset_parameters()
-
-    def _reset_parameters(self):
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-        for m in self.modules():
-            if isinstance(m, MSDeformAttn):
-                m._reset_parameters()
-        normal_(self.level_embed)
-
-    def get_valid_ratio(self, mask):
-        _, H, W = mask.shape
-        valid_H = torch.sum(~mask[:, :, 0], 1)
-        valid_W = torch.sum(~mask[:, 0, :], 1)
-        valid_ratio_h = valid_H.float() / H
-        valid_ratio_w = valid_W.float() / W
-        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
-        return valid_ratio
-
-    def forward(self, srcs, pos_embeds):
-        masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
-        # prepare input for encoder
-        src_flatten = []
-        mask_flatten = []
-        lvl_pos_embed_flatten = []
-        spatial_shapes = []
-        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
-            bs, c, h, w = src.shape
-            spatial_shape = (h, w)
-            spatial_shapes.append(spatial_shape)
-            src = src.flatten(2).transpose(1, 2)
-            mask = mask.flatten(1)
-            pos_embed = pos_embed.flatten(2).transpose(1, 2)
-            lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
-            lvl_pos_embed_flatten.append(lvl_pos_embed)
-            src_flatten.append(src)
-            mask_flatten.append(mask)
-        src_flatten = torch.cat(src_flatten, 1)
-        mask_flatten = torch.cat(mask_flatten, 1)
-        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
-        spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
-        level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
-        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
-
-        # encoder
-        memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
-
-        return memory, spatial_shapes, level_start_index, valid_ratios
-
-
-class MSDeformAttnTransformerEncoderLayer(nn.Module):
-    def __init__(self,
-                 d_model=256, d_ffn=1024,
-                 dropout=0.1, activation="relu",
-                 n_levels=4, n_heads=8, n_points=4):
-        super().__init__()
-
-        # self attention
-        self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
-        self.dropout1 = nn.Dropout(dropout)
-        self.norm1 = nn.LayerNorm(d_model)
-
-        # ffn
-        self.linear1 = nn.Linear(d_model, d_ffn)
-        self.activation = _get_activation_fn(activation)
-        self.dropout2 = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(d_ffn, d_model)
-        self.dropout3 = nn.Dropout(dropout)
-        self.norm2 = nn.LayerNorm(d_model)
-
-    @staticmethod
-    def with_pos_embed(tensor, pos):
-        return tensor if pos is None else tensor + pos
-
-    def forward_ffn(self, src):
-        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
-        src = src + self.dropout3(src2)
-        src = self.norm2(src)
-        return src
-
-    def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
-        # self attention
-        src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
-        src = src + self.dropout1(src2)
-        src = self.norm1(src)
-
-        # ffn
-        src = self.forward_ffn(src)
-
-        return src
-
-
-class MSDeformAttnTransformerEncoder(nn.Module):
-    def __init__(self, encoder_layer, num_layers):
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.num_layers = num_layers
-
-    @staticmethod
-    def get_reference_points(spatial_shapes, valid_ratios, device):
-        reference_points_list = []
-        for lvl, (H_, W_) in enumerate(spatial_shapes):
-
-            ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
-                                          torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
-            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
-            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
-            ref = torch.stack((ref_x, ref_y), -1)
-            reference_points_list.append(ref)
-        reference_points = torch.cat(reference_points_list, 1)
-        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
-        return reference_points
-
-    def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
-        output = src
-        reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
-        for _, layer in enumerate(self.layers):
-            output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
-
-        return output
-
-
-@SEM_SEG_HEADS_REGISTRY.register()
-class MSDeformAttnPixelDecoder(nn.Module):
-    @configurable
-    def __init__(
-        self,
-        input_shape: Dict[str, ShapeSpec],
-        *,
-        transformer_dropout: float,
-        transformer_nheads: int,
-        transformer_dim_feedforward: int,
-        transformer_enc_layers: int,
-        conv_dim: int,
-        mask_dim: int,
-        norm: Optional[Union[str, Callable]] = None,
-        # deformable transformer encoder args
-        transformer_in_features: List[str],
-        common_stride: int,
-    ):
-        """
-        NOTE: this interface is experimental.
-        Args:
-            input_shape: shapes (channels and stride) of the input features
-            transformer_dropout: dropout probability in transformer
-            transformer_nheads: number of heads in transformer
-            transformer_dim_feedforward: dimension of feedforward network
-            transformer_enc_layers: number of transformer encoder layers
-            conv_dims: number of output channels for the intermediate conv layers.
-            mask_dim: number of output channels for the final conv layer.
-            norm (str or callable): normalization for all conv layers
-        """
-        super().__init__()
-        transformer_input_shape = {
-            k: v for k, v in input_shape.items() if k in transformer_in_features
-        }
-
-        # this is the input shape of pixel decoder
-        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
-        self.in_features = [k for k, v in input_shape]  # starting from "res2" to "res5"
-        self.feature_strides = [v.stride for k, v in input_shape]
-        self.feature_channels = [v.channels for k, v in input_shape]
-        
-        # this is the input shape of transformer encoder (could use less features than pixel decoder
-        transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)
-        self.transformer_in_features = [k for k, v in transformer_input_shape]  # starting from "res2" to "res5"
-        transformer_in_channels = [v.channels for k, v in transformer_input_shape]
-        self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape]  # to decide extra FPN layers
-
-        self.transformer_num_feature_levels = len(self.transformer_in_features)
-        if self.transformer_num_feature_levels > 1:
-            input_proj_list = []
-            # from low resolution to high resolution (res5 -> res2)
-            for in_channels in transformer_in_channels[::-1]:
-                input_proj_list.append(nn.Sequential(
-                    nn.Conv2d(in_channels, conv_dim, kernel_size=1),
-                    nn.GroupNorm(32, conv_dim),
-                ))
-            self.input_proj = nn.ModuleList(input_proj_list)
-        else:
-            self.input_proj = nn.ModuleList([
-                nn.Sequential(
-                    nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
-                    nn.GroupNorm(32, conv_dim),
-                )])
-
-        for proj in self.input_proj:
-            nn.init.xavier_uniform_(proj[0].weight, gain=1)
-            nn.init.constant_(proj[0].bias, 0)
-
-        self.transformer = MSDeformAttnTransformerEncoderOnly(
-            d_model=conv_dim,
-            dropout=transformer_dropout,
-            nhead=transformer_nheads,
-            dim_feedforward=transformer_dim_feedforward,
-            num_encoder_layers=transformer_enc_layers,
-            num_feature_levels=self.transformer_num_feature_levels,
-        )
-        N_steps = conv_dim // 2
-        self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
-
-        self.mask_dim = mask_dim
-        # use 1x1 conv instead
-        self.mask_features = Conv2d(
-            conv_dim,
-            mask_dim,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-        )
-        weight_init.c2_xavier_fill(self.mask_features)
-        
-        self.oneformer_num_feature_levels = 3  # always use 3 scales
-        self.common_stride = common_stride
-
-        # extra fpn levels
-        stride = min(self.transformer_feature_strides)
-        self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
-
-        lateral_convs = []
-        output_convs = []
-
-        use_bias = norm == ""
-        for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):
-            lateral_norm = get_norm(norm, conv_dim)
-            output_norm = get_norm(norm, conv_dim)
-
-            lateral_conv = Conv2d(
-                in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
-            )
-            output_conv = Conv2d(
-                conv_dim,
-                conv_dim,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=use_bias,
-                norm=output_norm,
-                activation=F.relu,
-            )
-            weight_init.c2_xavier_fill(lateral_conv)
-            weight_init.c2_xavier_fill(output_conv)
-            self.add_module("adapter_{}".format(idx + 1), lateral_conv)
-            self.add_module("layer_{}".format(idx + 1), output_conv)
-
-            lateral_convs.append(lateral_conv)
-            output_convs.append(output_conv)
-        # Place convs into top-down order (from low to high resolution)
-        # to make the top-down computation in forward clearer.
-        self.lateral_convs = lateral_convs[::-1]
-        self.output_convs = output_convs[::-1]
-
-    @classmethod
-    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
-        ret = {}
-        ret["input_shape"] = {
-            k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
-        }
-        ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
-        ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
-        ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
-        ret["transformer_dropout"] = cfg.MODEL.ONE_FORMER.DROPOUT
-        ret["transformer_nheads"] = cfg.MODEL.ONE_FORMER.NHEADS
-        # ret["transformer_dim_feedforward"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD
-        ret["transformer_dim_feedforward"] = 1024  # use 1024 for deformable transformer encoder
-        ret[
-            "transformer_enc_layers"
-        ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS  # a separate config
-        ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES
-        ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE
-        return ret
-
-    @autocast(enabled=False)
-    def forward_features(self, features):
-        srcs = []
-        pos = []
-        # Reverse feature maps into top-down order (from low to high resolution)
-        for idx, f in enumerate(self.transformer_in_features[::-1]):
-            x = features[f].float()  # deformable detr does not support half precision
-            srcs.append(self.input_proj[idx](x))
-            pos.append(self.pe_layer(x))
-
-        y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos)
-        bs = y.shape[0]
-
-        split_size_or_sections = [None] * self.transformer_num_feature_levels
-        for i in range(self.transformer_num_feature_levels):
-            if i < self.transformer_num_feature_levels - 1:
-                split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
-            else:
-                split_size_or_sections[i] = y.shape[1] - level_start_index[i]
-        y = torch.split(y, split_size_or_sections, dim=1)
-
-        out = []
-        multi_scale_features = []
-        num_cur_levels = 0
-        for i, z in enumerate(y):
-            out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
-
-        # append `out` with extra FPN levels
-        # Reverse feature maps into top-down order (from low to high resolution)
-        for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):
-            x = features[f].float()
-            lateral_conv = self.lateral_convs[idx]
-            output_conv = self.output_convs[idx]
-            cur_fpn = lateral_conv(x)
-            # Following FPN implementation, we use nearest upsampling here
-            y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
-            y = output_conv(y)
-            out.append(y)
-
-        for o in out:
-            if num_cur_levels < self.oneformer_num_feature_levels:
-                multi_scale_features.append(o)
-                num_cur_levels += 1
-
-        return self.mask_features(out[-1]), out[0], multi_scale_features, spatial_shapes, level_start_index
diff --git a/spaces/PHZane/emrwa/cache/make_vocab.sh b/spaces/PHZane/emrwa/cache/make_vocab.sh
deleted file mode 100644
index ca9d73dec753025c5db18ecad3c043c3a4eb945a..0000000000000000000000000000000000000000
--- a/spaces/PHZane/emrwa/cache/make_vocab.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-python make_vocab.py \
-  --raw_data_path ../data/train.json \
-  --vocab_file vocab_user.txt \
-  --vocab_size 50000
\ No newline at end of file
diff --git a/spaces/PaddlePaddle/chinese-stable-diffusion/app.py b/spaces/PaddlePaddle/chinese-stable-diffusion/app.py
deleted file mode 100644
index 35c2e5d9c23d49c2db7746b7ffed21c4f425f2e5..0000000000000000000000000000000000000000
--- a/spaces/PaddlePaddle/chinese-stable-diffusion/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-import os
-import requests
-from uuid import uuid4
-from PIL import Image
-import gradio as gr
-
-
-def gen_image(prompt: str):
-    """generate the image from the chinese stable diffusion model of paddlenlp server
-
-    Args:
-        prompt (str): the source of the prompt
-    """
-    if not prompt:
-        return
-    access_token = os.environ['token']
-
-    url = f"https://aip.baidubce.com/rpc/2.0/nlp-itec/poc/chinese_stable_diffusion?access_token={access_token}"
-
-    content = requests.post(url, json={"text": prompt}).content
-    try:
-        new_content = content.decode(encoding='utf-8')
-
-        if new_content.startswith("error: "):
-            return []
-    except:
-        pass
-
-    cache_dir = 'images'
-    os.makedirs(cache_dir, exist_ok=True)
-
-    tempfile = os.path.join(cache_dir, f'{str(uuid4())}.png')
-    with open(tempfile, 'wb') as f:
-        f.write(content)
-    image = Image.open(tempfile)
-    os.remove(tempfile)
-    return [image]
-
-def read_content(file_path: str) -> str:
-    """read the content of target file
-    """
-    with open(file_path, 'r', encoding='utf-8') as f:
-        content = f.read()
-
-    return content
-
-block = gr.Blocks(css=read_content('style.css'))
-
-examples = [
-    '枯藤老树昏鸦，水墨画',
-    '小桥流水人家，水墨画',
-    '古道西风瘦马，水墨画',
-    '夕阳西下，水墨画',
-    '断肠人在天涯，水墨画',
-    '白日依山尽，水墨画',
-    '黄河入海流，水墨画',
-    '欲穷千里目，水墨画',
-    '更上一层楼，水墨画',
-]
-
-
-with block:
-    gr.HTML(read_content("header.html"))
-    gr.Markdown("> 非常抱歉，由于一些原因，此Space临时停止服务，等国庆之后会重新发布。", elem_id='warning', visible=True)
-    gr.Markdown("[![Stargazers repo roster for @PaddlePaddle/PaddleNLP](https://reporoster.com/stars/PaddlePaddle/PaddleNLP)](https://github.com/PaddlePaddle/PaddleNLP)")
-    with gr.Group():
-        with gr.Box():
-            with gr.Row().style(mobile_collapse=False, equal_height=True):
-                text = gr.Textbox(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="输入中文，生成图片",
-                ).style(
-                    border=(True, False, True, True),
-                    rounded=(True, False, False, True),
-                    container=False,
-                )
-
-                btn = gr.Button("Generate image").style(
-                    margin=False,
-                    rounded=(False, True, True, False),
-                )
-
-        gallery = gr.Gallery(
-            label="Generated images", show_label=False, elem_id="gallery"
-        ).style(grid=[1, 1], height="auto")
-
-        gr.Examples(examples=examples, fn=gen_image, inputs=text, outputs=gallery)
-        # text.submit(gen_image, inputs=text, outputs=gallery)
-        # btn.click(gen_image, inputs=text, outputs=gallery)
-
-    gr.Image('./paddlenlp-preview.jpeg')
-    gr.HTML(read_content("footer.html"))
-
-block.queue(concurrency_count=5).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/rnrs/base.go b/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/rnrs/base.go
deleted file mode 100644
index 3c578c35f139ded47540d79206ab034ffe9c9878..0000000000000000000000000000000000000000
Binary files a/spaces/Pattr/DrumClassification/lilypond-2.24.2/lib/guile/2.2/ccache/rnrs/base.go and /dev/null differ
diff --git a/spaces/PeepDaSlan9/AutoGPT/autogpt/speech/gtts.py b/spaces/PeepDaSlan9/AutoGPT/autogpt/speech/gtts.py
deleted file mode 100644
index 1c3e9cae0567428582891b11eca42f82a64f5c8e..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/AutoGPT/autogpt/speech/gtts.py
+++ /dev/null
@@ -1,22 +0,0 @@
-""" GTTS Voice. """
-import os
-
-import gtts
-from playsound import playsound
-
-from autogpt.speech.base import VoiceBase
-
-
-class GTTSVoice(VoiceBase):
-    """GTTS Voice."""
-
-    def _setup(self) -> None:
-        pass
-
-    def _speech(self, text: str, _: int = 0) -> bool:
-        """Play the given text."""
-        tts = gtts.gTTS(text)
-        tts.save("speech.mp3")
-        playsound("speech.mp3", True)
-        os.remove("speech.mp3")
-        return True
diff --git a/spaces/PeepDaSlan9/B2B-APG/README.md b/spaces/PeepDaSlan9/B2B-APG/README.md
deleted file mode 100644
index db13b8043ba62af154f8bee3628c3864f8cebfae..0000000000000000000000000000000000000000
--- a/spaces/PeepDaSlan9/B2B-APG/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: B2B APG
-emoji: 🚀
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 3.17.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Pie31415/control-animation/text_to_animation/models/unet_2d_condition_flax.py b/spaces/Pie31415/control-animation/text_to_animation/models/unet_2d_condition_flax.py
deleted file mode 100644
index cf2efcf7ef2bd32812154d3cdf3f73a3ab26113f..0000000000000000000000000000000000000000
--- a/spaces/Pie31415/control-animation/text_to_animation/models/unet_2d_condition_flax.py
+++ /dev/null
@@ -1,662 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# CUSTOM VERSION OF https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition_flax.py
-
-from typing import Tuple, Union
-
-import flax
-import flax.linen as nn
-import jax
-import jax.numpy as jnp
-from flax.core.frozen_dict import FrozenDict
-
-from diffusers.configuration_utils import ConfigMixin, flax_register_to_config
-from diffusers.utils import BaseOutput
-from diffusers.models.embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
-from diffusers.models.modeling_flax_utils import FlaxModelMixin
-from .unet_2d_blocks_flax import (
-    FlaxCrossAttnDownBlock2D,
-    FlaxCrossAttnUpBlock2D,
-    FlaxUNetCrossAttnMidBlock2D,
-    FlaxLoRACrossAttnDownBlock2D,
-    FlaxLoRACrossAttnUpBlock2D,
-    FlaxLoRAUNetCrossAttnMidBlock2D,
-    FlaxDownBlock2D,
-    FlaxUpBlock2D,
-)
-
-@flax.struct.dataclass
-class FlaxUNet2DConditionOutput(BaseOutput):
-    """
-    Args:
-        sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)`):
-            Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model.
-    """
-
-    sample: jnp.ndarray
-
-
-@flax_register_to_config
-class FlaxUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
-    r"""
-    FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
-    timestep and returns sample shaped output.
-
-    This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the models (such as downloading or saving, etc.)
-
-    Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
-    general usage and behavior.
-
-    Finally, this model supports inherent JAX features such as:
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        sample_size (`int`, *optional*):
-            The size of the input sample.
-        in_channels (`int`, *optional*, defaults to 4):
-            The number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4):
-            The number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
-            The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D",
-            "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D"
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
-            The tuple of upsample blocks to use. The corresponding class names will be: "FlaxUpBlock2D",
-            "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D"
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2):
-            The number of layers per block.
-        attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
-            The dimension of the attention heads.
-        cross_attention_dim (`int`, *optional*, defaults to 768):
-            The dimension of the cross attention features.
-        dropout (`float`, *optional*, defaults to 0):
-            Dropout probability for down, up and bottleneck blocks.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
-            enable memory efficient attention https://arxiv.org/abs/2112.05682
-
-    """
-
-    sample_size: int = 32
-    in_channels: int = 4
-    out_channels: int = 4
-    down_block_types: Tuple[str] = (
-        "CrossAttnDownBlock2D",
-        "CrossAttnDownBlock2D",
-        "CrossAttnDownBlock2D",
-        "DownBlock2D",
-    )
-    up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
-    only_cross_attention: Union[bool, Tuple[bool]] = False
-    block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
-    layers_per_block: int = 2
-    attention_head_dim: Union[int, Tuple[int]] = 8
-    cross_attention_dim: int = 1280
-    dropout: float = 0.0
-    use_linear_projection: bool = False
-    dtype: jnp.dtype = jnp.float16
-    flip_sin_to_cos: bool = True
-    freq_shift: int = 0
-    use_memory_efficient_attention: bool = False
-
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
-        # init input tensors
-        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
-        sample = jnp.zeros(sample_shape, dtype=self.dtype)
-        timesteps = jnp.ones((1,), dtype=jnp.int32)
-        encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=self.dtype)
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"]
-
-    def setup(self):
-        block_out_channels = self.block_out_channels
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.conv_in = nn.Conv(
-            block_out_channels[0],
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        # time
-        self.time_proj = FlaxTimesteps(
-            block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
-        )
-        self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
-
-        only_cross_attention = self.only_cross_attention
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = (only_cross_attention,) * len(self.down_block_types)
-
-        attention_head_dim = self.attention_head_dim
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(self.down_block_types)
-
-        # down
-        down_blocks = []
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(self.down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            if down_block_type == "CrossAttnDownBlock2D":
-                down_block = FlaxCrossAttnDownBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    dropout=self.dropout,
-                    num_layers=self.layers_per_block,
-                    attn_num_head_channels=attention_head_dim[i],
-                    add_downsample=not is_final_block,
-                    use_linear_projection=self.use_linear_projection,
-                    only_cross_attention=only_cross_attention[i],
-                    use_memory_efficient_attention=self.use_memory_efficient_attention,
-                    dtype=self.dtype,
-                )
-            else:
-                down_block = FlaxDownBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    dropout=self.dropout,
-                    num_layers=self.layers_per_block,
-                    add_downsample=not is_final_block,
-                    dtype=self.dtype,
-                )
-
-            down_blocks.append(down_block)
-        self.down_blocks = down_blocks
-
-        # mid
-        self.mid_block = FlaxUNetCrossAttnMidBlock2D(
-            in_channels=block_out_channels[-1],
-            dropout=self.dropout,
-            attn_num_head_channels=attention_head_dim[-1],
-            use_linear_projection=self.use_linear_projection,
-            use_memory_efficient_attention=self.use_memory_efficient_attention,
-            dtype=self.dtype,
-        )
-
-        # up
-        up_blocks = []
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_attention_head_dim = list(reversed(attention_head_dim))
-        only_cross_attention = list(reversed(only_cross_attention))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(self.up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            if up_block_type == "CrossAttnUpBlock2D":
-                up_block = FlaxCrossAttnUpBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    prev_output_channel=prev_output_channel,
-                    num_layers=self.layers_per_block + 1,
-                    attn_num_head_channels=reversed_attention_head_dim[i],
-                    add_upsample=not is_final_block,
-                    dropout=self.dropout,
-                    use_linear_projection=self.use_linear_projection,
-                    only_cross_attention=only_cross_attention[i],
-                    use_memory_efficient_attention=self.use_memory_efficient_attention,
-                    dtype=self.dtype,
-                )
-            else:
-                up_block = FlaxUpBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    prev_output_channel=prev_output_channel,
-                    num_layers=self.layers_per_block + 1,
-                    add_upsample=not is_final_block,
-                    dropout=self.dropout,
-                    dtype=self.dtype,
-                )
-
-            up_blocks.append(up_block)
-            prev_output_channel = output_channel
-        self.up_blocks = up_blocks
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5)
-        self.conv_out = nn.Conv(
-            self.out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-    def __call__(
-        self,
-        sample,
-        timesteps,
-        encoder_hidden_states,
-        down_block_additional_residuals=None,
-        mid_block_additional_residual=None,
-        return_dict: bool = True,
-        train: bool = False,
-    ) -> Union[FlaxUNet2DConditionOutput, Tuple]:
-        r"""
-        Args:
-            sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`jnp.ndarray` or `float` or `int`): timesteps
-            encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a
-                plain tuple.
-            train (`bool`, *optional*, defaults to `False`):
-                Use deterministic functions and disable dropout when not training.
-
-        Returns:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`.
-            When returning a tuple, the first element is the sample tensor.
-        """
-        # 1. time
-        if not isinstance(timesteps, jnp.ndarray):
-            timesteps = jnp.array([timesteps], dtype=jnp.int32)
-        elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:
-            timesteps = timesteps.astype(dtype=jnp.float32)
-            timesteps = jnp.expand_dims(timesteps, 0)
-
-        t_emb = self.time_proj(timesteps)
-        t_emb = self.time_embedding(t_emb)
-
-        # 2. pre-process
-        sample = jnp.transpose(sample, (0, 2, 3, 1))
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for down_block in self.down_blocks:
-            if isinstance(down_block, FlaxCrossAttnDownBlock2D):
-                sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
-            else:
-                sample, res_samples = down_block(sample, t_emb, deterministic=not train)
-            down_block_res_samples += res_samples
-
-        if down_block_additional_residuals is not None:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample += down_block_additional_residual
-                new_down_block_res_samples += (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # 4. mid
-        sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train)
-
-        if mid_block_additional_residual is not None:
-            sample += mid_block_additional_residual
-
-        # 5. up
-        for up_block in self.up_blocks:
-            res_samples = down_block_res_samples[-(self.layers_per_block + 1) :]
-            down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)]
-            if isinstance(up_block, FlaxCrossAttnUpBlock2D):
-                sample = up_block(
-                    sample,
-                    temb=t_emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    res_hidden_states_tuple=res_samples,
-                    deterministic=not train,
-                )
-            else:
-                sample = up_block(sample, temb=t_emb, res_hidden_states_tuple=res_samples, deterministic=not train)
-
-        # 6. post-process
-        sample = self.conv_norm_out(sample)
-        sample = nn.silu(sample)
-        sample = self.conv_out(sample)
-        sample = jnp.transpose(sample, (0, 3, 1, 2))
-
-        if not return_dict:
-            return (sample,)
-
-        return FlaxUNet2DConditionOutput(sample=sample)
-
-@flax_register_to_config
-class FlaxLoRAUNet2DConditionModel(nn.Module, FlaxModelMixin, ConfigMixin):
-    r"""
-
-    FlaxLoRAUNet2DConditionModel is a custom FlaxUNet2DConditionModel with a few tweaks:
-    - Cross Attention is replaced by Cross-Frame Attention
-    - Low Rank Adaptation (LoRA) layers are added to the Cross-Frame Attention
-    - An frame positional encoding is added to the encoder_hidden_states via a LoRA linear layer
-
-    FlaxUNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a
-    timestep and returns sample shaped output.
-
-    This model inherits from [`FlaxModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the models (such as downloading or saving, etc.)
-
-    Also, this model is a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
-    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
-    general usage and behavior.
-
-    Finally, this model supports inherent JAX features such as:
-    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
-    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
-    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
-    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
-
-    Parameters:
-        sample_size (`int`, *optional*):
-            The size of the input sample.
-        in_channels (`int`, *optional*, defaults to 4):
-            The number of channels in the input sample.
-        out_channels (`int`, *optional*, defaults to 4):
-            The number of channels in the output.
-        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
-            The tuple of downsample blocks to use. The corresponding class names will be: "FlaxCrossAttnDownBlock2D",
-            "FlaxCrossAttnDownBlock2D", "FlaxCrossAttnDownBlock2D", "FlaxDownBlock2D"
-        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`):
-            The tuple of upsample blocks to use. The corresponding class names will be: "FlaxUpBlock2D",
-            "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D", "FlaxCrossAttnUpBlock2D"
-        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
-            The tuple of output channels for each block.
-        layers_per_block (`int`, *optional*, defaults to 2):
-            The number of layers per block.
-        attention_head_dim (`int` or `Tuple[int]`, *optional*, defaults to 8):
-            The dimension of the attention heads.
-        cross_attention_dim (`int`, *optional*, defaults to 768):
-            The dimension of the cross attention features.
-        dropout (`float`, *optional*, defaults to 0):
-            Dropout probability for down, up and bottleneck blocks.
-        flip_sin_to_cos (`bool`, *optional*, defaults to `True`):
-            Whether to flip the sin to cos in the time embedding.
-        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
-        use_memory_efficient_attention (`bool`, *optional*, defaults to `False`):
-            enable memory efficient attention https://arxiv.org/abs/2112.05682
-
-    """
-
-    sample_size: int = 32
-    in_channels: int = 4
-    out_channels: int = 4
-    down_block_types: Tuple[str] = (
-        "CrossAttnDownBlock2D",
-        "CrossAttnDownBlock2D",
-        "CrossAttnDownBlock2D",
-        "DownBlock2D",
-    )
-    up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
-    only_cross_attention: Union[bool, Tuple[bool]] = False
-    block_out_channels: Tuple[int] = (320, 640, 1280, 1280)
-    layers_per_block: int = 2
-    attention_head_dim: Union[int, Tuple[int]] = 8
-    cross_attention_dim: int = 1280
-    dropout: float = 0.0
-    use_linear_projection: bool = False
-    dtype: jnp.dtype = jnp.float16
-    flip_sin_to_cos: bool = True
-    freq_shift: int = 0
-    use_memory_efficient_attention: bool = False
-
-    def init_weights(self, rng: jax.random.KeyArray) -> FrozenDict:
-        # init input tensors
-        sample_shape = (1, self.in_channels, self.sample_size, self.sample_size)
-        sample = jnp.zeros(sample_shape, dtype=self.dtype)
-        timesteps = jnp.ones((1,), dtype=jnp.int32)
-        encoder_hidden_states = jnp.zeros((1, 1, self.cross_attention_dim), dtype=self.dtype)
-
-        params_rng, dropout_rng = jax.random.split(rng)
-        rngs = {"params": params_rng, "dropout": dropout_rng}
-
-        return self.init(rngs, sample, timesteps, encoder_hidden_states)["params"]
-
-    def setup(self):
-        block_out_channels = self.block_out_channels
-        time_embed_dim = block_out_channels[0] * 4
-        
-        # input
-        self.conv_in = nn.Conv(
-            block_out_channels[0],
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-        # time
-        self.time_proj = FlaxTimesteps(
-            block_out_channels[0], flip_sin_to_cos=self.flip_sin_to_cos, freq_shift=self.config.freq_shift
-        )
-        self.time_embedding = FlaxTimestepEmbedding(time_embed_dim, dtype=self.dtype)
-
-        only_cross_attention = self.only_cross_attention
-        if isinstance(only_cross_attention, bool):
-            only_cross_attention = (only_cross_attention,) * len(self.down_block_types)
-
-        attention_head_dim = self.attention_head_dim
-        if isinstance(attention_head_dim, int):
-            attention_head_dim = (attention_head_dim,) * len(self.down_block_types)
-
-        # #frame positional embedding
-        # self.frame_pe = LoRAPositionalEncoding(self.cross_attention_dim)
-
-        # down
-        down_blocks = []
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(self.down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            if down_block_type == "CrossAttnDownBlock2D":
-                down_block = FlaxLoRACrossAttnDownBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    dropout=self.dropout,
-                    num_layers=self.layers_per_block,
-                    attn_num_head_channels=attention_head_dim[i],
-                    add_downsample=not is_final_block,
-                    use_linear_projection=self.use_linear_projection,
-                    only_cross_attention=only_cross_attention[i],
-                    use_memory_efficient_attention=self.use_memory_efficient_attention,
-                    dtype=self.dtype,
-                )
-            else:
-                down_block = FlaxDownBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    dropout=self.dropout,
-                    num_layers=self.layers_per_block,
-                    add_downsample=not is_final_block,
-                    dtype=self.dtype,
-                )
-
-            down_blocks.append(down_block)
-        self.down_blocks = down_blocks
-
-        # mid
-        self.mid_block = FlaxLoRAUNetCrossAttnMidBlock2D(
-            in_channels=block_out_channels[-1],
-            dropout=self.dropout,
-            attn_num_head_channels=attention_head_dim[-1],
-            use_linear_projection=self.use_linear_projection,
-            use_memory_efficient_attention=self.use_memory_efficient_attention,
-            dtype=self.dtype,
-        )
-
-        # up
-        up_blocks = []
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        reversed_attention_head_dim = list(reversed(attention_head_dim))
-        only_cross_attention = list(reversed(only_cross_attention))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(self.up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            if up_block_type == "CrossAttnUpBlock2D":
-                up_block = FlaxLoRACrossAttnUpBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    prev_output_channel=prev_output_channel,
-                    num_layers=self.layers_per_block + 1,
-                    attn_num_head_channels=reversed_attention_head_dim[i],
-                    add_upsample=not is_final_block,
-                    dropout=self.dropout,
-                    use_linear_projection=self.use_linear_projection,
-                    only_cross_attention=only_cross_attention[i],
-                    use_memory_efficient_attention=self.use_memory_efficient_attention,
-                    dtype=self.dtype,
-                )
-            else:
-                up_block = FlaxUpBlock2D(
-                    in_channels=input_channel,
-                    out_channels=output_channel,
-                    prev_output_channel=prev_output_channel,
-                    num_layers=self.layers_per_block + 1,
-                    add_upsample=not is_final_block,
-                    dropout=self.dropout,
-                    dtype=self.dtype,
-                )
-
-            up_blocks.append(up_block)
-            prev_output_channel = output_channel
-        self.up_blocks = up_blocks
-
-        # out
-        self.conv_norm_out = nn.GroupNorm(num_groups=32, epsilon=1e-5)
-        self.conv_out = nn.Conv(
-            self.out_channels,
-            kernel_size=(3, 3),
-            strides=(1, 1),
-            padding=((1, 1), (1, 1)),
-            dtype=self.dtype,
-        )
-
-    def __call__(
-        self,
-        sample,
-        timesteps,
-        encoder_hidden_states,
-        down_block_additional_residuals=None,
-        mid_block_additional_residual=None,
-        return_dict: bool = True,
-        train: bool = False,
-        scale: float = 1.,
-    ) -> Union[FlaxUNet2DConditionOutput, Tuple]:
-        r"""
-        Args:
-            sample (`jnp.ndarray`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`jnp.ndarray` or `float` or `int`): timesteps
-            encoder_hidden_states (`jnp.ndarray`): (batch_size, sequence_length, hidden_size) encoder hidden states
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] instead of a
-                plain tuple.
-            train (`bool`, *optional*, defaults to `False`):
-                Use deterministic functions and disable dropout when not training.
-
-        Returns:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] or `tuple`:
-            [`~models.unet_2d_condition_flax.FlaxUNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`.
-            When returning a tuple, the first element is the sample tensor.
-        """
-        # 1. time
-        if not isinstance(timesteps, jnp.ndarray):
-            timesteps = jnp.array([timesteps], dtype=jnp.int32)
-        elif isinstance(timesteps, jnp.ndarray) and len(timesteps.shape) == 0:
-            timesteps = timesteps.astype(dtype=jnp.float32)
-            timesteps = jnp.expand_dims(timesteps, 0)
-
-        t_emb = self.time_proj(timesteps)
-        t_emb = self.time_embedding(t_emb)
-
-        # 2. pre-process
-        sample = jnp.transpose(sample, (0, 2, 3, 1))
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for down_block in self.down_blocks:
-            if isinstance(down_block, FlaxLoRACrossAttnDownBlock2D):
-                sample, res_samples = down_block(sample, t_emb, encoder_hidden_states, deterministic=not train, scale=scale)
-            else:
-                sample, res_samples = down_block(sample, t_emb, deterministic=not train)
-            down_block_res_samples += res_samples
-
-        if down_block_additional_residuals is not None:
-            new_down_block_res_samples = ()
-
-            for down_block_res_sample, down_block_additional_residual in zip(
-                down_block_res_samples, down_block_additional_residuals
-            ):
-                down_block_res_sample += down_block_additional_residual
-                new_down_block_res_samples += (down_block_res_sample,)
-
-            down_block_res_samples = new_down_block_res_samples
-
-        # if encoder_hidden_states is not None:
-        #     #adding frame positional encoding
-        #     encoder_hidden_states = self.frame_pe(encoder_hidden_states, scale=scale)
-
-        # 4. mid
-        sample = self.mid_block(sample, t_emb, encoder_hidden_states, deterministic=not train, scale=scale)
-
-        if mid_block_additional_residual is not None:
-            sample += mid_block_additional_residual
-
-        # 5. up
-        for up_block in self.up_blocks:
-            res_samples = down_block_res_samples[-(self.layers_per_block + 1) :]
-            down_block_res_samples = down_block_res_samples[: -(self.layers_per_block + 1)]
-            if isinstance(up_block, FlaxLoRACrossAttnUpBlock2D):
-                sample = up_block(
-                    sample,
-                    temb=t_emb,
-                    encoder_hidden_states=encoder_hidden_states,
-                    res_hidden_states_tuple=res_samples,
-                    deterministic=not train,
-                    scale=scale,
-                )
-            else:
-                sample = up_block(sample, temb=t_emb, res_hidden_states_tuple=res_samples, deterministic=not train)
-
-        # 6. post-process
-        sample = self.conv_norm_out(sample)
-        sample = nn.silu(sample)
-        sample = self.conv_out(sample)
-        sample = jnp.transpose(sample, (0, 3, 1, 2))
-
-        if not return_dict:
-            return (sample,)
-
-        return FlaxUNet2DConditionOutput(sample=sample)
\ No newline at end of file
diff --git a/spaces/Qiukai/gpt/toolbox.py b/spaces/Qiukai/gpt/toolbox.py
deleted file mode 100644
index 3ced6534fd5779b6056a545708f0ca36b6c9afe2..0000000000000000000000000000000000000000
--- a/spaces/Qiukai/gpt/toolbox.py
+++ /dev/null
@@ -1,529 +0,0 @@
-import markdown
-import mdtex2html
-import threading
-import importlib
-import traceback
-import inspect
-import re
-from latex2mathml.converter import convert as tex2mathml
-from functools import wraps, lru_cache
-
-############################### 插件输入输出接驳区 #######################################
-class ChatBotWithCookies(list):
-    def __init__(self, cookie):
-        self._cookies = cookie
-
-    def write_list(self, list):
-        for t in list:
-            self.append(t)
-
-    def get_list(self):
-        return [t for t in self]
-
-    def get_cookies(self):
-        return self._cookies
-
-def ArgsGeneralWrapper(f):
-    """
-        装饰器函数，用于重组输入参数，改变输入参数的顺序与结构。
-    """
-    def decorated(cookies, txt, txt2, top_p, temperature, chatbot, history, system_prompt, *args):
-        txt_passon = txt
-        if txt == "" and txt2 != "": txt_passon = txt2
-        # 引入一个有cookie的chatbot
-        cookies.update({
-            'top_p':top_p, 
-            'temperature':temperature,
-        })
-        llm_kwargs = {
-            'api_key': cookies['api_key'],
-            'llm_model': cookies['llm_model'],
-            'top_p':top_p, 
-            'temperature':temperature,
-        }
-        plugin_kwargs = {
-            # 目前还没有
-        }
-        chatbot_with_cookie = ChatBotWithCookies(cookies)
-        chatbot_with_cookie.write_list(chatbot)
-        yield from f(txt_passon, llm_kwargs, plugin_kwargs, chatbot_with_cookie, history, system_prompt, *args)
-    return decorated
-
-def update_ui(chatbot, history, msg='正常', **kwargs):  # 刷新界面
-    """
-    刷新用户界面
-    """
-    assert isinstance(chatbot, ChatBotWithCookies), "在传递chatbot的过程中不要将其丢弃。必要时，可用clear将其清空，然后用for+append循环重新赋值。"
-    yield chatbot.get_cookies(), chatbot, history, msg
-############################### ################## #######################################
-##########################################################################################
-
-def get_reduce_token_percent(text):
-    """
-        * 此函数未来将被弃用
-    """
-    try:
-        # text = "maximum context length is 4097 tokens. However, your messages resulted in 4870 tokens"
-        pattern = r"(\d+)\s+tokens\b"
-        match = re.findall(pattern, text)
-        EXCEED_ALLO = 500  # 稍微留一点余地，否则在回复时会因余量太少出问题
-        max_limit = float(match[0]) - EXCEED_ALLO
-        current_tokens = float(match[1])
-        ratio = max_limit/current_tokens
-        assert ratio > 0 and ratio < 1
-        return ratio, str(int(current_tokens-max_limit))
-    except:
-        return 0.5, '不详'
-
-def predict_no_ui_but_counting_down(i_say, i_say_show_user, chatbot, llm_kwargs, history=[], sys_prompt='', long_connection=True):
-    """
-        * 此函数未来将被弃用（替代函数 request_gpt_model_in_new_thread_with_ui_alive 文件 chatgpt_academic/crazy_functions/crazy_utils）
-
-        调用简单的predict_no_ui接口，但是依然保留了些许界面心跳功能，当对话太长时，会自动采用二分法截断
-        i_say: 当前输入
-        i_say_show_user: 显示到对话界面上的当前输入，例如，输入整个文件时，你绝对不想把文件的内容都糊到对话界面上
-        chatbot: 对话界面句柄
-        top_p, temperature: gpt参数
-        history: gpt参数 对话历史
-        sys_prompt: gpt参数 sys_prompt
-        long_connection: 是否采用更稳定的连接方式（推荐）（已弃用）
-    """
-    import time
-    from request_llm.bridge_chatgpt import predict_no_ui_long_connection
-    from toolbox import get_conf
-    TIMEOUT_SECONDS, MAX_RETRY = get_conf('TIMEOUT_SECONDS', 'MAX_RETRY')
-    # 多线程的时候，需要一个mutable结构在不同线程之间传递信息
-    # list就是最简单的mutable结构，我们第一个位置放gpt输出，第二个位置传递报错信息
-    mutable = [None, '']
-    # multi-threading worker
-
-    def mt(i_say, history):
-        while True:
-            try:
-                mutable[0] = predict_no_ui_long_connection(
-                    inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt)
-
-            except ConnectionAbortedError as token_exceeded_error:
-                # 尝试计算比例，尽可能多地保留文本
-                p_ratio, n_exceed = get_reduce_token_percent(
-                    str(token_exceeded_error))
-                if len(history) > 0:
-                    history = [his[int(len(his) * p_ratio):]
-                               for his in history if his is not None]
-                else:
-                    i_say = i_say[:     int(len(i_say) * p_ratio)]
-                mutable[1] = f'警告，文本过长将进行截断，Token溢出数：{n_exceed}，截断比例：{(1-p_ratio):.0%}。'
-            except TimeoutError as e:
-                mutable[0] = '[Local Message] 请求超时。'
-                raise TimeoutError
-            except Exception as e:
-                mutable[0] = f'[Local Message] 异常：{str(e)}.'
-                raise RuntimeError(f'[Local Message] 异常：{str(e)}.')
-    # 创建新线程发出http请求
-    thread_name = threading.Thread(target=mt, args=(i_say, history))
-    thread_name.start()
-    # 原来的线程则负责持续更新UI，实现一个超时倒计时，并等待新线程的任务完成
-    cnt = 0
-    while thread_name.is_alive():
-        cnt += 1
-        chatbot[-1] = (i_say_show_user,
-                       f"[Local Message] {mutable[1]}waiting gpt response {cnt}/{TIMEOUT_SECONDS*2*(MAX_RETRY+1)}"+''.join(['.']*(cnt % 4)))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        time.sleep(1)
-    # 把gpt的输出从mutable中取出来
-    gpt_say = mutable[0]
-    if gpt_say == '[Local Message] Failed with timeout.':
-        raise TimeoutError
-    return gpt_say
-
-
-def write_results_to_file(history, file_name=None):
-    """
-        将对话记录history以Markdown格式写入文件中。如果没有指定文件名，则使用当前时间生成文件名。
-    """
-    import os
-    import time
-    if file_name is None:
-        # file_name = time.strftime("chatGPT分析报告%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-        file_name = 'chatGPT分析报告' + \
-            time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + '.md'
-    os.makedirs('./gpt_log/', exist_ok=True)
-    with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
-        f.write('# chatGPT 分析报告\n')
-        for i, content in enumerate(history):
-            try:    # 这个bug没找到触发条件，暂时先这样顶一下
-                if type(content) != str:
-                    content = str(content)
-            except:
-                continue
-            if i % 2 == 0:
-                f.write('## ')
-            f.write(content)
-            f.write('\n\n')
-    res = '以上材料已经被写入' + os.path.abspath(f'./gpt_log/{file_name}')
-    print(res)
-    return res
-
-
-def regular_txt_to_markdown(text):
-    """
-        将普通文本转换为Markdown格式的文本。
-    """
-    text = text.replace('\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    text = text.replace('\n\n\n', '\n\n')
-    return text
-
-
-def CatchException(f):
-    """
-        装饰器函数，捕捉函数f中的异常并封装到一个生成器中返回，并显示到聊天当中。
-    """
-    @wraps(f)
-    def decorated(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT):
-        try:
-            yield from f(txt, top_p, temperature, chatbot, history, systemPromptTxt, WEB_PORT)
-        except Exception as e:
-            from check_proxy import check_proxy
-            from toolbox import get_conf
-            proxies, = get_conf('proxies')
-            tb_str = '```\n' + traceback.format_exc() + '```'
-            if chatbot is None or len(chatbot) == 0:
-                chatbot = [["插件调度异常", "异常原因"]]
-            chatbot[-1] = (chatbot[-1][0],
-                           f"[Local Message] 实验性函数调用出错: \n\n{tb_str} \n\n当前代理可用性: \n\n{check_proxy(proxies)}")
-            yield from update_ui(chatbot=chatbot, history=history, msg=f'异常 {e}') # 刷新界面
-    return decorated
-
-
-def HotReload(f):
-    """
-    HotReload的装饰器函数，用于实现Python函数插件的热更新。
-    函数热更新是指在不停止程序运行的情况下，更新函数代码，从而达到实时更新功能。
-    在装饰器内部，使用wraps(f)来保留函数的元信息，并定义了一个名为decorated的内部函数。
-    内部函数通过使用importlib模块的reload函数和inspect模块的getmodule函数来重新加载并获取函数模块，
-    然后通过getattr函数获取函数名，并在新模块中重新加载函数。
-    最后，使用yield from语句返回重新加载过的函数，并在被装饰的函数上执行。
-    最终，装饰器函数返回内部函数。这个内部函数可以将函数的原始定义更新为最新版本，并执行函数的新版本。
-    """
-    @wraps(f)
-    def decorated(*args, **kwargs):
-        fn_name = f.__name__
-        f_hot_reload = getattr(importlib.reload(inspect.getmodule(f)), fn_name)
-        yield from f_hot_reload(*args, **kwargs)
-    return decorated
-
-
-def report_execption(chatbot, history, a, b):
-    """
-        向chatbot中添加错误信息
-    """
-    chatbot.append((a, b))
-    history.append(a)
-    history.append(b)
-
-
-def text_divide_paragraph(text):
-    """
-        将文本按照段落分隔符分割开，生成带有段落标签的HTML代码。
-    """
-    if '```' in text:
-        # careful input
-        return text
-    else:
-        # wtf input
-        lines = text.split("\n")
-        for i, line in enumerate(lines):
-            lines[i] = lines[i].replace(" ", "&nbsp;")
-        text = "</br>".join(lines)
-        return text
-
-
-def markdown_convertion(txt):
-    """
-        将Markdown格式的文本转换为HTML格式。如果包含数学公式，则先将公式转换为HTML格式。
-    """
-    pre = '<div class="markdown-body">'
-    suf = '</div>'
-    markdown_extension_configs = {
-        'mdx_math': {
-            'enable_dollar_delimiter': True,
-            'use_gitlab_delimiters': False,
-        },
-    }
-    find_equation_pattern = r'<script type="math/tex(?:.*?)>(.*?)</script>'
-
-    def tex2mathml_catch_exception(content, *args, **kwargs):
-        try:
-            content = tex2mathml(content, *args, **kwargs)
-        except:
-            content = content
-        return content
-
-    def replace_math_no_render(match):
-        content = match.group(1)
-        if 'mode=display' in match.group(0):
-            content = content.replace('\n', '</br>')
-            return f"<font color=\"#00FF00\">$$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$$</font>"
-        else:
-            return f"<font color=\"#00FF00\">$</font><font color=\"#FF00FF\">{content}</font><font color=\"#00FF00\">$</font>"
-
-    def replace_math_render(match):
-        content = match.group(1)
-        if 'mode=display' in match.group(0):
-            if '\\begin{aligned}' in content:
-                content = content.replace('\\begin{aligned}', '\\begin{array}')
-                content = content.replace('\\end{aligned}', '\\end{array}')
-                content = content.replace('&', ' ')
-            content = tex2mathml_catch_exception(content, display="block")
-            return content
-        else:
-            return tex2mathml_catch_exception(content)
-        
-    def markdown_bug_hunt(content):
-        """
-        解决一个mdx_math的bug（单$包裹begin命令时多余<script>）
-        """
-        content = content.replace('<script type="math/tex">\n<script type="math/tex; mode=display">', '<script type="math/tex; mode=display">')
-        content = content.replace('</script>\n</script>', '</script>')
-        return content
-    
-
-    if ('$' in txt) and ('```' not in txt):  # 有$标识的公式符号，且没有代码段```的标识
-        # convert everything to html format
-        split = markdown.markdown(text='---')
-        convert_stage_1 = markdown.markdown(text=txt, extensions=['mdx_math', 'fenced_code', 'tables', 'sane_lists'], extension_configs=markdown_extension_configs)
-        convert_stage_1 = markdown_bug_hunt(convert_stage_1)
-        # re.DOTALL: Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).
-        # 1. convert to easy-to-copy tex (do not render math)
-        convert_stage_2_1, n = re.subn(find_equation_pattern, replace_math_no_render, convert_stage_1, flags=re.DOTALL)
-        # 2. convert to rendered equation
-        convert_stage_2_2, n = re.subn(find_equation_pattern, replace_math_render, convert_stage_1, flags=re.DOTALL)
-        # cat them together
-        return pre + convert_stage_2_1 + f'{split}' + convert_stage_2_2 + suf
-    else:
-        return pre + markdown.markdown(txt, extensions=['fenced_code', 'codehilite', 'tables', 'sane_lists']) + suf
-
-
-def close_up_code_segment_during_stream(gpt_reply):
-    """
-    在gpt输出代码的中途（输出了前面的```，但还没输出完后面的```），补上后面的```
-    
-    Args:
-        gpt_reply (str): GPT模型返回的回复字符串。
-
-    Returns:
-        str: 返回一个新的字符串，将输出代码片段的“后面的```”补上。
-
-    """
-    if '```' not in gpt_reply:
-        return gpt_reply
-    if gpt_reply.endswith('```'):
-        return gpt_reply
-
-    # 排除了以上两个情况，我们
-    segments = gpt_reply.split('```')
-    n_mark = len(segments) - 1
-    if n_mark % 2 == 1:
-        # print('输出代码片段中！')
-        return gpt_reply+'\n```'
-    else:
-        return gpt_reply
-
-
-def format_io(self, y):
-    """
-        将输入和输出解析为HTML格式。将y中最后一项的输入部分段落化，并将输出部分的Markdown和数学公式转换为HTML格式。
-    """
-    if y is None or y == []:
-        return []
-    i_ask, gpt_reply = y[-1]
-    i_ask = text_divide_paragraph(i_ask)  # 输入部分太自由，预处理一波
-    gpt_reply = close_up_code_segment_during_stream(gpt_reply)  # 当代码输出半截的时候，试着补上后个```
-    y[-1] = (
-        None if i_ask is None else markdown.markdown(i_ask, extensions=['fenced_code', 'tables']),
-        None if gpt_reply is None else markdown_convertion(gpt_reply)
-    )
-    return y
-
-
-def find_free_port():
-    """
-        返回当前系统中可用的未使用端口。
-    """
-    import socket
-    from contextlib import closing
-    with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
-        s.bind(('', 0))
-        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-        return s.getsockname()[1]
-
-
-def extract_archive(file_path, dest_dir):
-    import zipfile
-    import tarfile
-    import os
-    # Get the file extension of the input file
-    file_extension = os.path.splitext(file_path)[1]
-
-    # Extract the archive based on its extension
-    if file_extension == '.zip':
-        with zipfile.ZipFile(file_path, 'r') as zipobj:
-            zipobj.extractall(path=dest_dir)
-            print("Successfully extracted zip archive to {}".format(dest_dir))
-
-    elif file_extension in ['.tar', '.gz', '.bz2']:
-        with tarfile.open(file_path, 'r:*') as tarobj:
-            tarobj.extractall(path=dest_dir)
-            print("Successfully extracted tar archive to {}".format(dest_dir))
-
-    # 第三方库，需要预先pip install rarfile
-    # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
-    elif file_extension == '.rar':
-        try:
-            import rarfile
-            with rarfile.RarFile(file_path) as rf:
-                rf.extractall(path=dest_dir)
-                print("Successfully extracted rar archive to {}".format(dest_dir))
-        except:
-            print("Rar format requires additional dependencies to install")
-            return '\n\n需要安装pip install rarfile来解压rar文件'
-
-    # 第三方库，需要预先pip install py7zr
-    elif file_extension == '.7z':
-        try:
-            import py7zr
-            with py7zr.SevenZipFile(file_path, mode='r') as f:
-                f.extractall(path=dest_dir)
-                print("Successfully extracted 7z archive to {}".format(dest_dir))
-        except:
-            print("7z format requires additional dependencies to install")
-            return '\n\n需要安装pip install py7zr来解压7z文件'
-    else:
-        return ''
-    return ''
-
-
-def find_recent_files(directory):
-    """
-        me: find files that is created with in one minutes under a directory with python, write a function
-        gpt: here it is!
-    """
-    import os
-    import time
-    current_time = time.time()
-    one_minute_ago = current_time - 60
-    recent_files = []
-
-    for filename in os.listdir(directory):
-        file_path = os.path.join(directory, filename)
-        if file_path.endswith('.log'):
-            continue
-        created_time = os.path.getmtime(file_path)
-        if created_time >= one_minute_ago:
-            if os.path.isdir(file_path):
-                continue
-            recent_files.append(file_path)
-
-    return recent_files
-
-
-def on_file_uploaded(files, chatbot, txt):
-    if len(files) == 0:
-        return chatbot, txt
-    import shutil
-    import os
-    import time
-    import glob
-    from toolbox import extract_archive
-    try:
-        shutil.rmtree('./private_upload/')
-    except:
-        pass
-    time_tag = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    os.makedirs(f'private_upload/{time_tag}', exist_ok=True)
-    err_msg = ''
-    for file in files:
-        file_origin_name = os.path.basename(file.orig_name)
-        shutil.copy(file.name, f'private_upload/{time_tag}/{file_origin_name}')
-        err_msg += extract_archive(f'private_upload/{time_tag}/{file_origin_name}',
-                                   dest_dir=f'private_upload/{time_tag}/{file_origin_name}.extract')
-    moved_files = [fp for fp in glob.glob(
-        'private_upload/**/*', recursive=True)]
-    txt = f'private_upload/{time_tag}'
-    moved_files_str = '\t\n\n'.join(moved_files)
-    chatbot.append(['我上传了文件，请查收',
-                    f'[Local Message] 收到以下文件: \n\n{moved_files_str}' +
-                    f'\n\n调用路径参数已自动修正到: \n\n{txt}' +
-                    f'\n\n现在您点击任意实验功能时，以上文件将被作为输入参数'+err_msg])
-    return chatbot, txt
-
-
-def on_report_generated(files, chatbot):
-    from toolbox import find_recent_files
-    report_files = find_recent_files('gpt_log')
-    if len(report_files) == 0:
-        return None, chatbot
-    # files.extend(report_files)
-    chatbot.append(['汇总报告如何远程获取？', '汇总报告已经添加到右侧“文件上传区”（可能处于折叠状态），请查收。'])
-    return report_files, chatbot
-
-def is_openai_api_key(key):
-    # 正确的 API_KEY 是 "sk-" + 48 位大小写字母数字的组合
-    API_MATCH = re.match(r"sk-[a-zA-Z0-9]{48}$", key)
-    return API_MATCH
-
-@lru_cache(maxsize=128)
-def read_single_conf_with_lru_cache(arg):
-    from colorful import print亮红, print亮绿
-    try:
-        r = getattr(importlib.import_module('config_private'), arg)
-    except:
-        r = getattr(importlib.import_module('config'), arg)
-    # 在读取API_KEY时，检查一下是不是忘了改config
-    if arg == 'API_KEY':
-        if is_openai_api_key(r):
-            print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
-        else:
-            print亮红( "[API_KEY] 正确的 API_KEY 是 'sk-' + '48 位大小写字母数字' 的组合，请在config文件中修改API密钥, 添加海外代理之后再运行。" + \
-                "（如果您刚更新过代码，请确保旧版config_private文件中没有遗留任何新增键值）")
-    if arg == 'proxies':
-        if r is None:
-            print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问。建议：检查USE_PROXY选项是否修改。')
-        else:
-            print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
-            assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
-    return r
-
-
-def get_conf(*args):
-    # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到
-    res = []
-    for arg in args:
-        r = read_single_conf_with_lru_cache(arg)
-        res.append(r)
-    return res
-
-
-def clear_line_break(txt):
-    txt = txt.replace('\n', ' ')
-    txt = txt.replace('  ', ' ')
-    txt = txt.replace('  ', ' ')
-    return txt
-
-
-class DummyWith():
-    """
-    这段代码定义了一个名为DummyWith的空上下文管理器，
-    它的作用是……额……没用，即在代码结构不变得情况下取代其他的上下文管理器。
-    上下文管理器是一种Python对象，用于与with语句一起使用，
-    以确保一些资源在代码块执行期间得到正确的初始化和清理。
-    上下文管理器必须实现两个方法，分别为 __enter__()和 __exit__()。 
-    在上下文执行开始的情况下，__enter__()方法会在代码块被执行前被调用，
-    而在上下文执行结束时，__exit__()方法则会被调用。
-    """
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        return
diff --git a/spaces/RamAnanth1/Youtube-to-HF-Dataset/dataset/transcript_dataset.py b/spaces/RamAnanth1/Youtube-to-HF-Dataset/dataset/transcript_dataset.py
deleted file mode 100644
index b8c34d339a34d58987167a39051d9a80b8b5d382..0000000000000000000000000000000000000000
--- a/spaces/RamAnanth1/Youtube-to-HF-Dataset/dataset/transcript_dataset.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import glob
-import os
-import validators
-import pandas as pd
-from downloader import WhisperPP, YoutubeDownloader
-from interpreter import WhisperInterpreter
-from datasets import load_dataset, concatenate_datasets, Dataset
-from dataset.hf_dataset import HFDataset
-
-class TranscriptDataset(HFDataset):
-
-  def __init__(self, name) -> None:
-    self.name = name
-    super().__init__(name)
-
-  def generate_dataset(self, input, download_path, overwrite, whisper_config):
-    if validators.url(input):
-      self.from_url(input, download_path, overwrite, **whisper_config)
-    else:
-      self.from_files(input, overwrite,  **whisper_config)
-
-  def from_url(self, url: str, download_path: str = "tmp/", overwrite: bool = False, **whisper_config: dict) -> None:
-    if self.is_empty:
-      emptyDataset = self.dataset
-    else:
-      #emptyDataset=self.dataset["train"].filter(lambda e: e["id"] is None)
-      emptyDataset=self.dataset["train"]
-    whisper_config["number_videos"] = 5
-    whisperPP = WhisperPP(emptyDataset, self.name, **whisper_config)
-    downloader = YoutubeDownloader(download_path)
-    if not overwrite:
-      downloader.config["download_archive"] = os.path.join(download_path,"video_record.txt")
-      self._fill_archive(downloader.config["download_archive"])
-    downloader.download(url, whisperPP)
-    self._concatenate_datasets(whisperPP.get_data())
-
-  def from_files(self, input:str, overwrite: bool = False, **whisper_config):
-    if (whisper_config.get("mode", None) is not None):
-      interpreter = WhisperInterpreter(whisper_config.pop("model_size"))
-      process = getattr(interpreter, whisper_config.pop("mode"))
-      result = process(input, **whisper_config)
-      if type(result) == list:
-        dataset = Dataset.from_list(result)
-      else:
-        dataset = Dataset.from_list([result])
-    else:
-      fileName = "tmp/*.json" if os.path.isdir(input) else input
-      dataset=load_dataset("json", data_files=glob.glob(fileName), split="train")
-    
-    self._concatenate_datasets(dataset)
-
-  def _fill_archive(self, archive_file):
-    if not self.is_empty:
-      with open(archive_file, "w") as f:
-        for id in self.dataset["train"]["id"]:
-          f.write(f"youtube {id}\n")
-
-  def _concatenate_datasets(self, dataset):
-    if not self.is_empty:
-      selectedIDs = list(set(dataset["id"])-set(self.dataset["train"]["id"]))
-      filteredDataset = dataset.filter(lambda element: element["id"] in selectedIDs)
-      self.dataset["train"] = concatenate_datasets([self.dataset["train"],filteredDataset])
-    else:
-      self.dataset = dataset
\ No newline at end of file
diff --git a/spaces/Ricecake123/RVC-demo/README.md b/spaces/Ricecake123/RVC-demo/README.md
deleted file mode 100644
index 7b5e9aef4745dff93640d06035e1456d299b4cf5..0000000000000000000000000000000000000000
--- a/spaces/Ricecake123/RVC-demo/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: RVC Demo
-emoji: 🏃
-colorFrom: indigo
-colorTo: green
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/__init__seg.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/__init__seg.py
deleted file mode 100644
index 3364d40997447a4ec15ca7a525a4d0e92ab211bd..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/__init__seg.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Uniformer
-# From https://github.com/Sense-X/UniFormer
-# # Apache-2.0 license
-
-import os
-
-from annotator.uniformer.mmseg.apis import init_segmentor, inference_segmentor, show_result_pyplot
-from annotator.uniformer.mmseg.core.evaluation import get_palette
-from annotator.util import annotator_ckpts_path
-
-
-checkpoint_file = "https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/upernet_global_small.pth"
-
-
-class UniformerDetector:
-    def __init__(self):
-        modelpath = os.path.join(annotator_ckpts_path, "upernet_global_small.pth")
-        if not os.path.exists(modelpath):
-            from basicsr.utils.download_util import load_file_from_url
-            load_file_from_url(checkpoint_file, model_dir=annotator_ckpts_path)
-        config_file = os.path.join(os.path.dirname(annotator_ckpts_path), "uniformer", "exp", "upernet_global_small", "config.py")
-        self.model = init_segmentor(config_file, modelpath).cuda()
-
-    def __call__(self, img):
-        result = inference_segmentor(self.model, img)
-        res_img = show_result_pyplot(self.model, img, result, get_palette('ade'), opacity=1)
-        return res_img
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/necks/multilevel_neck.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/necks/multilevel_neck.py
deleted file mode 100644
index 766144d8136326a1fab5906a153a0c0df69b6b60..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer/mmseg/models/necks/multilevel_neck.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-from annotator.uniformer.mmcv.cnn import ConvModule
-
-from ..builder import NECKS
-
-
-@NECKS.register_module()
-class MultiLevelNeck(nn.Module):
-    """MultiLevelNeck.
-
-    A neck structure connect vit backbone and decoder_heads.
-    Args:
-        in_channels (List[int]): Number of input channels per scale.
-        out_channels (int): Number of output channels (used at each scale).
-        scales (List[int]): Scale factors for each input feature map.
-        norm_cfg (dict): Config dict for normalization layer. Default: None.
-        act_cfg (dict): Config dict for activation layer in ConvModule.
-            Default: None.
-    """
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 scales=[0.5, 1, 2, 4],
-                 norm_cfg=None,
-                 act_cfg=None):
-        super(MultiLevelNeck, self).__init__()
-        assert isinstance(in_channels, list)
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.scales = scales
-        self.num_outs = len(scales)
-        self.lateral_convs = nn.ModuleList()
-        self.convs = nn.ModuleList()
-        for in_channel in in_channels:
-            self.lateral_convs.append(
-                ConvModule(
-                    in_channel,
-                    out_channels,
-                    kernel_size=1,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-        for _ in range(self.num_outs):
-            self.convs.append(
-                ConvModule(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    padding=1,
-                    stride=1,
-                    norm_cfg=norm_cfg,
-                    act_cfg=act_cfg))
-
-    def forward(self, inputs):
-        assert len(inputs) == len(self.in_channels)
-        print(inputs[0].shape)
-        inputs = [
-            lateral_conv(inputs[i])
-            for i, lateral_conv in enumerate(self.lateral_convs)
-        ]
-        # for len(inputs) not equal to self.num_outs
-        if len(inputs) == 1:
-            inputs = [inputs[0] for _ in range(self.num_outs)]
-        outs = []
-        for i in range(self.num_outs):
-            x_resize = F.interpolate(
-                inputs[i], scale_factor=self.scales[i], mode='bilinear')
-            outs.append(self.convs[i](x_resize))
-        return tuple(outs)
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/datasets/drive.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/datasets/drive.py
deleted file mode 100644
index 06e8ff606e0d2a4514ec8b7d2c6c436a32efcbf4..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/datasets/drive.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# dataset settings
-dataset_type = 'DRIVEDataset'
-data_root = 'data/DRIVE'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-img_scale = (584, 565)
-crop_size = (64, 64)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=img_scale, ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=img_scale,
-        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0],
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-
-data = dict(
-    samples_per_gpu=4,
-    workers_per_gpu=4,
-    train=dict(
-        type='RepeatDataset',
-        times=40000,
-        dataset=dict(
-            type=dataset_type,
-            data_root=data_root,
-            img_dir='images/training',
-            ann_dir='annotations/training',
-            pipeline=train_pipeline)),
-    val=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline),
-    test=dict(
-        type=dataset_type,
-        data_root=data_root,
-        img_dir='images/validation',
-        ann_dir='annotations/validation',
-        pipeline=test_pipeline))
diff --git a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/gcnet_r50-d8.py b/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/gcnet_r50-d8.py
deleted file mode 100644
index 3d2ad69f5c22adfe79d5fdabf920217628987166..0000000000000000000000000000000000000000
--- a/spaces/Robert001/UniControl-Demo/annotator/uniformer_base/configs/_base_/models/gcnet_r50-d8.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# model settings
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='open-mmlab://resnet50_v1c',
-    backbone=dict(
-        type='ResNetV1c',
-        depth=50,
-        num_stages=4,
-        out_indices=(0, 1, 2, 3),
-        dilations=(1, 1, 2, 4),
-        strides=(1, 2, 1, 1),
-        norm_cfg=norm_cfg,
-        norm_eval=False,
-        style='pytorch',
-        contract_dilation=True),
-    decode_head=dict(
-        type='GCHead',
-        in_channels=2048,
-        in_index=3,
-        channels=512,
-        ratio=1 / 4.,
-        pooling_type='att',
-        fusion_types=('channel_add', ),
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=1024,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=19,
-        norm_cfg=norm_cfg,
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    # model training and testing settings
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
diff --git a/spaces/Sakil/sakil_text_summarization_app/app.py b/spaces/Sakil/sakil_text_summarization_app/app.py
deleted file mode 100644
index d59b6491494ca6b9a66f31b3cdbca480b4e5bc85..0000000000000000000000000000000000000000
--- a/spaces/Sakil/sakil_text_summarization_app/app.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-summarizer= pipeline("summarization")
-def get_summarize(input_text):
-  return summarizer(input_text)
-iface=gr.Interface(fn=get_summarize,inputs="text",outputs=['text'],title='Sakil Text Summarization APP')
-iface.launch(inline=False)
\ No newline at end of file
diff --git a/spaces/Salesforce/EDICT/my_half_diffusers/models/unet_2d.py b/spaces/Salesforce/EDICT/my_half_diffusers/models/unet_2d.py
deleted file mode 100644
index ca8931b2ed6db1e5b4561b510785e5a69c20fa59..0000000000000000000000000000000000000000
--- a/spaces/Salesforce/EDICT/my_half_diffusers/models/unet_2d.py
+++ /dev/null
@@ -1,246 +0,0 @@
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from ..modeling_utils import ModelMixin
-from ..utils import BaseOutput
-from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
-from .unet_blocks import UNetMidBlock2D, get_down_block, get_up_block
-
-
-@dataclass
-class UNet2DOutput(BaseOutput):
-    """
-    Args:
-        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
-            Hidden states output. Output of last layer of model.
-    """
-
-    sample: torch.DoubleTensor
-
-
-class UNet2DModel(ModelMixin, ConfigMixin):
-    r"""
-    UNet2DModel is a 2D UNet model that takes in a noisy sample and a timestep and returns sample shaped output.
-
-    This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library
-    implements for all the model (such as downloading or saving, etc.)
-
-    Parameters:
-        sample_size (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
-            Input sample size.
-        in_channels (`int`, *optional*, defaults to 3): Number of channels in the input image.
-        out_channels (`int`, *optional*, defaults to 3): Number of channels in the output.
-        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
-        time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use.
-        freq_shift (`int`, *optional*, defaults to 0): Frequency shift for fourier time embedding.
-        flip_sin_to_cos (`bool`, *optional*, defaults to :
-            obj:`False`): Whether to flip sin to cos for fourier time embedding.
-        down_block_types (`Tuple[str]`, *optional*, defaults to :
-            obj:`("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block
-            types.
-        up_block_types (`Tuple[str]`, *optional*, defaults to :
-            obj:`("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types.
-        block_out_channels (`Tuple[int]`, *optional*, defaults to :
-            obj:`(224, 448, 672, 896)`): Tuple of block output channels.
-        layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block.
-        mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block.
-        downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution.
-        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
-        attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension.
-        norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for the normalization.
-        norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for the normalization.
-    """
-
-    @register_to_config
-    def __init__(
-        self,
-        sample_size: Optional[int] = None,
-        in_channels: int = 3,
-        out_channels: int = 3,
-        center_input_sample: bool = False,
-        time_embedding_type: str = "positional",
-        freq_shift: int = 0,
-        flip_sin_to_cos: bool = True,
-        down_block_types: Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"),
-        up_block_types: Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"),
-        block_out_channels: Tuple[int] = (224, 448, 672, 896),
-        layers_per_block: int = 2,
-        mid_block_scale_factor = 1,
-        downsample_padding: int = 1,
-        act_fn: str = "silu",
-        attention_head_dim: int = 8,
-        norm_num_groups: int = 32,
-        norm_eps = 1e-5,
-    ):
-        super().__init__()
-
-        self.sample_size = sample_size
-        time_embed_dim = block_out_channels[0] * 4
-
-        # input
-        self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
-
-        # time
-        if time_embedding_type == "fourier":
-            self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16)
-            timestep_input_dim = 2 * block_out_channels[0]
-        elif time_embedding_type == "positional":
-            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
-            timestep_input_dim = block_out_channels[0]
-
-        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
-
-        self.down_blocks = nn.ModuleList([])
-        self.mid_block = None
-        self.up_blocks = nn.ModuleList([])
-
-        # down
-        output_channel = block_out_channels[0]
-        for i, down_block_type in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-
-            down_block = get_down_block(
-                down_block_type,
-                num_layers=layers_per_block,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                temb_channels=time_embed_dim,
-                add_downsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                attn_num_head_channels=attention_head_dim,
-                downsample_padding=downsample_padding,
-            )
-            self.down_blocks.append(down_block)
-
-        # mid
-        self.mid_block = UNetMidBlock2D(
-            in_channels=block_out_channels[-1],
-            temb_channels=time_embed_dim,
-            resnet_eps=norm_eps,
-            resnet_act_fn=act_fn,
-            output_scale_factor=mid_block_scale_factor,
-            resnet_time_scale_shift="default",
-            attn_num_head_channels=attention_head_dim,
-            resnet_groups=norm_num_groups,
-        )
-
-        # up
-        reversed_block_out_channels = list(reversed(block_out_channels))
-        output_channel = reversed_block_out_channels[0]
-        for i, up_block_type in enumerate(up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_block_out_channels[i]
-            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
-
-            is_final_block = i == len(block_out_channels) - 1
-
-            up_block = get_up_block(
-                up_block_type,
-                num_layers=layers_per_block + 1,
-                in_channels=input_channel,
-                out_channels=output_channel,
-                prev_output_channel=prev_output_channel,
-                temb_channels=time_embed_dim,
-                add_upsample=not is_final_block,
-                resnet_eps=norm_eps,
-                resnet_act_fn=act_fn,
-                attn_num_head_channels=attention_head_dim,
-            )
-            self.up_blocks.append(up_block)
-            prev_output_channel = output_channel
-
-        # out
-        num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32)
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps)
-        self.conv_act = nn.SiLU()
-        self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1)
-
-    def forward(
-        self,
-        sample: torch.DoubleTensor,
-        timestep: Union[torch.Tensor, float, int],
-        return_dict: bool = True,
-    ) -> Union[UNet2DOutput, Tuple]:
-        """r
-        Args:
-            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
-            timestep (`torch.FloatTensor` or `float` or `int): (batch) timesteps
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple.
-
-        Returns:
-            [`~models.unet_2d.UNet2DOutput`] or `tuple`: [`~models.unet_2d.UNet2DOutput`] if `return_dict` is True,
-            otherwise a `tuple`. When returning a tuple, the first element is the sample tensor.
-        """
-        # 0. center input if necessary
-        if self.config.center_input_sample:
-            sample = 2 * sample - 1.0
-
-        # 1. time
-        timesteps = timestep
-        if not torch.is_tensor(timesteps):
-            timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device)
-        elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0:
-            timesteps = timesteps[None].to(sample.device)
-
-        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-        timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device)
-
-        t_emb = self.time_proj(timesteps)
-        emb = self.time_embedding(t_emb)
-
-        # 2. pre-process
-        skip_sample = sample
-        sample = self.conv_in(sample)
-
-        # 3. down
-        down_block_res_samples = (sample,)
-        for downsample_block in self.down_blocks:
-            if hasattr(downsample_block, "skip_conv"):
-                sample, res_samples, skip_sample = downsample_block(
-                    hidden_states=sample, temb=emb, skip_sample=skip_sample
-                )
-            else:
-                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
-
-            down_block_res_samples += res_samples
-
-        # 4. mid
-        sample = self.mid_block(sample, emb)
-
-        # 5. up
-        skip_sample = None
-        for upsample_block in self.up_blocks:
-            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
-            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
-
-            if hasattr(upsample_block, "skip_conv"):
-                sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample)
-            else:
-                sample = upsample_block(sample, res_samples, emb)
-
-        # 6. post-process
-        # make sure hidden states is in float32
-        # when running in half-precision
-        sample = self.conv_norm_out(sample).type(sample.dtype)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-
-        if skip_sample is not None:
-            sample += skip_sample
-
-        if self.config.time_embedding_type == "fourier":
-            timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
-            sample = sample / timesteps
-
-        if not return_dict:
-            return (sample,)
-
-        return UNet2DOutput(sample=sample)
diff --git a/spaces/Sambhavnoobcoder/pneumonia-detector-v1/README.md b/spaces/Sambhavnoobcoder/pneumonia-detector-v1/README.md
deleted file mode 100644
index 7e3572403fdd008a1a042b4f9093e86f6b2953c0..0000000000000000000000000000000000000000
--- a/spaces/Sambhavnoobcoder/pneumonia-detector-v1/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Pneumonia Detector V1
-emoji: 🏆
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.27.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/ServerX/PorcoDiaz/tools/torchgate/utils.py b/spaces/ServerX/PorcoDiaz/tools/torchgate/utils.py
deleted file mode 100644
index dc97d45a399c112c76e80cdd8c73cfebaf3ef6ad..0000000000000000000000000000000000000000
--- a/spaces/ServerX/PorcoDiaz/tools/torchgate/utils.py
+++ /dev/null
@@ -1,66 +0,0 @@
-import torch
-from torch.types import Number
-
-
-@torch.no_grad()
-def amp_to_db(x: torch.Tensor, eps=torch.finfo(torch.float64).eps, top_db=40) -> torch.Tensor:
-    """
-    Convert the input tensor from amplitude to decibel scale.
-
-    Arguments:
-        x {[torch.Tensor]} -- [Input tensor.]
-
-    Keyword Arguments:
-        eps {[float]} -- [Small value to avoid numerical instability.]
-                          (default: {torch.finfo(torch.float64).eps})
-        top_db {[float]} -- [threshold the output at ``top_db`` below the peak]
-            `             (default: {40})
-
-    Returns:
-        [torch.Tensor] -- [Output tensor in decibel scale.]
-    """
-    x_db = 20 * torch.log10(x.abs() + eps)
-    return torch.max(x_db, (x_db.max(-1).values - top_db).unsqueeze(-1))
-
-
-@torch.no_grad()
-def temperature_sigmoid(x: torch.Tensor, x0: float, temp_coeff: float) -> torch.Tensor:
-    """
-    Apply a sigmoid function with temperature scaling.
-
-    Arguments:
-        x {[torch.Tensor]} -- [Input tensor.]
-        x0 {[float]} -- [Parameter that controls the threshold of the sigmoid.]
-        temp_coeff {[float]} -- [Parameter that controls the slope of the sigmoid.]
-
-    Returns:
-        [torch.Tensor] -- [Output tensor after applying the sigmoid with temperature scaling.]
-    """
-    return torch.sigmoid((x - x0) / temp_coeff)
-
-
-@torch.no_grad()
-def linspace(start: Number, stop: Number, num: int = 50, endpoint: bool = True, **kwargs) -> torch.Tensor:
-    """
-    Generate a linearly spaced 1-D tensor.
-
-    Arguments:
-        start {[Number]} -- [The starting value of the sequence.]
-        stop {[Number]} -- [The end value of the sequence, unless `endpoint` is set to False.
-                            In that case, the sequence consists of all but the last of ``num + 1``
-                            evenly spaced samples, so that `stop` is excluded. Note that the step
-                            size changes when `endpoint` is False.]
-
-    Keyword Arguments:
-        num {[int]} -- [Number of samples to generate. Default is 50. Must be non-negative.]
-        endpoint {[bool]} -- [If True, `stop` is the last sample. Otherwise, it is not included.
-                              Default is True.]
-        **kwargs -- [Additional arguments to be passed to the underlying PyTorch `linspace` function.]
-
-    Returns:
-        [torch.Tensor] -- [1-D tensor of `num` equally spaced samples from `start` to `stop`.]
-    """
-    if endpoint:
-        return torch.linspace(start, stop, num, **kwargs)
-    else:
-        return torch.linspace(start, stop, num + 1, **kwargs)[:-1]
diff --git a/spaces/SkyYeXianer/vits-uma-genshin-honkai/text/cleaners.py b/spaces/SkyYeXianer/vits-uma-genshin-honkai/text/cleaners.py
deleted file mode 100644
index d26581deb399609163518054718ad80ecca5d934..0000000000000000000000000000000000000000
--- a/spaces/SkyYeXianer/vits-uma-genshin-honkai/text/cleaners.py
+++ /dev/null
@@ -1,475 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-import re
-from unidecode import unidecode
-import pyopenjtalk
-from jamo import h2j, j2hcj
-from pypinyin import lazy_pinyin, BOPOMOFO
-import jieba, cn2an
-
-
-# This is a list of Korean classifiers preceded by pure Korean numerals.
-_korean_classifiers = '군데 권 개 그루 닢 대 두 마리 모 모금 뭇 발 발짝 방 번 벌 보루 살 수 술 시 쌈 움큼 정 짝 채 척 첩 축 켤레 톨 통'
-
-# Regular expression matching whitespace:
-_whitespace_re = re.compile(r'\s+')
-
-# Regular expression matching Japanese without punctuation marks:
-_japanese_characters = re.compile(r'[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# Regular expression matching non-Japanese characters or punctuation marks:
-_japanese_marks = re.compile(r'[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('mrs', 'misess'),
-  ('mr', 'mister'),
-  ('dr', 'doctor'),
-  ('st', 'saint'),
-  ('co', 'company'),
-  ('jr', 'junior'),
-  ('maj', 'major'),
-  ('gen', 'general'),
-  ('drs', 'doctors'),
-  ('rev', 'reverend'),
-  ('lt', 'lieutenant'),
-  ('hon', 'honorable'),
-  ('sgt', 'sergeant'),
-  ('capt', 'captain'),
-  ('esq', 'esquire'),
-  ('ltd', 'limited'),
-  ('col', 'colonel'),
-  ('ft', 'fort'),
-]]
-
-# List of (hangul, hangul divided) pairs:
-_hangul_divided = [(re.compile('%s' % x[0]), x[1]) for x in [
-  ('ㄳ', 'ㄱㅅ'),
-  ('ㄵ', 'ㄴㅈ'),
-  ('ㄶ', 'ㄴㅎ'),
-  ('ㄺ', 'ㄹㄱ'),
-  ('ㄻ', 'ㄹㅁ'),
-  ('ㄼ', 'ㄹㅂ'),
-  ('ㄽ', 'ㄹㅅ'),
-  ('ㄾ', 'ㄹㅌ'),
-  ('ㄿ', 'ㄹㅍ'),
-  ('ㅀ', 'ㄹㅎ'),
-  ('ㅄ', 'ㅂㅅ'),
-  ('ㅘ', 'ㅗㅏ'),
-  ('ㅙ', 'ㅗㅐ'),
-  ('ㅚ', 'ㅗㅣ'),
-  ('ㅝ', 'ㅜㅓ'),
-  ('ㅞ', 'ㅜㅔ'),
-  ('ㅟ', 'ㅜㅣ'),
-  ('ㅢ', 'ㅡㅣ'),
-  ('ㅑ', 'ㅣㅏ'),
-  ('ㅒ', 'ㅣㅐ'),
-  ('ㅕ', 'ㅣㅓ'),
-  ('ㅖ', 'ㅣㅔ'),
-  ('ㅛ', 'ㅣㅗ'),
-  ('ㅠ', 'ㅣㅜ')
-]]
-
-# List of (Latin alphabet, hangul) pairs:
-_latin_to_hangul = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', '에이'),
-  ('b', '비'),
-  ('c', '시'),
-  ('d', '디'),
-  ('e', '이'),
-  ('f', '에프'),
-  ('g', '지'),
-  ('h', '에이치'),
-  ('i', '아이'),
-  ('j', '제이'),
-  ('k', '케이'),
-  ('l', '엘'),
-  ('m', '엠'),
-  ('n', '엔'),
-  ('o', '오'),
-  ('p', '피'),
-  ('q', '큐'),
-  ('r', '아르'),
-  ('s', '에스'),
-  ('t', '티'),
-  ('u', '유'),
-  ('v', '브이'),
-  ('w', '더블유'),
-  ('x', '엑스'),
-  ('y', '와이'),
-  ('z', '제트')
-]]
-
-# List of (Latin alphabet, bopomofo) pairs:
-_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('a', 'ㄟˉ'),
-  ('b', 'ㄅㄧˋ'),
-  ('c', 'ㄙㄧˉ'),
-  ('d', 'ㄉㄧˋ'),
-  ('e', 'ㄧˋ'),
-  ('f', 'ㄝˊㄈㄨˋ'),
-  ('g', 'ㄐㄧˋ'),
-  ('h', 'ㄝˇㄑㄩˋ'),
-  ('i', 'ㄞˋ'),
-  ('j', 'ㄐㄟˋ'),
-  ('k', 'ㄎㄟˋ'),
-  ('l', 'ㄝˊㄛˋ'),
-  ('m', 'ㄝˊㄇㄨˋ'),
-  ('n', 'ㄣˉ'),
-  ('o', 'ㄡˉ'),
-  ('p', 'ㄆㄧˉ'),
-  ('q', 'ㄎㄧㄡˉ'),
-  ('r', 'ㄚˋ'),
-  ('s', 'ㄝˊㄙˋ'),
-  ('t', 'ㄊㄧˋ'),
-  ('u', 'ㄧㄡˉ'),
-  ('v', 'ㄨㄧˉ'),
-  ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'),
-  ('x', 'ㄝˉㄎㄨˋㄙˋ'),
-  ('y', 'ㄨㄞˋ'),
-  ('z', 'ㄗㄟˋ')
-]]
-
-
-# List of (bopomofo, romaji) pairs:
-_bopomofo_to_romaji = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [
-  ('ㄅㄛ', 'p⁼wo'),
-  ('ㄆㄛ', 'pʰwo'),
-  ('ㄇㄛ', 'mwo'),
-  ('ㄈㄛ', 'fwo'),
-  ('ㄅ', 'p⁼'),
-  ('ㄆ', 'pʰ'),
-  ('ㄇ', 'm'),
-  ('ㄈ', 'f'),
-  ('ㄉ', 't⁼'),
-  ('ㄊ', 'tʰ'),
-  ('ㄋ', 'n'),
-  ('ㄌ', 'l'),
-  ('ㄍ', 'k⁼'),
-  ('ㄎ', 'kʰ'),
-  ('ㄏ', 'h'),
-  ('ㄐ', 'ʧ⁼'),
-  ('ㄑ', 'ʧʰ'),
-  ('ㄒ', 'ʃ'),
-  ('ㄓ', 'ʦ`⁼'),
-  ('ㄔ', 'ʦ`ʰ'),
-  ('ㄕ', 's`'),
-  ('ㄖ', 'ɹ`'),
-  ('ㄗ', 'ʦ⁼'),
-  ('ㄘ', 'ʦʰ'),
-  ('ㄙ', 's'),
-  ('ㄚ', 'a'),
-  ('ㄛ', 'o'),
-  ('ㄜ', 'ə'),
-  ('ㄝ', 'e'),
-  ('ㄞ', 'ai'),
-  ('ㄟ', 'ei'),
-  ('ㄠ', 'au'),
-  ('ㄡ', 'ou'),
-  ('ㄧㄢ', 'yeNN'),
-  ('ㄢ', 'aNN'),
-  ('ㄧㄣ', 'iNN'),
-  ('ㄣ', 'əNN'),
-  ('ㄤ', 'aNg'),
-  ('ㄧㄥ', 'iNg'),
-  ('ㄨㄥ', 'uNg'),
-  ('ㄩㄥ', 'yuNg'),
-  ('ㄥ', 'əNg'),
-  ('ㄦ', 'əɻ'),
-  ('ㄧ', 'i'),
-  ('ㄨ', 'u'),
-  ('ㄩ', 'ɥ'),
-  ('ˉ', '→'),
-  ('ˊ', '↑'),
-  ('ˇ', '↓↑'),
-  ('ˋ', '↓'),
-  ('˙', ''),
-  ('，', ','),
-  ('。', '.'),
-  ('！', '!'),
-  ('？', '?'),
-  ('—', '-')
-]]
-
-
-def expand_abbreviations(text):
-  for regex, replacement in _abbreviations:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def lowercase(text):
-  return text.lower()
-
-
-def collapse_whitespace(text):
-  return re.sub(_whitespace_re, ' ', text)
-
-
-def convert_to_ascii(text):
-  return unidecode(text)
-
-
-def japanese_to_romaji_with_accent(text):
-  '''Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html'''
-  sentences = re.split(_japanese_marks, text)
-  marks = re.findall(_japanese_marks, text)
-  text = ''
-  for i, sentence in enumerate(sentences):
-    if re.match(_japanese_characters, sentence):
-      if text!='':
-        text+=' '
-      labels = pyopenjtalk.extract_fullcontext(sentence)
-      for n, label in enumerate(labels):
-        phoneme = re.search(r'\-([^\+]*)\+', label).group(1)
-        if phoneme not in ['sil','pau']:
-          text += phoneme.replace('ch','ʧ').replace('sh','ʃ').replace('cl','Q')
-        else:
-          continue
-        n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
-        a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
-        a2 = int(re.search(r"\+(\d+)\+", label).group(1))
-        a3 = int(re.search(r"\+(\d+)/", label).group(1))
-        if re.search(r'\-([^\+]*)\+', labels[n + 1]).group(1) in ['sil','pau']:
-          a2_next=-1
-        else:
-          a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
-        # Accent phrase boundary
-        if a3 == 1 and a2_next == 1:
-          text += ' '
-        # Falling
-        elif a1 == 0 and a2_next == a2 + 1 and a2 != n_moras:
-          text += '↓'
-        # Rising
-        elif a2 == 1 and a2_next == 2:
-          text += '↑'
-    if i<len(marks):
-      text += unidecode(marks[i]).replace(' ','')
-  return text
-
-
-def latin_to_hangul(text):
-  for regex, replacement in _latin_to_hangul:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def divide_hangul(text):
-  for regex, replacement in _hangul_divided:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def hangul_number(num, sino=True):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  num = re.sub(',', '', num)
-
-  if num == '0':
-      return '영'
-  if not sino and num == '20':
-      return '스무'
-
-  digits = '123456789'
-  names = '일이삼사오육칠팔구'
-  digit2name = {d: n for d, n in zip(digits, names)}
-  
-  modifiers = '한 두 세 네 다섯 여섯 일곱 여덟 아홉'
-  decimals = '열 스물 서른 마흔 쉰 예순 일흔 여든 아흔'
-  digit2mod = {d: mod for d, mod in zip(digits, modifiers.split())}
-  digit2dec = {d: dec for d, dec in zip(digits, decimals.split())}
-
-  spelledout = []
-  for i, digit in enumerate(num):
-    i = len(num) - i - 1
-    if sino:
-      if i == 0:
-        name = digit2name.get(digit, '')
-      elif i == 1:
-        name = digit2name.get(digit, '') + '십'
-        name = name.replace('일십', '십')
-    else:
-      if i == 0:
-        name = digit2mod.get(digit, '')
-      elif i == 1:
-        name = digit2dec.get(digit, '')
-    if digit == '0':
-      if i % 4 == 0:
-        last_three = spelledout[-min(3, len(spelledout)):]
-        if ''.join(last_three) == '':
-          spelledout.append('')
-          continue
-      else:
-        spelledout.append('')
-        continue
-    if i == 2:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 3:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 4:
-      name = digit2name.get(digit, '') + '만'
-      name = name.replace('일만', '만')
-    elif i == 5:
-      name = digit2name.get(digit, '') + '십'
-      name = name.replace('일십', '십')
-    elif i == 6:
-      name = digit2name.get(digit, '') + '백'
-      name = name.replace('일백', '백')
-    elif i == 7:
-      name = digit2name.get(digit, '') + '천'
-      name = name.replace('일천', '천')
-    elif i == 8:
-      name = digit2name.get(digit, '') + '억'
-    elif i == 9:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 10:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 11:
-      name = digit2name.get(digit, '') + '천'
-    elif i == 12:
-      name = digit2name.get(digit, '') + '조'
-    elif i == 13:
-      name = digit2name.get(digit, '') + '십'
-    elif i == 14:
-      name = digit2name.get(digit, '') + '백'
-    elif i == 15:
-      name = digit2name.get(digit, '') + '천'
-    spelledout.append(name)
-  return ''.join(elem for elem in spelledout)
-
-
-def number_to_hangul(text):
-  '''Reference https://github.com/Kyubyong/g2pK'''
-  tokens = set(re.findall(r'(\d[\d,]*)([\uac00-\ud71f]+)', text))
-  for token in tokens:
-    num, classifier = token
-    if classifier[:2] in _korean_classifiers or classifier[0] in _korean_classifiers:
-      spelledout = hangul_number(num, sino=False)
-    else:
-      spelledout = hangul_number(num, sino=True)
-    text = text.replace(f'{num}{classifier}', f'{spelledout}{classifier}')
-  # digit by digit for remaining digits
-  digits = '0123456789'
-  names = '영일이삼사오육칠팔구'
-  for d, n in zip(digits, names):
-    text = text.replace(d, n)
-  return text
-
-
-def number_to_chinese(text):
-  numbers = re.findall(r'\d+(?:\.?\d+)?', text)
-  for number in numbers:
-    text = text.replace(number, cn2an.an2cn(number),1)
-  return text
-
-
-def chinese_to_bopomofo(text):
-  text=text.replace('、','，').replace('；','，').replace('：','，')
-  words=jieba.lcut(text,cut_all=False)
-  text=''
-  for word in words:
-    bopomofos=lazy_pinyin(word,BOPOMOFO)
-    if not re.search('[\u4e00-\u9fff]',word):
-      text+=word
-      continue
-    for i in range(len(bopomofos)):
-      if re.match('[\u3105-\u3129]',bopomofos[i][-1]):
-        bopomofos[i]+='ˉ'
-    if text!='':
-      text+=' '
-    text+=''.join(bopomofos)
-  return text
-
-
-def latin_to_bopomofo(text):
-  for regex, replacement in _latin_to_bopomofo:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def bopomofo_to_romaji(text):
-  for regex, replacement in _bopomofo_to_romaji:
-    text = re.sub(regex, replacement, text)
-  return text
-
-
-def basic_cleaners(text):
-  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def transliteration_cleaners(text):
-  '''Pipeline for non-English text that transliterates to ASCII.'''
-  text = convert_to_ascii(text)
-  text = lowercase(text)
-  text = collapse_whitespace(text)
-  return text
-
-
-def japanese_cleaners(text):
-  text=japanese_to_romaji_with_accent(text)
-  if re.match('[A-Za-z]',text[-1]):
-    text += '.'
-  return text
-
-
-def japanese_cleaners2(text):
-  return japanese_cleaners(text).replace('ts','ʦ').replace('...','…')
-
-
-def korean_cleaners(text):
-  '''Pipeline for Korean text'''
-  text = latin_to_hangul(text)
-  text = number_to_hangul(text)
-  text = j2hcj(h2j(text))
-  text = divide_hangul(text)
-  if re.match('[\u3131-\u3163]',text[-1]):
-    text += '.'
-  return text
-
-
-def chinese_cleaners(text):
-  '''Pipeline for Chinese text'''
-  text=number_to_chinese(text)
-  text=chinese_to_bopomofo(text)
-  text=latin_to_bopomofo(text)
-  if re.match('[ˉˊˇˋ˙]',text[-1]):
-    text += '。'
-  return text
-
-
-def zh_ja_mixture_cleaners(text):
-  chinese_texts=re.findall(r'\[ZH\].*?\[ZH\]',text)
-  japanese_texts=re.findall(r'\[JA\].*?\[JA\]',text)
-  for chinese_text in chinese_texts:
-    cleaned_text=number_to_chinese(chinese_text[4:-4])
-    cleaned_text=chinese_to_bopomofo(cleaned_text)
-    cleaned_text=latin_to_bopomofo(cleaned_text)
-    cleaned_text=bopomofo_to_romaji(cleaned_text)
-    cleaned_text=re.sub('i[aoe]',lambda x:'y'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('u[aoəe]',lambda x:'w'+x.group(0)[1:],cleaned_text)
-    cleaned_text=re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ`'+x.group(2),cleaned_text).replace('ɻ','ɹ`')
-    cleaned_text=re.sub('([ʦs][⁼ʰ]?)([→↓↑]+)',lambda x:x.group(1)+'ɹ'+x.group(2),cleaned_text)
-    text = text.replace(chinese_text,cleaned_text+' ',1)
-  for japanese_text in japanese_texts:
-    cleaned_text=japanese_to_romaji_with_accent(japanese_text[4:-4]).replace('ts','ʦ').replace('u','ɯ').replace('...','…')
-    text = text.replace(japanese_text,cleaned_text+' ',1)
-  text=text[:-1]
-  if re.match('[A-Za-zɯɹəɥ→↓↑]',text[-1]):
-    text += '.'
-  return text
\ No newline at end of file
diff --git a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/longcode/jpgd.cpp b/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/longcode/jpgd.cpp
deleted file mode 100644
index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000
--- a/spaces/SouthCity/ShuruiXu/crazy_functions/test_project/cpp/longcode/jpgd.cpp
+++ /dev/null
@@ -1,3276 +0,0 @@
-// jpgd.cpp - C++ class for JPEG decompression.
-// Public domain, Rich Geldreich <richgel99@gmail.com>
-// Last updated Apr. 16, 2011
-// Alex Evans: Linear memory allocator (taken from jpge.h).
-//
-// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2.
-//
-// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling.
-// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain"
-// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html
-
-#include "jpgd.h"
-#include <string.h>
-
-#include <assert.h>
-// BEGIN EPIC MOD
-#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0
-// END EPIC MOD
-
-#ifdef _MSC_VER
-#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable
-#endif
-
-// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling).
-// This is slower, but results in higher quality on images with highly saturated colors.
-#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1
-
-#define JPGD_TRUE (1)
-#define JPGD_FALSE (0)
-
-#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b))
-#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b))
-
-namespace jpgd {
-
-	static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); }
-	static inline void jpgd_free(void *p) { FMemory::Free(p); }
-
-// BEGIN EPIC MOD
-//@UE3 - use UE3 BGRA encoding instead of assuming RGBA
-	// stolen from IImageWrapper.h
-	enum ERGBFormatJPG
-	{
-		Invalid = -1,
-		RGBA =  0,
-		BGRA =  1,
-		Gray =  2,
-	};
-	static ERGBFormatJPG jpg_format;
-// END EPIC MOD
-
-	// DCT coefficients are stored in this sequence.
-	static int g_ZAG[64] = {  0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 };
-
-	enum JPEG_MARKER
-	{
-		M_SOF0  = 0xC0, M_SOF1  = 0xC1, M_SOF2  = 0xC2, M_SOF3  = 0xC3, M_SOF5  = 0xC5, M_SOF6  = 0xC6, M_SOF7  = 0xC7, M_JPG   = 0xC8,
-		M_SOF9  = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT   = 0xC4, M_DAC   = 0xCC,
-		M_RST0  = 0xD0, M_RST1  = 0xD1, M_RST2  = 0xD2, M_RST3  = 0xD3, M_RST4  = 0xD4, M_RST5  = 0xD5, M_RST6  = 0xD6, M_RST7  = 0xD7,
-		M_SOI   = 0xD8, M_EOI   = 0xD9, M_SOS   = 0xDA, M_DQT   = 0xDB, M_DNL   = 0xDC, M_DRI   = 0xDD, M_DHP   = 0xDE, M_EXP   = 0xDF,
-		M_APP0  = 0xE0, M_APP15 = 0xEF, M_JPG0  = 0xF0, M_JPG13 = 0xFD, M_COM   = 0xFE, M_TEM   = 0x01, M_ERROR = 0x100, RST0   = 0xD0
-	};
-
-	enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 };
-
-#define CONST_BITS  13
-#define PASS1_BITS  2
-#define SCALEDONE ((int32)1)
-
-#define FIX_0_298631336  ((int32)2446)        /* FIX(0.298631336) */
-#define FIX_0_390180644  ((int32)3196)        /* FIX(0.390180644) */
-#define FIX_0_541196100  ((int32)4433)        /* FIX(0.541196100) */
-#define FIX_0_765366865  ((int32)6270)        /* FIX(0.765366865) */
-#define FIX_0_899976223  ((int32)7373)        /* FIX(0.899976223) */
-#define FIX_1_175875602  ((int32)9633)        /* FIX(1.175875602) */
-#define FIX_1_501321110  ((int32)12299)       /* FIX(1.501321110) */
-#define FIX_1_847759065  ((int32)15137)       /* FIX(1.847759065) */
-#define FIX_1_961570560  ((int32)16069)       /* FIX(1.961570560) */
-#define FIX_2_053119869  ((int32)16819)       /* FIX(2.053119869) */
-#define FIX_2_562915447  ((int32)20995)       /* FIX(2.562915447) */
-#define FIX_3_072711026  ((int32)25172)       /* FIX(3.072711026) */
-
-#define DESCALE(x,n)  (((x) + (SCALEDONE << ((n)-1))) >> (n))
-#define DESCALE_ZEROSHIFT(x,n)  (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n))
-
-#define MULTIPLY(var, cnst)  ((var) * (cnst))
-
-#define CLAMP(i) ((static_cast<uint>(i) > 255) ? (((~i) >> 31) & 0xFF) : (i))
-
-	// Compiler creates a fast path 1D IDCT for X non-zero columns
-	template <int NONZERO_COLS>
-	struct Row
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			// ACCESS_COL() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0)
-
-			const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS);
-			pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS);
-			pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS);
-			pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS);
-			pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS);
-		}
-	};
-
-	template <>
-	struct Row<0>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-#ifdef _MSC_VER
-			pTemp; pSrc;
-#endif
-		}
-	};
-
-	template <>
-	struct Row<1>
-	{
-		static void idct(int* pTemp, const jpgd_block_t* pSrc)
-		{
-			const int dcval = (pSrc[0] << PASS1_BITS);
-
-			pTemp[0] = dcval;
-			pTemp[1] = dcval;
-			pTemp[2] = dcval;
-			pTemp[3] = dcval;
-			pTemp[4] = dcval;
-			pTemp[5] = dcval;
-			pTemp[6] = dcval;
-			pTemp[7] = dcval;
-		}
-	};
-
-	// Compiler creates a fast path 1D IDCT for X non-zero rows
-	template <int NONZERO_ROWS>
-	struct Col
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			// ACCESS_ROW() will be optimized at compile time to either an array access, or 0.
-#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0)
-
-			const int z2 = ACCESS_ROW(2);
-			const int z3 = ACCESS_ROW(6);
-
-			const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100);
-			const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065);
-			const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865);
-
-			const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS;
-			const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS;
-
-			const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2;
-
-			const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1);
-
-			const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3;
-			const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602);
-
-			const int az1 = MULTIPLY(bz1, - FIX_0_899976223);
-			const int az2 = MULTIPLY(bz2, - FIX_2_562915447);
-			const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5;
-			const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5;
-
-			const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3;
-			const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4;
-			const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3;
-			const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4;
-
-			int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*0] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*7] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*1] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*6] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*2] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*5] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*3] = (uint8)CLAMP(i);
-
-			i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3);
-			pDst_ptr[8*4] = (uint8)CLAMP(i);
-		}
-	};
-
-	template <>
-	struct Col<1>
-	{
-		static void idct(uint8* pDst_ptr, const int* pTemp)
-		{
-			int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3);
-			const uint8 dcval_clamped = (uint8)CLAMP(dcval);
-			pDst_ptr[0*8] = dcval_clamped;
-			pDst_ptr[1*8] = dcval_clamped;
-			pDst_ptr[2*8] = dcval_clamped;
-			pDst_ptr[3*8] = dcval_clamped;
-			pDst_ptr[4*8] = dcval_clamped;
-			pDst_ptr[5*8] = dcval_clamped;
-			pDst_ptr[6*8] = dcval_clamped;
-			pDst_ptr[7*8] = dcval_clamped;
-		}
-	};
-
-	static const uint8 s_idct_row_table[] =
-	{
-		1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0,
-		4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0,
-		6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0,
-		6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0,
-		8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2,
-		8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2,
-		8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4,
-		8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8,
-	};
-
-	static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 };
-
-	void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag)
-	{
-		JPGD_ASSERT(block_max_zag >= 1);
-		JPGD_ASSERT(block_max_zag <= 64);
-
-		if (block_max_zag == 1)
-		{
-			int k = ((pSrc_ptr[0] + 4) >> 3) + 128;
-			k = CLAMP(k);
-			k = k | (k<<8);
-			k = k | (k<<16);
-
-			for (int i = 8; i > 0; i--)
-			{
-				*(int*)&pDst_ptr[0] = k;
-				*(int*)&pDst_ptr[4] = k;
-				pDst_ptr += 8;
-			}
-			return;
-		}
-
-		int temp[64];
-
-		const jpgd_block_t* pSrc = pSrc_ptr;
-		int* pTemp = temp;
-
-		const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8];
-		int i;
-		for (i = 8; i > 0; i--, pRow_tab++)
-		{
-			switch (*pRow_tab)
-			{
-			case 0: Row<0>::idct(pTemp, pSrc); break;
-			case 1: Row<1>::idct(pTemp, pSrc); break;
-			case 2: Row<2>::idct(pTemp, pSrc); break;
-			case 3: Row<3>::idct(pTemp, pSrc); break;
-			case 4: Row<4>::idct(pTemp, pSrc); break;
-			case 5: Row<5>::idct(pTemp, pSrc); break;
-			case 6: Row<6>::idct(pTemp, pSrc); break;
-			case 7: Row<7>::idct(pTemp, pSrc); break;
-			case 8: Row<8>::idct(pTemp, pSrc); break;
-			}
-
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-
-		const int nonzero_rows = s_idct_col_table[block_max_zag - 1];
-		for (i = 8; i > 0; i--)
-		{
-			switch (nonzero_rows)
-			{
-			case 1: Col<1>::idct(pDst_ptr, pTemp); break;
-			case 2: Col<2>::idct(pDst_ptr, pTemp); break;
-			case 3: Col<3>::idct(pDst_ptr, pTemp); break;
-			case 4: Col<4>::idct(pDst_ptr, pTemp); break;
-			case 5: Col<5>::idct(pDst_ptr, pTemp); break;
-			case 6: Col<6>::idct(pDst_ptr, pTemp); break;
-			case 7: Col<7>::idct(pDst_ptr, pTemp); break;
-			case 8: Col<8>::idct(pDst_ptr, pTemp); break;
-			}
-
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr)
-	{
-		int temp[64];
-		int* pTemp = temp;
-		const jpgd_block_t* pSrc = pSrc_ptr;
-
-		for (int i = 4; i > 0; i--)
-		{
-			Row<4>::idct(pTemp, pSrc);
-			pSrc += 8;
-			pTemp += 8;
-		}
-
-		pTemp = temp;
-		for (int i = 8; i > 0; i--)
-		{
-			Col<4>::idct(pDst_ptr, pTemp);
-			pTemp++;
-			pDst_ptr++;
-		}
-	}
-
-	// Retrieve one character from the input stream.
-	inline uint jpeg_decoder::get_char()
-	{
-		// Any bytes remaining in buffer?
-		if (!m_in_buf_left)
-		{
-			// Try to get more bytes.
-			prep_in_buffer();
-			// Still nothing to get?
-			if (!m_in_buf_left)
-			{
-				// Pad the end of the stream with 0xFF 0xD9 (EOI marker)
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Same as previous method, except can indicate if the character is a pad character or not.
-	inline uint jpeg_decoder::get_char(bool *pPadding_flag)
-	{
-		if (!m_in_buf_left)
-		{
-			prep_in_buffer();
-			if (!m_in_buf_left)
-			{
-				*pPadding_flag = true;
-				int t = m_tem_flag;
-				m_tem_flag ^= 1;
-				if (t)
-					return 0xD9;
-				else
-					return 0xFF;
-			}
-		}
-
-		*pPadding_flag = false;
-
-		uint c = *m_pIn_buf_ofs++;
-		m_in_buf_left--;
-
-		return c;
-	}
-
-	// Inserts a previously retrieved character back into the input buffer.
-	inline void jpeg_decoder::stuff_char(uint8 q)
-	{
-		*(--m_pIn_buf_ofs) = q;
-		m_in_buf_left++;
-	}
-
-	// Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered.
-	inline uint8 jpeg_decoder::get_octet()
-	{
-		bool padding_flag;
-		int c = get_char(&padding_flag);
-
-		if (c == 0xFF)
-		{
-			if (padding_flag)
-				return 0xFF;
-
-			c = get_char(&padding_flag);
-			if (padding_flag)
-			{
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-
-			if (c == 0x00)
-				return 0xFF;
-			else
-			{
-				stuff_char(static_cast<uint8>(c));
-				stuff_char(0xFF);
-				return 0xFF;
-			}
-		}
-
-		return static_cast<uint8>(c);
-	}
-
-	// Retrieves a variable number of bits from the input stream. Does not recognize markers.
-	inline uint jpeg_decoder::get_bits(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			uint c1 = get_char();
-			uint c2 = get_char();
-			m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2;
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered.
-	inline uint jpeg_decoder::get_bits_no_markers(int num_bits)
-	{
-		if (!num_bits)
-			return 0;
-
-		uint i = m_bit_buf >> (32 - num_bits);
-
-		if ((m_bits_left -= num_bits) <= 0)
-		{
-			m_bit_buf <<= (num_bits += m_bits_left);
-
-			if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF))
-			{
-				uint c1 = get_octet();
-				uint c2 = get_octet();
-				m_bit_buf |= (c1 << 8) | c2;
-			}
-			else
-			{
-				m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1];
-				m_in_buf_left -= 2;
-				m_pIn_buf_ofs += 2;
-			}
-
-			m_bit_buf <<= -m_bits_left;
-
-			m_bits_left += 16;
-
-			JPGD_ASSERT(m_bits_left >= 0);
-		}
-		else
-			m_bit_buf <<= num_bits;
-
-		return i;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0)
-		{
-			// Decode more bits, use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-		}
-		else
-			get_bits_no_markers(pH->code_size[symbol]);
-
-		return symbol;
-	}
-
-	// Decodes a Huffman encoded symbol.
-	inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits)
-	{
-		int symbol;
-
-		// Check first 8-bits: do we have a complete symbol?
-		if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0)
-		{
-			// Use a tree traversal to find symbol.
-			int ofs = 23;
-			do
-			{
-				symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))];
-				ofs--;
-			} while (symbol < 0);
-
-			get_bits_no_markers(8 + (23 - ofs));
-
-			extra_bits = get_bits_no_markers(symbol & 0xF);
-		}
-		else
-		{
-			JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0));
-
-			if (symbol & 0x8000)
-			{
-				get_bits_no_markers((symbol >> 8) & 31);
-				extra_bits = symbol >> 16;
-			}
-			else
-			{
-				int code_size = (symbol >> 8) & 31;
-				int num_extra_bits = symbol & 0xF;
-				int bits = code_size + num_extra_bits;
-				if (bits <= (m_bits_left + 16))
-					extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1);
-				else
-				{
-					get_bits_no_markers(code_size);
-					extra_bits = get_bits_no_markers(num_extra_bits);
-				}
-			}
-
-			symbol &= 0xFF;
-		}
-
-		return symbol;
-	}
-
-	// Tables and macro used to fully decode the DPCM differences.
-	static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 };
-	static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 };
-	static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) };
-#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x))
-
-	// Clamps a value between 0-255.
-	inline uint8 jpeg_decoder::clamp(int i)
-	{
-		if (static_cast<uint>(i) > 255)
-			i = (((~i) >> 31) & 0xFF);
-
-		return static_cast<uint8>(i);
-	}
-
-	namespace DCT_Upsample
-	{
-		struct Matrix44
-		{
-			typedef int Element_Type;
-			enum { NUM_ROWS = 4, NUM_COLS = 4 };
-
-			Element_Type v[NUM_ROWS][NUM_COLS];
-
-			inline int rows() const { return NUM_ROWS; }
-			inline int cols() const { return NUM_COLS; }
-
-			inline const Element_Type & at(int r, int c) const { return v[r][c]; }
-			inline       Element_Type & at(int r, int c)       { return v[r][c]; }
-
-			inline Matrix44() { }
-
-			inline Matrix44& operator += (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) += a.at(r, 0);
-					at(r, 1) += a.at(r, 1);
-					at(r, 2) += a.at(r, 2);
-					at(r, 3) += a.at(r, 3);
-				}
-				return *this;
-			}
-
-			inline Matrix44& operator -= (const Matrix44& a)
-			{
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					at(r, 0) -= a.at(r, 0);
-					at(r, 1) -= a.at(r, 1);
-					at(r, 2) -= a.at(r, 2);
-					at(r, 3) -= a.at(r, 3);
-				}
-				return *this;
-			}
-
-			friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) + b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) + b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) + b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) + b.at(r, 3);
-				}
-				return ret;
-			}
-
-			friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b)
-			{
-				Matrix44 ret;
-				for (int r = 0; r < NUM_ROWS; r++)
-				{
-					ret.at(r, 0) = a.at(r, 0) - b.at(r, 0);
-					ret.at(r, 1) = a.at(r, 1) - b.at(r, 1);
-					ret.at(r, 2) = a.at(r, 2) - b.at(r, 2);
-					ret.at(r, 3) = a.at(r, 3) - b.at(r, 3);
-				}
-				return ret;
-			}
-
-			static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) + b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) + b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) + b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) + b.at(r, 3));
-				}
-			}
-
-			static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b)
-			{
-				for (int r = 0; r < 4; r++)
-				{
-					pDst[0*8 + r] = static_cast<jpgd_block_t>(a.at(r, 0) - b.at(r, 0));
-					pDst[1*8 + r] = static_cast<jpgd_block_t>(a.at(r, 1) - b.at(r, 1));
-					pDst[2*8 + r] = static_cast<jpgd_block_t>(a.at(r, 2) - b.at(r, 2));
-					pDst[3*8 + r] = static_cast<jpgd_block_t>(a.at(r, 3) - b.at(r, 3));
-				}
-			}
-		};
-
-		const int FRACT_BITS = 10;
-		const int SCALE = 1 << FRACT_BITS;
-
-		typedef int Temp_Type;
-#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS)
-#define F(i) ((int)((i) * SCALE + .5f))
-
-		// Any decent C++ compiler will optimize this at compile time to a 0, or an array access.
-#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8])
-
-		// NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix
-		template<int NUM_ROWS, int NUM_COLS>
-		struct P_Q
-		{
-			static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X000 = AT(0, 0);
-				const Temp_Type X001 = AT(0, 1);
-				const Temp_Type X002 = AT(0, 2);
-				const Temp_Type X003 = AT(0, 3);
-				const Temp_Type X004 = AT(0, 4);
-				const Temp_Type X005 = AT(0, 5);
-				const Temp_Type X006 = AT(0, 6);
-				const Temp_Type X007 = AT(0, 7);
-				const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0));
-				const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1));
-				const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2));
-				const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3));
-				const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4));
-				const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5));
-				const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6));
-				const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7));
-				const Temp_Type X020 = AT(4, 0);
-				const Temp_Type X021 = AT(4, 1);
-				const Temp_Type X022 = AT(4, 2);
-				const Temp_Type X023 = AT(4, 3);
-				const Temp_Type X024 = AT(4, 4);
-				const Temp_Type X025 = AT(4, 5);
-				const Temp_Type X026 = AT(4, 6);
-				const Temp_Type X027 = AT(4, 7);
-				const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0));
-				const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1));
-				const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2));
-				const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3));
-				const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4));
-				const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5));
-				const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6));
-				const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7));
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				P.at(0, 0) = X000;
-				P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f));
-				P.at(0, 2) = X004;
-				P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f));
-				P.at(1, 0) = X010;
-				P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f));
-				P.at(1, 2) = X014;
-				P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f));
-				P.at(2, 0) = X020;
-				P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f));
-				P.at(2, 2) = X024;
-				P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f));
-				P.at(3, 0) = X030;
-				P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f));
-				P.at(3, 2) = X034;
-				P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f));
-				// 40 muls 24 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f));
-				Q.at(0, 1) = X002;
-				Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f));
-				Q.at(0, 3) = X006;
-				Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f));
-				Q.at(1, 1) = X012;
-				Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f));
-				Q.at(1, 3) = X016;
-				Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f));
-				Q.at(2, 1) = X022;
-				Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f));
-				Q.at(2, 3) = X026;
-				Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f));
-				Q.at(3, 1) = X032;
-				Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f));
-				Q.at(3, 3) = X036;
-				// 40 muls 24 adds
-			}
-		};
-
-		template<int NUM_ROWS, int NUM_COLS>
-		struct R_S
-		{
-			static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc)
-			{
-				// 4x8 = 4x8 times 8x8, matrix 0 is constant
-				const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0));
-				const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1));
-				const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2));
-				const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3));
-				const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4));
-				const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5));
-				const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6));
-				const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7));
-				const Temp_Type X110 = AT(2, 0);
-				const Temp_Type X111 = AT(2, 1);
-				const Temp_Type X112 = AT(2, 2);
-				const Temp_Type X113 = AT(2, 3);
-				const Temp_Type X114 = AT(2, 4);
-				const Temp_Type X115 = AT(2, 5);
-				const Temp_Type X116 = AT(2, 6);
-				const Temp_Type X117 = AT(2, 7);
-				const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0));
-				const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1));
-				const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2));
-				const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3));
-				const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4));
-				const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5));
-				const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6));
-				const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7));
-				const Temp_Type X130 = AT(6, 0);
-				const Temp_Type X131 = AT(6, 1);
-				const Temp_Type X132 = AT(6, 2);
-				const Temp_Type X133 = AT(6, 3);
-				const Temp_Type X134 = AT(6, 4);
-				const Temp_Type X135 = AT(6, 5);
-				const Temp_Type X136 = AT(6, 6);
-				const Temp_Type X137 = AT(6, 7);
-				// 80 muls 48 adds
-
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				R.at(0, 0) = X100;
-				R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f));
-				R.at(0, 2) = X104;
-				R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f));
-				R.at(1, 0) = X110;
-				R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f));
-				R.at(1, 2) = X114;
-				R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f));
-				R.at(2, 0) = X120;
-				R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f));
-				R.at(2, 2) = X124;
-				R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f));
-				R.at(3, 0) = X130;
-				R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f));
-				R.at(3, 2) = X134;
-				R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f));
-				// 40 muls 24 adds
-				// 4x4 = 4x8 times 8x4, matrix 1 is constant
-				S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f));
-				S.at(0, 1) = X102;
-				S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f));
-				S.at(0, 3) = X106;
-				S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f));
-				S.at(1, 1) = X112;
-				S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f));
-				S.at(1, 3) = X116;
-				S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f));
-				S.at(2, 1) = X122;
-				S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f));
-				S.at(2, 3) = X126;
-				S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f));
-				S.at(3, 1) = X132;
-				S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f));
-				S.at(3, 3) = X136;
-				// 40 muls 24 adds
-			}
-		};
-	} // end namespace DCT_Upsample
-
-	// Unconditionally frees all allocated m_blocks.
-	void jpeg_decoder::free_all_blocks()
-	{
-		m_pStream = NULL;
-		for (mem_block *b = m_pMem_blocks; b; )
-		{
-			mem_block *n = b->m_pNext;
-			jpgd_free(b);
-			b = n;
-		}
-		m_pMem_blocks = NULL;
-	}
-
-	// This method handles all errors.
-	// It could easily be changed to use C++ exceptions.
-	void jpeg_decoder::stop_decoding(jpgd_status status)
-	{
-		m_error_code = status;
-		free_all_blocks();
-		longjmp(m_jmp_state, status);
-
-		// we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit
-		// that this function doesn't return, otherwise we get this error:
-		// 
-		// error : function declared 'noreturn' should not return
-		exit(1);
-	}
-
-	void *jpeg_decoder::alloc(size_t nSize, bool zero)
-	{
-		nSize = (JPGD_MAX(nSize, 1) + 3) & ~3;
-		char *rv = NULL;
-		for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext)
-		{
-			if ((b->m_used_count + nSize) <= b->m_size)
-			{
-				rv = b->m_data + b->m_used_count;
-				b->m_used_count += nSize;
-				break;
-			}
-		}
-		if (!rv)
-		{
-			int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047);
-			mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity);
-			if (!b) stop_decoding(JPGD_NOTENOUGHMEM);
-			b->m_pNext = m_pMem_blocks; m_pMem_blocks = b;
-			b->m_used_count = nSize;
-			b->m_size = capacity;
-			rv = b->m_data;
-		}
-		if (zero) memset(rv, 0, nSize);
-		return rv;
-	}
-
-	void jpeg_decoder::word_clear(void *p, uint16 c, uint n)
-	{
-		uint8 *pD = (uint8*)p;
-		const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF;
-		while (n)
-		{
-			pD[0] = l; pD[1] = h; pD += 2;
-			n--;
-		}
-	}
-
-	// Refill the input buffer.
-	// This method will sit in a loop until (A) the buffer is full or (B)
-	// the stream's read() method reports and end of file condition.
-	void jpeg_decoder::prep_in_buffer()
-	{
-		m_in_buf_left = 0;
-		m_pIn_buf_ofs = m_in_buf;
-
-		if (m_eof_flag)
-			return;
-
-		do
-		{
-			int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag);
-			if (bytes_read == -1)
-				stop_decoding(JPGD_STREAM_READ);
-
-			m_in_buf_left += bytes_read;
-		} while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag));
-
-		m_total_bytes_read += m_in_buf_left;
-
-		// Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid).
-		// (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.)
-		word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64);
-	}
-
-	// Read a Huffman code table.
-	void jpeg_decoder::read_dht_marker()
-	{
-		int i, index, count;
-		uint8 huff_num[17];
-		uint8 huff_val[256];
-
-		uint num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DHT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			index = get_bits(8);
-
-			huff_num[0] = 0;
-
-			count = 0;
-
-			for (i = 1; i <= 16; i++)
-			{
-				huff_num[i] = static_cast<uint8>(get_bits(8));
-				count += huff_num[i];
-			}
-
-			if (count > 255)
-				stop_decoding(JPGD_BAD_DHT_COUNTS);
-
-			for (i = 0; i < count; i++)
-				huff_val[i] = static_cast<uint8>(get_bits(8));
-
-			i = 1 + 16 + count;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DHT_MARKER);
-
-			num_left -= i;
-
-			if ((index & 0x10) > 0x10)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1);
-
-			if (index >= JPGD_MAX_HUFF_TABLES)
-				stop_decoding(JPGD_BAD_DHT_INDEX);
-
-			if (!m_huff_num[index])
-				m_huff_num[index] = (uint8 *)alloc(17);
-
-			if (!m_huff_val[index])
-				m_huff_val[index] = (uint8 *)alloc(256);
-
-			m_huff_ac[index] = (index & 0x10) != 0;
-			memcpy(m_huff_num[index], huff_num, 17);
-			memcpy(m_huff_val[index], huff_val, 256);
-		}
-	}
-
-	// Read a quantization table.
-	void jpeg_decoder::read_dqt_marker()
-	{
-		int n, i, prec;
-		uint num_left;
-		uint temp;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_DQT_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			n = get_bits(8);
-			prec = n >> 4;
-			n &= 0x0F;
-
-			if (n >= JPGD_MAX_QUANT_TABLES)
-				stop_decoding(JPGD_BAD_DQT_TABLE);
-
-			if (!m_quant[n])
-				m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t));
-
-			// read quantization entries, in zag order
-			for (i = 0; i < 64; i++)
-			{
-				temp = get_bits(8);
-
-				if (prec)
-					temp = (temp << 8) + get_bits(8);
-
-				m_quant[n][i] = static_cast<jpgd_quant_t>(temp);
-			}
-
-			i = 64 + 1;
-
-			if (prec)
-				i += 64;
-
-			if (num_left < (uint)i)
-				stop_decoding(JPGD_BAD_DQT_LENGTH);
-
-			num_left -= i;
-		}
-	}
-
-	// Read the start of frame (SOF) marker.
-	void jpeg_decoder::read_sof_marker()
-	{
-		int i;
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (get_bits(8) != 8)   /* precision: sorry, only 8-bit precision is supported right now */
-			stop_decoding(JPGD_BAD_PRECISION);
-
-		m_image_y_size = get_bits(16);
-
-		if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT))
-			stop_decoding(JPGD_BAD_HEIGHT);
-
-		m_image_x_size = get_bits(16);
-
-		if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH))
-			stop_decoding(JPGD_BAD_WIDTH);
-
-		m_comps_in_frame = get_bits(8);
-
-		if (m_comps_in_frame > JPGD_MAX_COMPONENTS)
-			stop_decoding(JPGD_TOO_MANY_COMPONENTS);
-
-		if (num_left != (uint)(m_comps_in_frame * 3 + 8))
-			stop_decoding(JPGD_BAD_SOF_LENGTH);
-
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_comp_ident[i]  = get_bits(8);
-			m_comp_h_samp[i] = get_bits(4);
-			m_comp_v_samp[i] = get_bits(4);
-			m_comp_quant[i]  = get_bits(8);
-		}
-	}
-
-	// Used to skip unrecognized markers.
-	void jpeg_decoder::skip_variable_marker()
-	{
-		uint num_left;
-
-		num_left = get_bits(16);
-
-		if (num_left < 2)
-			stop_decoding(JPGD_BAD_VARIABLE_MARKER);
-
-		num_left -= 2;
-
-		while (num_left)
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Read a define restart interval (DRI) marker.
-	void jpeg_decoder::read_dri_marker()
-	{
-		if (get_bits(16) != 4)
-			stop_decoding(JPGD_BAD_DRI_LENGTH);
-
-		m_restart_interval = get_bits(16);
-	}
-
-	// Read a start of scan (SOS) marker.
-	void jpeg_decoder::read_sos_marker()
-	{
-		uint num_left;
-		int i, ci, n, c, cc;
-
-		num_left = get_bits(16);
-
-		n = get_bits(8);
-
-		m_comps_in_scan = n;
-
-		num_left -= 3;
-
-		if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) )
-			stop_decoding(JPGD_BAD_SOS_LENGTH);
-
-		for (i = 0; i < n; i++)
-		{
-			cc = get_bits(8);
-			c = get_bits(8);
-			num_left -= 2;
-
-			for (ci = 0; ci < m_comps_in_frame; ci++)
-				if (cc == m_comp_ident[ci])
-					break;
-
-			if (ci >= m_comps_in_frame)
-				stop_decoding(JPGD_BAD_SOS_COMP_ID);
-
-			m_comp_list[i]    = ci;
-			m_comp_dc_tab[ci] = (c >> 4) & 15;
-			m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1);
-		}
-
-		m_spectral_start  = get_bits(8);
-		m_spectral_end    = get_bits(8);
-		m_successive_high = get_bits(4);
-		m_successive_low  = get_bits(4);
-
-		if (!m_progressive_flag)
-		{
-			m_spectral_start = 0;
-			m_spectral_end = 63;
-		}
-
-		num_left -= 3;
-
-		while (num_left)                  /* read past whatever is num_left */
-		{
-			get_bits(8);
-			num_left--;
-		}
-	}
-
-	// Finds the next marker.
-	int jpeg_decoder::next_marker()
-	{
-		uint c, bytes;
-
-		bytes = 0;
-
-		do
-		{
-			do
-			{
-				bytes++;
-				c = get_bits(8);
-			} while (c != 0xFF);
-
-			do
-			{
-				c = get_bits(8);
-			} while (c == 0xFF);
-
-		} while (c == 0);
-
-		// If bytes > 0 here, there where extra bytes before the marker (not good).
-
-		return c;
-	}
-
-	// Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is
-	// encountered.
-	int jpeg_decoder::process_markers()
-	{
-		int c;
-
-		for ( ; ; )
-		{
-			c = next_marker();
-
-			switch (c)
-			{
-			case M_SOF0:
-			case M_SOF1:
-			case M_SOF2:
-			case M_SOF3:
-			case M_SOF5:
-			case M_SOF6:
-			case M_SOF7:
-				//      case M_JPG:
-			case M_SOF9:
-			case M_SOF10:
-			case M_SOF11:
-			case M_SOF13:
-			case M_SOF14:
-			case M_SOF15:
-			case M_SOI:
-			case M_EOI:
-			case M_SOS:
-				{
-					return c;
-				}
-			case M_DHT:
-				{
-					read_dht_marker();
-					break;
-				}
-				// No arithmitic support - dumb patents!
-			case M_DAC:
-				{
-					stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-					break;
-				}
-			case M_DQT:
-				{
-					read_dqt_marker();
-					break;
-				}
-			case M_DRI:
-				{
-					read_dri_marker();
-					break;
-				}
-				//case M_APP0:  /* no need to read the JFIF marker */
-
-			case M_JPG:
-			case M_RST0:    /* no parameters */
-			case M_RST1:
-			case M_RST2:
-			case M_RST3:
-			case M_RST4:
-			case M_RST5:
-			case M_RST6:
-			case M_RST7:
-			case M_TEM:
-				{
-					stop_decoding(JPGD_UNEXPECTED_MARKER);
-					break;
-				}
-			default:    /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */
-				{
-					skip_variable_marker();
-					break;
-				}
-			}
-		}
-	}
-
-	// Finds the start of image (SOI) marker.
-	// This code is rather defensive: it only checks the first 512 bytes to avoid
-	// false positives.
-	void jpeg_decoder::locate_soi_marker()
-	{
-		uint lastchar, thischar;
-		uint bytesleft;
-
-		lastchar = get_bits(8);
-
-		thischar = get_bits(8);
-
-		/* ok if it's a normal JPEG file without a special header */
-
-		if ((lastchar == 0xFF) && (thischar == M_SOI))
-			return;
-
-		bytesleft = 4096; //512;
-
-		for ( ; ; )
-		{
-			if (--bytesleft == 0)
-				stop_decoding(JPGD_NOT_JPEG);
-
-			lastchar = thischar;
-
-			thischar = get_bits(8);
-
-			if (lastchar == 0xFF)
-			{
-				if (thischar == M_SOI)
-					break;
-				else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end
-					stop_decoding(JPGD_NOT_JPEG);
-			}
-		}
-
-		// Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad.
-		thischar = (m_bit_buf >> 24) & 0xFF;
-
-		if (thischar != 0xFF)
-			stop_decoding(JPGD_NOT_JPEG);
-	}
-
-	// Find a start of frame (SOF) marker.
-	void jpeg_decoder::locate_sof_marker()
-	{
-		locate_soi_marker();
-
-		int c = process_markers();
-
-		switch (c)
-		{
-		case M_SOF2:
-			m_progressive_flag = JPGD_TRUE;
-		case M_SOF0:  /* baseline DCT */
-		case M_SOF1:  /* extended sequential DCT */
-			{
-				read_sof_marker();
-				break;
-			}
-		case M_SOF9:  /* Arithmitic coding */
-			{
-				stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT);
-				break;
-			}
-		default:
-			{
-				stop_decoding(JPGD_UNSUPPORTED_MARKER);
-				break;
-			}
-		}
-	}
-
-	// Find a start of scan (SOS) marker.
-	int jpeg_decoder::locate_sos_marker()
-	{
-		int c;
-
-		c = process_markers();
-
-		if (c == M_EOI)
-			return JPGD_FALSE;
-		else if (c != M_SOS)
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-
-		read_sos_marker();
-
-		return JPGD_TRUE;
-	}
-
-	// Reset everything to default/uninitialized state.
-	void jpeg_decoder::init(jpeg_decoder_stream *pStream)
-	{
-		m_pMem_blocks = NULL;
-		m_error_code = JPGD_SUCCESS;
-		m_ready_flag = false;
-		m_image_x_size = m_image_y_size = 0;
-		m_pStream = pStream;
-		m_progressive_flag = JPGD_FALSE;
-
-		memset(m_huff_ac, 0, sizeof(m_huff_ac));
-		memset(m_huff_num, 0, sizeof(m_huff_num));
-		memset(m_huff_val, 0, sizeof(m_huff_val));
-		memset(m_quant, 0, sizeof(m_quant));
-
-		m_scan_type = 0;
-		m_comps_in_frame = 0;
-
-		memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp));
-		memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp));
-		memset(m_comp_quant, 0, sizeof(m_comp_quant));
-		memset(m_comp_ident, 0, sizeof(m_comp_ident));
-		memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks));
-		memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks));
-
-		m_comps_in_scan = 0;
-		memset(m_comp_list, 0, sizeof(m_comp_list));
-		memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab));
-		memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab));
-
-		m_spectral_start = 0;
-		m_spectral_end = 0;
-		m_successive_low = 0;
-		m_successive_high = 0;
-		m_max_mcu_x_size = 0;
-		m_max_mcu_y_size = 0;
-		m_blocks_per_mcu = 0;
-		m_max_blocks_per_row = 0;
-		m_mcus_per_row = 0;
-		m_mcus_per_col = 0;
-		m_expanded_blocks_per_component = 0;
-		m_expanded_blocks_per_mcu = 0;
-		m_expanded_blocks_per_row = 0;
-		m_freq_domain_chroma_upsample = false;
-
-		memset(m_mcu_org, 0, sizeof(m_mcu_org));
-
-		m_total_lines_left = 0;
-		m_mcu_lines_left = 0;
-		m_real_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_scan_line = 0;
-		m_dest_bytes_per_pixel = 0;
-
-		memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs));
-
-		memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs));
-		memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs));
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_eob_run = 0;
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		m_pIn_buf_ofs = m_in_buf;
-		m_in_buf_left = 0;
-		m_eof_flag = false;
-		m_tem_flag = 0;
-
-		memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start));
-		memset(m_in_buf, 0, sizeof(m_in_buf));
-		memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end));
-
-		m_restart_interval = 0;
-		m_restarts_left    = 0;
-		m_next_restart_num = 0;
-
-		m_max_mcus_per_row = 0;
-		m_max_blocks_per_mcu = 0;
-		m_max_mcus_per_col = 0;
-
-		memset(m_last_dc_val, 0, sizeof(m_last_dc_val));
-		m_pMCU_coefficients = NULL;
-		m_pSample_buf = NULL;
-
-		m_total_bytes_read = 0;
-
-		m_pScan_line_0 = NULL;
-		m_pScan_line_1 = NULL;
-
-		// Ready the input buffer.
-		prep_in_buffer();
-
-		// Prime the bit buffer.
-		m_bits_left = 16;
-		m_bit_buf = 0;
-
-		get_bits(16);
-		get_bits(16);
-
-		for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++)
-			m_mcu_block_max_zag[i] = 64;
-	}
-
-#define SCALEBITS 16
-#define ONE_HALF  ((int) 1 << (SCALEBITS-1))
-#define FIX(x)    ((int) ((x) * (1L<<SCALEBITS) + 0.5f))
-
-	// Create a few tables that allow us to quickly convert YCbCr to RGB.
-	void jpeg_decoder::create_look_ups()
-	{
-		for (int i = 0; i <= 255; i++)
-		{
-			int k = i - 128;
-			m_crr[i] = ( FIX(1.40200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_cbb[i] = ( FIX(1.77200f)  * k + ONE_HALF) >> SCALEBITS;
-			m_crg[i] = (-FIX(0.71414f)) * k;
-			m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF;
-		}
-	}
-
-	// This method throws back into the stream any bytes that where read
-	// into the bit buffer during initial marker scanning.
-	void jpeg_decoder::fix_in_buffer()
-	{
-		// In case any 0xFF's where pulled into the buffer during marker scanning.
-		JPGD_ASSERT((m_bits_left & 7) == 0);
-
-		if (m_bits_left == 16)
-			stuff_char( (uint8)(m_bit_buf & 0xFF));
-
-		if (m_bits_left >= 8)
-			stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF));
-
-		stuff_char((uint8)((m_bit_buf >> 16) & 0xFF));
-		stuff_char((uint8)((m_bit_buf >> 24) & 0xFF));
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	void jpeg_decoder::transform_mcu(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64;
-
-		for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-	}
-
-	static const uint8 s_max_rc[64] =
-	{
-		17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86,
-		102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
-		136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136
-	};
-
-	void jpeg_decoder::transform_mcu_expand(int mcu_row)
-	{
-		jpgd_block_t* pSrc_ptr = m_pMCU_coefficients;
-		uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64;
-
-		// Y IDCT
-		int mcu_block;
-		for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++)
-		{
-			idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]);
-			pSrc_ptr += 64;
-			pDst_ptr += 64;
-		}
-
-		// Chroma IDCT, with upsampling
-		jpgd_block_t temp_block[64];
-
-		for (int i = 0; i < 2; i++)
-		{
-			DCT_Upsample::Matrix44 P, Q, R, S;
-
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1);
-			JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64);
-
-			switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1])
-			{
-			case 1*16+1:
-				DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr);
-				break;
-			case 1*16+2:
-				DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 2*16+2:
-				DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+2:
-				DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+3:
-				DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr);
-				break;
-			case 3*16+4:
-				DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 4*16+4:
-				DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+4:
-				DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+5:
-				DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr);
-				break;
-			case 5*16+6:
-				DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 6*16+6:
-				DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+6:
-				DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+7:
-				DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr);
-				break;
-			case 7*16+8:
-				DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr);
-				break;
-			case 8*16+8:
-				DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr);
-				DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr);
-				break;
-			default:
-				JPGD_ASSERT(false);
-			}
-
-			DCT_Upsample::Matrix44 a(P + Q); P -= Q;
-			DCT_Upsample::Matrix44& b = P;
-			DCT_Upsample::Matrix44 c(R + S); R -= S;
-			DCT_Upsample::Matrix44& d = R;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::add_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d);
-			idct_4x4(temp_block, pDst_ptr);
-			pDst_ptr += 64;
-
-			pSrc_ptr += 64;
-		}
-	}
-
-	// Loads and dequantizes the next row of (already decoded) coefficients.
-	// Progressive images only.
-	void jpeg_decoder::load_next_row()
-	{
-		int i;
-		jpgd_block_t *p;
-		jpgd_quant_t *q;
-		int mcu_row, mcu_block, row_block = 0;
-		int component_num, component_id;
-		int block_x_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int));
-
-		for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-			for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-			{
-				component_id = m_mcu_org[mcu_block];
-				q = m_quant[m_comp_quant[component_id]];
-
-				p = m_pMCU_coefficients + 64 * mcu_block;
-
-				jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-				p[0] = pDC[0];
-				memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t));
-
-				for (i = 63; i > 0; i--)
-					if (p[g_ZAG[i]])
-						break;
-
-				m_mcu_block_max_zag[mcu_block] = i + 1;
-
-				for ( ; i >= 0; i--)
-					if (p[g_ZAG[i]])
-						p[g_ZAG[i]] = static_cast<jpgd_block_t>(p[g_ZAG[i]] * q[i]);
-
-				row_block++;
-
-				if (m_comps_in_scan == 1)
-					block_x_mcu[component_id]++;
-				else
-				{
-					if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-					{
-						block_x_mcu_ofs = 0;
-
-						if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-						{
-							block_y_mcu_ofs = 0;
-
-							block_x_mcu[component_id] += m_comp_h_samp[component_id];
-						}
-					}
-				}
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-		}
-
-		if (m_comps_in_scan == 1)
-			m_block_y_mcu[m_comp_list[0]]++;
-		else
-		{
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				component_id = m_comp_list[component_num];
-
-				m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-			}
-		}
-	}
-
-	// Restart interval processing.
-	void jpeg_decoder::process_restart()
-	{
-		int i;
-		int c = 0;
-
-		// Align to a byte boundry
-		// FIXME: Is this really necessary? get_bits_no_markers() never reads in markers!
-		//get_bits_no_markers(m_bits_left & 7);
-
-		// Let's scan a little bit to find the marker, but not _too_ far.
-		// 1536 is a "fudge factor" that determines how much to scan.
-		for (i = 1536; i > 0; i--)
-			if (get_char() == 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		for ( ; i > 0; i--)
-			if ((c = get_char()) != 0xFF)
-				break;
-
-		if (i == 0)
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Is it the expected marker? If not, something bad happened.
-		if (c != (m_next_restart_num + M_RST0))
-			stop_decoding(JPGD_BAD_RESTART_MARKER);
-
-		// Reset each component's DC prediction values.
-		memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		m_restarts_left = m_restart_interval;
-
-		m_next_restart_num = (m_next_restart_num + 1) & 7;
-
-		// Get the bit buffer going again...
-
-		m_bits_left = 16;
-		get_bits_no_markers(16);
-		get_bits_no_markers(16);
-	}
-
-	static inline int dequantize_ac(int c, int q) {	c *= q;	return c; }
-
-	// Decodes and dequantizes the next row of coefficients.
-	void jpeg_decoder::decode_next_row()
-	{
-		int row_block = 0;
-
-		for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-		{
-			if ((m_restart_interval) && (m_restarts_left == 0))
-				process_restart();
-
-			jpgd_block_t* p = m_pMCU_coefficients;
-			for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64)
-			{
-				int component_id = m_mcu_org[mcu_block];
-				jpgd_quant_t* q = m_quant[m_comp_quant[component_id]];
-
-				int r, s;
-				s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r);
-				s = HUFF_EXTEND(r, s);
-
-				m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]);
-
-				p[0] = static_cast<jpgd_block_t>(s * q[0]);
-
-				int prev_num_set = m_mcu_block_max_zag[mcu_block];
-
-				huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]];
-
-				int k;
-				for (k = 1; k < 64; k++)
-				{
-					int extra_bits;
-					s = huff_decode(pH, extra_bits);
-
-					r = s >> 4;
-					s &= 15;
-
-					if (s)
-					{
-						if (r)
-						{
-							if ((k + r) > 63)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(r, prev_num_set - k);
-								int kt = k;
-								while (n--)
-									p[g_ZAG[kt++]] = 0;
-							}
-
-							k += r;
-						}
-
-						s = HUFF_EXTEND(extra_bits, s);
-
-						JPGD_ASSERT(k < 64);
-
-						p[g_ZAG[k]] = static_cast<jpgd_block_t>(dequantize_ac(s, q[k])); //s * q[k];
-					}
-					else
-					{
-						if (r == 15)
-						{
-							if ((k + 16) > 64)
-								stop_decoding(JPGD_DECODE_ERROR);
-
-							if (k < prev_num_set)
-							{
-								int n = JPGD_MIN(16, prev_num_set - k);
-								int kt = k;
-								while (n--)
-								{
-									JPGD_ASSERT(kt <= 63);
-									p[g_ZAG[kt++]] = 0;
-								}
-							}
-
-							k += 16 - 1; // - 1 because the loop counter is k
-							// BEGIN EPIC MOD
-							JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0);
-							// END EPIC MOD
-						}
-						else
-							break;
-					}
-				}
-
-				if (k < prev_num_set)
-				{
-					int kt = k;
-					while (kt < prev_num_set)
-						p[g_ZAG[kt++]] = 0;
-				}
-
-				m_mcu_block_max_zag[mcu_block] = k;
-
-				row_block++;
-			}
-
-			if (m_freq_domain_chroma_upsample)
-				transform_mcu_expand(mcu_row);
-			else
-				transform_mcu(mcu_row);
-
-			m_restarts_left--;
-		}
-	}
-
-	// YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int y = s[j];
-				int cb = s[64+j];
-				int cr = s[128+j];
-
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d[0] = clamp(y + m_cbb[cb]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_crr[cr]);
-					d[3] = 255;
-				}
-				else
-				{
-					d[0] = clamp(y + m_crr[cr]);
-					d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-					d[2] = clamp(y + m_cbb[cb]);
-					d[3] = 255;
-				}
-				d += 4;
-			}
-
-			s += 64*3;
-		}
-	}
-
-	// YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V1Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *y = m_pSample_buf + row * 8;
-		uint8 *c = m_pSample_buf + 2*64 + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 4; j++)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j<<1];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[(j<<1)+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-					}
-
-					d0 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*4 - 64*2;
-			c += 64*4 - 8;
-		}
-	}
-
-	// YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB
-	void jpeg_decoder::H1V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*1 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*2 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int j = 0; j < 8; j++)
-			{
-				int cb = c[0+j];
-				int cr = c[64+j];
-
-				int rc = m_crr[cr];
-				int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-				int bc = m_cbb[cb];
-
-				int yy = y[j];
-				if (jpg_format == ERGBFormatJPG::BGRA)
-				{
-					d0[0] = clamp(yy+bc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+rc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+bc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+rc);
-					d1[3] = 255;
-				}
-				else
-				{
-					d0[0] = clamp(yy+rc);
-					d0[1] = clamp(yy+gc);
-					d0[2] = clamp(yy+bc);
-					d0[3] = 255;
-					yy = y[8+j];
-					d1[0] = clamp(yy+rc);
-					d1[1] = clamp(yy+gc);
-					d1[2] = clamp(yy+bc);
-					d1[3] = 255;
-				}
-
-				d0 += 4;
-				d1 += 4;
-			}
-
-			y += 64*4;
-			c += 64*4;
-		}
-	}
-
-	// YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB
-	void jpeg_decoder::H2V2Convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d0 = m_pScan_line_0;
-		uint8 *d1 = m_pScan_line_1;
-		uint8 *y;
-		uint8 *c;
-
-		if (row < 8)
-			y = m_pSample_buf + row * 8;
-		else
-			y = m_pSample_buf + 64*2 + (row & 7) * 8;
-
-		c = m_pSample_buf + 64*4 + (row >> 1) * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int l = 0; l < 2; l++)
-			{
-				for (int j = 0; j < 8; j += 2)
-				{
-					int cb = c[0];
-					int cr = c[64];
-
-					int rc = m_crr[cr];
-					int gc = ((m_crg[cr] + m_cbg[cb]) >> 16);
-					int bc = m_cbb[cb];
-
-					int yy = y[j];
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d0[0] = clamp(yy+bc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+rc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+bc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+rc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+bc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+rc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+bc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+rc);
-						d1[7] = 255;
-					}
-					else
-					{
-						d0[0] = clamp(yy+rc);
-						d0[1] = clamp(yy+gc);
-						d0[2] = clamp(yy+bc);
-						d0[3] = 255;
-						yy = y[j+1];
-						d0[4] = clamp(yy+rc);
-						d0[5] = clamp(yy+gc);
-						d0[6] = clamp(yy+bc);
-						d0[7] = 255;
-						yy = y[j+8];
-						d1[0] = clamp(yy+rc);
-						d1[1] = clamp(yy+gc);
-						d1[2] = clamp(yy+bc);
-						d1[3] = 255;
-						yy = y[j+8+1];
-						d1[4] = clamp(yy+rc);
-						d1[5] = clamp(yy+gc);
-						d1[6] = clamp(yy+bc);
-						d1[7] = 255;
-					}
-
-					d0 += 8;
-					d1 += 8;
-
-					c++;
-				}
-				y += 64;
-			}
-
-			y += 64*6 - 64*2;
-			c += 64*6 - 8;
-		}
-	}
-
-	// Y (1 block per MCU) to 8-bit grayscale
-	void jpeg_decoder::gray_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-		uint8 *d = m_pScan_line_0;
-		uint8 *s = m_pSample_buf + row * 8;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			*(uint *)d = *(uint *)s;
-			*(uint *)(&d[4]) = *(uint *)(&s[4]);
-
-			s += 64;
-			d += 8;
-		}
-	}
-
-	void jpeg_decoder::expanded_convert()
-	{
-		int row = m_max_mcu_y_size - m_mcu_lines_left;
-
-		uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8;
-
-		uint8* d = m_pScan_line_0;
-
-		for (int i = m_max_mcus_per_row; i > 0; i--)
-		{
-			for (int k = 0; k < m_max_mcu_x_size; k += 8)
-			{
-				const int Y_ofs = k * 8;
-				const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component;
-				const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2;
-				for (int j = 0; j < 8; j++)
-				{
-					int y = Py[Y_ofs + j];
-					int cb = Py[Cb_ofs + j];
-					int cr = Py[Cr_ofs + j];
-
-					if (jpg_format == ERGBFormatJPG::BGRA)
-					{
-						d[0] = clamp(y + m_cbb[cb]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_crr[cr]);
-						d[3] = 255;
-					}
-					else
-					{
-						d[0] = clamp(y + m_crr[cr]);
-						d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16));
-						d[2] = clamp(y + m_cbb[cb]);
-						d[3] = 255;
-					}
-
-					d += 4;
-				}
-			}
-
-			Py += 64 * m_expanded_blocks_per_mcu;
-		}
-	}
-
-	// Find end of image (EOI) marker, so we can return to the user the exact size of the input stream.
-	void jpeg_decoder::find_eoi()
-	{
-		if (!m_progressive_flag)
-		{
-			// Attempt to read the EOI marker.
-			//get_bits_no_markers(m_bits_left & 7);
-
-			// Prime the bit buffer
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-
-			// The next marker _should_ be EOI
-			process_markers();
-		}
-
-		m_total_bytes_read -= m_in_buf_left;
-	}
-
-	int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len)
-	{
-		if ((m_error_code) || (!m_ready_flag))
-			return JPGD_FAILED;
-
-		if (m_total_lines_left == 0)
-			return JPGD_DONE;
-
-		if (m_mcu_lines_left == 0)
-		{
-			if (setjmp(m_jmp_state))
-				return JPGD_FAILED;
-
-			if (m_progressive_flag)
-				load_next_row();
-			else
-				decode_next_row();
-
-			// Find the EOI marker if that was the last row.
-			if (m_total_lines_left <= m_max_mcu_y_size)
-				find_eoi();
-
-			m_mcu_lines_left = m_max_mcu_y_size;
-		}
-
-		if (m_freq_domain_chroma_upsample)
-		{
-			expanded_convert();
-			*pScan_line = m_pScan_line_0;
-		}
-		else
-		{
-			switch (m_scan_type)
-			{
-			case JPGD_YH2V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H2V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH2V1:
-				{
-					H2V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_YH1V2:
-				{
-					if ((m_mcu_lines_left & 1) == 0)
-					{
-						H1V2Convert();
-						*pScan_line = m_pScan_line_0;
-					}
-					else
-						*pScan_line = m_pScan_line_1;
-
-					break;
-				}
-			case JPGD_YH1V1:
-				{
-					H1V1Convert();
-					*pScan_line = m_pScan_line_0;
-					break;
-				}
-			case JPGD_GRAYSCALE:
-				{
-					gray_convert();
-					*pScan_line = m_pScan_line_0;
-
-					break;
-				}
-			}
-		}
-
-		*pScan_line_len = m_real_dest_bytes_per_scan_line;
-
-		m_mcu_lines_left--;
-		m_total_lines_left--;
-
-		return JPGD_SUCCESS;
-	}
-
-	// Creates the tables needed for efficient Huffman decoding.
-	void jpeg_decoder::make_huff_table(int index, huff_tables *pH)
-	{
-		int p, i, l, si;
-		uint8 huffsize[257];
-		uint huffcode[257];
-		uint code;
-		uint subtree;
-		int code_size;
-		int lastp;
-		int nextfreeentry;
-		int currententry;
-
-		pH->ac_table = m_huff_ac[index] != 0;
-
-		p = 0;
-
-		for (l = 1; l <= 16; l++)
-		{
-			for (i = 1; i <= m_huff_num[index][l]; i++)
-				huffsize[p++] = static_cast<uint8>(l);
-		}
-
-		huffsize[p] = 0;
-
-		lastp = p;
-
-		code = 0;
-		si = huffsize[0];
-		p = 0;
-
-		while (huffsize[p])
-		{
-			while (huffsize[p] == si)
-			{
-				huffcode[p++] = code;
-				code++;
-			}
-
-			code <<= 1;
-			si++;
-		}
-
-		memset(pH->look_up, 0, sizeof(pH->look_up));
-		memset(pH->look_up2, 0, sizeof(pH->look_up2));
-		memset(pH->tree, 0, sizeof(pH->tree));
-		memset(pH->code_size, 0, sizeof(pH->code_size));
-
-		nextfreeentry = -1;
-
-		p = 0;
-
-		while (p < lastp)
-		{
-			i = m_huff_val[index][p];
-			code = huffcode[p];
-			code_size = huffsize[p];
-
-			pH->code_size[i] = static_cast<uint8>(code_size);
-
-			if (code_size <= 8)
-			{
-				code <<= (8 - code_size);
-
-				for (l = 1 << (8 - code_size); l > 0; l--)
-				{
-					JPGD_ASSERT(i < 256);
-
-					pH->look_up[code] = i;
-
-					bool has_extrabits = false;
-					int extra_bits = 0;
-					int num_extra_bits = i & 15;
-
-					int bits_to_fetch = code_size;
-					if (num_extra_bits)
-					{
-						int total_codesize = code_size + num_extra_bits;
-						if (total_codesize <= 8)
-						{
-							has_extrabits = true;
-							extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize));
-							JPGD_ASSERT(extra_bits <= 0x7FFF);
-							bits_to_fetch += num_extra_bits;
-						}
-					}
-
-					if (!has_extrabits)
-						pH->look_up2[code] = i | (bits_to_fetch << 8);
-					else
-						pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8);
-
-					code++;
-				}
-			}
-			else
-			{
-				subtree = (code >> (code_size - 8)) & 0xFF;
-
-				currententry = pH->look_up[subtree];
-
-				if (currententry == 0)
-				{
-					pH->look_up[subtree] = currententry = nextfreeentry;
-					pH->look_up2[subtree] = currententry = nextfreeentry;
-
-					nextfreeentry -= 2;
-				}
-
-				code <<= (16 - (code_size - 8));
-
-				for (l = code_size; l > 9; l--)
-				{
-					if ((code & 0x8000) == 0)
-						currententry--;
-
-					if (pH->tree[-currententry - 1] == 0)
-					{
-						pH->tree[-currententry - 1] = nextfreeentry;
-
-						currententry = nextfreeentry;
-
-						nextfreeentry -= 2;
-					}
-					else
-						currententry = pH->tree[-currententry - 1];
-
-					code <<= 1;
-				}
-
-				if ((code & 0x8000) == 0)
-					currententry--;
-
-				pH->tree[-currententry - 1] = i;
-			}
-
-			p++;
-		}
-	}
-
-	// Verifies the quantization tables needed for this scan are available.
-	void jpeg_decoder::check_quant_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-			if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL)
-				stop_decoding(JPGD_UNDEFINED_QUANT_TABLE);
-	}
-
-	// Verifies that all the Huffman tables needed for this scan are available.
-	void jpeg_decoder::check_huff_tables()
-	{
-		for (int i = 0; i < m_comps_in_scan; i++)
-		{
-			if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-
-			if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL))
-				stop_decoding(JPGD_UNDEFINED_HUFF_TABLE);
-		}
-
-		for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++)
-			if (m_huff_num[i])
-			{
-				if (!m_pHuff_tabs[i])
-					m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables));
-
-				make_huff_table(i, m_pHuff_tabs[i]);
-			}
-	}
-
-	// Determines the component order inside each MCU.
-	// Also calcs how many MCU's are on each row, etc.
-	void jpeg_decoder::calc_mcu_block_order()
-	{
-		int component_num, component_id;
-		int max_h_samp = 0, max_v_samp = 0;
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			if (m_comp_h_samp[component_id] > max_h_samp)
-				max_h_samp = m_comp_h_samp[component_id];
-
-			if (m_comp_v_samp[component_id] > max_v_samp)
-				max_v_samp = m_comp_v_samp[component_id];
-		}
-
-		for (component_id = 0; component_id < m_comps_in_frame; component_id++)
-		{
-			m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8;
-			m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]];
-			m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]];
-		}
-		else
-		{
-			m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp;
-			m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp;
-		}
-
-		if (m_comps_in_scan == 1)
-		{
-			m_mcu_org[0] = m_comp_list[0];
-
-			m_blocks_per_mcu = 1;
-		}
-		else
-		{
-			m_blocks_per_mcu = 0;
-
-			for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-			{
-				int num_blocks;
-
-				component_id = m_comp_list[component_num];
-
-				num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id];
-
-				while (num_blocks--)
-					m_mcu_org[m_blocks_per_mcu++] = component_id;
-			}
-		}
-	}
-
-	// Starts a new scan.
-	int jpeg_decoder::init_scan()
-	{
-		if (!locate_sos_marker())
-			return JPGD_FALSE;
-
-		calc_mcu_block_order();
-
-		check_huff_tables();
-
-		check_quant_tables();
-
-		memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint));
-
-		m_eob_run = 0;
-
-		if (m_restart_interval)
-		{
-			m_restarts_left = m_restart_interval;
-			m_next_restart_num = 0;
-		}
-
-		fix_in_buffer();
-
-		return JPGD_TRUE;
-	}
-
-	// Starts a frame. Determines if the number of components or sampling factors
-	// are supported.
-	void jpeg_decoder::init_frame()
-	{
-		int i;
-
-		if (m_comps_in_frame == 1)
-		{
-			if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1))
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			m_scan_type = JPGD_GRAYSCALE;
-			m_max_blocks_per_mcu = 1;
-			m_max_mcu_x_size = 8;
-			m_max_mcu_y_size = 8;
-		}
-		else if (m_comps_in_frame == 3)
-		{
-			if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) ||
-				((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) )
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-
-			if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH1V1;
-
-				m_max_blocks_per_mcu = 3;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1))
-			{
-				m_scan_type = JPGD_YH2V1;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 8;
-			}
-			else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH1V2;
-				m_max_blocks_per_mcu = 4;
-				m_max_mcu_x_size = 8;
-				m_max_mcu_y_size = 16;
-			}
-			else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2))
-			{
-				m_scan_type = JPGD_YH2V2;
-				m_max_blocks_per_mcu = 6;
-				m_max_mcu_x_size = 16;
-				m_max_mcu_y_size = 16;
-			}
-			else
-				stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS);
-		}
-		else
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size;
-		m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size;
-
-		// These values are for the *destination* pixels: after conversion.
-		if (m_scan_type == JPGD_GRAYSCALE)
-			m_dest_bytes_per_pixel = 1;
-		else
-			m_dest_bytes_per_pixel = 4;
-
-		m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel;
-
-		m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel);
-
-		// Initialize two scan line buffers.
-		m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-		if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2))
-			m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true);
-
-		m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu;
-
-		// Should never happen
-		if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW)
-			stop_decoding(JPGD_ASSERTION_ERROR);
-
-		// Allocate the coefficient buffer, enough for one MCU
-		m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t));
-
-		for (i = 0; i < m_max_blocks_per_mcu; i++)
-			m_mcu_block_max_zag[i] = 64;
-
-		m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0];
-		m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame;
-		m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu;
-		// Freq. domain chroma upsampling is only supported for H2V2 subsampling factor.
-// BEGIN EPIC MOD
-#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING
-		m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3);
-#else
-		m_freq_domain_chroma_upsample = 0;
-#endif
-// END EPIC MOD
-
-		if (m_freq_domain_chroma_upsample)
-			m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64);
-		else
-			m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64);
-
-		m_total_lines_left = m_image_y_size;
-
-		m_mcu_lines_left = 0;
-
-		create_look_ups();
-	}
-
-	// The coeff_buf series of methods originally stored the coefficients
-	// into a "virtual" file which was located in EMS, XMS, or a disk file. A cache
-	// was used to make this process more efficient. Now, we can store the entire
-	// thing in RAM.
-	jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y)
-	{
-		coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf));
-
-		cb->block_num_x = block_num_x;
-		cb->block_num_y = block_num_y;
-		cb->block_len_x = block_len_x;
-		cb->block_len_y = block_len_y;
-		cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t);
-		cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true);
-		return cb;
-	}
-
-	inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y)
-	{
-		JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y));
-		return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x));
-	}
-
-	// The following methods decode the various types of m_blocks encountered
-	// in progressively encoded images.
-	void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, r;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-		if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0)
-		{
-			r = pD->get_bits_no_markers(s);
-			s = HUFF_EXTEND(r, s);
-		}
-
-		pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]);
-
-		p[0] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-	}
-
-	void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		if (pD->get_bits_no_markers(1))
-		{
-			jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y);
-
-			p[0] |= (1 << pD->m_successive_low);
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int k, s, r;
-
-		if (pD->m_eob_run)
-		{
-			pD->m_eob_run--;
-			return;
-		}
-
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++)
-		{
-			s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-			r = s >> 4;
-			s &= 15;
-
-			if (s)
-			{
-				if ((k += r) > 63)
-					pD->stop_decoding(JPGD_DECODE_ERROR);
-
-				r = pD->get_bits_no_markers(s);
-				s = HUFF_EXTEND(r, s);
-
-				p[g_ZAG[k]] = static_cast<jpgd_block_t>(s << pD->m_successive_low);
-			}
-			else
-			{
-				if (r == 15)
-				{
-					if ((k += 15) > 63)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-				}
-				else
-				{
-					pD->m_eob_run = 1 << r;
-
-					if (r)
-						pD->m_eob_run += pD->get_bits_no_markers(r);
-
-					pD->m_eob_run--;
-
-					break;
-				}
-			}
-		}
-	}
-
-	void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y)
-	{
-		int s, k, r;
-		int p1 = 1 << pD->m_successive_low;
-		int m1 = (-1) << pD->m_successive_low;
-		jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y);
-
-		k = pD->m_spectral_start;
-
-		if (pD->m_eob_run == 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]);
-
-				r = s >> 4;
-				s &= 15;
-
-				if (s)
-				{
-					if (s != 1)
-						pD->stop_decoding(JPGD_DECODE_ERROR);
-
-					if (pD->get_bits_no_markers(1))
-						s = p1;
-					else
-						s = m1;
-				}
-				else
-				{
-					if (r != 15)
-					{
-						pD->m_eob_run = 1 << r;
-
-						if (r)
-							pD->m_eob_run += pD->get_bits_no_markers(r);
-
-						break;
-					}
-				}
-
-				do
-				{
-					// BEGIN EPIC MOD
-					JPGD_ASSERT(k < 64);
-					// END EPIC MOD
-
-					jpgd_block_t *this_coef = p + g_ZAG[k];
-
-					if (*this_coef != 0)
-					{
-						if (pD->get_bits_no_markers(1))
-						{
-							if ((*this_coef & p1) == 0)
-							{
-								if (*this_coef >= 0)
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-								else
-									*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-							}
-						}
-					}
-					else
-					{
-						if (--r < 0)
-							break;
-					}
-
-					k++;
-
-				} while (k <= pD->m_spectral_end);
-
-				if ((s) && (k < 64))
-				{
-					p[g_ZAG[k]] = static_cast<jpgd_block_t>(s);
-				}
-			}
-		}
-
-		if (pD->m_eob_run > 0)
-		{
-			for ( ; k <= pD->m_spectral_end; k++)
-			{
-				// BEGIN EPIC MOD
-				JPGD_ASSERT(k < 64);
-				// END EPIC MOD
-
-				jpgd_block_t *this_coef = p + g_ZAG[k];
-
-				if (*this_coef != 0)
-				{
-					if (pD->get_bits_no_markers(1))
-					{
-						if ((*this_coef & p1) == 0)
-						{
-							if (*this_coef >= 0)
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + p1);
-							else
-								*this_coef = static_cast<jpgd_block_t>(*this_coef + m1);
-						}
-					}
-				}
-			}
-
-			pD->m_eob_run--;
-		}
-	}
-
-	// Decode a scan in a progressively encoded image.
-	void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func)
-	{
-		int mcu_row, mcu_col, mcu_block;
-		int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS];
-
-		memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu));
-
-		for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++)
-		{
-			int component_num, component_id;
-
-			memset(block_x_mcu, 0, sizeof(block_x_mcu));
-
-			for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++)
-			{
-				int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0;
-
-				if ((m_restart_interval) && (m_restarts_left == 0))
-					process_restart();
-
-				for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++)
-				{
-					component_id = m_mcu_org[mcu_block];
-
-					decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs);
-
-					if (m_comps_in_scan == 1)
-						block_x_mcu[component_id]++;
-					else
-					{
-						if (++block_x_mcu_ofs == m_comp_h_samp[component_id])
-						{
-							block_x_mcu_ofs = 0;
-
-							if (++block_y_mcu_ofs == m_comp_v_samp[component_id])
-							{
-								block_y_mcu_ofs = 0;
-								block_x_mcu[component_id] += m_comp_h_samp[component_id];
-							}
-						}
-					}
-				}
-
-				m_restarts_left--;
-			}
-
-			if (m_comps_in_scan == 1)
-				m_block_y_mcu[m_comp_list[0]]++;
-			else
-			{
-				for (component_num = 0; component_num < m_comps_in_scan; component_num++)
-				{
-					component_id = m_comp_list[component_num];
-					m_block_y_mcu[component_id] += m_comp_v_samp[component_id];
-				}
-			}
-		}
-	}
-
-	// Decode a progressively encoded image.
-	void jpeg_decoder::init_progressive()
-	{
-		int i;
-
-		if (m_comps_in_frame == 4)
-			stop_decoding(JPGD_UNSUPPORTED_COLORSPACE);
-
-		// Allocate the coefficient buffers.
-		for (i = 0; i < m_comps_in_frame; i++)
-		{
-			m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1);
-			m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8);
-		}
-
-		for ( ; ; )
-		{
-			int dc_only_scan, refinement_scan;
-			pDecode_block_func decode_block_func;
-
-			if (!init_scan())
-				break;
-
-			dc_only_scan = (m_spectral_start == 0);
-			refinement_scan = (m_successive_high != 0);
-
-			if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63))
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if (dc_only_scan)
-			{
-				if (m_spectral_end)
-					stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-			}
-			else if (m_comps_in_scan != 1)  /* AC scans can only contain one component */
-				stop_decoding(JPGD_BAD_SOS_SPECTRAL);
-
-			if ((refinement_scan) && (m_successive_low != m_successive_high - 1))
-				stop_decoding(JPGD_BAD_SOS_SUCCESSIVE);
-
-			if (dc_only_scan)
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_dc_refine;
-				else
-					decode_block_func = decode_block_dc_first;
-			}
-			else
-			{
-				if (refinement_scan)
-					decode_block_func = decode_block_ac_refine;
-				else
-					decode_block_func = decode_block_ac_first;
-			}
-
-			decode_scan(decode_block_func);
-
-			m_bits_left = 16;
-			get_bits(16);
-			get_bits(16);
-		}
-
-		m_comps_in_scan = m_comps_in_frame;
-
-		for (i = 0; i < m_comps_in_frame; i++)
-			m_comp_list[i] = i;
-
-		calc_mcu_block_order();
-	}
-
-	void jpeg_decoder::init_sequential()
-	{
-		if (!init_scan())
-			stop_decoding(JPGD_UNEXPECTED_MARKER);
-	}
-
-	void jpeg_decoder::decode_start()
-	{
-		init_frame();
-
-		if (m_progressive_flag)
-			init_progressive();
-		else
-			init_sequential();
-	}
-
-	void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream)
-	{
-		init(pStream);
-		locate_sof_marker();
-	}
-
-	jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream)
-	{
-		if (setjmp(m_jmp_state))
-			return;
-		decode_init(pStream);
-	}
-
-	int jpeg_decoder::begin_decoding()
-	{
-		if (m_ready_flag)
-			return JPGD_SUCCESS;
-
-		if (m_error_code)
-			return JPGD_FAILED;
-
-		if (setjmp(m_jmp_state))
-			return JPGD_FAILED;
-
-		decode_start();
-
-		m_ready_flag = true;
-
-		return JPGD_SUCCESS;
-	}
-
-	jpeg_decoder::~jpeg_decoder()
-	{
-		free_all_blocks();
-	}
-
-	jpeg_decoder_file_stream::jpeg_decoder_file_stream()
-	{
-		m_pFile = NULL;
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	void jpeg_decoder_file_stream::close()
-	{
-		if (m_pFile)
-		{
-			fclose(m_pFile);
-			m_pFile = NULL;
-		}
-
-		m_eof_flag = false;
-		m_error_flag = false;
-	}
-
-	jpeg_decoder_file_stream::~jpeg_decoder_file_stream()
-	{
-		close();
-	}
-
-	bool jpeg_decoder_file_stream::open(const char *Pfilename)
-	{
-		close();
-
-		m_eof_flag = false;
-		m_error_flag = false;
-
-#if defined(_MSC_VER)
-		m_pFile = NULL;
-		fopen_s(&m_pFile, Pfilename, "rb");
-#else
-		m_pFile = fopen(Pfilename, "rb");
-#endif
-		return m_pFile != NULL;
-	}
-
-	int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		if (!m_pFile)
-			return -1;
-
-		if (m_eof_flag)
-		{
-			*pEOF_flag = true;
-			return 0;
-		}
-
-		if (m_error_flag)
-			return -1;
-
-		int bytes_read = static_cast<int>(fread(pBuf, 1, max_bytes_to_read, m_pFile));
-		if (bytes_read < max_bytes_to_read)
-		{
-			if (ferror(m_pFile))
-			{
-				m_error_flag = true;
-				return -1;
-			}
-
-			m_eof_flag = true;
-			*pEOF_flag = true;
-		}
-
-		return bytes_read;
-	}
-
-	bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size)
-	{
-		close();
-		m_pSrc_data = pSrc_data;
-		m_ofs = 0;
-		m_size = size;
-		return true;
-	}
-
-	int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag)
-	{
-		*pEOF_flag = false;
-
-		if (!m_pSrc_data)
-			return -1;
-
-		uint bytes_remaining = m_size - m_ofs;
-		if ((uint)max_bytes_to_read > bytes_remaining)
-		{
-			max_bytes_to_read = bytes_remaining;
-			*pEOF_flag = true;
-		}
-
-		memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read);
-		m_ofs += max_bytes_to_read;
-
-		return max_bytes_to_read;
-	}
-
-	unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		if (!actual_comps)
-			return NULL;
-		*actual_comps = 0;
-
-		if ((!pStream) || (!width) || (!height) || (!req_comps))
-			return NULL;
-
-		if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4))
-			return NULL;
-
-		jpeg_decoder decoder(pStream);
-		if (decoder.get_error_code() != JPGD_SUCCESS)
-			return NULL;
-
-		const int image_width = decoder.get_width(), image_height = decoder.get_height();
-		*width = image_width;
-		*height = image_height;
-		*actual_comps = decoder.get_num_components();
-
-		if (decoder.begin_decoding() != JPGD_SUCCESS)
-			return NULL;
-
-		const int dst_bpl = image_width * req_comps;
-
-		uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height);
-		if (!pImage_data)
-			return NULL;
-
-		for (int y = 0; y < image_height; y++)
-		{
-			const uint8* pScan_line = 0;
-			uint scan_line_len;
-			if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS)
-			{
-				jpgd_free(pImage_data);
-				return NULL;
-			}
-
-			uint8 *pDst = pImage_data + y * dst_bpl;
-
-			if (((req_comps == 4) && (decoder.get_num_components() == 3)) ||
-				((req_comps == 1) && (decoder.get_num_components() == 1)))
-			{
-				memcpy(pDst, pScan_line, dst_bpl);
-			}
-			else if (decoder.get_num_components() == 1)
-			{
-				if (req_comps == 3)
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst += 3;
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						uint8 luma = pScan_line[x];
-						pDst[0] = luma;
-						pDst[1] = luma;
-						pDst[2] = luma;
-						pDst[3] = 255;
-						pDst += 4;
-					}
-				}
-			}
-			else if (decoder.get_num_components() == 3)
-			{
-				if (req_comps == 1)
-				{
-					const int YR = 19595, YG = 38470, YB = 7471;
-					for (int x = 0; x < image_width; x++)
-					{
-						int r = pScan_line[x*4+0];
-						int g = pScan_line[x*4+1];
-						int b = pScan_line[x*4+2];
-						*pDst++ = static_cast<uint8>((r * YR + g * YG + b * YB + 32768) >> 16);
-					}
-				}
-				else
-				{
-					for (int x = 0; x < image_width; x++)
-					{
-						pDst[0] = pScan_line[x*4+0];
-						pDst[1] = pScan_line[x*4+1];
-						pDst[2] = pScan_line[x*4+2];
-						pDst += 3;
-					}
-				}
-			}
-		}
-
-		return pImage_data;
-	}
-
-// BEGIN EPIC MOD
-	unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format)
-	{
-		jpg_format = (ERGBFormatJPG)format;
-// EMD EPIC MOD
-		jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size);
-		return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps);
-	}
-
-	unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps)
-	{
-		jpgd::jpeg_decoder_file_stream file_stream;
-		if (!file_stream.open(pSrc_filename))
-			return NULL;
-		return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps);
-	}
-
-} // namespace jpgd
diff --git a/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/README.md b/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/README.md
deleted file mode 100644
index b61f96a3f0f5df541bd4e0dfba3a468ceb1c54e9..0000000000000000000000000000000000000000
--- a/spaces/SuSung-boy/LoRA-DreamBooth-Training-UI/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-title: LoRA DreamBooth Training UI
-emoji: ⚡
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.16.2
-python_version: 3.10.9
-app_file: app.py
-pinned: false
-license: mit
-duplicated_from: lora-library/LoRA-DreamBooth-Training-UI
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Sujal7/shikshaconnect/style2.css b/spaces/Sujal7/shikshaconnect/style2.css
deleted file mode 100644
index c935241c5c9084ea38c691d38a73ad7c02032bc8..0000000000000000000000000000000000000000
--- a/spaces/Sujal7/shikshaconnect/style2.css
+++ /dev/null
@@ -1,94 +0,0 @@
-* {
-	box-sizing: border-box;
-	margin: 0;
-	padding: 0;
-}
-
-body {
-	font-family: Arial, sans-serif;
-    background-color: rgba(135, 202, 211, 0.89);
-}
-
-header {
-	background-color:#0c1a44b0;
-	color: #fff;
-	display: flex;
-	justify-content: space-between;
-	align-items: center;
-	padding: 10px;
-}
-
-nav ul {
-	display: flex;
-	list-style: none;
-    background-color: #333;
-    margin: 23px 30px ;
-    margin-top: 10px;
-    padding: 5px;
-}
-
-nav li {
-	margin-left: 20px;
-    justify-content: space-around;
-    text-align: center;
-
-}
-
-nav a {
-	color: #fff;
-	text-decoration: none;
-}
-
-main {
-	padding: 20px;
-}
-
-section {
-	margin-bottom: 20px;
-}
-
-form label {
-	display: block;
-	margin-bottom: 5px;
-}
-
-form input[type="text"],
-form input[type="password"],
-form input[type="date"],
-form input[type="number"],
-form textarea {
-	display: block;
-	margin-bottom: 10px;
-	padding: 5px;
-	width: 100%;
-}
-
-form input[type="submit"] {
-	background-color: #333;
-	border: none;
-	color: #fff;
-	cursor: pointer;
-	padding: 10px;
-}
-
-nav ul {
-	display: flex;
-	list-style: none;
-}
-
-nav li {
-	margin-left: 20px;
-}
-
-nav a {
-	color: #333;
-	text-decoration: none;
-}
-
-article {
-	margin-bottom: 20px;
-	padding: 20px;
-	background-color: #f2f2f2;
-	border-radius: 5px;
-	box-shadow: 0 0 5px rgba(0, 0, 0, 0.1);
-}
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/guarded_eval.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/guarded_eval.py
deleted file mode 100644
index d576a2a769d671c265b35afa696b68208b43ba6b..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/IPython/core/guarded_eval.py
+++ /dev/null
@@ -1,733 +0,0 @@
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Set,
-    Sequence,
-    Tuple,
-    NamedTuple,
-    Type,
-    Literal,
-    Union,
-    TYPE_CHECKING,
-)
-import ast
-import builtins
-import collections
-import operator
-import sys
-from functools import cached_property
-from dataclasses import dataclass, field
-from types import MethodDescriptorType, ModuleType
-
-from IPython.utils.docs import GENERATING_DOCUMENTATION
-from IPython.utils.decorators import undoc
-
-
-if TYPE_CHECKING or GENERATING_DOCUMENTATION:
-    from typing_extensions import Protocol
-else:
-    # do not require on runtime
-    Protocol = object  # requires Python >=3.8
-
-
-@undoc
-class HasGetItem(Protocol):
-    def __getitem__(self, key) -> None:
-        ...
-
-
-@undoc
-class InstancesHaveGetItem(Protocol):
-    def __call__(self, *args, **kwargs) -> HasGetItem:
-        ...
-
-
-@undoc
-class HasGetAttr(Protocol):
-    def __getattr__(self, key) -> None:
-        ...
-
-
-@undoc
-class DoesNotHaveGetAttr(Protocol):
-    pass
-
-
-# By default `__getattr__` is not explicitly implemented on most objects
-MayHaveGetattr = Union[HasGetAttr, DoesNotHaveGetAttr]
-
-
-def _unbind_method(func: Callable) -> Union[Callable, None]:
-    """Get unbound method for given bound method.
-
-    Returns None if cannot get unbound method, or method is already unbound.
-    """
-    owner = getattr(func, "__self__", None)
-    owner_class = type(owner)
-    name = getattr(func, "__name__", None)
-    instance_dict_overrides = getattr(owner, "__dict__", None)
-    if (
-        owner is not None
-        and name
-        and (
-            not instance_dict_overrides
-            or (instance_dict_overrides and name not in instance_dict_overrides)
-        )
-    ):
-        return getattr(owner_class, name)
-    return None
-
-
-@undoc
-@dataclass
-class EvaluationPolicy:
-    """Definition of evaluation policy."""
-
-    allow_locals_access: bool = False
-    allow_globals_access: bool = False
-    allow_item_access: bool = False
-    allow_attr_access: bool = False
-    allow_builtins_access: bool = False
-    allow_all_operations: bool = False
-    allow_any_calls: bool = False
-    allowed_calls: Set[Callable] = field(default_factory=set)
-
-    def can_get_item(self, value, item):
-        return self.allow_item_access
-
-    def can_get_attr(self, value, attr):
-        return self.allow_attr_access
-
-    def can_operate(self, dunders: Tuple[str, ...], a, b=None):
-        if self.allow_all_operations:
-            return True
-
-    def can_call(self, func):
-        if self.allow_any_calls:
-            return True
-
-        if func in self.allowed_calls:
-            return True
-
-        owner_method = _unbind_method(func)
-
-        if owner_method and owner_method in self.allowed_calls:
-            return True
-
-
-def _get_external(module_name: str, access_path: Sequence[str]):
-    """Get value from external module given a dotted access path.
-
-    Raises:
-    * `KeyError` if module is removed not found, and
-    * `AttributeError` if acess path does not match an exported object
-    """
-    member_type = sys.modules[module_name]
-    for attr in access_path:
-        member_type = getattr(member_type, attr)
-    return member_type
-
-
-def _has_original_dunder_external(
-    value,
-    module_name: str,
-    access_path: Sequence[str],
-    method_name: str,
-):
-    if module_name not in sys.modules:
-        # LBYLB as it is faster
-        return False
-    try:
-        member_type = _get_external(module_name, access_path)
-        value_type = type(value)
-        if type(value) == member_type:
-            return True
-        if method_name == "__getattribute__":
-            # we have to short-circuit here due to an unresolved issue in
-            # `isinstance` implementation: https://bugs.python.org/issue32683
-            return False
-        if isinstance(value, member_type):
-            method = getattr(value_type, method_name, None)
-            member_method = getattr(member_type, method_name, None)
-            if member_method == method:
-                return True
-    except (AttributeError, KeyError):
-        return False
-
-
-def _has_original_dunder(
-    value, allowed_types, allowed_methods, allowed_external, method_name
-):
-    # note: Python ignores `__getattr__`/`__getitem__` on instances,
-    # we only need to check at class level
-    value_type = type(value)
-
-    # strict type check passes → no need to check method
-    if value_type in allowed_types:
-        return True
-
-    method = getattr(value_type, method_name, None)
-
-    if method is None:
-        return None
-
-    if method in allowed_methods:
-        return True
-
-    for module_name, *access_path in allowed_external:
-        if _has_original_dunder_external(value, module_name, access_path, method_name):
-            return True
-
-    return False
-
-
-@undoc
-@dataclass
-class SelectivePolicy(EvaluationPolicy):
-    allowed_getitem: Set[InstancesHaveGetItem] = field(default_factory=set)
-    allowed_getitem_external: Set[Tuple[str, ...]] = field(default_factory=set)
-
-    allowed_getattr: Set[MayHaveGetattr] = field(default_factory=set)
-    allowed_getattr_external: Set[Tuple[str, ...]] = field(default_factory=set)
-
-    allowed_operations: Set = field(default_factory=set)
-    allowed_operations_external: Set[Tuple[str, ...]] = field(default_factory=set)
-
-    _operation_methods_cache: Dict[str, Set[Callable]] = field(
-        default_factory=dict, init=False
-    )
-
-    def can_get_attr(self, value, attr):
-        has_original_attribute = _has_original_dunder(
-            value,
-            allowed_types=self.allowed_getattr,
-            allowed_methods=self._getattribute_methods,
-            allowed_external=self.allowed_getattr_external,
-            method_name="__getattribute__",
-        )
-        has_original_attr = _has_original_dunder(
-            value,
-            allowed_types=self.allowed_getattr,
-            allowed_methods=self._getattr_methods,
-            allowed_external=self.allowed_getattr_external,
-            method_name="__getattr__",
-        )
-
-        accept = False
-
-        # Many objects do not have `__getattr__`, this is fine.
-        if has_original_attr is None and has_original_attribute:
-            accept = True
-        else:
-            # Accept objects without modifications to `__getattr__` and `__getattribute__`
-            accept = has_original_attr and has_original_attribute
-
-        if accept:
-            # We still need to check for overriden properties.
-
-            value_class = type(value)
-            if not hasattr(value_class, attr):
-                return True
-
-            class_attr_val = getattr(value_class, attr)
-            is_property = isinstance(class_attr_val, property)
-
-            if not is_property:
-                return True
-
-            # Properties in allowed types are ok (although we do not include any
-            # properties in our default allow list currently).
-            if type(value) in self.allowed_getattr:
-                return True  # pragma: no cover
-
-            # Properties in subclasses of allowed types may be ok if not changed
-            for module_name, *access_path in self.allowed_getattr_external:
-                try:
-                    external_class = _get_external(module_name, access_path)
-                    external_class_attr_val = getattr(external_class, attr)
-                except (KeyError, AttributeError):
-                    return False  # pragma: no cover
-                return class_attr_val == external_class_attr_val
-
-        return False
-
-    def can_get_item(self, value, item):
-        """Allow accessing `__getiitem__` of allow-listed instances unless it was not modified."""
-        return _has_original_dunder(
-            value,
-            allowed_types=self.allowed_getitem,
-            allowed_methods=self._getitem_methods,
-            allowed_external=self.allowed_getitem_external,
-            method_name="__getitem__",
-        )
-
-    def can_operate(self, dunders: Tuple[str, ...], a, b=None):
-        objects = [a]
-        if b is not None:
-            objects.append(b)
-        return all(
-            [
-                _has_original_dunder(
-                    obj,
-                    allowed_types=self.allowed_operations,
-                    allowed_methods=self._operator_dunder_methods(dunder),
-                    allowed_external=self.allowed_operations_external,
-                    method_name=dunder,
-                )
-                for dunder in dunders
-                for obj in objects
-            ]
-        )
-
-    def _operator_dunder_methods(self, dunder: str) -> Set[Callable]:
-        if dunder not in self._operation_methods_cache:
-            self._operation_methods_cache[dunder] = self._safe_get_methods(
-                self.allowed_operations, dunder
-            )
-        return self._operation_methods_cache[dunder]
-
-    @cached_property
-    def _getitem_methods(self) -> Set[Callable]:
-        return self._safe_get_methods(self.allowed_getitem, "__getitem__")
-
-    @cached_property
-    def _getattr_methods(self) -> Set[Callable]:
-        return self._safe_get_methods(self.allowed_getattr, "__getattr__")
-
-    @cached_property
-    def _getattribute_methods(self) -> Set[Callable]:
-        return self._safe_get_methods(self.allowed_getattr, "__getattribute__")
-
-    def _safe_get_methods(self, classes, name) -> Set[Callable]:
-        return {
-            method
-            for class_ in classes
-            for method in [getattr(class_, name, None)]
-            if method
-        }
-
-
-class _DummyNamedTuple(NamedTuple):
-    """Used internally to retrieve methods of named tuple instance."""
-
-
-class EvaluationContext(NamedTuple):
-    #: Local namespace
-    locals: dict
-    #: Global namespace
-    globals: dict
-    #: Evaluation policy identifier
-    evaluation: Literal[
-        "forbidden", "minimal", "limited", "unsafe", "dangerous"
-    ] = "forbidden"
-    #: Whether the evalution of code takes place inside of a subscript.
-    #: Useful for evaluating ``:-1, 'col'`` in ``df[:-1, 'col']``.
-    in_subscript: bool = False
-
-
-class _IdentitySubscript:
-    """Returns the key itself when item is requested via subscript."""
-
-    def __getitem__(self, key):
-        return key
-
-
-IDENTITY_SUBSCRIPT = _IdentitySubscript()
-SUBSCRIPT_MARKER = "__SUBSCRIPT_SENTINEL__"
-
-
-class GuardRejection(Exception):
-    """Exception raised when guard rejects evaluation attempt."""
-
-    pass
-
-
-def guarded_eval(code: str, context: EvaluationContext):
-    """Evaluate provided code in the evaluation context.
-
-    If evaluation policy given by context is set to ``forbidden``
-    no evaluation will be performed; if it is set to ``dangerous``
-    standard :func:`eval` will be used; finally, for any other,
-    policy :func:`eval_node` will be called on parsed AST.
-    """
-    locals_ = context.locals
-
-    if context.evaluation == "forbidden":
-        raise GuardRejection("Forbidden mode")
-
-    # note: not using `ast.literal_eval` as it does not implement
-    # getitem at all, for example it fails on simple `[0][1]`
-
-    if context.in_subscript:
-        # syntatic sugar for ellipsis (:) is only available in susbcripts
-        # so we need to trick the ast parser into thinking that we have
-        # a subscript, but we need to be able to later recognise that we did
-        # it so we can ignore the actual __getitem__ operation
-        if not code:
-            return tuple()
-        locals_ = locals_.copy()
-        locals_[SUBSCRIPT_MARKER] = IDENTITY_SUBSCRIPT
-        code = SUBSCRIPT_MARKER + "[" + code + "]"
-        context = EvaluationContext(**{**context._asdict(), **{"locals": locals_}})
-
-    if context.evaluation == "dangerous":
-        return eval(code, context.globals, context.locals)
-
-    expression = ast.parse(code, mode="eval")
-
-    return eval_node(expression, context)
-
-
-BINARY_OP_DUNDERS: Dict[Type[ast.operator], Tuple[str]] = {
-    ast.Add: ("__add__",),
-    ast.Sub: ("__sub__",),
-    ast.Mult: ("__mul__",),
-    ast.Div: ("__truediv__",),
-    ast.FloorDiv: ("__floordiv__",),
-    ast.Mod: ("__mod__",),
-    ast.Pow: ("__pow__",),
-    ast.LShift: ("__lshift__",),
-    ast.RShift: ("__rshift__",),
-    ast.BitOr: ("__or__",),
-    ast.BitXor: ("__xor__",),
-    ast.BitAnd: ("__and__",),
-    ast.MatMult: ("__matmul__",),
-}
-
-COMP_OP_DUNDERS: Dict[Type[ast.cmpop], Tuple[str, ...]] = {
-    ast.Eq: ("__eq__",),
-    ast.NotEq: ("__ne__", "__eq__"),
-    ast.Lt: ("__lt__", "__gt__"),
-    ast.LtE: ("__le__", "__ge__"),
-    ast.Gt: ("__gt__", "__lt__"),
-    ast.GtE: ("__ge__", "__le__"),
-    ast.In: ("__contains__",),
-    # Note: ast.Is, ast.IsNot, ast.NotIn are handled specially
-}
-
-UNARY_OP_DUNDERS: Dict[Type[ast.unaryop], Tuple[str, ...]] = {
-    ast.USub: ("__neg__",),
-    ast.UAdd: ("__pos__",),
-    # we have to check both __inv__ and __invert__!
-    ast.Invert: ("__invert__", "__inv__"),
-    ast.Not: ("__not__",),
-}
-
-
-def _find_dunder(node_op, dunders) -> Union[Tuple[str, ...], None]:
-    dunder = None
-    for op, candidate_dunder in dunders.items():
-        if isinstance(node_op, op):
-            dunder = candidate_dunder
-    return dunder
-
-
-def eval_node(node: Union[ast.AST, None], context: EvaluationContext):
-    """Evaluate AST node in provided context.
-
-    Applies evaluation restrictions defined in the context. Currently does not support evaluation of functions with keyword arguments.
-
-    Does not evaluate actions that always have side effects:
-
-    - class definitions (``class sth: ...``)
-    - function definitions (``def sth: ...``)
-    - variable assignments (``x = 1``)
-    - augmented assignments (``x += 1``)
-    - deletions (``del x``)
-
-    Does not evaluate operations which do not return values:
-
-    - assertions (``assert x``)
-    - pass (``pass``)
-    - imports (``import x``)
-    - control flow:
-
-        - conditionals (``if x:``) except for ternary IfExp (``a if x else b``)
-        - loops (``for`` and `while``)
-        - exception handling
-
-    The purpose of this function is to guard against unwanted side-effects;
-    it does not give guarantees on protection from malicious code execution.
-    """
-    policy = EVALUATION_POLICIES[context.evaluation]
-    if node is None:
-        return None
-    if isinstance(node, ast.Expression):
-        return eval_node(node.body, context)
-    if isinstance(node, ast.BinOp):
-        left = eval_node(node.left, context)
-        right = eval_node(node.right, context)
-        dunders = _find_dunder(node.op, BINARY_OP_DUNDERS)
-        if dunders:
-            if policy.can_operate(dunders, left, right):
-                return getattr(left, dunders[0])(right)
-            else:
-                raise GuardRejection(
-                    f"Operation (`{dunders}`) for",
-                    type(left),
-                    f"not allowed in {context.evaluation} mode",
-                )
-    if isinstance(node, ast.Compare):
-        left = eval_node(node.left, context)
-        all_true = True
-        negate = False
-        for op, right in zip(node.ops, node.comparators):
-            right = eval_node(right, context)
-            dunder = None
-            dunders = _find_dunder(op, COMP_OP_DUNDERS)
-            if not dunders:
-                if isinstance(op, ast.NotIn):
-                    dunders = COMP_OP_DUNDERS[ast.In]
-                    negate = True
-                if isinstance(op, ast.Is):
-                    dunder = "is_"
-                if isinstance(op, ast.IsNot):
-                    dunder = "is_"
-                    negate = True
-            if not dunder and dunders:
-                dunder = dunders[0]
-            if dunder:
-                a, b = (right, left) if dunder == "__contains__" else (left, right)
-                if dunder == "is_" or dunders and policy.can_operate(dunders, a, b):
-                    result = getattr(operator, dunder)(a, b)
-                    if negate:
-                        result = not result
-                    if not result:
-                        all_true = False
-                    left = right
-                else:
-                    raise GuardRejection(
-                        f"Comparison (`{dunder}`) for",
-                        type(left),
-                        f"not allowed in {context.evaluation} mode",
-                    )
-            else:
-                raise ValueError(
-                    f"Comparison `{dunder}` not supported"
-                )  # pragma: no cover
-        return all_true
-    if isinstance(node, ast.Constant):
-        return node.value
-    if isinstance(node, ast.Tuple):
-        return tuple(eval_node(e, context) for e in node.elts)
-    if isinstance(node, ast.List):
-        return [eval_node(e, context) for e in node.elts]
-    if isinstance(node, ast.Set):
-        return {eval_node(e, context) for e in node.elts}
-    if isinstance(node, ast.Dict):
-        return dict(
-            zip(
-                [eval_node(k, context) for k in node.keys],
-                [eval_node(v, context) for v in node.values],
-            )
-        )
-    if isinstance(node, ast.Slice):
-        return slice(
-            eval_node(node.lower, context),
-            eval_node(node.upper, context),
-            eval_node(node.step, context),
-        )
-    if isinstance(node, ast.UnaryOp):
-        value = eval_node(node.operand, context)
-        dunders = _find_dunder(node.op, UNARY_OP_DUNDERS)
-        if dunders:
-            if policy.can_operate(dunders, value):
-                return getattr(value, dunders[0])()
-            else:
-                raise GuardRejection(
-                    f"Operation (`{dunders}`) for",
-                    type(value),
-                    f"not allowed in {context.evaluation} mode",
-                )
-    if isinstance(node, ast.Subscript):
-        value = eval_node(node.value, context)
-        slice_ = eval_node(node.slice, context)
-        if policy.can_get_item(value, slice_):
-            return value[slice_]
-        raise GuardRejection(
-            "Subscript access (`__getitem__`) for",
-            type(value),  # not joined to avoid calling `repr`
-            f" not allowed in {context.evaluation} mode",
-        )
-    if isinstance(node, ast.Name):
-        if policy.allow_locals_access and node.id in context.locals:
-            return context.locals[node.id]
-        if policy.allow_globals_access and node.id in context.globals:
-            return context.globals[node.id]
-        if policy.allow_builtins_access and hasattr(builtins, node.id):
-            # note: do not use __builtins__, it is implementation detail of cPython
-            return getattr(builtins, node.id)
-        if not policy.allow_globals_access and not policy.allow_locals_access:
-            raise GuardRejection(
-                f"Namespace access not allowed in {context.evaluation} mode"
-            )
-        else:
-            raise NameError(f"{node.id} not found in locals, globals, nor builtins")
-    if isinstance(node, ast.Attribute):
-        value = eval_node(node.value, context)
-        if policy.can_get_attr(value, node.attr):
-            return getattr(value, node.attr)
-        raise GuardRejection(
-            "Attribute access (`__getattr__`) for",
-            type(value),  # not joined to avoid calling `repr`
-            f"not allowed in {context.evaluation} mode",
-        )
-    if isinstance(node, ast.IfExp):
-        test = eval_node(node.test, context)
-        if test:
-            return eval_node(node.body, context)
-        else:
-            return eval_node(node.orelse, context)
-    if isinstance(node, ast.Call):
-        func = eval_node(node.func, context)
-        if policy.can_call(func) and not node.keywords:
-            args = [eval_node(arg, context) for arg in node.args]
-            return func(*args)
-        raise GuardRejection(
-            "Call for",
-            func,  # not joined to avoid calling `repr`
-            f"not allowed in {context.evaluation} mode",
-        )
-    raise ValueError("Unhandled node", ast.dump(node))
-
-
-SUPPORTED_EXTERNAL_GETITEM = {
-    ("pandas", "core", "indexing", "_iLocIndexer"),
-    ("pandas", "core", "indexing", "_LocIndexer"),
-    ("pandas", "DataFrame"),
-    ("pandas", "Series"),
-    ("numpy", "ndarray"),
-    ("numpy", "void"),
-}
-
-
-BUILTIN_GETITEM: Set[InstancesHaveGetItem] = {
-    dict,
-    str,  # type: ignore[arg-type]
-    bytes,  # type: ignore[arg-type]
-    list,
-    tuple,
-    collections.defaultdict,
-    collections.deque,
-    collections.OrderedDict,
-    collections.ChainMap,
-    collections.UserDict,
-    collections.UserList,
-    collections.UserString,  # type: ignore[arg-type]
-    _DummyNamedTuple,
-    _IdentitySubscript,
-}
-
-
-def _list_methods(cls, source=None):
-    """For use on immutable objects or with methods returning a copy"""
-    return [getattr(cls, k) for k in (source if source else dir(cls))]
-
-
-dict_non_mutating_methods = ("copy", "keys", "values", "items")
-list_non_mutating_methods = ("copy", "index", "count")
-set_non_mutating_methods = set(dir(set)) & set(dir(frozenset))
-
-
-dict_keys: Type[collections.abc.KeysView] = type({}.keys())
-
-NUMERICS = {int, float, complex}
-
-ALLOWED_CALLS = {
-    bytes,
-    *_list_methods(bytes),
-    dict,
-    *_list_methods(dict, dict_non_mutating_methods),
-    dict_keys.isdisjoint,
-    list,
-    *_list_methods(list, list_non_mutating_methods),
-    set,
-    *_list_methods(set, set_non_mutating_methods),
-    frozenset,
-    *_list_methods(frozenset),
-    range,
-    str,
-    *_list_methods(str),
-    tuple,
-    *_list_methods(tuple),
-    *NUMERICS,
-    *[method for numeric_cls in NUMERICS for method in _list_methods(numeric_cls)],
-    collections.deque,
-    *_list_methods(collections.deque, list_non_mutating_methods),
-    collections.defaultdict,
-    *_list_methods(collections.defaultdict, dict_non_mutating_methods),
-    collections.OrderedDict,
-    *_list_methods(collections.OrderedDict, dict_non_mutating_methods),
-    collections.UserDict,
-    *_list_methods(collections.UserDict, dict_non_mutating_methods),
-    collections.UserList,
-    *_list_methods(collections.UserList, list_non_mutating_methods),
-    collections.UserString,
-    *_list_methods(collections.UserString, dir(str)),
-    collections.Counter,
-    *_list_methods(collections.Counter, dict_non_mutating_methods),
-    collections.Counter.elements,
-    collections.Counter.most_common,
-}
-
-BUILTIN_GETATTR: Set[MayHaveGetattr] = {
-    *BUILTIN_GETITEM,
-    set,
-    frozenset,
-    object,
-    type,  # `type` handles a lot of generic cases, e.g. numbers as in `int.real`.
-    *NUMERICS,
-    dict_keys,
-    MethodDescriptorType,
-    ModuleType,
-}
-
-
-BUILTIN_OPERATIONS = {*BUILTIN_GETATTR}
-
-EVALUATION_POLICIES = {
-    "minimal": EvaluationPolicy(
-        allow_builtins_access=True,
-        allow_locals_access=False,
-        allow_globals_access=False,
-        allow_item_access=False,
-        allow_attr_access=False,
-        allowed_calls=set(),
-        allow_any_calls=False,
-        allow_all_operations=False,
-    ),
-    "limited": SelectivePolicy(
-        allowed_getitem=BUILTIN_GETITEM,
-        allowed_getitem_external=SUPPORTED_EXTERNAL_GETITEM,
-        allowed_getattr=BUILTIN_GETATTR,
-        allowed_getattr_external={
-            # pandas Series/Frame implements custom `__getattr__`
-            ("pandas", "DataFrame"),
-            ("pandas", "Series"),
-        },
-        allowed_operations=BUILTIN_OPERATIONS,
-        allow_builtins_access=True,
-        allow_locals_access=True,
-        allow_globals_access=True,
-        allowed_calls=ALLOWED_CALLS,
-    ),
-    "unsafe": EvaluationPolicy(
-        allow_builtins_access=True,
-        allow_locals_access=True,
-        allow_globals_access=True,
-        allow_attr_access=True,
-        allow_item_access=True,
-        allow_any_calls=True,
-        allow_all_operations=True,
-    ),
-}
-
-
-__all__ = [
-    "guarded_eval",
-    "eval_node",
-    "GuardRejection",
-    "EvaluationContext",
-    "_unbind_method",
-]
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/to_process.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/to_process.py
deleted file mode 100644
index 7ba9d44198233b94bea1b01c6135416170eac925..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/anyio/to_process.py
+++ /dev/null
@@ -1,249 +0,0 @@
-from __future__ import annotations
-
-import os
-import pickle
-import subprocess
-import sys
-from collections import deque
-from importlib.util import module_from_spec, spec_from_file_location
-from typing import Callable, TypeVar, cast
-
-from ._core._eventloop import current_time, get_asynclib, get_cancelled_exc_class
-from ._core._exceptions import BrokenWorkerProcess
-from ._core._subprocesses import open_process
-from ._core._synchronization import CapacityLimiter
-from ._core._tasks import CancelScope, fail_after
-from .abc import ByteReceiveStream, ByteSendStream, Process
-from .lowlevel import RunVar, checkpoint_if_cancelled
-from .streams.buffered import BufferedByteReceiveStream
-
-WORKER_MAX_IDLE_TIME = 300  # 5 minutes
-
-T_Retval = TypeVar("T_Retval")
-_process_pool_workers: RunVar[set[Process]] = RunVar("_process_pool_workers")
-_process_pool_idle_workers: RunVar[deque[tuple[Process, float]]] = RunVar(
-    "_process_pool_idle_workers"
-)
-_default_process_limiter: RunVar[CapacityLimiter] = RunVar("_default_process_limiter")
-
-
-async def run_sync(
-    func: Callable[..., T_Retval],
-    *args: object,
-    cancellable: bool = False,
-    limiter: CapacityLimiter | None = None,
-) -> T_Retval:
-    """
-    Call the given function with the given arguments in a worker process.
-
-    If the ``cancellable`` option is enabled and the task waiting for its completion is cancelled,
-    the worker process running it will be abruptly terminated using SIGKILL (or
-    ``terminateProcess()`` on Windows).
-
-    :param func: a callable
-    :param args: positional arguments for the callable
-    :param cancellable: ``True`` to allow cancellation of the operation while it's running
-    :param limiter: capacity limiter to use to limit the total amount of processes running
-        (if omitted, the default limiter is used)
-    :return: an awaitable that yields the return value of the function.
-
-    """
-
-    async def send_raw_command(pickled_cmd: bytes) -> object:
-        try:
-            await stdin.send(pickled_cmd)
-            response = await buffered.receive_until(b"\n", 50)
-            status, length = response.split(b" ")
-            if status not in (b"RETURN", b"EXCEPTION"):
-                raise RuntimeError(
-                    f"Worker process returned unexpected response: {response!r}"
-                )
-
-            pickled_response = await buffered.receive_exactly(int(length))
-        except BaseException as exc:
-            workers.discard(process)
-            try:
-                process.kill()
-                with CancelScope(shield=True):
-                    await process.aclose()
-            except ProcessLookupError:
-                pass
-
-            if isinstance(exc, get_cancelled_exc_class()):
-                raise
-            else:
-                raise BrokenWorkerProcess from exc
-
-        retval = pickle.loads(pickled_response)
-        if status == b"EXCEPTION":
-            assert isinstance(retval, BaseException)
-            raise retval
-        else:
-            return retval
-
-    # First pickle the request before trying to reserve a worker process
-    await checkpoint_if_cancelled()
-    request = pickle.dumps(("run", func, args), protocol=pickle.HIGHEST_PROTOCOL)
-
-    # If this is the first run in this event loop thread, set up the necessary variables
-    try:
-        workers = _process_pool_workers.get()
-        idle_workers = _process_pool_idle_workers.get()
-    except LookupError:
-        workers = set()
-        idle_workers = deque()
-        _process_pool_workers.set(workers)
-        _process_pool_idle_workers.set(idle_workers)
-        get_asynclib().setup_process_pool_exit_at_shutdown(workers)
-
-    async with (limiter or current_default_process_limiter()):
-        # Pop processes from the pool (starting from the most recently used) until we find one that
-        # hasn't exited yet
-        process: Process
-        while idle_workers:
-            process, idle_since = idle_workers.pop()
-            if process.returncode is None:
-                stdin = cast(ByteSendStream, process.stdin)
-                buffered = BufferedByteReceiveStream(
-                    cast(ByteReceiveStream, process.stdout)
-                )
-
-                # Prune any other workers that have been idle for WORKER_MAX_IDLE_TIME seconds or
-                # longer
-                now = current_time()
-                killed_processes: list[Process] = []
-                while idle_workers:
-                    if now - idle_workers[0][1] < WORKER_MAX_IDLE_TIME:
-                        break
-
-                    process, idle_since = idle_workers.popleft()
-                    process.kill()
-                    workers.remove(process)
-                    killed_processes.append(process)
-
-                with CancelScope(shield=True):
-                    for process in killed_processes:
-                        await process.aclose()
-
-                break
-
-            workers.remove(process)
-        else:
-            command = [sys.executable, "-u", "-m", __name__]
-            process = await open_process(
-                command, stdin=subprocess.PIPE, stdout=subprocess.PIPE
-            )
-            try:
-                stdin = cast(ByteSendStream, process.stdin)
-                buffered = BufferedByteReceiveStream(
-                    cast(ByteReceiveStream, process.stdout)
-                )
-                with fail_after(20):
-                    message = await buffered.receive(6)
-
-                if message != b"READY\n":
-                    raise BrokenWorkerProcess(
-                        f"Worker process returned unexpected response: {message!r}"
-                    )
-
-                main_module_path = getattr(sys.modules["__main__"], "__file__", None)
-                pickled = pickle.dumps(
-                    ("init", sys.path, main_module_path),
-                    protocol=pickle.HIGHEST_PROTOCOL,
-                )
-                await send_raw_command(pickled)
-            except (BrokenWorkerProcess, get_cancelled_exc_class()):
-                raise
-            except BaseException as exc:
-                process.kill()
-                raise BrokenWorkerProcess(
-                    "Error during worker process initialization"
-                ) from exc
-
-            workers.add(process)
-
-        with CancelScope(shield=not cancellable):
-            try:
-                return cast(T_Retval, await send_raw_command(request))
-            finally:
-                if process in workers:
-                    idle_workers.append((process, current_time()))
-
-
-def current_default_process_limiter() -> CapacityLimiter:
-    """
-    Return the capacity limiter that is used by default to limit the number of worker processes.
-
-    :return: a capacity limiter object
-
-    """
-    try:
-        return _default_process_limiter.get()
-    except LookupError:
-        limiter = CapacityLimiter(os.cpu_count() or 2)
-        _default_process_limiter.set(limiter)
-        return limiter
-
-
-def process_worker() -> None:
-    # Redirect standard streams to os.devnull so that user code won't interfere with the
-    # parent-worker communication
-    stdin = sys.stdin
-    stdout = sys.stdout
-    sys.stdin = open(os.devnull)
-    sys.stdout = open(os.devnull, "w")
-
-    stdout.buffer.write(b"READY\n")
-    while True:
-        retval = exception = None
-        try:
-            command, *args = pickle.load(stdin.buffer)
-        except EOFError:
-            return
-        except BaseException as exc:
-            exception = exc
-        else:
-            if command == "run":
-                func, args = args
-                try:
-                    retval = func(*args)
-                except BaseException as exc:
-                    exception = exc
-            elif command == "init":
-                main_module_path: str | None
-                sys.path, main_module_path = args
-                del sys.modules["__main__"]
-                if main_module_path:
-                    # Load the parent's main module but as __mp_main__ instead of __main__
-                    # (like multiprocessing does) to avoid infinite recursion
-                    try:
-                        spec = spec_from_file_location("__mp_main__", main_module_path)
-                        if spec and spec.loader:
-                            main = module_from_spec(spec)
-                            spec.loader.exec_module(main)
-                            sys.modules["__main__"] = main
-                    except BaseException as exc:
-                        exception = exc
-
-        try:
-            if exception is not None:
-                status = b"EXCEPTION"
-                pickled = pickle.dumps(exception, pickle.HIGHEST_PROTOCOL)
-            else:
-                status = b"RETURN"
-                pickled = pickle.dumps(retval, pickle.HIGHEST_PROTOCOL)
-        except BaseException as exc:
-            exception = exc
-            status = b"EXCEPTION"
-            pickled = pickle.dumps(exc, pickle.HIGHEST_PROTOCOL)
-
-        stdout.buffer.write(b"%s %d\n" % (status, len(pickled)))
-        stdout.buffer.write(pickled)
-
-        # Respect SIGTERM
-        if isinstance(exception, SystemExit):
-            raise exception
-
-
-if __name__ == "__main__":
-    process_worker()
diff --git a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/server/api.py b/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/server/api.py
deleted file mode 100644
index 8fa8767a193b6cc09800c8a4128b9803044f3825..0000000000000000000000000000000000000000
--- a/spaces/SungBeom/chatwine-korean/.venv/Lib/site-packages/debugpy/server/api.py
+++ /dev/null
@@ -1,365 +0,0 @@
-# Copyright (c) Microsoft Corporation. All rights reserved.
-# Licensed under the MIT License. See LICENSE in the project root
-# for license information.
-
-import codecs
-import os
-import pydevd
-import socket
-import sys
-import threading
-
-import debugpy
-from debugpy import adapter
-from debugpy.common import json, log, sockets
-from _pydevd_bundle.pydevd_constants import get_global_debugger
-from pydevd_file_utils import absolute_path
-from debugpy.common.util import hide_debugpy_internals
-
-_tls = threading.local()
-
-# TODO: "gevent", if possible.
-_config = {
-    "qt": "none",
-    "subProcess": True,
-    "python": sys.executable,
-    "pythonEnv": {},
-}
-
-_config_valid_values = {
-    # If property is not listed here, any value is considered valid, so long as
-    # its type matches that of the default value in _config.
-    "qt": ["auto", "none", "pyside", "pyside2", "pyqt4", "pyqt5"],
-}
-
-# This must be a global to prevent it from being garbage collected and triggering
-# https://bugs.python.org/issue37380.
-_adapter_process = None
-
-
-def _settrace(*args, **kwargs):
-    log.debug("pydevd.settrace(*{0!r}, **{1!r})", args, kwargs)
-    # The stdin in notification is not acted upon in debugpy, so, disable it.
-    kwargs.setdefault("notify_stdin", False)
-    try:
-        return pydevd.settrace(*args, **kwargs)
-    except Exception:
-        raise
-    else:
-        _settrace.called = True
-
-
-_settrace.called = False
-
-
-def ensure_logging():
-    """Starts logging to log.log_dir, if it hasn't already been done."""
-    if ensure_logging.ensured:
-        return
-    ensure_logging.ensured = True
-    log.to_file(prefix="debugpy.server")
-    log.describe_environment("Initial environment:")
-    if log.log_dir is not None:
-        pydevd.log_to(log.log_dir + "/debugpy.pydevd.log")
-
-
-ensure_logging.ensured = False
-
-
-def log_to(path):
-    if ensure_logging.ensured:
-        raise RuntimeError("logging has already begun")
-
-    log.debug("log_to{0!r}", (path,))
-    if path is sys.stderr:
-        log.stderr.levels |= set(log.LEVELS)
-    else:
-        log.log_dir = path
-
-
-def configure(properties=None, **kwargs):
-    if _settrace.called:
-        raise RuntimeError("debug adapter is already running")
-
-    ensure_logging()
-    log.debug("configure{0!r}", (properties, kwargs))
-
-    if properties is None:
-        properties = kwargs
-    else:
-        properties = dict(properties)
-        properties.update(kwargs)
-
-    for k, v in properties.items():
-        if k not in _config:
-            raise ValueError("Unknown property {0!r}".format(k))
-        expected_type = type(_config[k])
-        if type(v) is not expected_type:
-            raise ValueError("{0!r} must be a {1}".format(k, expected_type.__name__))
-        valid_values = _config_valid_values.get(k)
-        if (valid_values is not None) and (v not in valid_values):
-            raise ValueError("{0!r} must be one of: {1!r}".format(k, valid_values))
-        _config[k] = v
-
-
-def _starts_debugging(func):
-    def debug(address, **kwargs):
-        if _settrace.called:
-            raise RuntimeError("this process already has a debug adapter")
-
-        try:
-            _, port = address
-        except Exception:
-            port = address
-            address = ("127.0.0.1", port)
-        try:
-            port.__index__()  # ensure it's int-like
-        except Exception:
-            raise ValueError("expected port or (host, port)")
-        if not (0 <= port < 2 ** 16):
-            raise ValueError("invalid port number")
-
-        ensure_logging()
-        log.debug("{0}({1!r}, **{2!r})", func.__name__, address, kwargs)
-        log.info("Initial debug configuration: {0}", json.repr(_config))
-
-        qt_mode = _config.get("qt", "none")
-        if qt_mode != "none":
-            pydevd.enable_qt_support(qt_mode)
-
-        settrace_kwargs = {
-            "suspend": False,
-            "patch_multiprocessing": _config.get("subProcess", True),
-        }
-
-        if hide_debugpy_internals():
-            debugpy_path = os.path.dirname(absolute_path(debugpy.__file__))
-            settrace_kwargs["dont_trace_start_patterns"] = (debugpy_path,)
-            settrace_kwargs["dont_trace_end_patterns"] = (str("debugpy_launcher.py"),)
-
-        try:
-            return func(address, settrace_kwargs, **kwargs)
-        except Exception:
-            log.reraise_exception("{0}() failed:", func.__name__, level="info")
-
-    return debug
-
-
-@_starts_debugging
-def listen(address, settrace_kwargs, in_process_debug_adapter=False):
-    # Errors below are logged with level="info", because the caller might be catching
-    # and handling exceptions, and we don't want to spam their stderr unnecessarily.
-
-    if in_process_debug_adapter:
-        host, port = address
-        log.info("Listening: pydevd without debugpy adapter: {0}:{1}", host, port)
-        settrace_kwargs['patch_multiprocessing'] = False
-        _settrace(
-            host=host,
-            port=port,
-            wait_for_ready_to_run=False,
-            block_until_connected=False,
-            **settrace_kwargs
-        )
-        return
-
-    import subprocess
-
-    server_access_token = codecs.encode(os.urandom(32), "hex").decode("ascii")
-
-    try:
-        endpoints_listener = sockets.create_server("127.0.0.1", 0, timeout=10)
-    except Exception as exc:
-        log.swallow_exception("Can't listen for adapter endpoints:")
-        raise RuntimeError("can't listen for adapter endpoints: " + str(exc))
-
-    try:
-        endpoints_host, endpoints_port = endpoints_listener.getsockname()
-        log.info(
-            "Waiting for adapter endpoints on {0}:{1}...",
-            endpoints_host,
-            endpoints_port,
-        )
-
-        host, port = address
-        adapter_args = [
-            _config.get("python", sys.executable),
-            os.path.dirname(adapter.__file__),
-            "--for-server",
-            str(endpoints_port),
-            "--host",
-            host,
-            "--port",
-            str(port),
-            "--server-access-token",
-            server_access_token,
-        ]
-        if log.log_dir is not None:
-            adapter_args += ["--log-dir", log.log_dir]
-        log.info("debugpy.listen() spawning adapter: {0}", json.repr(adapter_args))
-
-        # On Windows, detach the adapter from our console, if any, so that it doesn't
-        # receive Ctrl+C from it, and doesn't keep it open once we exit.
-        creationflags = 0
-        if sys.platform == "win32":
-            creationflags |= 0x08000000  # CREATE_NO_WINDOW
-            creationflags |= 0x00000200  # CREATE_NEW_PROCESS_GROUP
-
-        # On embedded applications, environment variables might not contain
-        # Python environment settings.
-        python_env = _config.get("pythonEnv")
-        if not bool(python_env):
-            python_env = None
-
-        # Adapter will outlive this process, so we shouldn't wait for it. However, we
-        # need to ensure that the Popen instance for it doesn't get garbage-collected
-        # by holding a reference to it in a non-local variable, to avoid triggering
-        # https://bugs.python.org/issue37380.
-        try:
-            global _adapter_process
-            _adapter_process = subprocess.Popen(
-                adapter_args, close_fds=True, creationflags=creationflags, env=python_env
-            )
-            if os.name == "posix":
-                # It's going to fork again to daemonize, so we need to wait on it to
-                # clean it up properly.
-                _adapter_process.wait()
-            else:
-                # Suppress misleading warning about child process still being alive when
-                # this process exits (https://bugs.python.org/issue38890).
-                _adapter_process.returncode = 0
-                pydevd.add_dont_terminate_child_pid(_adapter_process.pid)
-        except Exception as exc:
-            log.swallow_exception("Error spawning debug adapter:", level="info")
-            raise RuntimeError("error spawning debug adapter: " + str(exc))
-
-        try:
-            sock, _ = endpoints_listener.accept()
-            try:
-                sock.settimeout(None)
-                sock_io = sock.makefile("rb", 0)
-                try:
-                    endpoints = json.loads(sock_io.read().decode("utf-8"))
-                finally:
-                    sock_io.close()
-            finally:
-                sockets.close_socket(sock)
-        except socket.timeout:
-            log.swallow_exception(
-                "Timed out waiting for adapter to connect:", level="info"
-            )
-            raise RuntimeError("timed out waiting for adapter to connect")
-        except Exception as exc:
-            log.swallow_exception("Error retrieving adapter endpoints:", level="info")
-            raise RuntimeError("error retrieving adapter endpoints: " + str(exc))
-
-    finally:
-        endpoints_listener.close()
-
-    log.info("Endpoints received from adapter: {0}", json.repr(endpoints))
-
-    if "error" in endpoints:
-        raise RuntimeError(str(endpoints["error"]))
-
-    try:
-        server_host = str(endpoints["server"]["host"])
-        server_port = int(endpoints["server"]["port"])
-        client_host = str(endpoints["client"]["host"])
-        client_port = int(endpoints["client"]["port"])
-    except Exception as exc:
-        log.swallow_exception(
-            "Error parsing adapter endpoints:\n{0}\n",
-            json.repr(endpoints),
-            level="info",
-        )
-        raise RuntimeError("error parsing adapter endpoints: " + str(exc))
-    log.info(
-        "Adapter is accepting incoming client connections on {0}:{1}",
-        client_host,
-        client_port,
-    )
-
-    _settrace(
-        host=server_host,
-        port=server_port,
-        wait_for_ready_to_run=False,
-        block_until_connected=True,
-        access_token=server_access_token,
-        **settrace_kwargs
-    )
-    log.info("pydevd is connected to adapter at {0}:{1}", server_host, server_port)
-    return client_host, client_port
-
-
-@_starts_debugging
-def connect(address, settrace_kwargs, access_token=None):
-    host, port = address
-    _settrace(host=host, port=port, client_access_token=access_token, **settrace_kwargs)
-
-
-class wait_for_client:
-    def __call__(self):
-        ensure_logging()
-        log.debug("wait_for_client()")
-
-        pydb = get_global_debugger()
-        if pydb is None:
-            raise RuntimeError("listen() or connect() must be called first")
-
-        cancel_event = threading.Event()
-        self.cancel = cancel_event.set
-        pydevd._wait_for_attach(cancel=cancel_event)
-
-    @staticmethod
-    def cancel():
-        raise RuntimeError("wait_for_client() must be called first")
-
-
-wait_for_client = wait_for_client()
-
-
-def is_client_connected():
-    return pydevd._is_attached()
-
-
-def breakpoint():
-    ensure_logging()
-    if not is_client_connected():
-        log.info("breakpoint() ignored - debugger not attached")
-        return
-    log.debug("breakpoint()")
-
-    # Get the first frame in the stack that's not an internal frame.
-    pydb = get_global_debugger()
-    stop_at_frame = sys._getframe().f_back
-    while (
-        stop_at_frame is not None
-        and pydb.get_file_type(stop_at_frame) == pydb.PYDEV_FILE
-    ):
-        stop_at_frame = stop_at_frame.f_back
-
-    _settrace(
-        suspend=True,
-        trace_only_current_thread=True,
-        patch_multiprocessing=False,
-        stop_at_frame=stop_at_frame,
-    )
-    stop_at_frame = None
-
-
-def debug_this_thread():
-    ensure_logging()
-    log.debug("debug_this_thread()")
-
-    _settrace(suspend=False)
-
-
-def trace_this_thread(should_trace):
-    ensure_logging()
-    log.debug("trace_this_thread({0!r})", should_trace)
-
-    pydb = get_global_debugger()
-    if should_trace:
-        pydb.enable_tracing()
-    else:
-        pydb.disable_tracing()
diff --git a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/models/__init__.py b/spaces/Suniilkumaar/MusicGen-updated/audiocraft/models/__init__.py
deleted file mode 100644
index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000
--- a/spaces/Suniilkumaar/MusicGen-updated/audiocraft/models/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-# flake8: noqa
-from .musicgen import MusicGen
-from .lm import LMModel
-from .encodec import CompressionModel, EncodecModel
diff --git a/spaces/Superlang/ImageProcessor/annotator/leres/pix2pix/models/__init__.py b/spaces/Superlang/ImageProcessor/annotator/leres/pix2pix/models/__init__.py
deleted file mode 100644
index f96e5c7f032f2154c6bb433b68fc968d0a19b5a8..0000000000000000000000000000000000000000
--- a/spaces/Superlang/ImageProcessor/annotator/leres/pix2pix/models/__init__.py
+++ /dev/null
@@ -1,67 +0,0 @@
-"""This package contains modules related to objective functions, optimizations, and network architectures.
-
-To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
-You need to implement the following five functions:
-    -- <__init__>:                      initialize the class; first call BaseModel.__init__(self, opt).
-    -- <set_input>:                     unpack data from dataset and apply preprocessing.
-    -- <forward>:                       produce intermediate results.
-    -- <optimize_parameters>:           calculate loss, gradients, and update network weights.
-    -- <modify_commandline_options>:    (optionally) add model-specific options and set default options.
-
-In the function <__init__>, you need to define four lists:
-    -- self.loss_names (str list):          specify the training losses that you want to plot and save.
-    -- self.model_names (str list):         define networks used in our training.
-    -- self.visual_names (str list):        specify the images that you want to display and save.
-    -- self.optimizers (optimizer list):    define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
-
-Now you can use the model class by specifying flag '--model dummy'.
-See our template model class 'template_model.py' for more details.
-"""
-
-import importlib
-from .base_model import BaseModel
-
-
-def find_model_using_name(model_name):
-    """Import the module "models/[model_name]_model.py".
-
-    In the file, the class called DatasetNameModel() will
-    be instantiated. It has to be a subclass of BaseModel,
-    and it is case-insensitive.
-    """
-    model_filename = "annotator.leres.pix2pix.models." + model_name + "_model"
-    modellib = importlib.import_module(model_filename)
-    model = None
-    target_model_name = model_name.replace('_', '') + 'model'
-    for name, cls in modellib.__dict__.items():
-        if name.lower() == target_model_name.lower() \
-           and issubclass(cls, BaseModel):
-            model = cls
-
-    if model is None:
-        print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
-        exit(0)
-
-    return model
-
-
-def get_option_setter(model_name):
-    """Return the static method <modify_commandline_options> of the model class."""
-    model_class = find_model_using_name(model_name)
-    return model_class.modify_commandline_options
-
-
-def create_model(opt):
-    """Create a model given the option.
-
-    This function warps the class CustomDatasetDataLoader.
-    This is the main interface between this package and 'train.py'/'test.py'
-
-    Example:
-        >>> from models import create_model
-        >>> model = create_model(opt)
-    """
-    model = find_model_using_name(opt.model)
-    instance = model(opt)
-    print("model [%s] was created" % type(instance).__name__)
-    return instance
diff --git a/spaces/TEnngal/bingo/src/components/chat-history.tsx b/spaces/TEnngal/bingo/src/components/chat-history.tsx
deleted file mode 100644
index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000
--- a/spaces/TEnngal/bingo/src/components/chat-history.tsx
+++ /dev/null
@@ -1,48 +0,0 @@
-import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons"
-
-export function ChatHistory() {
-  return (
-    <div className="chat-history fixed top-18 right-4">
-      <div className="chat-history-header text-sm font-semibold text-left w-[280px] px-4 py-6">
-        历史记录
-      </div>
-      <div className="chat-history-main">
-        <div className="scroller">
-          <div className="surface">
-            <div className="threads">
-              <div className="thread">
-                <div className="primary-row">
-                  <button type="button" aria-label="加载聊天">
-
-                  </button>
-                  <div className="description">
-                    <h3 className="name">无标题的聊天</h3>
-                  </div>
-                  <h4 className="time">上午1:42</h4>
-                  <div className="controls">
-
-                    <button className="edit icon-button" type="button" aria-label="重命名">
-                      <IconEdit />
-                    </button>
-
-                    <button className="delete icon-button" type="button" aria-label="删除">
-                      <IconTrash />
-                    </button>
-
-                    <button className="more icon-button" type="button" aria-haspopup="true" aria-expanded="false" aria-label="更多">
-                      <IconMore />
-                    </button>
-
-                    <button className="export icon-button" type="button" aria-label="导出">
-                      <IconDownload />
-                    </button>
-                  </div>
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      </div>
-    </div>
-  )
-}
diff --git a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/_version.py b/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/_version.py
deleted file mode 100644
index d69ca3145704364d2dcb49150e1ad0b4dd761425..0000000000000000000000000000000000000000
--- a/spaces/TandCAcceptMe/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/urllib3/_version.py
+++ /dev/null
@@ -1,2 +0,0 @@
-# This file is protected via CODEOWNERS
-__version__ = "1.26.16"
diff --git a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp b/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp
deleted file mode 100644
index d7556e645b604aa83d86cc702b783fd8ecedffcc..0000000000000000000000000000000000000000
--- a/spaces/TencentARC/VLog/models/grit_src/third_party/CenterNet2/detectron2/layers/csrc/nms_rotated/nms_rotated_cpu.cpp
+++ /dev/null
@@ -1,75 +0,0 @@
-// Copyright (c) Facebook, Inc. and its affiliates.
-#include "../box_iou_rotated/box_iou_rotated_utils.h"
-#include "nms_rotated.h"
-
-namespace detectron2 {
-
-template <typename scalar_t>
-at::Tensor nms_rotated_cpu_kernel(
-    const at::Tensor& dets,
-    const at::Tensor& scores,
-    const double iou_threshold) {
-  // nms_rotated_cpu_kernel is modified from torchvision's nms_cpu_kernel,
-  // however, the code in this function is much shorter because
-  // we delegate the IoU computation for rotated boxes to
-  // the single_box_iou_rotated function in box_iou_rotated_utils.h
-  AT_ASSERTM(dets.device().is_cpu(), "dets must be a CPU tensor");
-  AT_ASSERTM(scores.device().is_cpu(), "scores must be a CPU tensor");
-  AT_ASSERTM(
-      dets.scalar_type() == scores.scalar_type(),
-      "dets should have the same type as scores");
-
-  if (dets.numel() == 0) {
-    return at::empty({0}, dets.options().dtype(at::kLong));
-  }
-
-  auto order_t = std::get<1>(scores.sort(0, /* descending=*/true));
-
-  auto ndets = dets.size(0);
-  at::Tensor suppressed_t = at::zeros({ndets}, dets.options().dtype(at::kByte));
-  at::Tensor keep_t = at::zeros({ndets}, dets.options().dtype(at::kLong));
-
-  auto suppressed = suppressed_t.data_ptr<uint8_t>();
-  auto keep = keep_t.data_ptr<int64_t>();
-  auto order = order_t.data_ptr<int64_t>();
-
-  int64_t num_to_keep = 0;
-
-  for (int64_t _i = 0; _i < ndets; _i++) {
-    auto i = order[_i];
-    if (suppressed[i] == 1) {
-      continue;
-    }
-
-    keep[num_to_keep++] = i;
-
-    for (int64_t _j = _i + 1; _j < ndets; _j++) {
-      auto j = order[_j];
-      if (suppressed[j] == 1) {
-        continue;
-      }
-
-      auto ovr = single_box_iou_rotated<scalar_t>(
-          dets[i].data_ptr<scalar_t>(), dets[j].data_ptr<scalar_t>());
-      if (ovr >= iou_threshold) {
-        suppressed[j] = 1;
-      }
-    }
-  }
-  return keep_t.narrow(/*dim=*/0, /*start=*/0, /*length=*/num_to_keep);
-}
-
-at::Tensor nms_rotated_cpu(
-    // input must be contiguous
-    const at::Tensor& dets,
-    const at::Tensor& scores,
-    const double iou_threshold) {
-  auto result = at::empty({0}, dets.options());
-
-  AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms_rotated", [&] {
-    result = nms_rotated_cpu_kernel<scalar_t>(dets, scores, iou_threshold);
-  });
-  return result;
-}
-
-} // namespace detectron2
diff --git a/spaces/TheKitten/Pictures/README.md b/spaces/TheKitten/Pictures/README.md
deleted file mode 100644
index aa50b664b66cd9198ed706def40a9674ec8c5221..0000000000000000000000000000000000000000
--- a/spaces/TheKitten/Pictures/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Pictures
-emoji: 👀
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.47.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Tritkoman/Tritkoman-EnglishtoChurchSlavonicV2/README.md b/spaces/Tritkoman/Tritkoman-EnglishtoChurchSlavonicV2/README.md
deleted file mode 100644
index b1360327c7cea95f3bcfbccf4725f1d1d06e8063..0000000000000000000000000000000000000000
--- a/spaces/Tritkoman/Tritkoman-EnglishtoChurchSlavonicV2/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Tritkoman EnglishtoChurchSlavonicV2
-emoji: 🌖
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/Unachstudents/README/README.md b/spaces/Unachstudents/README/README.md
deleted file mode 100644
index 91b17787a06d796532e89e4ee534eef55ec33526..0000000000000000000000000000000000000000
--- a/spaces/Unachstudents/README/README.md
+++ /dev/null
@@ -1,10 +0,0 @@
----
-title: README
-emoji: 💻
-colorFrom: pink
-colorTo: green
-sdk: static
-pinned: false
----
-
-Edit this `README.md` markdown file to author your organization card 🔥
diff --git a/spaces/VlaTal/facial_beauty_analysis/app.py b/spaces/VlaTal/facial_beauty_analysis/app.py
deleted file mode 100644
index ef8387bfd6ba646cbc560f0e05be8b171cdf6cd1..0000000000000000000000000000000000000000
--- a/spaces/VlaTal/facial_beauty_analysis/app.py
+++ /dev/null
@@ -1,54 +0,0 @@
-import os
-
-import cv2
-import gradio as gr
-import numpy as np
-import joblib
-
-from facial_image import FacialImage, convert_to_numpy_array
-
-title = "Sytoss System: Beauty Recognition"
-description = "A model to classify is face beautiful or not."
-
-target_width = 512
-facial = FacialImage()
-loaded_svm_classifier = joblib.load('svm_model.pkl')
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-
-def process_image(input_image: np.ndarray):
-    if input_image is not None:
-
-        output_image = input_image
-
-        input_height, input_width, _ = input_image.shape
-        if input_width > target_width:
-            scale_factor = float(target_width / input_width)
-            output_width = int(input_width * scale_factor)
-            output_height = int(input_height * scale_factor)
-            dsize = (output_width, output_height)
-            output_image = cv2.resize(input_image, dsize)
-
-        ratios, output_image = facial.calculate_ratios(output_image)
-        ratios_vector = convert_to_numpy_array(ratios)
-
-        class_probabilities = loaded_svm_classifier.predict_proba([ratios_vector])
-
-        predicted_class = np.argmax(class_probabilities)
-
-        probability_of_predicted_class = class_probabilities[0, predicted_class]
-
-        return output_image, predicted_class, probability_of_predicted_class
-    return None, None
-
-
-iface = gr.Interface(
-    process_image,
-    inputs=gr.inputs.Image(),
-    outputs=["image", gr.Number(label="Predicted class"), gr.Number(label="Probability")],
-    title=title,
-    description=description,
-    examples=example_list)
-
-iface.launch()
diff --git a/spaces/Xenova/next-server-example-app/src/app/page.js b/spaces/Xenova/next-server-example-app/src/app/page.js
deleted file mode 100644
index 6cd6aec541c1e3880e2adb73e94b9bc163651057..0000000000000000000000000000000000000000
--- a/spaces/Xenova/next-server-example-app/src/app/page.js
+++ /dev/null
@@ -1,45 +0,0 @@
-'use client'
-
-import { useState } from 'react'
-
-export default function Home() {
-
-  // Keep track of the classification result and the model loading status.
-  const [result, setResult] = useState(null);
-  const [ready, setReady] = useState(null);
-
-  const classify = async (text) => {
-    if (!text) return;
-    if (ready === null) setReady(false);
-
-    // Make a request to the /classify route on the server.
-    const result = await fetch(`/classify?text=${encodeURIComponent(text)}`);
-
-    // If this is the first time we've made a request, set the ready flag.
-    if (!ready) setReady(true);
-
-    const json = await result.json();
-    setResult(json);
-  };
-  return (
-    <main className="flex min-h-screen flex-col items-center justify-center p-12">
-      <h1 className="text-5xl font-bold mb-2 text-center">Transformers.js</h1>
-      <h2 className="text-2xl mb-4 text-center">Next.js template (server-side)</h2>
-      <input
-        type="text"
-        className="w-full max-w-xs p-2 border border-gray-300 rounded mb-4"
-        placeholder="Enter text here"
-        onInput={e => {
-          classify(e.target.value);
-        }}
-      />
-
-      {ready !== null && (
-        <pre className="bg-gray-100 p-2 rounded">
-          {
-            (!ready || !result) ? 'Loading...' : JSON.stringify(result, null, 2)}
-        </pre>
-      )}
-    </main>
-  )
-}
diff --git a/spaces/XzJosh/Jianmo-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md b/spaces/XzJosh/Jianmo-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
deleted file mode 100644
index 7bce039b7f81ee328fdf8efe3f14409200aacbef..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/Jianmo-Bert-VITS2/bert/chinese-roberta-wwm-ext-large/README.md
+++ /dev/null
@@ -1,57 +0,0 @@
----
-language: 
-- zh
-tags:
-- bert
-license: "apache-2.0"
----
-
-# Please use 'Bert' related functions to load this model!
-
-## Chinese BERT with Whole Word Masking
-For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. 
-
-**[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)**  
-Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu
-
-This repository is developed based on：https://github.com/google-research/bert
-
-You may also interested in,
-- Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm
-- Chinese MacBERT: https://github.com/ymcui/MacBERT
-- Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
-- Chinese XLNet: https://github.com/ymcui/Chinese-XLNet
-- Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer
-
-More resources by HFL: https://github.com/ymcui/HFL-Anthology
-
-## Citation
-If you find the technical report or resource is useful, please cite the following technical report in your paper.
-- Primary: https://arxiv.org/abs/2004.13922
-```
-@inproceedings{cui-etal-2020-revisiting,
-    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
-    author = "Cui, Yiming  and
-      Che, Wanxiang  and
-      Liu, Ting  and
-      Qin, Bing  and
-      Wang, Shijin  and
-      Hu, Guoping",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
-    month = nov,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
-    pages = "657--668",
-}
-```
-- Secondary: https://arxiv.org/abs/1906.08101  
-```
-@article{chinese-bert-wwm,
-  title={Pre-Training with Whole Word Masking for Chinese BERT},
-  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
-  journal={arXiv preprint arXiv:1906.08101},
-  year={2019}
- }
-```
\ No newline at end of file
diff --git a/spaces/XzJosh/XingTong-Bert-VITS2/text/english.py b/spaces/XzJosh/XingTong-Bert-VITS2/text/english.py
deleted file mode 100644
index 781d0a56cef71f66fc67db51d76538be90d3ddd2..0000000000000000000000000000000000000000
--- a/spaces/XzJosh/XingTong-Bert-VITS2/text/english.py
+++ /dev/null
@@ -1,138 +0,0 @@
-import pickle
-import os
-import re
-from g2p_en import G2p
-from string import punctuation
-
-from text import symbols
-
-current_file_path = os.path.dirname(__file__)
-CMU_DICT_PATH = os.path.join(current_file_path, 'cmudict.rep')
-CACHE_PATH = os.path.join(current_file_path, 'cmudict_cache.pickle')
-_g2p = G2p()
-
-arpa = {'AH0', 'S', 'AH1', 'EY2', 'AE2', 'EH0', 'OW2', 'UH0', 'NG', 'B', 'G', 'AY0', 'M', 'AA0', 'F', 'AO0', 'ER2', 'UH1', 'IY1', 'AH2', 'DH', 'IY0', 'EY1', 'IH0', 'K', 'N', 'W', 'IY2', 'T', 'AA1', 'ER1', 'EH2', 'OY0', 'UH2', 'UW1', 'Z', 'AW2', 'AW1', 'V', 'UW2', 'AA2', 'ER', 'AW0', 'UW0', 'R', 'OW1', 'EH1', 'ZH', 'AE0', 'IH2', 'IH', 'Y', 'JH', 'P', 'AY1', 'EY0', 'OY2', 'TH', 'HH', 'D', 'ER0', 'CH', 'AO1', 'AE1', 'AO2', 'OY1', 'AY2', 'IH1', 'OW0', 'L', 'SH'}
-
-
-def post_replace_ph(ph):
-    rep_map = {
-        '：': ',',
-        '；': ',',
-        '，': ',',
-        '。': '.',
-        '！': '!',
-        '？': '?',
-        '\n': '.',
-        "·": ",",
-        '、': ",",
-        '...': '…',
-        'v': "V"
-    }
-    if ph in rep_map.keys():
-        ph = rep_map[ph]
-    if ph in symbols:
-        return ph
-    if ph not in symbols:
-        ph = 'UNK'
-    return ph
-
-def read_dict():
-    g2p_dict = {}
-    start_line = 49
-    with open(CMU_DICT_PATH) as f:
-        line = f.readline()
-        line_index = 1
-        while line:
-            if line_index >= start_line:
-                line = line.strip()
-                word_split = line.split('  ')
-                word = word_split[0]
-
-                syllable_split = word_split[1].split(' - ')
-                g2p_dict[word] = []
-                for syllable in syllable_split:
-                    phone_split = syllable.split(' ')
-                    g2p_dict[word].append(phone_split)
-
-            line_index = line_index + 1
-            line = f.readline()
-
-    return g2p_dict
-
-
-def cache_dict(g2p_dict, file_path):
-    with open(file_path, 'wb') as pickle_file:
-        pickle.dump(g2p_dict, pickle_file)
-
-
-def get_dict():
-    if os.path.exists(CACHE_PATH):
-        with open(CACHE_PATH, 'rb') as pickle_file:
-            g2p_dict = pickle.load(pickle_file)
-    else:
-        g2p_dict = read_dict()
-        cache_dict(g2p_dict, CACHE_PATH)
-
-    return g2p_dict
-
-eng_dict = get_dict()
-
-def refine_ph(phn):
-    tone = 0
-    if re.search(r'\d$', phn):
-        tone = int(phn[-1]) + 1
-        phn = phn[:-1]
-    return phn.lower(), tone
-
-def refine_syllables(syllables):
-    tones = []
-    phonemes = []
-    for phn_list in syllables:
-        for i in range(len(phn_list)):
-            phn = phn_list[i]
-            phn, tone = refine_ph(phn)
-            phonemes.append(phn)
-            tones.append(tone)
-    return phonemes, tones
-
-
-def text_normalize(text):
-    # todo: eng text normalize
-    return text
-
-def g2p(text):
-
-    phones = []
-    tones = []
-    words = re.split(r"([,;.\-\?\!\s+])", text)
-    for w in words:
-        if w.upper() in eng_dict:
-            phns, tns = refine_syllables(eng_dict[w.upper()])
-            phones += phns
-            tones += tns
-        else:
-            phone_list = list(filter(lambda p: p != " ", _g2p(w)))
-            for ph in phone_list:
-                if ph in arpa:
-                    ph, tn = refine_ph(ph)
-                    phones.append(ph)
-                    tones.append(tn)
-                else:
-                    phones.append(ph)
-                    tones.append(0)
-    # todo: implement word2ph
-    word2ph = [1 for i in phones]
-
-    phones = [post_replace_ph(i) for i in phones]
-    return phones, tones, word2ph
-
-if __name__ == "__main__":
-    # print(get_dict())
-    # print(eng_word_to_phoneme("hello"))
-    print(g2p("In this paper, we propose 1 DSPGAN, a GAN-based universal vocoder."))
-    # all_phones = set()
-    # for k, syllables in eng_dict.items():
-    #     for group in syllables:
-    #         for ph in group:
-    #             all_phones.add(ph)
-    # print(all_phones)
\ No newline at end of file
diff --git a/spaces/Y-T-G/Blur-Anything/tracker/base_tracker.py b/spaces/Y-T-G/Blur-Anything/tracker/base_tracker.py
deleted file mode 100644
index 0dba74857c61312f8b31091974f1a0c72e4d798c..0000000000000000000000000000000000000000
--- a/spaces/Y-T-G/Blur-Anything/tracker/base_tracker.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# import for debugging
-import os
-import glob
-import numpy as np
-from PIL import Image
-
-# import for base_tracker
-import torch
-import yaml
-import torch.nn.functional as F
-from tracker.model.network import XMem
-from inference.inference_core import InferenceCore
-from tracker.util.mask_mapper import MaskMapper
-from torchvision import transforms
-from tracker.util.range_transform import im_normalization
-
-from utils.painter import mask_painter
-
-dir_path = os.path.dirname(os.path.realpath(__file__))
-
-
-class BaseTracker:
-    def __init__(
-        self, xmem_checkpoint, device, sam_model=None, model_type=None
-    ) -> None:
-        """
-        device: model device
-        xmem_checkpoint: checkpoint of XMem model
-        """
-        # load configurations
-        with open(f"{dir_path}/config/config.yaml", "r") as stream:
-            config = yaml.safe_load(stream)
-        # initialise XMem
-        network = XMem(config, xmem_checkpoint, map_location=device).eval()
-        # initialise IncerenceCore
-        self.tracker = InferenceCore(network, config)
-        # data transformation
-        self.im_transform = transforms.Compose(
-            [
-                transforms.ToTensor(),
-                im_normalization,
-            ]
-        )
-        self.device = device
-
-        # changable properties
-        self.mapper = MaskMapper()
-        self.initialised = False
-
-        # # SAM-based refinement
-        # self.sam_model = sam_model
-        # self.resizer = Resize([256, 256])
-
-    @torch.no_grad()
-    def resize_mask(self, mask):
-        # mask transform is applied AFTER mapper, so we need to post-process it in eval.py
-        h, w = mask.shape[-2:]
-        min_hw = min(h, w)
-        return F.interpolate(
-            mask,
-            (int(h / min_hw * self.size), int(w / min_hw * self.size)),
-            mode="nearest",
-        )
-
-    @torch.no_grad()
-    def track(self, frame, first_frame_annotation=None):
-        """
-        Input:
-        frames: numpy arrays (H, W, 3)
-        logit: numpy array (H, W), logit
-
-        Output:
-        mask: numpy arrays (H, W)
-        logit: numpy arrays, probability map (H, W)
-        painted_image: numpy array (H, W, 3)
-        """
-
-        if first_frame_annotation is not None:  # first frame mask
-            # initialisation
-            mask, labels = self.mapper.convert_mask(first_frame_annotation)
-            mask = torch.Tensor(mask).to(self.device)
-            self.tracker.set_all_labels(list(self.mapper.remappings.values()))
-        else:
-            mask = None
-            labels = None
-        # prepare inputs
-        frame_tensor = self.im_transform(frame).to(self.device)
-        # track one frame
-        probs, _ = self.tracker.step(frame_tensor, mask, labels)  # logits 2 (bg fg) H W
-        # # refine
-        # if first_frame_annotation is None:
-        #     out_mask = self.sam_refinement(frame, logits[1], ti)
-
-        # convert to mask
-        out_mask = torch.argmax(probs, dim=0)
-        out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
-
-        final_mask = np.zeros_like(out_mask)
-
-        # map back
-        for k, v in self.mapper.remappings.items():
-            final_mask[out_mask == v] = k
-
-        num_objs = final_mask.max()
-        painted_image = frame
-        for obj in range(1, num_objs + 1):
-            if np.max(final_mask == obj) == 0:
-                continue
-            painted_image = mask_painter(
-                painted_image, (final_mask == obj).astype("uint8"), mask_color=obj + 1
-            )
-
-        # print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')
-
-        return final_mask, final_mask, painted_image
-
-    @torch.no_grad()
-    def sam_refinement(self, frame, logits, ti):
-        """
-        refine segmentation results with mask prompt
-        """
-        # convert to 1, 256, 256
-        self.sam_model.set_image(frame)
-        mode = "mask"
-        logits = logits.unsqueeze(0)
-        logits = self.resizer(logits).cpu().numpy()
-        prompts = {"mask_input": logits}  # 1 256 256
-        masks, scores, logits = self.sam_model.predict(
-            prompts, mode, multimask=True
-        )  # masks (n, h, w), scores (n,), logits (n, 256, 256)
-        painted_image = mask_painter(
-            frame, masks[np.argmax(scores)].astype("uint8"), mask_alpha=0.8
-        )
-        painted_image = Image.fromarray(painted_image)
-        painted_image.save(f"/ssd1/gaomingqi/refine/{ti:05d}.png")
-        self.sam_model.reset_image()
-
-    @torch.no_grad()
-    def clear_memory(self):
-        self.tracker.clear_memory()
-        self.mapper.clear_labels()
-        torch.cuda.empty_cache()
diff --git a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/configs/common/coco_schedule.py b/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/configs/common/coco_schedule.py
deleted file mode 100644
index 355e66a1d213cb599a7ffe55089d854089c8ead2..0000000000000000000000000000000000000000
--- a/spaces/Yiqin/ChatVID/model/vision/grit_src/third_party/CenterNet2/configs/common/coco_schedule.py
+++ /dev/null
@@ -1,47 +0,0 @@
-from fvcore.common.param_scheduler import MultiStepParamScheduler
-
-from detectron2.config import LazyCall as L
-from detectron2.solver import WarmupParamScheduler
-
-
-def default_X_scheduler(num_X):
-    """
-    Returns the config for a default multi-step LR scheduler such as "1x", "3x",
-    commonly referred to in papers, where every 1x has the total length of 1440k
-    training images (~12 COCO epochs). LR is decayed twice at the end of training
-    following the strategy defined in "Rethinking ImageNet Pretraining", Sec 4.
-
-    Args:
-        num_X: a positive real number
-
-    Returns:
-        DictConfig: configs that define the multiplier for LR during training
-    """
-    # total number of iterations assuming 16 batch size, using 1440000/16=90000
-    total_steps_16bs = num_X * 90000
-
-    if num_X <= 2:
-        scheduler = L(MultiStepParamScheduler)(
-            values=[1.0, 0.1, 0.01],
-            # note that scheduler is scale-invariant. This is equivalent to
-            # milestones=[6, 8, 9]
-            milestones=[60000, 80000, 90000],
-        )
-    else:
-        scheduler = L(MultiStepParamScheduler)(
-            values=[1.0, 0.1, 0.01],
-            milestones=[total_steps_16bs - 60000, total_steps_16bs - 20000, total_steps_16bs],
-        )
-    return L(WarmupParamScheduler)(
-        scheduler=scheduler,
-        warmup_length=1000 / total_steps_16bs,
-        warmup_method="linear",
-        warmup_factor=0.001,
-    )
-
-
-lr_multiplier_1x = default_X_scheduler(1)
-lr_multiplier_2x = default_X_scheduler(2)
-lr_multiplier_3x = default_X_scheduler(3)
-lr_multiplier_6x = default_X_scheduler(6)
-lr_multiplier_9x = default_X_scheduler(9)
diff --git a/spaces/Yuliang/ECON/lib/torch_utils/custom_ops.py b/spaces/Yuliang/ECON/lib/torch_utils/custom_ops.py
deleted file mode 100644
index c76fc0e6a9c41c9e5b8e861079865eae41189226..0000000000000000000000000000000000000000
--- a/spaces/Yuliang/ECON/lib/torch_utils/custom_ops.py
+++ /dev/null
@@ -1,145 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import glob
-import hashlib
-import importlib
-import os
-import shutil
-from pathlib import Path
-
-import torch
-import torch.utils.cpp_extension
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief'    # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try:    # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(
-                    f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".'
-                )
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(
-                list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file())
-            )
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(
-                module_name, verbose=verbose_build
-            )    # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(
-                                src, os.path.join(digest_build_dir, os.path.basename(src))
-                            )
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(
-                name=module_name,
-                build_directory=build_dir,
-                verbose=verbose_build,
-                sources=digest_sources,
-                **build_kwargs
-            )
-        else:
-            torch.utils.cpp_extension.load(
-                name=module_name, verbose=verbose_build, sources=sources, **build_kwargs
-            )
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-
-#----------------------------------------------------------------------------
diff --git a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/tester.py b/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/tester.py
deleted file mode 100644
index 50d622e7edb0ed989fcd3273d35e74d66f11ce75..0000000000000000000000000000000000000000
--- a/spaces/aaaaaabbbbbbbdddddddduuuuulllll/Ashaar/poetry_diacritizer/tester.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from .config_manager import ConfigManager
-import os
-from typing import Dict
-
-from torch import nn
-from tqdm import tqdm
-from tqdm import trange
-
-from dataset import load_iterators
-from trainer import GeneralTrainer
-
-
-class DiacritizationTester(GeneralTrainer):
-    def __init__(self, config_path: str, model_kind: str) -> None:
-        self.config_path = config_path
-        self.model_kind = model_kind
-        self.config_manager = ConfigManager(
-            config_path=config_path, model_kind=model_kind
-        )
-        self.config = self.config_manager.config
-        self.pad_idx = 0
-        self.criterion = nn.CrossEntropyLoss(ignore_index=self.pad_idx)
-        self.set_device()
-
-        self.text_encoder = self.config_manager.text_encoder
-        self.start_symbol_id = self.text_encoder.start_symbol_id
-
-        self.model = self.config_manager.get_model()
-
-        self.model = self.model.to(self.device)
-
-        self.load_model(model_path=self.config["test_model_path"], load_optimizer=False)
-        self.load_diacritizer()
-        self.diacritizer.set_model(self.model)
-
-        self.initialize_model()
-
-        self.print_config()
-
-    def run(self):
-        self.config_manager.config["load_training_data"] = False
-        self.config_manager.config["load_validation_data"] = False
-        self.config_manager.config["load_test_data"] = True
-        _, test_iterator, _ = load_iterators(self.config_manager)
-        tqdm_eval = trange(0, len(test_iterator), leave=True)
-        tqdm_error_rates = trange(0, len(test_iterator), leave=True)
-
-        loss, acc = self.evaluate(test_iterator, tqdm_eval, log = False)
-        error_rates, _ = self.evaluate_with_error_rates(test_iterator, tqdm_error_rates, log = False)
-
-        tqdm_eval.close()
-        tqdm_error_rates.close()
-
-        WER = error_rates["WER"]
-        DER = error_rates["DER"]
-        DER1 = error_rates["DER*"]
-        WER1 = error_rates["WER*"]
-
-        error_rates = f"DER: {DER}, WER: {WER}, DER*: {DER1}, WER*: {WER1}"
-
-        print(f"global step : {self.global_step}")
-        print(f"Evaluate {self.global_step}: accuracy, {acc}, loss: {loss}")
-        print(f"WER/DER {self.global_step}: {error_rates}")
diff --git a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/snowflake/configuration.md b/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/snowflake/configuration.md
deleted file mode 100644
index 30e568d54a1b1cf93fbd696431865ed9381cb021..0000000000000000000000000000000000000000
--- a/spaces/abdvl/datahub_qa_bot/docs/quick-ingestion-guides/snowflake/configuration.md
+++ /dev/null
@@ -1,145 +0,0 @@
----
-title: Configuration
----
-# Configuring Your Snowflake Connector to DataHub
-
-Now that you have created a DataHub-specific user with the relevant roles in Snowflake in [the prior step](setup.md), it's now time to set up a connection via the DataHub UI.
-
-## Configure Secrets
-
-1. Within DataHub, navigate to the **Ingestion** tab in the top, right corner of your screen
-
-<p align="center">
-  <img width="75%" alt="Navigate to the &quot;Ingestion Tab&quot;" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/common/common_ingestion_ingestion_button.png"/>
-</p>
-
-:::note
-If you do not see the Ingestion tab, please contact your DataHub admin to grant you the correct permissions
-:::
-
-2. Navigate to the **Secrets** tab and click **Create new secret**
-
-<p align="center">
-   <img width="75%" alt="Secrets Tab" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/common/common_ingestion_secrets_tab.png"/>
-</p>
-
-3. Create a Password secret
-
-  This will securely store your Snowflake password within DataHub
-
-   * Enter a name like `SNOWFLAKE_PASSWORD` - we will use this later to refer to the secret
-   * Enter the password configured for the DataHub user in the previous step
-   * Optionally add a description
-   * Click **Create**
-
-<p align="center">
-   <img width="70%" alt="Snowflake Password Secret" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_password_secret.png"/>
-</p>
-
-## Configure Recipe
-
-4. Navigate to the **Sources** tab and click **Create new source**
-
-<p align="center">
-  <img width="75%" alt="Click &quot;Create new source&quot;" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/common/common_ingestion_click_create_new_source_button.png"/>
-</p>
-
-5. Select Snowflake
-
-<p align="center">
-  <img width="70%" alt="Select Snowflake from the options" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_snowflake_source.png"/>
-</p>
-
-6. Fill out the Snowflake Recipe
-
-Enter the Snowflake Account Identifier as **Account ID** field. Account identifier is the part before `.snowflakecomputing.com` in your snowflake host URL:
-
-<p align="center">
-   <img width="70%" alt="Account Id Field" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_account_id.png"/>
-</p>
-
-*Learn more about Snowflake Account Identifiers [here](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#account-identifiers)*
-
-Add the previously added Password secret to **Password** field:
-   * Click on the Password input field
-   * Select `SNOWFLAKE_PASSWORD` secret
-
-<p align="center">
-     <img width="70%" alt="Password field" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_password_secret_field.png"/>
-</p>
-
-Populate the relevant fields using the same **Username**, **Role**, and **Warehouse** you created and/or specified in [Snowflake Prerequisites](setup.md).
-
-<p align="center">
-   <img width="70%" alt="Warehouse Field" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_warehouse_username_role_fields.png"/>
-</p>
-
-7. Click **Test Connection**
-
-This step will ensure you have configured your credentials accurately and confirm you have the required permissions to extract all relevant metadata.
-
-<p align="center">
-  <img width="75%" alt="Test Snoflake connection" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_test_connection.png"/>
-</p>
-
-After you have successfully tested your connection, click **Next**.
-
-## Schedule Execution
-
-Now it's time to schedule a recurring ingestion pipeline to regularly extract metadata from your Snowflake instance.
-
-8. Decide how regularly you want this ingestion to run-- day, month, year, hour, minute, etc. Select from the dropdown
-
-<p align="center">
-    <img width="75%" alt="schedule selector" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/common/common_ingestion_set_execution_schedule.png"/>
-</p>  
-
-9. Ensure you've configured your correct timezone
-<p align="center">
-    <img width="75%" alt="timezone_selector" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/common/common_ingestion_set_execution_timezone.png"/>
-</p>  
-
-10. Click **Next** when you are done
-
-## Finish Up
-
-11. Name your ingestion source, then click **Save and Run**
-<p align="center">
-  <img width="75%" alt="Name your ingestion" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/common/common_ingestion_name_ingestion_source.png"/>
-</p>  
-
-You will now find your new ingestion source running
-
-<p align="center">
-  <img width="75%" alt="ingestion_running" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_source_running.png"/>
-</p>  
-
-## Validate Ingestion Runs
-
-12. View the latest status of ingestion runs on the Ingestion page
-
-<p align="center">
-  <img width="75%" alt="ingestion succeeded" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_ingestion_succeded.png"/>
-</p>  
-
-13. Click the plus sign to expand the full list of historical runs and outcomes; click **Details** to see the outcomes of a specific run
-
-<p align="center">
-  <img width="75%" alt="ingestion_details" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_ingestion_details.png"/>
-</p>
-
-14. From the Ingestion Run Details page, pick **View All** to see which entities were ingested
-
-<p align="center">
-  <img width="75%" alt="ingestion_details_view_all" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_details_view_all.png"/>
-</p>  
-
-15. Pick an entity from the list to manually validate if it contains the detail you expected  
-
-<p align="center">
-  <img width="75%" alt="ingestion_details_view_all" src="https://raw.githubusercontent.com/datahub-project/static-assets/main/imgs/guides/snowflake/snowflake_ingestion_view_ingested_assets.png"/>
-</p>  
-
-**Congratulations!** You've successfully set up Snowflake as an ingestion source for DataHub!
-
-*Need more help? Join the conversation in [Slack](http://slack.datahubproject.io)!*
\ No newline at end of file
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/apis/train.py b/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/apis/train.py
deleted file mode 100644
index 7f2f1f95c0a8e7c9232f7aa490e8104f8e37c4f5..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer/mmdet/apis/train.py
+++ /dev/null
@@ -1,185 +0,0 @@
-import random
-import warnings
-
-import numpy as np
-import torch
-from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
-from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner,
-                         Fp16OptimizerHook, OptimizerHook, build_optimizer,
-                         build_runner)
-from mmcv.utils import build_from_cfg
-
-from mmdet.core import DistEvalHook, EvalHook
-from mmdet.datasets import (build_dataloader, build_dataset,
-                            replace_ImageToTensor)
-from mmdet.utils import get_root_logger
-from mmcv_custom.runner import EpochBasedRunnerAmp
-try:
-    import apex
-except:
-    print('apex is not installed')
-
-
-def set_random_seed(seed, deterministic=False):
-    """Set random seed.
-
-    Args:
-        seed (int): Seed to be used.
-        deterministic (bool): Whether to set the deterministic option for
-            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
-            to True and `torch.backends.cudnn.benchmark` to False.
-            Default: False.
-    """
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-    if deterministic:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-
-
-def train_detector(model,
-                   dataset,
-                   cfg,
-                   distributed=False,
-                   validate=False,
-                   timestamp=None,
-                   meta=None):
-    logger = get_root_logger(cfg.log_level)
-
-    # prepare data loaders
-    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
-    if 'imgs_per_gpu' in cfg.data:
-        logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
-                       'Please use "samples_per_gpu" instead')
-        if 'samples_per_gpu' in cfg.data:
-            logger.warning(
-                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
-                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
-                f'={cfg.data.imgs_per_gpu} is used in this experiments')
-        else:
-            logger.warning(
-                'Automatically set "samples_per_gpu"="imgs_per_gpu"='
-                f'{cfg.data.imgs_per_gpu} in this experiments')
-        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
-
-    data_loaders = [
-        build_dataloader(
-            ds,
-            cfg.data.samples_per_gpu,
-            cfg.data.workers_per_gpu,
-            # cfg.gpus will be ignored if distributed
-            len(cfg.gpu_ids),
-            dist=distributed,
-            seed=cfg.seed) for ds in dataset
-    ]
-
-    # build optimizer
-    optimizer = build_optimizer(model, cfg.optimizer)
-
-    # use apex fp16 optimizer
-    if cfg.optimizer_config.get("type", None) and cfg.optimizer_config["type"] == "DistOptimizerHook":
-        if cfg.optimizer_config.get("use_fp16", False):
-            model, optimizer = apex.amp.initialize(
-                model.cuda(), optimizer, opt_level="O1")
-            for m in model.modules():
-                if hasattr(m, "fp16_enabled"):
-                    m.fp16_enabled = True
-
-    # put model on gpus
-    if distributed:
-        find_unused_parameters = cfg.get('find_unused_parameters', False)
-        # Sets the `find_unused_parameters` parameter in
-        # torch.nn.parallel.DistributedDataParallel
-        model = MMDistributedDataParallel(
-            model.cuda(),
-            device_ids=[torch.cuda.current_device()],
-            broadcast_buffers=False,
-            find_unused_parameters=find_unused_parameters)
-    else:
-        model = MMDataParallel(
-            model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids)
-
-    if 'runner' not in cfg:
-        cfg.runner = {
-            'type': 'EpochBasedRunner',
-            'max_epochs': cfg.total_epochs
-        }
-        warnings.warn(
-            'config is now expected to have a `runner` section, '
-            'please set `runner` in your config.', UserWarning)
-    else:
-        if 'total_epochs' in cfg:
-            assert cfg.total_epochs == cfg.runner.max_epochs
-
-    # build runner
-    runner = build_runner(
-        cfg.runner,
-        default_args=dict(
-            model=model,
-            optimizer=optimizer,
-            work_dir=cfg.work_dir,
-            logger=logger,
-            meta=meta))
-
-    # an ugly workaround to make .log and .log.json filenames the same
-    runner.timestamp = timestamp
-
-    # fp16 setting
-    fp16_cfg = cfg.get('fp16', None)
-    if fp16_cfg is not None:
-        optimizer_config = Fp16OptimizerHook(
-            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
-    elif distributed and 'type' not in cfg.optimizer_config:
-        optimizer_config = OptimizerHook(**cfg.optimizer_config)
-    else:
-        optimizer_config = cfg.optimizer_config
-
-    # register hooks
-    runner.register_training_hooks(cfg.lr_config, optimizer_config,
-                                   cfg.checkpoint_config, cfg.log_config,
-                                   cfg.get('momentum_config', None))
-    if distributed:
-        if isinstance(runner, EpochBasedRunner):
-            runner.register_hook(DistSamplerSeedHook())
-
-    # register eval hooks
-    if validate:
-        # Support batch_size > 1 in validation
-        val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1)
-        if val_samples_per_gpu > 1:
-            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
-            cfg.data.val.pipeline = replace_ImageToTensor(
-                cfg.data.val.pipeline)
-        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
-        val_dataloader = build_dataloader(
-            val_dataset,
-            samples_per_gpu=val_samples_per_gpu,
-            workers_per_gpu=cfg.data.workers_per_gpu,
-            dist=distributed,
-            shuffle=False)
-        eval_cfg = cfg.get('evaluation', {})
-        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
-        eval_hook = DistEvalHook if distributed else EvalHook
-        runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
-
-    # user-defined hooks
-    if cfg.get('custom_hooks', None):
-        custom_hooks = cfg.custom_hooks
-        assert isinstance(custom_hooks, list), \
-            f'custom_hooks expect list type, but got {type(custom_hooks)}'
-        for hook_cfg in cfg.custom_hooks:
-            assert isinstance(hook_cfg, dict), \
-                'Each item in custom_hooks expects dict type, but got ' \
-                f'{type(hook_cfg)}'
-            hook_cfg = hook_cfg.copy()
-            priority = hook_cfg.pop('priority', 'NORMAL')
-            hook = build_from_cfg(hook_cfg, HOOKS)
-            runner.register_hook(hook, priority=priority)
-
-    if cfg.resume_from:
-        runner.resume(cfg.resume_from)
-    elif cfg.load_from:
-        runner.load_checkpoint(cfg.load_from)
-    runner.run(data_loaders, cfg.workflow)
diff --git a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/checkpoint.py b/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/checkpoint.py
deleted file mode 100644
index 6af3fae43ac4b35532641a81eb13557edfc7dfba..0000000000000000000000000000000000000000
--- a/spaces/abhishek/sketch-to-image/annotator/uniformer_base/mmcv/runner/hooks/checkpoint.py
+++ /dev/null
@@ -1,167 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import os.path as osp
-import warnings
-
-from annotator.uniformer.mmcv.fileio import FileClient
-from ..dist_utils import allreduce_params, master_only
-from .hook import HOOKS, Hook
-
-
-@HOOKS.register_module()
-class CheckpointHook(Hook):
-    """Save checkpoints periodically.
-
-    Args:
-        interval (int): The saving period. If ``by_epoch=True``, interval
-            indicates epochs, otherwise it indicates iterations.
-            Default: -1, which means "never".
-        by_epoch (bool): Saving checkpoints by epoch or by iteration.
-            Default: True.
-        save_optimizer (bool): Whether to save optimizer state_dict in the
-            checkpoint. It is usually used for resuming experiments.
-            Default: True.
-        out_dir (str, optional): The root directory to save checkpoints. If not
-            specified, ``runner.work_dir`` will be used by default. If
-            specified, the ``out_dir`` will be the concatenation of ``out_dir``
-            and the last level directory of ``runner.work_dir``.
-            `Changed in version 1.3.16.`
-        max_keep_ckpts (int, optional): The maximum checkpoints to keep.
-            In some cases we want only the latest few checkpoints and would
-            like to delete old ones to save the disk space.
-            Default: -1, which means unlimited.
-        save_last (bool, optional): Whether to force the last checkpoint to be
-            saved regardless of interval. Default: True.
-        sync_buffer (bool, optional): Whether to synchronize buffers in
-            different gpus. Default: False.
-        file_client_args (dict, optional): Arguments to instantiate a
-            FileClient. See :class:`mmcv.fileio.FileClient` for details.
-            Default: None.
-            `New in version 1.3.16.`
-
-    .. warning::
-        Before v1.3.16, the ``out_dir`` argument indicates the path where the
-        checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the
-        root directory and the final path to save checkpoint is the
-        concatenation of ``out_dir`` and the last level directory of
-        ``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A"
-        and the value of ``runner.work_dir`` is "/path/of/B", then the final
-        path will be "/path/of/A/B".
-    """
-
-    def __init__(self,
-                 interval=-1,
-                 by_epoch=True,
-                 save_optimizer=True,
-                 out_dir=None,
-                 max_keep_ckpts=-1,
-                 save_last=True,
-                 sync_buffer=False,
-                 file_client_args=None,
-                 **kwargs):
-        self.interval = interval
-        self.by_epoch = by_epoch
-        self.save_optimizer = save_optimizer
-        self.out_dir = out_dir
-        self.max_keep_ckpts = max_keep_ckpts
-        self.save_last = save_last
-        self.args = kwargs
-        self.sync_buffer = sync_buffer
-        self.file_client_args = file_client_args
-
-    def before_run(self, runner):
-        if not self.out_dir:
-            self.out_dir = runner.work_dir
-
-        self.file_client = FileClient.infer_client(self.file_client_args,
-                                                   self.out_dir)
-
-        # if `self.out_dir` is not equal to `runner.work_dir`, it means that
-        # `self.out_dir` is set so the final `self.out_dir` is the
-        # concatenation of `self.out_dir` and the last level directory of
-        # `runner.work_dir`
-        if self.out_dir != runner.work_dir:
-            basename = osp.basename(runner.work_dir.rstrip(osp.sep))
-            self.out_dir = self.file_client.join_path(self.out_dir, basename)
-
-        runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
-                            f'{self.file_client.name}.'))
-
-        # disable the create_symlink option because some file backends do not
-        # allow to create a symlink
-        if 'create_symlink' in self.args:
-            if self.args[
-                    'create_symlink'] and not self.file_client.allow_symlink:
-                self.args['create_symlink'] = False
-                warnings.warn(
-                    ('create_symlink is set as True by the user but is changed'
-                     'to be False because creating symbolic link is not '
-                     f'allowed in {self.file_client.name}'))
-        else:
-            self.args['create_symlink'] = self.file_client.allow_symlink
-
-    def after_train_epoch(self, runner):
-        if not self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` epochs
-        # 2. reach the last epoch of training
-        if self.every_n_epochs(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_epoch(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.epoch + 1} epochs')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
-
-    @master_only
-    def _save_checkpoint(self, runner):
-        """Save the current checkpoint and delete unwanted checkpoint."""
-        runner.save_checkpoint(
-            self.out_dir, save_optimizer=self.save_optimizer, **self.args)
-        if runner.meta is not None:
-            if self.by_epoch:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
-            else:
-                cur_ckpt_filename = self.args.get(
-                    'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
-            runner.meta.setdefault('hook_msgs', dict())
-            runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path(
-                self.out_dir, cur_ckpt_filename)
-        # remove other checkpoints
-        if self.max_keep_ckpts > 0:
-            if self.by_epoch:
-                name = 'epoch_{}.pth'
-                current_ckpt = runner.epoch + 1
-            else:
-                name = 'iter_{}.pth'
-                current_ckpt = runner.iter + 1
-            redundant_ckpts = range(
-                current_ckpt - self.max_keep_ckpts * self.interval, 0,
-                -self.interval)
-            filename_tmpl = self.args.get('filename_tmpl', name)
-            for _step in redundant_ckpts:
-                ckpt_path = self.file_client.join_path(
-                    self.out_dir, filename_tmpl.format(_step))
-                if self.file_client.isfile(ckpt_path):
-                    self.file_client.remove(ckpt_path)
-                else:
-                    break
-
-    def after_train_iter(self, runner):
-        if self.by_epoch:
-            return
-
-        # save checkpoint for following cases:
-        # 1. every ``self.interval`` iterations
-        # 2. reach the last iteration of training
-        if self.every_n_iters(
-                runner, self.interval) or (self.save_last
-                                           and self.is_last_iter(runner)):
-            runner.logger.info(
-                f'Saving checkpoint at {runner.iter + 1} iterations')
-            if self.sync_buffer:
-                allreduce_params(runner.model.buffers())
-            self._save_checkpoint(runner)
diff --git a/spaces/akhaliq/GPEN/face_parse/parse_model.py b/spaces/akhaliq/GPEN/face_parse/parse_model.py
deleted file mode 100644
index b0717eedb97bab83331312c13cfd48a1e268b862..0000000000000000000000000000000000000000
--- a/spaces/akhaliq/GPEN/face_parse/parse_model.py
+++ /dev/null
@@ -1,77 +0,0 @@
-'''
-@Created by chaofengc (chaofenghust@gmail.com)
-
-@Modified by yangxy (yangtao9009@gmail.com)
-'''
-
-from blocks import *
-import torch
-from torch import nn
-import numpy as np
-
-def define_P(in_size=512, out_size=512, min_feat_size=32, relu_type='LeakyReLU', isTrain=False, weight_path=None):
-    net = ParseNet(in_size, out_size, min_feat_size, 64, 19, norm_type='bn', relu_type=relu_type, ch_range=[32, 256])
-    if not isTrain:
-        net.eval()  
-    if weight_path is not None:
-        net.load_state_dict(torch.load(weight_path))
-    return net
-
-
-class ParseNet(nn.Module):
-    def __init__(self,
-                in_size=128,
-                out_size=128,
-                min_feat_size=32,
-                base_ch=64,
-                parsing_ch=19,
-                res_depth=10,
-                relu_type='prelu',
-                norm_type='bn',
-                ch_range=[32, 512],
-                ):
-        super().__init__()
-        self.res_depth = res_depth
-        act_args = {'norm_type': norm_type, 'relu_type': relu_type}
-        min_ch, max_ch = ch_range
-
-        ch_clip = lambda x: max(min_ch, min(x, max_ch))
-        min_feat_size = min(in_size, min_feat_size)
-
-        down_steps = int(np.log2(in_size//min_feat_size))
-        up_steps = int(np.log2(out_size//min_feat_size))
-
-        # =============== define encoder-body-decoder ==================== 
-        self.encoder = []
-        self.encoder.append(ConvLayer(3, base_ch, 3, 1))
-        head_ch = base_ch
-        for i in range(down_steps):
-            cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2)
-            self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args))
-            head_ch = head_ch * 2
-
-        self.body = []
-        for i in range(res_depth):
-            self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args))
-
-        self.decoder = []
-        for i in range(up_steps):
-            cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2)
-            self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args))
-            head_ch = head_ch // 2
-
-        self.encoder = nn.Sequential(*self.encoder)
-        self.body = nn.Sequential(*self.body)
-        self.decoder = nn.Sequential(*self.decoder)
-        self.out_img_conv = ConvLayer(ch_clip(head_ch), 3)
-        self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch)
-
-    def forward(self, x):
-        feat = self.encoder(x)
-        x = feat + self.body(feat)
-        x = self.decoder(x)
-        out_img = self.out_img_conv(x) 
-        out_mask = self.out_mask_conv(x)
-        return out_mask, out_img
-
-
diff --git a/spaces/akhaliq/Music_Source_Separation/bytesep/dataset_creation/__init__.py b/spaces/akhaliq/Music_Source_Separation/bytesep/dataset_creation/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/cachecontrol/heuristics.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/cachecontrol/heuristics.py
deleted file mode 100644
index ebe4a96f589474f6f441858de2bb961c5e473c6d..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/cachecontrol/heuristics.py
+++ /dev/null
@@ -1,139 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-import calendar
-import time
-
-from email.utils import formatdate, parsedate, parsedate_tz
-
-from datetime import datetime, timedelta
-
-TIME_FMT = "%a, %d %b %Y %H:%M:%S GMT"
-
-
-def expire_after(delta, date=None):
-    date = date or datetime.utcnow()
-    return date + delta
-
-
-def datetime_to_header(dt):
-    return formatdate(calendar.timegm(dt.timetuple()))
-
-
-class BaseHeuristic(object):
-
-    def warning(self, response):
-        """
-        Return a valid 1xx warning header value describing the cache
-        adjustments.
-
-        The response is provided too allow warnings like 113
-        http://tools.ietf.org/html/rfc7234#section-5.5.4 where we need
-        to explicitly say response is over 24 hours old.
-        """
-        return '110 - "Response is Stale"'
-
-    def update_headers(self, response):
-        """Update the response headers with any new headers.
-
-        NOTE: This SHOULD always include some Warning header to
-              signify that the response was cached by the client, not
-              by way of the provided headers.
-        """
-        return {}
-
-    def apply(self, response):
-        updated_headers = self.update_headers(response)
-
-        if updated_headers:
-            response.headers.update(updated_headers)
-            warning_header_value = self.warning(response)
-            if warning_header_value is not None:
-                response.headers.update({"Warning": warning_header_value})
-
-        return response
-
-
-class OneDayCache(BaseHeuristic):
-    """
-    Cache the response by providing an expires 1 day in the
-    future.
-    """
-
-    def update_headers(self, response):
-        headers = {}
-
-        if "expires" not in response.headers:
-            date = parsedate(response.headers["date"])
-            expires = expire_after(timedelta(days=1), date=datetime(*date[:6]))
-            headers["expires"] = datetime_to_header(expires)
-            headers["cache-control"] = "public"
-        return headers
-
-
-class ExpiresAfter(BaseHeuristic):
-    """
-    Cache **all** requests for a defined time period.
-    """
-
-    def __init__(self, **kw):
-        self.delta = timedelta(**kw)
-
-    def update_headers(self, response):
-        expires = expire_after(self.delta)
-        return {"expires": datetime_to_header(expires), "cache-control": "public"}
-
-    def warning(self, response):
-        tmpl = "110 - Automatically cached for %s. Response might be stale"
-        return tmpl % self.delta
-
-
-class LastModified(BaseHeuristic):
-    """
-    If there is no Expires header already, fall back on Last-Modified
-    using the heuristic from
-    http://tools.ietf.org/html/rfc7234#section-4.2.2
-    to calculate a reasonable value.
-
-    Firefox also does something like this per
-    https://developer.mozilla.org/en-US/docs/Web/HTTP/Caching_FAQ
-    http://lxr.mozilla.org/mozilla-release/source/netwerk/protocol/http/nsHttpResponseHead.cpp#397
-    Unlike mozilla we limit this to 24-hr.
-    """
-    cacheable_by_default_statuses = {
-        200, 203, 204, 206, 300, 301, 404, 405, 410, 414, 501
-    }
-
-    def update_headers(self, resp):
-        headers = resp.headers
-
-        if "expires" in headers:
-            return {}
-
-        if "cache-control" in headers and headers["cache-control"] != "public":
-            return {}
-
-        if resp.status not in self.cacheable_by_default_statuses:
-            return {}
-
-        if "date" not in headers or "last-modified" not in headers:
-            return {}
-
-        date = calendar.timegm(parsedate_tz(headers["date"]))
-        last_modified = parsedate(headers["last-modified"])
-        if date is None or last_modified is None:
-            return {}
-
-        now = time.time()
-        current_age = max(0, now - date)
-        delta = date - calendar.timegm(last_modified)
-        freshness_lifetime = max(0, min(delta / 10, 24 * 3600))
-        if freshness_lifetime <= current_age:
-            return {}
-
-        expires = date + freshness_lifetime
-        return {"expires": time.strftime(TIME_FMT, time.gmtime(expires))}
-
-    def warning(self, resp):
-        return None
diff --git a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/requests/models.py b/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/requests/models.py
deleted file mode 100644
index f538c1054d5bba01be1825feada16e2fc2ea7167..0000000000000000000000000000000000000000
--- a/spaces/alexray/btc_predictor/venv/lib/python3.10/site-packages/pip/_vendor/requests/models.py
+++ /dev/null
@@ -1,973 +0,0 @@
-# -*- coding: utf-8 -*-
-
-"""
-requests.models
-~~~~~~~~~~~~~~~
-
-This module contains the primary objects that power Requests.
-"""
-
-import datetime
-import sys
-
-# Import encoding now, to avoid implicit import later.
-# Implicit import within threads may cause LookupError when standard library is in a ZIP,
-# such as in Embedded Python. See https://github.com/psf/requests/issues/3578.
-import encodings.idna
-
-from pip._vendor.urllib3.fields import RequestField
-from pip._vendor.urllib3.filepost import encode_multipart_formdata
-from pip._vendor.urllib3.util import parse_url
-from pip._vendor.urllib3.exceptions import (
-    DecodeError, ReadTimeoutError, ProtocolError, LocationParseError)
-
-from io import UnsupportedOperation
-from .hooks import default_hooks
-from .structures import CaseInsensitiveDict
-
-from .auth import HTTPBasicAuth
-from .cookies import cookiejar_from_dict, get_cookie_header, _copy_cookie_jar
-from .exceptions import (
-    HTTPError, MissingSchema, InvalidURL, ChunkedEncodingError,
-    ContentDecodingError, ConnectionError, StreamConsumedError,
-    InvalidJSONError)
-from .exceptions import JSONDecodeError as RequestsJSONDecodeError
-from ._internal_utils import to_native_string, unicode_is_ascii
-from .utils import (
-    guess_filename, get_auth_from_url, requote_uri,
-    stream_decode_response_unicode, to_key_val_list, parse_header_links,
-    iter_slices, guess_json_utf, super_len, check_header_validity)
-from .compat import (
-    Callable, Mapping,
-    cookielib, urlunparse, urlsplit, urlencode, str, bytes,
-    is_py2, chardet, builtin_str, basestring, JSONDecodeError)
-from .compat import json as complexjson
-from .status_codes import codes
-
-#: The set of HTTP status codes that indicate an automatically
-#: processable redirect.
-REDIRECT_STATI = (
-    codes.moved,               # 301
-    codes.found,               # 302
-    codes.other,               # 303
-    codes.temporary_redirect,  # 307
-    codes.permanent_redirect,  # 308
-)
-
-DEFAULT_REDIRECT_LIMIT = 30
-CONTENT_CHUNK_SIZE = 10 * 1024
-ITER_CHUNK_SIZE = 512
-
-
-class RequestEncodingMixin(object):
-    @property
-    def path_url(self):
-        """Build the path URL to use."""
-
-        url = []
-
-        p = urlsplit(self.url)
-
-        path = p.path
-        if not path:
-            path = '/'
-
-        url.append(path)
-
-        query = p.query
-        if query:
-            url.append('?')
-            url.append(query)
-
-        return ''.join(url)
-
-    @staticmethod
-    def _encode_params(data):
-        """Encode parameters in a piece of data.
-
-        Will successfully encode parameters when passed as a dict or a list of
-        2-tuples. Order is retained if data is a list of 2-tuples but arbitrary
-        if parameters are supplied as a dict.
-        """
-
-        if isinstance(data, (str, bytes)):
-            return data
-        elif hasattr(data, 'read'):
-            return data
-        elif hasattr(data, '__iter__'):
-            result = []
-            for k, vs in to_key_val_list(data):
-                if isinstance(vs, basestring) or not hasattr(vs, '__iter__'):
-                    vs = [vs]
-                for v in vs:
-                    if v is not None:
-                        result.append(
-                            (k.encode('utf-8') if isinstance(k, str) else k,
-                             v.encode('utf-8') if isinstance(v, str) else v))
-            return urlencode(result, doseq=True)
-        else:
-            return data
-
-    @staticmethod
-    def _encode_files(files, data):
-        """Build the body for a multipart/form-data request.
-
-        Will successfully encode files when passed as a dict or a list of
-        tuples. Order is retained if data is a list of tuples but arbitrary
-        if parameters are supplied as a dict.
-        The tuples may be 2-tuples (filename, fileobj), 3-tuples (filename, fileobj, contentype)
-        or 4-tuples (filename, fileobj, contentype, custom_headers).
-        """
-        if (not files):
-            raise ValueError("Files must be provided.")
-        elif isinstance(data, basestring):
-            raise ValueError("Data must not be a string.")
-
-        new_fields = []
-        fields = to_key_val_list(data or {})
-        files = to_key_val_list(files or {})
-
-        for field, val in fields:
-            if isinstance(val, basestring) or not hasattr(val, '__iter__'):
-                val = [val]
-            for v in val:
-                if v is not None:
-                    # Don't call str() on bytestrings: in Py3 it all goes wrong.
-                    if not isinstance(v, bytes):
-                        v = str(v)
-
-                    new_fields.append(
-                        (field.decode('utf-8') if isinstance(field, bytes) else field,
-                         v.encode('utf-8') if isinstance(v, str) else v))
-
-        for (k, v) in files:
-            # support for explicit filename
-            ft = None
-            fh = None
-            if isinstance(v, (tuple, list)):
-                if len(v) == 2:
-                    fn, fp = v
-                elif len(v) == 3:
-                    fn, fp, ft = v
-                else:
-                    fn, fp, ft, fh = v
-            else:
-                fn = guess_filename(v) or k
-                fp = v
-
-            if isinstance(fp, (str, bytes, bytearray)):
-                fdata = fp
-            elif hasattr(fp, 'read'):
-                fdata = fp.read()
-            elif fp is None:
-                continue
-            else:
-                fdata = fp
-
-            rf = RequestField(name=k, data=fdata, filename=fn, headers=fh)
-            rf.make_multipart(content_type=ft)
-            new_fields.append(rf)
-
-        body, content_type = encode_multipart_formdata(new_fields)
-
-        return body, content_type
-
-
-class RequestHooksMixin(object):
-    def register_hook(self, event, hook):
-        """Properly register a hook."""
-
-        if event not in self.hooks:
-            raise ValueError('Unsupported event specified, with event name "%s"' % (event))
-
-        if isinstance(hook, Callable):
-            self.hooks[event].append(hook)
-        elif hasattr(hook, '__iter__'):
-            self.hooks[event].extend(h for h in hook if isinstance(h, Callable))
-
-    def deregister_hook(self, event, hook):
-        """Deregister a previously registered hook.
-        Returns True if the hook existed, False if not.
-        """
-
-        try:
-            self.hooks[event].remove(hook)
-            return True
-        except ValueError:
-            return False
-
-
-class Request(RequestHooksMixin):
-    """A user-created :class:`Request <Request>` object.
-
-    Used to prepare a :class:`PreparedRequest <PreparedRequest>`, which is sent to the server.
-
-    :param method: HTTP method to use.
-    :param url: URL to send.
-    :param headers: dictionary of headers to send.
-    :param files: dictionary of {filename: fileobject} files to multipart upload.
-    :param data: the body to attach to the request. If a dictionary or
-        list of tuples ``[(key, value)]`` is provided, form-encoding will
-        take place.
-    :param json: json for the body to attach to the request (if files or data is not specified).
-    :param params: URL parameters to append to the URL. If a dictionary or
-        list of tuples ``[(key, value)]`` is provided, form-encoding will
-        take place.
-    :param auth: Auth handler or (user, pass) tuple.
-    :param cookies: dictionary or CookieJar of cookies to attach to this request.
-    :param hooks: dictionary of callback hooks, for internal usage.
-
-    Usage::
-
-      >>> import requests
-      >>> req = requests.Request('GET', 'https://httpbin.org/get')
-      >>> req.prepare()
-      <PreparedRequest [GET]>
-    """
-
-    def __init__(self,
-            method=None, url=None, headers=None, files=None, data=None,
-            params=None, auth=None, cookies=None, hooks=None, json=None):
-
-        # Default empty dicts for dict params.
-        data = [] if data is None else data
-        files = [] if files is None else files
-        headers = {} if headers is None else headers
-        params = {} if params is None else params
-        hooks = {} if hooks is None else hooks
-
-        self.hooks = default_hooks()
-        for (k, v) in list(hooks.items()):
-            self.register_hook(event=k, hook=v)
-
-        self.method = method
-        self.url = url
-        self.headers = headers
-        self.files = files
-        self.data = data
-        self.json = json
-        self.params = params
-        self.auth = auth
-        self.cookies = cookies
-
-    def __repr__(self):
-        return '<Request [%s]>' % (self.method)
-
-    def prepare(self):
-        """Constructs a :class:`PreparedRequest <PreparedRequest>` for transmission and returns it."""
-        p = PreparedRequest()
-        p.prepare(
-            method=self.method,
-            url=self.url,
-            headers=self.headers,
-            files=self.files,
-            data=self.data,
-            json=self.json,
-            params=self.params,
-            auth=self.auth,
-            cookies=self.cookies,
-            hooks=self.hooks,
-        )
-        return p
-
-
-class PreparedRequest(RequestEncodingMixin, RequestHooksMixin):
-    """The fully mutable :class:`PreparedRequest <PreparedRequest>` object,
-    containing the exact bytes that will be sent to the server.
-
-    Instances are generated from a :class:`Request <Request>` object, and
-    should not be instantiated manually; doing so may produce undesirable
-    effects.
-
-    Usage::
-
-      >>> import requests
-      >>> req = requests.Request('GET', 'https://httpbin.org/get')
-      >>> r = req.prepare()
-      >>> r
-      <PreparedRequest [GET]>
-
-      >>> s = requests.Session()
-      >>> s.send(r)
-      <Response [200]>
-    """
-
-    def __init__(self):
-        #: HTTP verb to send to the server.
-        self.method = None
-        #: HTTP URL to send the request to.
-        self.url = None
-        #: dictionary of HTTP headers.
-        self.headers = None
-        # The `CookieJar` used to create the Cookie header will be stored here
-        # after prepare_cookies is called
-        self._cookies = None
-        #: request body to send to the server.
-        self.body = None
-        #: dictionary of callback hooks, for internal usage.
-        self.hooks = default_hooks()
-        #: integer denoting starting position of a readable file-like body.
-        self._body_position = None
-
-    def prepare(self,
-            method=None, url=None, headers=None, files=None, data=None,
-            params=None, auth=None, cookies=None, hooks=None, json=None):
-        """Prepares the entire request with the given parameters."""
-
-        self.prepare_method(method)
-        self.prepare_url(url, params)
-        self.prepare_headers(headers)
-        self.prepare_cookies(cookies)
-        self.prepare_body(data, files, json)
-        self.prepare_auth(auth, url)
-
-        # Note that prepare_auth must be last to enable authentication schemes
-        # such as OAuth to work on a fully prepared request.
-
-        # This MUST go after prepare_auth. Authenticators could add a hook
-        self.prepare_hooks(hooks)
-
-    def __repr__(self):
-        return '<PreparedRequest [%s]>' % (self.method)
-
-    def copy(self):
-        p = PreparedRequest()
-        p.method = self.method
-        p.url = self.url
-        p.headers = self.headers.copy() if self.headers is not None else None
-        p._cookies = _copy_cookie_jar(self._cookies)
-        p.body = self.body
-        p.hooks = self.hooks
-        p._body_position = self._body_position
-        return p
-
-    def prepare_method(self, method):
-        """Prepares the given HTTP method."""
-        self.method = method
-        if self.method is not None:
-            self.method = to_native_string(self.method.upper())
-
-    @staticmethod
-    def _get_idna_encoded_host(host):
-        from pip._vendor import idna
-
-        try:
-            host = idna.encode(host, uts46=True).decode('utf-8')
-        except idna.IDNAError:
-            raise UnicodeError
-        return host
-
-    def prepare_url(self, url, params):
-        """Prepares the given HTTP URL."""
-        #: Accept objects that have string representations.
-        #: We're unable to blindly call unicode/str functions
-        #: as this will include the bytestring indicator (b'')
-        #: on python 3.x.
-        #: https://github.com/psf/requests/pull/2238
-        if isinstance(url, bytes):
-            url = url.decode('utf8')
-        else:
-            url = unicode(url) if is_py2 else str(url)
-
-        # Remove leading whitespaces from url
-        url = url.lstrip()
-
-        # Don't do any URL preparation for non-HTTP schemes like `mailto`,
-        # `data` etc to work around exceptions from `url_parse`, which
-        # handles RFC 3986 only.
-        if ':' in url and not url.lower().startswith('http'):
-            self.url = url
-            return
-
-        # Support for unicode domain names and paths.
-        try:
-            scheme, auth, host, port, path, query, fragment = parse_url(url)
-        except LocationParseError as e:
-            raise InvalidURL(*e.args)
-
-        if not scheme:
-            error = ("Invalid URL {0!r}: No scheme supplied. Perhaps you meant http://{0}?")
-            error = error.format(to_native_string(url, 'utf8'))
-
-            raise MissingSchema(error)
-
-        if not host:
-            raise InvalidURL("Invalid URL %r: No host supplied" % url)
-
-        # In general, we want to try IDNA encoding the hostname if the string contains
-        # non-ASCII characters. This allows users to automatically get the correct IDNA
-        # behaviour. For strings containing only ASCII characters, we need to also verify
-        # it doesn't start with a wildcard (*), before allowing the unencoded hostname.
-        if not unicode_is_ascii(host):
-            try:
-                host = self._get_idna_encoded_host(host)
-            except UnicodeError:
-                raise InvalidURL('URL has an invalid label.')
-        elif host.startswith((u'*', u'.')):
-            raise InvalidURL('URL has an invalid label.')
-
-        # Carefully reconstruct the network location
-        netloc = auth or ''
-        if netloc:
-            netloc += '@'
-        netloc += host
-        if port:
-            netloc += ':' + str(port)
-
-        # Bare domains aren't valid URLs.
-        if not path:
-            path = '/'
-
-        if is_py2:
-            if isinstance(scheme, str):
-                scheme = scheme.encode('utf-8')
-            if isinstance(netloc, str):
-                netloc = netloc.encode('utf-8')
-            if isinstance(path, str):
-                path = path.encode('utf-8')
-            if isinstance(query, str):
-                query = query.encode('utf-8')
-            if isinstance(fragment, str):
-                fragment = fragment.encode('utf-8')
-
-        if isinstance(params, (str, bytes)):
-            params = to_native_string(params)
-
-        enc_params = self._encode_params(params)
-        if enc_params:
-            if query:
-                query = '%s&%s' % (query, enc_params)
-            else:
-                query = enc_params
-
-        url = requote_uri(urlunparse([scheme, netloc, path, None, query, fragment]))
-        self.url = url
-
-    def prepare_headers(self, headers):
-        """Prepares the given HTTP headers."""
-
-        self.headers = CaseInsensitiveDict()
-        if headers:
-            for header in headers.items():
-                # Raise exception on invalid header value.
-                check_header_validity(header)
-                name, value = header
-                self.headers[to_native_string(name)] = value
-
-    def prepare_body(self, data, files, json=None):
-        """Prepares the given HTTP body data."""
-
-        # Check if file, fo, generator, iterator.
-        # If not, run through normal process.
-
-        # Nottin' on you.
-        body = None
-        content_type = None
-
-        if not data and json is not None:
-            # urllib3 requires a bytes-like body. Python 2's json.dumps
-            # provides this natively, but Python 3 gives a Unicode string.
-            content_type = 'application/json'
-
-            try:
-                body = complexjson.dumps(json, allow_nan=False)
-            except ValueError as ve:
-                raise InvalidJSONError(ve, request=self)
-
-            if not isinstance(body, bytes):
-                body = body.encode('utf-8')
-
-        is_stream = all([
-            hasattr(data, '__iter__'),
-            not isinstance(data, (basestring, list, tuple, Mapping))
-        ])
-
-        if is_stream:
-            try:
-                length = super_len(data)
-            except (TypeError, AttributeError, UnsupportedOperation):
-                length = None
-
-            body = data
-
-            if getattr(body, 'tell', None) is not None:
-                # Record the current file position before reading.
-                # This will allow us to rewind a file in the event
-                # of a redirect.
-                try:
-                    self._body_position = body.tell()
-                except (IOError, OSError):
-                    # This differentiates from None, allowing us to catch
-                    # a failed `tell()` later when trying to rewind the body
-                    self._body_position = object()
-
-            if files:
-                raise NotImplementedError('Streamed bodies and files are mutually exclusive.')
-
-            if length:
-                self.headers['Content-Length'] = builtin_str(length)
-            else:
-                self.headers['Transfer-Encoding'] = 'chunked'
-        else:
-            # Multi-part file uploads.
-            if files:
-                (body, content_type) = self._encode_files(files, data)
-            else:
-                if data:
-                    body = self._encode_params(data)
-                    if isinstance(data, basestring) or hasattr(data, 'read'):
-                        content_type = None
-                    else:
-                        content_type = 'application/x-www-form-urlencoded'
-
-            self.prepare_content_length(body)
-
-            # Add content-type if it wasn't explicitly provided.
-            if content_type and ('content-type' not in self.headers):
-                self.headers['Content-Type'] = content_type
-
-        self.body = body
-
-    def prepare_content_length(self, body):
-        """Prepare Content-Length header based on request method and body"""
-        if body is not None:
-            length = super_len(body)
-            if length:
-                # If length exists, set it. Otherwise, we fallback
-                # to Transfer-Encoding: chunked.
-                self.headers['Content-Length'] = builtin_str(length)
-        elif self.method not in ('GET', 'HEAD') and self.headers.get('Content-Length') is None:
-            # Set Content-Length to 0 for methods that can have a body
-            # but don't provide one. (i.e. not GET or HEAD)
-            self.headers['Content-Length'] = '0'
-
-    def prepare_auth(self, auth, url=''):
-        """Prepares the given HTTP auth data."""
-
-        # If no Auth is explicitly provided, extract it from the URL first.
-        if auth is None:
-            url_auth = get_auth_from_url(self.url)
-            auth = url_auth if any(url_auth) else None
-
-        if auth:
-            if isinstance(auth, tuple) and len(auth) == 2:
-                # special-case basic HTTP auth
-                auth = HTTPBasicAuth(*auth)
-
-            # Allow auth to make its changes.
-            r = auth(self)
-
-            # Update self to reflect the auth changes.
-            self.__dict__.update(r.__dict__)
-
-            # Recompute Content-Length
-            self.prepare_content_length(self.body)
-
-    def prepare_cookies(self, cookies):
-        """Prepares the given HTTP cookie data.
-
-        This function eventually generates a ``Cookie`` header from the
-        given cookies using cookielib. Due to cookielib's design, the header
-        will not be regenerated if it already exists, meaning this function
-        can only be called once for the life of the
-        :class:`PreparedRequest <PreparedRequest>` object. Any subsequent calls
-        to ``prepare_cookies`` will have no actual effect, unless the "Cookie"
-        header is removed beforehand.
-        """
-        if isinstance(cookies, cookielib.CookieJar):
-            self._cookies = cookies
-        else:
-            self._cookies = cookiejar_from_dict(cookies)
-
-        cookie_header = get_cookie_header(self._cookies, self)
-        if cookie_header is not None:
-            self.headers['Cookie'] = cookie_header
-
-    def prepare_hooks(self, hooks):
-        """Prepares the given hooks."""
-        # hooks can be passed as None to the prepare method and to this
-        # method. To prevent iterating over None, simply use an empty list
-        # if hooks is False-y
-        hooks = hooks or []
-        for event in hooks:
-            self.register_hook(event, hooks[event])
-
-
-class Response(object):
-    """The :class:`Response <Response>` object, which contains a
-    server's response to an HTTP request.
-    """
-
-    __attrs__ = [
-        '_content', 'status_code', 'headers', 'url', 'history',
-        'encoding', 'reason', 'cookies', 'elapsed', 'request'
-    ]
-
-    def __init__(self):
-        self._content = False
-        self._content_consumed = False
-        self._next = None
-
-        #: Integer Code of responded HTTP Status, e.g. 404 or 200.
-        self.status_code = None
-
-        #: Case-insensitive Dictionary of Response Headers.
-        #: For example, ``headers['content-encoding']`` will return the
-        #: value of a ``'Content-Encoding'`` response header.
-        self.headers = CaseInsensitiveDict()
-
-        #: File-like object representation of response (for advanced usage).
-        #: Use of ``raw`` requires that ``stream=True`` be set on the request.
-        #: This requirement does not apply for use internally to Requests.
-        self.raw = None
-
-        #: Final URL location of Response.
-        self.url = None
-
-        #: Encoding to decode with when accessing r.text.
-        self.encoding = None
-
-        #: A list of :class:`Response <Response>` objects from
-        #: the history of the Request. Any redirect responses will end
-        #: up here. The list is sorted from the oldest to the most recent request.
-        self.history = []
-
-        #: Textual reason of responded HTTP Status, e.g. "Not Found" or "OK".
-        self.reason = None
-
-        #: A CookieJar of Cookies the server sent back.
-        self.cookies = cookiejar_from_dict({})
-
-        #: The amount of time elapsed between sending the request
-        #: and the arrival of the response (as a timedelta).
-        #: This property specifically measures the time taken between sending
-        #: the first byte of the request and finishing parsing the headers. It
-        #: is therefore unaffected by consuming the response content or the
-        #: value of the ``stream`` keyword argument.
-        self.elapsed = datetime.timedelta(0)
-
-        #: The :class:`PreparedRequest <PreparedRequest>` object to which this
-        #: is a response.
-        self.request = None
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, *args):
-        self.close()
-
-    def __getstate__(self):
-        # Consume everything; accessing the content attribute makes
-        # sure the content has been fully read.
-        if not self._content_consumed:
-            self.content
-
-        return {attr: getattr(self, attr, None) for attr in self.__attrs__}
-
-    def __setstate__(self, state):
-        for name, value in state.items():
-            setattr(self, name, value)
-
-        # pickled objects do not have .raw
-        setattr(self, '_content_consumed', True)
-        setattr(self, 'raw', None)
-
-    def __repr__(self):
-        return '<Response [%s]>' % (self.status_code)
-
-    def __bool__(self):
-        """Returns True if :attr:`status_code` is less than 400.
-
-        This attribute checks if the status code of the response is between
-        400 and 600 to see if there was a client error or a server error. If
-        the status code, is between 200 and 400, this will return True. This
-        is **not** a check to see if the response code is ``200 OK``.
-        """
-        return self.ok
-
-    def __nonzero__(self):
-        """Returns True if :attr:`status_code` is less than 400.
-
-        This attribute checks if the status code of the response is between
-        400 and 600 to see if there was a client error or a server error. If
-        the status code, is between 200 and 400, this will return True. This
-        is **not** a check to see if the response code is ``200 OK``.
-        """
-        return self.ok
-
-    def __iter__(self):
-        """Allows you to use a response as an iterator."""
-        return self.iter_content(128)
-
-    @property
-    def ok(self):
-        """Returns True if :attr:`status_code` is less than 400, False if not.
-
-        This attribute checks if the status code of the response is between
-        400 and 600 to see if there was a client error or a server error. If
-        the status code is between 200 and 400, this will return True. This
-        is **not** a check to see if the response code is ``200 OK``.
-        """
-        try:
-            self.raise_for_status()
-        except HTTPError:
-            return False
-        return True
-
-    @property
-    def is_redirect(self):
-        """True if this Response is a well-formed HTTP redirect that could have
-        been processed automatically (by :meth:`Session.resolve_redirects`).
-        """
-        return ('location' in self.headers and self.status_code in REDIRECT_STATI)
-
-    @property
-    def is_permanent_redirect(self):
-        """True if this Response one of the permanent versions of redirect."""
-        return ('location' in self.headers and self.status_code in (codes.moved_permanently, codes.permanent_redirect))
-
-    @property
-    def next(self):
-        """Returns a PreparedRequest for the next request in a redirect chain, if there is one."""
-        return self._next
-
-    @property
-    def apparent_encoding(self):
-        """The apparent encoding, provided by the charset_normalizer or chardet libraries."""
-        return chardet.detect(self.content)['encoding']
-
-    def iter_content(self, chunk_size=1, decode_unicode=False):
-        """Iterates over the response data.  When stream=True is set on the
-        request, this avoids reading the content at once into memory for
-        large responses.  The chunk size is the number of bytes it should
-        read into memory.  This is not necessarily the length of each item
-        returned as decoding can take place.
-
-        chunk_size must be of type int or None. A value of None will
-        function differently depending on the value of `stream`.
-        stream=True will read data as it arrives in whatever size the
-        chunks are received. If stream=False, data is returned as
-        a single chunk.
-
-        If decode_unicode is True, content will be decoded using the best
-        available encoding based on the response.
-        """
-
-        def generate():
-            # Special case for urllib3.
-            if hasattr(self.raw, 'stream'):
-                try:
-                    for chunk in self.raw.stream(chunk_size, decode_content=True):
-                        yield chunk
-                except ProtocolError as e:
-                    raise ChunkedEncodingError(e)
-                except DecodeError as e:
-                    raise ContentDecodingError(e)
-                except ReadTimeoutError as e:
-                    raise ConnectionError(e)
-            else:
-                # Standard file-like object.
-                while True:
-                    chunk = self.raw.read(chunk_size)
-                    if not chunk:
-                        break
-                    yield chunk
-
-            self._content_consumed = True
-
-        if self._content_consumed and isinstance(self._content, bool):
-            raise StreamConsumedError()
-        elif chunk_size is not None and not isinstance(chunk_size, int):
-            raise TypeError("chunk_size must be an int, it is instead a %s." % type(chunk_size))
-        # simulate reading small chunks of the content
-        reused_chunks = iter_slices(self._content, chunk_size)
-
-        stream_chunks = generate()
-
-        chunks = reused_chunks if self._content_consumed else stream_chunks
-
-        if decode_unicode:
-            chunks = stream_decode_response_unicode(chunks, self)
-
-        return chunks
-
-    def iter_lines(self, chunk_size=ITER_CHUNK_SIZE, decode_unicode=False, delimiter=None):
-        """Iterates over the response data, one line at a time.  When
-        stream=True is set on the request, this avoids reading the
-        content at once into memory for large responses.
-
-        .. note:: This method is not reentrant safe.
-        """
-
-        pending = None
-
-        for chunk in self.iter_content(chunk_size=chunk_size, decode_unicode=decode_unicode):
-
-            if pending is not None:
-                chunk = pending + chunk
-
-            if delimiter:
-                lines = chunk.split(delimiter)
-            else:
-                lines = chunk.splitlines()
-
-            if lines and lines[-1] and chunk and lines[-1][-1] == chunk[-1]:
-                pending = lines.pop()
-            else:
-                pending = None
-
-            for line in lines:
-                yield line
-
-        if pending is not None:
-            yield pending
-
-    @property
-    def content(self):
-        """Content of the response, in bytes."""
-
-        if self._content is False:
-            # Read the contents.
-            if self._content_consumed:
-                raise RuntimeError(
-                    'The content for this response was already consumed')
-
-            if self.status_code == 0 or self.raw is None:
-                self._content = None
-            else:
-                self._content = b''.join(self.iter_content(CONTENT_CHUNK_SIZE)) or b''
-
-        self._content_consumed = True
-        # don't need to release the connection; that's been handled by urllib3
-        # since we exhausted the data.
-        return self._content
-
-    @property
-    def text(self):
-        """Content of the response, in unicode.
-
-        If Response.encoding is None, encoding will be guessed using
-        ``charset_normalizer`` or ``chardet``.
-
-        The encoding of the response content is determined based solely on HTTP
-        headers, following RFC 2616 to the letter. If you can take advantage of
-        non-HTTP knowledge to make a better guess at the encoding, you should
-        set ``r.encoding`` appropriately before accessing this property.
-        """
-
-        # Try charset from content-type
-        content = None
-        encoding = self.encoding
-
-        if not self.content:
-            return str('')
-
-        # Fallback to auto-detected encoding.
-        if self.encoding is None:
-            encoding = self.apparent_encoding
-
-        # Decode unicode from given encoding.
-        try:
-            content = str(self.content, encoding, errors='replace')
-        except (LookupError, TypeError):
-            # A LookupError is raised if the encoding was not found which could
-            # indicate a misspelling or similar mistake.
-            #
-            # A TypeError can be raised if encoding is None
-            #
-            # So we try blindly encoding.
-            content = str(self.content, errors='replace')
-
-        return content
-
-    def json(self, **kwargs):
-        r"""Returns the json-encoded content of a response, if any.
-
-        :param \*\*kwargs: Optional arguments that ``json.loads`` takes.
-        :raises requests.exceptions.JSONDecodeError: If the response body does not
-            contain valid json.
-        """
-
-        if not self.encoding and self.content and len(self.content) > 3:
-            # No encoding set. JSON RFC 4627 section 3 states we should expect
-            # UTF-8, -16 or -32. Detect which one to use; If the detection or
-            # decoding fails, fall back to `self.text` (using charset_normalizer to make
-            # a best guess).
-            encoding = guess_json_utf(self.content)
-            if encoding is not None:
-                try:
-                    return complexjson.loads(
-                        self.content.decode(encoding), **kwargs
-                    )
-                except UnicodeDecodeError:
-                    # Wrong UTF codec detected; usually because it's not UTF-8
-                    # but some other 8-bit codec.  This is an RFC violation,
-                    # and the server didn't bother to tell us what codec *was*
-                    # used.
-                    pass
-
-        try:
-            return complexjson.loads(self.text, **kwargs)
-        except JSONDecodeError as e:
-            # Catch JSON-related errors and raise as requests.JSONDecodeError
-            # This aliases json.JSONDecodeError and simplejson.JSONDecodeError
-            if is_py2: # e is a ValueError
-                raise RequestsJSONDecodeError(e.message)
-            else:
-                raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
-
-    @property
-    def links(self):
-        """Returns the parsed header links of the response, if any."""
-
-        header = self.headers.get('link')
-
-        # l = MultiDict()
-        l = {}
-
-        if header:
-            links = parse_header_links(header)
-
-            for link in links:
-                key = link.get('rel') or link.get('url')
-                l[key] = link
-
-        return l
-
-    def raise_for_status(self):
-        """Raises :class:`HTTPError`, if one occurred."""
-
-        http_error_msg = ''
-        if isinstance(self.reason, bytes):
-            # We attempt to decode utf-8 first because some servers
-            # choose to localize their reason strings. If the string
-            # isn't utf-8, we fall back to iso-8859-1 for all other
-            # encodings. (See PR #3538)
-            try:
-                reason = self.reason.decode('utf-8')
-            except UnicodeDecodeError:
-                reason = self.reason.decode('iso-8859-1')
-        else:
-            reason = self.reason
-
-        if 400 <= self.status_code < 500:
-            http_error_msg = u'%s Client Error: %s for url: %s' % (self.status_code, reason, self.url)
-
-        elif 500 <= self.status_code < 600:
-            http_error_msg = u'%s Server Error: %s for url: %s' % (self.status_code, reason, self.url)
-
-        if http_error_msg:
-            raise HTTPError(http_error_msg, response=self)
-
-    def close(self):
-        """Releases the connection back to the pool. Once this method has been
-        called the underlying ``raw`` object must not be accessed again.
-
-        *Note: Should not normally need to be called explicitly.*
-        """
-        if not self._content_consumed:
-            self.raw.close()
-
-        release_conn = getattr(self.raw, 'release_conn', None)
-        if release_conn is not None:
-            release_conn()
diff --git a/spaces/aliabd/SummerTime/model/query_based/base_query_based_model.py b/spaces/aliabd/SummerTime/model/query_based/base_query_based_model.py
deleted file mode 100644
index 9b94b5a3c7f4cc0bb894c7e0863524330887d6e5..0000000000000000000000000000000000000000
--- a/spaces/aliabd/SummerTime/model/query_based/base_query_based_model.py
+++ /dev/null
@@ -1,147 +0,0 @@
-from model.base_model import SummModel
-from model.single_doc import TextRankModel
-from typing import List, Union
-
-from nltk import sent_tokenize, word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import PorterStemmer
-
-
-class QueryBasedSummModel(SummModel):
-
-    is_query_based = True
-
-    def __init__(
-        self,
-        trained_domain: str = None,
-        max_input_length: int = None,
-        max_output_length: int = None,
-        model_backend: SummModel = TextRankModel,
-        retrieval_ratio: float = 0.5,
-        preprocess: bool = True,
-        **kwargs,
-    ):
-        super(QueryBasedSummModel, self).__init__(
-            trained_domain=trained_domain,
-            max_input_length=max_input_length,
-            max_output_length=max_output_length,
-        )
-        self.model = model_backend(**kwargs)
-        self.retrieval_ratio = retrieval_ratio
-        self.preprocess = preprocess
-
-    def _retrieve(self, instance: List[str], query: List[str], n_best) -> List[str]:
-        raise NotImplementedError()
-
-    def summarize(
-        self,
-        corpus: Union[List[str], List[List[str]]],
-        queries: List[str] = None,
-    ) -> List[str]:
-        self.assert_summ_input_type(corpus, queries)
-
-        retrieval_output = []  # List[str]
-        for instance, query in zip(corpus, queries):
-            if isinstance(instance, str):
-                is_dialogue = False
-                instance = sent_tokenize(instance)
-            else:
-                is_dialogue = True
-            query = [query]
-
-            # instance & query now are List[str] for sure
-            if self.preprocess:
-                preprocessor = Preprocessor()
-                instance = preprocessor.preprocess(instance)
-                query = preprocessor.preprocess(query)
-
-            n_best = max(int(len(instance) * self.retrieval_ratio), 1)
-            top_n_sent = self._retrieve(instance, query, n_best)
-
-            if not is_dialogue:
-                top_n_sent = " ".join(top_n_sent)  # str
-            retrieval_output.append(top_n_sent)
-
-        summaries = self.model.summarize(
-            retrieval_output
-        )  # List[str] or List[List[str]]
-        return summaries
-
-    def generate_specific_description(self):
-        is_neural = self.model.is_neural & self.is_neural
-        is_extractive = self.model.is_extractive | self.is_extractive
-        model_name = "Pipeline with retriever: {}, summarizer: {}".format(
-            self.model_name, self.model.model_name
-        )
-
-        extractive_abstractive = "extractive" if is_extractive else "abstractive"
-        neural = "neural" if is_neural else "non-neural"
-
-        basic_description = (
-            f"{model_name} is a "
-            f"{'query-based' if self.is_query_based else ''} "
-            f"{extractive_abstractive}, {neural} model for summarization."
-        )
-
-        return basic_description
-
-    @classmethod
-    def assert_summ_input_type(cls, corpus, query):
-        if query is None:
-            raise TypeError(
-                "Query-based summarization models summarize instances of query-text pairs， however, query is missing."
-            )
-
-        if not isinstance(query, list):
-            raise TypeError(
-                "Query-based single-document summarization requires query of `List[str]`."
-            )
-        if not all([isinstance(q, str) for q in query]):
-            raise TypeError(
-                "Query-based single-document summarization requires query of `List[str]`."
-            )
-
-    @classmethod
-    def generate_basic_description(cls) -> str:
-        basic_description = (
-            "QueryBasedSummModel performs query-based summarization. Given a query-text pair,"
-            "the model will first extract the most relevant sentences in articles or turns in "
-            "dialogues, then use the single document summarization model to generate the summary"
-        )
-        return basic_description
-
-    @classmethod
-    def show_capability(cls):
-        basic_description = cls.generate_basic_description()
-        more_details = (
-            "A query-based summarization model."
-            " Allows for custom model backend selection at initialization."
-            " Retrieve relevant turns and then summarize the retrieved turns\n"
-            "Strengths: \n - Allows for control of backend model.\n"
-            "Weaknesses: \n - Heavily depends on the performance of both retriever and summarizer.\n"
-        )
-        print(f"{basic_description}\n{'#' * 20}\n{more_details}")
-
-
-class Preprocessor:
-    def __init__(self, remove_stopwords=True, lower_case=True, stem=False):
-        self.sw = stopwords.words("english")
-        self.stemmer = PorterStemmer()
-        self.remove_stopwords = remove_stopwords
-        self.lower_case = lower_case
-        self.stem = stem
-
-    def preprocess(self, corpus: List[str]) -> List[str]:
-        if self.lower_case:
-            corpus = [sent.lower() for sent in corpus]
-        tokenized_corpus = [word_tokenize(sent) for sent in corpus]
-        if self.remove_stopwords:
-            tokenized_corpus = [
-                [word for word in sent if word not in self.sw]
-                for sent in tokenized_corpus
-            ]
-        if self.stem:
-            tokenized_corpus = [
-                [self.stemmer.stem(word) for word in sent] for sent in tokenized_corpus
-            ]
-        return [" ".join(sent) for sent in tokenized_corpus]
diff --git a/spaces/aliabid94/AutoGPT/tests/milvus_memory_test.py b/spaces/aliabid94/AutoGPT/tests/milvus_memory_test.py
deleted file mode 100644
index 84fd6e6d5006e781fa5e1065f949b2160537d913..0000000000000000000000000000000000000000
--- a/spaces/aliabid94/AutoGPT/tests/milvus_memory_test.py
+++ /dev/null
@@ -1,72 +0,0 @@
-# sourcery skip: snake-case-functions
-"""Tests for the MilvusMemory class."""
-import os
-import sys
-import unittest
-
-try:
-    from autogpt.memory.milvus import MilvusMemory
-
-    def mock_config() -> dict:
-        """Mock the Config class"""
-        return type(
-            "MockConfig",
-            (object,),
-            {
-                "debug_mode": False,
-                "continuous_mode": False,
-                "speak_mode": False,
-                "milvus_collection": "autogpt",
-                "milvus_addr": "localhost:19530",
-            },
-        )
-
-    class TestMilvusMemory(unittest.TestCase):
-        """Tests for the MilvusMemory class."""
-
-        def setUp(self) -> None:
-            """Set up the test environment"""
-            self.cfg = mock_config()
-            self.memory = MilvusMemory(self.cfg)
-
-        def test_add(self) -> None:
-            """Test adding a text to the cache"""
-            text = "Sample text"
-            self.memory.clear()
-            self.memory.add(text)
-            result = self.memory.get(text)
-            self.assertEqual([text], result)
-
-        def test_clear(self) -> None:
-            """Test clearing the cache"""
-            self.memory.clear()
-            self.assertEqual(self.memory.collection.num_entities, 0)
-
-        def test_get(self) -> None:
-            """Test getting a text from the cache"""
-            text = "Sample text"
-            self.memory.clear()
-            self.memory.add(text)
-            result = self.memory.get(text)
-            self.assertEqual(result, [text])
-
-        def test_get_relevant(self) -> None:
-            """Test getting relevant texts from the cache"""
-            text1 = "Sample text 1"
-            text2 = "Sample text 2"
-            self.memory.clear()
-            self.memory.add(text1)
-            self.memory.add(text2)
-            result = self.memory.get_relevant(text1, 1)
-            self.assertEqual(result, [text1])
-
-        def test_get_stats(self) -> None:
-            """Test getting the cache stats"""
-            text = "Sample text"
-            self.memory.clear()
-            self.memory.add(text)
-            stats = self.memory.get_stats()
-            self.assertEqual(15, len(stats))
-
-except:
-    print("Milvus not installed, skipping tests")
diff --git a/spaces/amish1729/LFUNet/utils/architectures.py b/spaces/amish1729/LFUNet/utils/architectures.py
deleted file mode 100644
index db7f2ba93eff9122ffbed02cd8d19748f72582cc..0000000000000000000000000000000000000000
--- a/spaces/amish1729/LFUNet/utils/architectures.py
+++ /dev/null
@@ -1,344 +0,0 @@
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import Tuple, Optional
-
-import tensorflow as tf
-from tensorflow.keras.layers import *
-from tensorflow.keras.models import *
-
-class BaseUNet(ABC):
-    """
-    Base Interface for UNet
-    """
-
-    def __init__(self, model: Model):
-        self.model: Model = model
-
-    def get_model(self):
-        return self.model
-
-    @staticmethod
-    @abstractmethod
-    def build_model(input_size: Tuple[int, int, int], filters: Tuple, kernels: Tuple):
-        pass
-
-
-class UNet(Enum):
-    """
-    Enum class defining different architecture types available
-    """
-    DEFAULT = 0
-    DEFAULT_IMAGENET_EMBEDDING = 1
-    RESNET = 3
-    RESIDUAL_ATTENTION_UNET_SEPARABLE_CONV = 4
-
-    def build_model(self, input_size: Tuple[int, int, int], filters: Optional[Tuple] = None,
-                    kernels: Optional[Tuple] = None) -> BaseUNet:
-
-        # set default filters
-        if filters is None:
-            filters = (16, 32, 64, 128, 256)
-
-        # set default kernels
-        if kernels is None:
-            kernels = list(3 for _ in range(len(filters)))
-
-        # check kernels and filters
-        if len(filters) != len(kernels):
-            raise Exception('Kernels and filter count has to match.')
-
-        if self == UNet.DEFAULT_IMAGENET_EMBEDDING:
-            print('Using default UNet model with imagenet embedding')
-            return UNetDefault.build_model(input_size, filters, kernels, use_embedding=True)
-        elif self == UNet.RESNET:
-            print('Using UNet Resnet model')
-            return UNet_resnet.build_model(input_size, filters, kernels)
-        elif self == UNet.RESIDUAL_ATTENTION_UNET_SEPARABLE_CONV:
-            print('Using UNet Resnet model with attention mechanism and separable convolutions')
-            return UNet_ResNet_Attention_SeparableConv.build_model(input_size, filters, kernels)
-
-        print('Using default UNet model')
-        return UNetDefault.build_model(input_size, filters, kernels, use_embedding=False)
-    
-
-class Attention(Layer):
-    def __init__(self, **kwargs):
-        super(Attention, self).__init__(**kwargs)
-
-    def build(self, input_shape):
-        # Create a trainable weight variable for this layer.
-        self.kernel = self.add_weight(name='kernel', 
-                                    shape=(input_shape[-1], 1),
-                                    initializer='glorot_normal',
-                                    trainable=True)
-        self.bias = self.add_weight(name='bias',
-                                   shape=(1,),
-                                   initializer='zeros',
-                                   trainable=True)
-        super(Attention, self).build(input_shape)  # Be sure to call this at the end
-
-    def call(self, x):
-        attention = tf.nn.softmax(tf.matmul(x, self.kernel) + self.bias, axis=-1)
-        return tf.multiply(x, attention)
-
-    def compute_output_shape(self, input_shape):
-        return input_shape
-
-
-class UNet_ResNet_Attention_SeparableConv(BaseUNet):
-    """
-    UNet architecture with resnet blocks, attention mechanism and separable convolutions
-    """
-    @staticmethod
-    def build_model(input_size: Tuple[int, int, int], filters: Tuple, kernels: Tuple):
-
-        p0 = Input(shape=input_size)
-        conv_outputs = []
-        first_layer = SeparableConv2D(filters[0], kernels[0], padding='same')(p0)
-        int_layer = first_layer
-        for i, f in enumerate(filters):
-            int_layer, skip = UNet_ResNet_Attention_SeparableConv.down_block(int_layer, f, kernels[i])
-            conv_outputs.append(skip)
-
-        int_layer = UNet_ResNet_Attention_SeparableConv.bottleneck(int_layer, filters[-1], kernels[-1])
-
-        conv_outputs = list(reversed(conv_outputs))
-        reversed_filter = list(reversed(filters))
-        reversed_kernels = list(reversed(kernels))
-        for i, f in enumerate(reversed_filter):
-            if i + 1 < len(reversed_filter):
-                num_filters_next = reversed_filter[i + 1]
-                num_kernels_next = reversed_kernels[i + 1]
-            else:
-                num_filters_next = f
-                num_kernels_next = reversed_kernels[i]
-            int_layer = UNet_ResNet_Attention_SeparableConv.up_block(int_layer, conv_outputs[i], f, num_filters_next, num_kernels_next)
-            int_layer = Attention()(int_layer)
-            
-        # concat. with the first layer
-        int_layer = Concatenate()([first_layer, int_layer])
-        int_layer = SeparableConv2D(filters[0], kernels[0], padding="same", activation="relu")(int_layer)
-        outputs = SeparableConv2D(3, (1, 1), padding="same", activation="sigmoid")(int_layer)
-        model = Model(p0, outputs)
-        return UNet_ResNet_Attention_SeparableConv(model)
-
-    @staticmethod
-    def down_block(x, num_filters: int = 64, kernel: int = 3):
-        # down-sample inputs
-        x = SeparableConv2D(num_filters, kernel, padding='same', strides=2, dilation_rate = 2)(x)
-
-        # inner block
-        out = SeparableConv2D(num_filters, kernel, padding='same')(x)
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-        out = SeparableConv2D(num_filters, kernel, padding='same')(out)
-
-        # merge with the skip connection
-        out = Add()([out, x])
-        # out = BatchNormalization()(out)
-        return Activation('relu')(out), x
-
-    @staticmethod
-    def up_block(x, skip, num_filters: int = 64, num_filters_next: int = 64, kernel: int = 3):
-        # add U-Net skip connection - before up-sampling
-        concat = Concatenate()([x, skip])
-
-        # inner block
-        out = SeparableConv2D(num_filters, kernel, padding='same', dilation_rate = 2)(concat)
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-        out = SeparableConv2D(num_filters, kernel, padding='same')(out)
-
-        # merge with the skip connection
-        out = Add()([out, x])
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-
-        # up-sample
-        out = UpSampling2D((2, 2))(out)
-        out = SeparableConv2D(num_filters_next, kernel, padding='same')(out)
-        # out = BatchNormalization()(out)
-        return Activation('relu')(out)
-
-    @staticmethod
-    def bottleneck(x, num_filters: int = 64, kernel: int = 3):
-        # inner block
-        out = SeparableConv2D(num_filters, kernel, padding='same', dilation_rate = 2)(x)
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-        out = SeparableConv2D(num_filters, kernel, padding='same')(out)
-        out = Add()([out, x])
-        # out = BatchNormalization()(out)
-        return Activation('relu')(out)
-
-    
-
-
-
-
-
-
-# Class for UNet with Resnet blocks
-
-class UNet_resnet(BaseUNet):
-    """
-    UNet architecture with resnet blocks
-    """
-
-    @staticmethod
-    def build_model(input_size: Tuple[int, int, int], filters: Tuple, kernels: Tuple):
-
-        p0 = Input(shape=input_size)
-        conv_outputs = []
-        first_layer = Conv2D(filters[0], kernels[0], padding='same')(p0)
-        int_layer = first_layer
-        for i, f in enumerate(filters):
-            int_layer, skip = UNet_resnet.down_block(int_layer, f, kernels[i])
-            conv_outputs.append(skip)
-
-        int_layer = UNet_resnet.bottleneck(int_layer, filters[-1], kernels[-1])
-
-        conv_outputs = list(reversed(conv_outputs))
-        reversed_filter = list(reversed(filters))
-        reversed_kernels = list(reversed(kernels))
-        for i, f in enumerate(reversed_filter):
-            if i + 1 < len(reversed_filter):
-                num_filters_next = reversed_filter[i + 1]
-                num_kernels_next = reversed_kernels[i + 1]
-            else:
-                num_filters_next = f
-                num_kernels_next = reversed_kernels[i]
-            int_layer = UNet_resnet.up_block(int_layer, conv_outputs[i], f, num_filters_next, num_kernels_next)
-
-        # concat. with the first layer
-        int_layer = Concatenate()([first_layer, int_layer])
-        int_layer = Conv2D(filters[0], kernels[0], padding="same", activation="relu")(int_layer)
-        outputs = Conv2D(3, (1, 1), padding="same", activation="sigmoid")(int_layer)
-        model = Model(p0, outputs)
-        return UNet_resnet(model)
-
-    @staticmethod
-    def down_block(x, num_filters: int = 64, kernel: int = 3):
-        # down-sample inputs
-        x = Conv2D(num_filters, kernel, padding='same', strides=2)(x)
-
-        # inner block
-        out = Conv2D(num_filters, kernel, padding='same')(x)
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-        out = Conv2D(num_filters, kernel, padding='same')(out)
-
-        # merge with the skip connection
-        out = Add()([out, x])
-        # out = BatchNormalization()(out)
-        return Activation('relu')(out), x
-
-    @staticmethod
-    def up_block(x, skip, num_filters: int = 64, num_filters_next: int = 64, kernel: int = 3):
-
-        # add U-Net skip connection - before up-sampling
-        concat = Concatenate()([x, skip])
-
-        # inner block
-        out = Conv2D(num_filters, kernel, padding='same')(concat)
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-        out = Conv2D(num_filters, kernel, padding='same')(out)
-
-        # merge with the skip connection
-        out = Add()([out, x])
-        # out = BatchNormalization()(out)
-        out = Activation('relu')(out)
-
-        # add U-Net skip connection - before up-sampling
-        concat = Concatenate()([out, skip])
-
-        # up-sample
-        # out = UpSampling2D((2, 2))(concat)
-        out = Conv2DTranspose(num_filters_next, kernel, padding='same', strides=2)(concat)
-        out = Conv2D(num_filters_next, kernel, padding='same')(out)
-        # out = BatchNormalization()(out)
-        return Activation('relu')(out)
-
-    @staticmethod
-    def bottleneck(x, filters, kernel: int = 3):
-        x = Conv2D(filters, kernel, padding='same', name='bottleneck')(x)
-        # x = BatchNormalization()(x)
-        return Activation('relu')(x)
-
-
-class UNetDefault(BaseUNet):
-    """
-     UNet architecture from following github notebook for image segmentation:
-    https://github.com/nikhilroxtomar/UNet-Segmentation-in-Keras-TensorFlow/blob/master/unet-segmentation.ipynb
-    https://github.com/nikhilroxtomar/Polyp-Segmentation-using-UNET-in-TensorFlow-2.0
-    """
-
-    @staticmethod
-    def build_model(input_size: Tuple[int, int, int], filters: Tuple, kernels: Tuple, use_embedding: bool = True):
-
-        p0 = Input(input_size)
-
-        if use_embedding:
-            mobilenet_model = tf.keras.applications.MobileNetV2(
-                input_shape=input_size, include_top=False, weights='imagenet'
-            )
-            mobilenet_model.trainable = False
-            mn1 = mobilenet_model(p0)
-            mn1 = Reshape((16, 16, 320))(mn1)
-
-        conv_outputs = []
-        int_layer = p0
-
-        for f in filters:
-            conv_output, int_layer = UNetDefault.down_block(int_layer, f)
-            conv_outputs.append(conv_output)
-
-        int_layer = UNetDefault.bottleneck(int_layer, filters[-1])
-
-        if use_embedding:
-            int_layer = Concatenate()([int_layer, mn1])
-
-        conv_outputs = list(reversed(conv_outputs))
-        for i, f in enumerate(reversed(filters)):
-            int_layer = UNetDefault.up_block(int_layer, conv_outputs[i], f)
-
-        int_layer = Conv2D(filters[0] // 2, 3, padding="same", activation="relu")(int_layer)
-        outputs = Conv2D(3, (1, 1), padding="same", activation="sigmoid")(int_layer)
-        model = Model(p0, outputs)
-        return UNetDefault(model)
-
-    @staticmethod
-    def down_block(x, filters, kernel_size=(3, 3), padding="same", strides=1):
-        c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(x)
-        # c = BatchNormalization()(c)
-        p = MaxPool2D((2, 2), (2, 2))(c)
-        return c, p
-
-    @staticmethod
-    def up_block(x, skip, filters, kernel_size=(3, 3), padding="same", strides=1):
-        us = UpSampling2D((2, 2))(x)
-        c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(us)
-        # c = BatchNormalization()(c)
-        concat = Concatenate()([c, skip])
-        c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(concat)
-        # c = BatchNormalization()(c)
-        return c
-
-    @staticmethod
-    def bottleneck(x, filters, kernel_size=(3, 3), padding="same", strides=1):
-        c = Conv2D(filters, kernel_size, padding=padding, strides=strides, activation="relu")(x)
-        # c = BatchNormalization()(c)
-        return c
-
-
-if __name__ == "__main__":
-    filters = (64, 128, 128, 256, 256, 512)
-    kernels = (7, 7, 7, 3, 3, 3)
-    input_image_size = (256, 256, 3)
-    # model = UNet_resnet()
-    # model = model.build_model(input_size=input_image_size,filters=filters,kernels=kernels)
-    # print(model.summary())
-    # __init__() missing 1 required positional argument: 'model'
-    model = UNetDefault.build_model(input_size=input_image_size, filters=filters, kernels=kernels)
-    print(model.summary())
diff --git a/spaces/amulyaprasanth/car_price_prediction/helper_functions.py b/spaces/amulyaprasanth/car_price_prediction/helper_functions.py
deleted file mode 100644
index e1b3dbec67e6bd529a10b56a8c37dbb764d4443c..0000000000000000000000000000000000000000
--- a/spaces/amulyaprasanth/car_price_prediction/helper_functions.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import tensorflow as tf
-from tensorflow import keras
-from keras.layers import Input, Dense
-from keras.optimizers import Adam
-
-
-def build_model():
-    "This model builds and compiles the model with mean absolute loss and adam optimizer and returns it"
-    model = keras.Sequential([
-        Dense(32, activation='relu', input_shape=(10,)),
-        Dense(16, activation='relu'),
-        Dense(8, activation='relu'),
-        Dense(1)
-    ])
-    model.compile(loss='mean_squared_error',
-                  optimizer=Adam(0.001))
-    return model
-
-# This code is taken from geeks for geeks page (https://www.geeksforgeeks.org/convert-the-number-from-international-system-to-indian-system/)
-
-
-def convert(input):
-    """This function converts numbers from international system to indian system numbers"""
-
-    # Convert the input to string format for len method and slicing
-    input = str(input)
-
-    # Find the length of the
-    # input string
-    Len = len(input)
-
-    # Removing all the separators(, )
-    # from the input string
-    i = 0
-    while(i < Len):
-        if(input[i] == ","):
-            input = input[:i] + input[i + 1:]
-            Len -= 1
-            i -= 1
-        elif(input[i] == " "):
-            input = input[:i] + input[i + 1:]
-            Len -= 1
-            i -= 1
-        else:
-            i += 1
-        # Reverse the input string
-        input = input[::-1]
-
-        # Declaring the output string
-        output = ""
-
-        # Process the input string
-        for i in range(Len):
-
-            # Add a separator(, ) after the
-            # third number
-            if(i == 2):
-                output += input[i]
-                output += ","
-
-            # Then add a separator(, ) after
-            # every second number
-            elif(i > 2 and i % 2 == 0 and
-                    i + 1 < Len):
-                output += input[i]
-                output += ","
-            else:
-                output += input[i]
-
-        # Reverse the output string
-        output = output[::-1]
-
-        # Return the output string back
-        # to the main function
-        return output
diff --git a/spaces/amulyaprasanth/car_price_prediction/requirements.py b/spaces/amulyaprasanth/car_price_prediction/requirements.py
deleted file mode 100644
index 722df69f49cc1fcc44252757f615e3df200ae1a2..0000000000000000000000000000000000000000
--- a/spaces/amulyaprasanth/car_price_prediction/requirements.py
+++ /dev/null
@@ -1,6 +0,0 @@
-scipy=1.9.3
-scikit-learn==1.1.3
-numpy==1.23.3
-tensorflow==2.11.0
-keras==2.11.0
-gradio==3.15.0
\ No newline at end of file
diff --git a/spaces/anaclaudia13ct/insect_detection/utils/dataloaders.py b/spaces/anaclaudia13ct/insect_detection/utils/dataloaders.py
deleted file mode 100644
index 6d2b27ea5e60f64fb83d679b75cc97624f4c96ad..0000000000000000000000000000000000000000
--- a/spaces/anaclaudia13ct/insect_detection/utils/dataloaders.py
+++ /dev/null
@@ -1,1220 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Dataloaders and dataset utils
-"""
-
-import contextlib
-import glob
-import hashlib
-import json
-import math
-import os
-import random
-import shutil
-import time
-from itertools import repeat
-from multiprocessing.pool import Pool, ThreadPool
-from pathlib import Path
-from threading import Thread
-from urllib.parse import urlparse
-
-import numpy as np
-import psutil
-import torch
-import torch.nn.functional as F
-import torchvision
-import yaml
-from PIL import ExifTags, Image, ImageOps
-from torch.utils.data import DataLoader, Dataset, dataloader, distributed
-from tqdm import tqdm
-
-from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
-                                 letterbox, mixup, random_perspective)
-from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
-                           check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
-                           xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
-from utils.torch_utils import torch_distributed_zero_first
-
-# Parameters
-HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
-IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
-VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
-
-# Get orientation exif tag
-for orientation in ExifTags.TAGS.keys():
-    if ExifTags.TAGS[orientation] == 'Orientation':
-        break
-
-
-def get_hash(paths):
-    # Returns a single hash value of a list of paths (files or dirs)
-    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
-    h = hashlib.md5(str(size).encode())  # hash sizes
-    h.update(''.join(paths).encode())  # hash paths
-    return h.hexdigest()  # return hash
-
-
-def exif_size(img):
-    # Returns exif-corrected PIL size
-    s = img.size  # (width, height)
-    with contextlib.suppress(Exception):
-        rotation = dict(img._getexif().items())[orientation]
-        if rotation in [6, 8]:  # rotation 270 or 90
-            s = (s[1], s[0])
-    return s
-
-
-def exif_transpose(image):
-    """
-    Transpose a PIL image accordingly if it has an EXIF Orientation tag.
-    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
-
-    :param image: The image to transpose.
-    :return: An image.
-    """
-    exif = image.getexif()
-    orientation = exif.get(0x0112, 1)  # default 1
-    if orientation > 1:
-        method = {
-            2: Image.FLIP_LEFT_RIGHT,
-            3: Image.ROTATE_180,
-            4: Image.FLIP_TOP_BOTTOM,
-            5: Image.TRANSPOSE,
-            6: Image.ROTATE_270,
-            7: Image.TRANSVERSE,
-            8: Image.ROTATE_90}.get(orientation)
-        if method is not None:
-            image = image.transpose(method)
-            del exif[0x0112]
-            image.info["exif"] = exif.tobytes()
-    return image
-
-
-def seed_worker(worker_id):
-    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
-    worker_seed = torch.initial_seed() % 2 ** 32
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
-
-
-def create_dataloader(path,
-                      imgsz,
-                      batch_size,
-                      stride,
-                      single_cls=False,
-                      hyp=None,
-                      augment=False,
-                      cache=False,
-                      pad=0.0,
-                      rect=False,
-                      rank=-1,
-                      workers=8,
-                      image_weights=False,
-                      quad=False,
-                      prefix='',
-                      shuffle=False):
-    if rect and shuffle:
-        LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
-        shuffle = False
-    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
-        dataset = LoadImagesAndLabels(
-            path,
-            imgsz,
-            batch_size,
-            augment=augment,  # augmentation
-            hyp=hyp,  # hyperparameters
-            rect=rect,  # rectangular batches
-            cache_images=cache,
-            single_cls=single_cls,
-            stride=int(stride),
-            pad=pad,
-            image_weights=image_weights,
-            prefix=prefix)
-
-    batch_size = min(batch_size, len(dataset))
-    nd = torch.cuda.device_count()  # number of CUDA devices
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
-    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
-    generator = torch.Generator()
-    generator.manual_seed(6148914691236517205 + RANK)
-    return loader(dataset,
-                  batch_size=batch_size,
-                  shuffle=shuffle and sampler is None,
-                  num_workers=nw,
-                  sampler=sampler,
-                  pin_memory=PIN_MEMORY,
-                  collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
-                  worker_init_fn=seed_worker,
-                  generator=generator), dataset
-
-
-class InfiniteDataLoader(dataloader.DataLoader):
-    """ Dataloader that reuses workers
-
-    Uses same syntax as vanilla DataLoader
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
-        self.iterator = super().__iter__()
-
-    def __len__(self):
-        return len(self.batch_sampler.sampler)
-
-    def __iter__(self):
-        for _ in range(len(self)):
-            yield next(self.iterator)
-
-
-class _RepeatSampler:
-    """ Sampler that repeats forever
-
-    Args:
-        sampler (Sampler)
-    """
-
-    def __init__(self, sampler):
-        self.sampler = sampler
-
-    def __iter__(self):
-        while True:
-            yield from iter(self.sampler)
-
-
-class LoadScreenshots:
-    # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
-    def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
-        # source = [screen_number left top width height] (pixels)
-        check_requirements('mss')
-        import mss
-
-        source, *params = source.split()
-        self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0
-        if len(params) == 1:
-            self.screen = int(params[0])
-        elif len(params) == 4:
-            left, top, width, height = (int(x) for x in params)
-        elif len(params) == 5:
-            self.screen, left, top, width, height = (int(x) for x in params)
-        self.img_size = img_size
-        self.stride = stride
-        self.transforms = transforms
-        self.auto = auto
-        self.mode = 'stream'
-        self.frame = 0
-        self.sct = mss.mss()
-
-        # Parse monitor shape
-        monitor = self.sct.monitors[self.screen]
-        self.top = monitor["top"] if top is None else (monitor["top"] + top)
-        self.left = monitor["left"] if left is None else (monitor["left"] + left)
-        self.width = width or monitor["width"]
-        self.height = height or monitor["height"]
-        self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        # mss screen capture: get raw pixels from the screen as np array
-        im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
-        s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
-
-        if self.transforms:
-            im = self.transforms(im0)  # transforms
-        else:
-            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
-        self.frame += 1
-        return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s
-
-
-class LoadImages:
-    # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
-    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
-        if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
-            path = Path(path).read_text().rsplit()
-        files = []
-        for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
-            p = str(Path(p).resolve())
-            if '*' in p:
-                files.extend(sorted(glob.glob(p, recursive=True)))  # glob
-            elif os.path.isdir(p):
-                files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))  # dir
-            elif os.path.isfile(p):
-                files.append(p)  # files
-            else:
-                raise FileNotFoundError(f'{p} does not exist')
-
-        images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
-        videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
-        ni, nv = len(images), len(videos)
-
-        self.img_size = img_size
-        self.stride = stride
-        self.files = images + videos
-        self.nf = ni + nv  # number of files
-        self.video_flag = [False] * ni + [True] * nv
-        self.mode = 'image'
-        self.auto = auto
-        self.transforms = transforms  # optional
-        self.vid_stride = vid_stride  # video frame-rate stride
-        if any(videos):
-            self._new_video(videos[0])  # new video
-        else:
-            self.cap = None
-        assert self.nf > 0, f'No images or videos found in {p}. ' \
-                            f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
-
-    def __iter__(self):
-        self.count = 0
-        return self
-
-    def __next__(self):
-        if self.count == self.nf:
-            raise StopIteration
-        path = self.files[self.count]
-
-        if self.video_flag[self.count]:
-            # Read video
-            self.mode = 'video'
-            for _ in range(self.vid_stride):
-                self.cap.grab()
-            ret_val, im0 = self.cap.retrieve()
-            while not ret_val:
-                self.count += 1
-                self.cap.release()
-                if self.count == self.nf:  # last video
-                    raise StopIteration
-                path = self.files[self.count]
-                self._new_video(path)
-                ret_val, im0 = self.cap.read()
-
-            self.frame += 1
-            # im0 = self._cv2_rotate(im0)  # for use if cv2 autorotation is False
-            s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
-
-        else:
-            # Read image
-            self.count += 1
-            im0 = cv2.imread(path)  # BGR
-            assert im0 is not None, f'Image Not Found {path}'
-            s = f'image {self.count}/{self.nf} {path}: '
-
-        if self.transforms:
-            im = self.transforms(im0)  # transforms
-        else:
-            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
-
-        return path, im, im0, self.cap, s
-
-    def _new_video(self, path):
-        # Create a new video capture object
-        self.frame = 0
-        self.cap = cv2.VideoCapture(path)
-        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
-        self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META))  # rotation degrees
-        # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493
-
-    def _cv2_rotate(self, im):
-        # Rotate a cv2 video manually
-        if self.orientation == 0:
-            return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
-        elif self.orientation == 180:
-            return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
-        elif self.orientation == 90:
-            return cv2.rotate(im, cv2.ROTATE_180)
-        return im
-
-    def __len__(self):
-        return self.nf  # number of files
-
-
-class LoadStreams:
-    # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
-    def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
-        torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
-        self.mode = 'stream'
-        self.img_size = img_size
-        self.stride = stride
-        self.vid_stride = vid_stride  # video frame-rate stride
-        sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
-        n = len(sources)
-        self.sources = [clean_str(x) for x in sources]  # clean source names for later
-        self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
-        for i, s in enumerate(sources):  # index, source
-            # Start thread to read frames from video stream
-            st = f'{i + 1}/{n}: {s}... '
-            if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video
-                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
-                check_requirements(('pafy', 'youtube_dl==2020.12.2'))
-                import pafy
-                s = pafy.new(s).getbest(preftype="mp4").url  # YouTube URL
-            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
-            if s == 0:
-                assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
-                assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.'
-            cap = cv2.VideoCapture(s)
-            assert cap.isOpened(), f'{st}Failed to open {s}'
-            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-            fps = cap.get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan
-            self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf')  # infinite stream fallback
-            self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback
-
-            _, self.imgs[i] = cap.read()  # guarantee first frame
-            self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
-            LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
-            self.threads[i].start()
-        LOGGER.info('')  # newline
-
-        # check for common shapes
-        s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])
-        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
-        self.auto = auto and self.rect
-        self.transforms = transforms  # optional
-        if not self.rect:
-            LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
-
-    def update(self, i, cap, stream):
-        # Read stream `i` frames in daemon thread
-        n, f = 0, self.frames[i]  # frame number, frame array
-        while cap.isOpened() and n < f:
-            n += 1
-            cap.grab()  # .read() = .grab() followed by .retrieve()
-            if n % self.vid_stride == 0:
-                success, im = cap.retrieve()
-                if success:
-                    self.imgs[i] = im
-                else:
-                    LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
-                    self.imgs[i] = np.zeros_like(self.imgs[i])
-                    cap.open(stream)  # re-open stream if signal was lost
-            time.sleep(0.0)  # wait time
-
-    def __iter__(self):
-        self.count = -1
-        return self
-
-    def __next__(self):
-        self.count += 1
-        if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'):  # q to quit
-            cv2.destroyAllWindows()
-            raise StopIteration
-
-        im0 = self.imgs.copy()
-        if self.transforms:
-            im = np.stack([self.transforms(x) for x in im0])  # transforms
-        else:
-            im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0])  # resize
-            im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
-            im = np.ascontiguousarray(im)  # contiguous
-
-        return self.sources, im, im0, None, ''
-
-    def __len__(self):
-        return len(self.sources)  # 1E12 frames = 32 streams at 30 FPS for 30 years
-
-
-def img2label_paths(img_paths):
-    # Define label paths as a function of image paths
-    sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings
-    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
-
-
-class LoadImagesAndLabels(Dataset):
-    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
-    cache_version = 0.6  # dataset labels *.cache version
-    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
-
-    def __init__(self,
-                 path,
-                 img_size=640,
-                 batch_size=16,
-                 augment=False,
-                 hyp=None,
-                 rect=False,
-                 image_weights=False,
-                 cache_images=False,
-                 single_cls=False,
-                 stride=32,
-                 pad=0.0,
-                 min_items=0,
-                 prefix=''):
-        self.img_size = img_size
-        self.augment = augment
-        self.hyp = hyp
-        self.image_weights = image_weights
-        self.rect = False if image_weights else rect
-        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
-        self.mosaic_border = [-img_size // 2, -img_size // 2]
-        self.stride = stride
-        self.path = path
-        self.albumentations = Albumentations(size=img_size) if augment else None
-
-        try:
-            f = []  # image files
-            for p in path if isinstance(path, list) else [path]:
-                p = Path(p)  # os-agnostic
-                if p.is_dir():  # dir
-                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
-                    # f = list(p.rglob('*.*'))  # pathlib
-                elif p.is_file():  # file
-                    with open(p) as t:
-                        t = t.read().strip().splitlines()
-                        parent = str(p.parent) + os.sep
-                        f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t]  # to global path
-                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # to global path (pathlib)
-                else:
-                    raise FileNotFoundError(f'{prefix}{p} does not exist')
-            self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
-            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
-            assert self.im_files, f'{prefix}No images found'
-        except Exception as e:
-            raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e
-
-        # Check cache
-        self.label_files = img2label_paths(self.im_files)  # labels
-        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
-        try:
-            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
-            assert cache['version'] == self.cache_version  # matches current version
-            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash
-        except Exception:
-            cache, exists = self.cache_labels(cache_path, prefix), False  # run cache ops
-
-        # Display cache
-        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
-        if exists and LOCAL_RANK in {-1, 0}:
-            d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
-            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)  # display cache results
-            if cache['msgs']:
-                LOGGER.info('\n'.join(cache['msgs']))  # display warnings
-        assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'
-
-        # Read cache
-        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
-        labels, shapes, self.segments = zip(*cache.values())
-        nl = len(np.concatenate(labels, 0))  # number of labels
-        assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'
-        self.labels = list(labels)
-        self.shapes = np.array(shapes)
-        self.im_files = list(cache.keys())  # update
-        self.label_files = img2label_paths(cache.keys())  # update
-
-        # Filter images
-        if min_items:
-            include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)
-            LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset')
-            self.im_files = [self.im_files[i] for i in include]
-            self.label_files = [self.label_files[i] for i in include]
-            self.labels = [self.labels[i] for i in include]
-            self.segments = [self.segments[i] for i in include]
-            self.shapes = self.shapes[include]  # wh
-
-        # Create indices
-        n = len(self.shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
-        nb = bi[-1] + 1  # number of batches
-        self.batch = bi  # batch index of image
-        self.n = n
-        self.indices = range(n)
-
-        # Update labels
-        include_class = []  # filter labels to include only these classes (optional)
-        include_class_array = np.array(include_class).reshape(1, -1)
-        for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
-            if include_class:
-                j = (label[:, 0:1] == include_class_array).any(1)
-                self.labels[i] = label[j]
-                if segment:
-                    self.segments[i] = segment[j]
-            if single_cls:  # single-class training, merge all classes into 0
-                self.labels[i][:, 0] = 0
-
-        # Rectangular Training
-        if self.rect:
-            # Sort by aspect ratio
-            s = self.shapes  # wh
-            ar = s[:, 1] / s[:, 0]  # aspect ratio
-            irect = ar.argsort()
-            self.im_files = [self.im_files[i] for i in irect]
-            self.label_files = [self.label_files[i] for i in irect]
-            self.labels = [self.labels[i] for i in irect]
-            self.segments = [self.segments[i] for i in irect]
-            self.shapes = s[irect]  # wh
-            ar = ar[irect]
-
-            # Set training image shapes
-            shapes = [[1, 1]] * nb
-            for i in range(nb):
-                ari = ar[bi == i]
-                mini, maxi = ari.min(), ari.max()
-                if maxi < 1:
-                    shapes[i] = [maxi, 1]
-                elif mini > 1:
-                    shapes[i] = [1, 1 / mini]
-
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
-
-        # Cache images into RAM/disk for faster training
-        if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix):
-            cache_images = False
-        self.ims = [None] * n
-        self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
-        if cache_images:
-            b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
-            self.im_hw0, self.im_hw = [None] * n, [None] * n
-            fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
-            results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
-            pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
-            for i, x in pbar:
-                if cache_images == 'disk':
-                    b += self.npy_files[i].stat().st_size
-                else:  # 'ram'
-                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
-                    b += self.ims[i].nbytes
-                pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})'
-            pbar.close()
-
-    def check_cache_ram(self, safety_margin=0.1, prefix=''):
-        # Check image caching requirements vs available memory
-        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
-        n = min(self.n, 30)  # extrapolate from 30 random images
-        for _ in range(n):
-            im = cv2.imread(random.choice(self.im_files))  # sample image
-            ratio = self.img_size / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
-            b += im.nbytes * ratio ** 2
-        mem_required = b * self.n / n  # GB required to cache dataset into RAM
-        mem = psutil.virtual_memory()
-        cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
-        if not cache:
-            LOGGER.info(f"{prefix}{mem_required / gb:.1f}GB RAM required, "
-                        f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, "
-                        f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
-        return cache
-
-    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
-        # Cache dataset labels, check images and read shapes
-        x = {}  # dict
-        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
-        desc = f"{prefix}Scanning {path.parent / path.stem}..."
-        with Pool(NUM_THREADS) as pool:
-            pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
-                        desc=desc,
-                        total=len(self.im_files),
-                        bar_format=TQDM_BAR_FORMAT)
-            for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
-                nm += nm_f
-                nf += nf_f
-                ne += ne_f
-                nc += nc_f
-                if im_file:
-                    x[im_file] = [lb, shape, segments]
-                if msg:
-                    msgs.append(msg)
-                pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
-
-        pbar.close()
-        if msgs:
-            LOGGER.info('\n'.join(msgs))
-        if nf == 0:
-            LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')
-        x['hash'] = get_hash(self.label_files + self.im_files)
-        x['results'] = nf, nm, ne, nc, len(self.im_files)
-        x['msgs'] = msgs  # warnings
-        x['version'] = self.cache_version  # cache version
-        try:
-            np.save(path, x)  # save cache for next time
-            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
-            LOGGER.info(f'{prefix}New cache created: {path}')
-        except Exception as e:
-            LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}')  # not writeable
-        return x
-
-    def __len__(self):
-        return len(self.im_files)
-
-    # def __iter__(self):
-    #     self.count = -1
-    #     print('ran dataset iter')
-    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
-    #     return self
-
-    def __getitem__(self, index):
-        index = self.indices[index]  # linear, shuffled, or image_weights
-
-        hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp['mosaic']
-        if mosaic:
-            # Load mosaic
-            img, labels = self.load_mosaic(index)
-            shapes = None
-
-            # MixUp augmentation
-            if random.random() < hyp['mixup']:
-                img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))
-
-        else:
-            # Load image
-            img, (h0, w0), (h, w) = self.load_image(index)
-
-            # Letterbox
-            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
-            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
-            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
-
-            labels = self.labels[index].copy()
-            if labels.size:  # normalized xywh to pixel xyxy format
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
-
-            if self.augment:
-                img, labels = random_perspective(img,
-                                                 labels,
-                                                 degrees=hyp['degrees'],
-                                                 translate=hyp['translate'],
-                                                 scale=hyp['scale'],
-                                                 shear=hyp['shear'],
-                                                 perspective=hyp['perspective'])
-
-        nl = len(labels)  # number of labels
-        if nl:
-            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)
-
-        if self.augment:
-            # Albumentations
-            img, labels = self.albumentations(img, labels)
-            nl = len(labels)  # update after albumentations
-
-            # HSV color-space
-            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
-            # Flip up-down
-            if random.random() < hyp['flipud']:
-                img = np.flipud(img)
-                if nl:
-                    labels[:, 2] = 1 - labels[:, 2]
-
-            # Flip left-right
-            if random.random() < hyp['fliplr']:
-                img = np.fliplr(img)
-                if nl:
-                    labels[:, 1] = 1 - labels[:, 1]
-
-            # Cutouts
-            # labels = cutout(img, labels, p=0.5)
-            # nl = len(labels)  # update after cutout
-
-        labels_out = torch.zeros((nl, 6))
-        if nl:
-            labels_out[:, 1:] = torch.from_numpy(labels)
-
-        # Convert
-        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-        img = np.ascontiguousarray(img)
-
-        return torch.from_numpy(img), labels_out, self.im_files[index], shapes
-
-    def load_image(self, i):
-        # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
-        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
-        if im is None:  # not cached in RAM
-            if fn.exists():  # load npy
-                im = np.load(fn)
-            else:  # read image
-                im = cv2.imread(f)  # BGR
-                assert im is not None, f'Image Not Found {f}'
-            h0, w0 = im.shape[:2]  # orig hw
-            r = self.img_size / max(h0, w0)  # ratio
-            if r != 1:  # if sizes are not equal
-                interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
-                im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp)
-            return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
-        return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized
-
-    def cache_images_to_disk(self, i):
-        # Saves an image as an *.npy file for faster loading
-        f = self.npy_files[i]
-        if not f.exists():
-            np.save(f.as_posix(), cv2.imread(self.im_files[i]))
-
-    def load_mosaic(self, index):
-        # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
-        labels4, segments4 = [], []
-        s = self.img_size
-        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y
-        indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
-        random.shuffle(indices)
-        for i, index in enumerate(indices):
-            # Load image
-            img, _, (h, w) = self.load_image(index)
-
-            # place img in img4
-            if i == 0:  # top left
-                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
-                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
-            elif i == 1:  # top right
-                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
-                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
-            elif i == 2:  # bottom left
-                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
-                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
-            elif i == 3:  # bottom right
-                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
-                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
-            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-            padw = x1a - x1b
-            padh = y1a - y1b
-
-            # Labels
-            labels, segments = self.labels[index].copy(), self.segments[index].copy()
-            if labels.size:
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
-                segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
-            labels4.append(labels)
-            segments4.extend(segments)
-
-        # Concat/clip labels
-        labels4 = np.concatenate(labels4, 0)
-        for x in (labels4[:, 1:], *segments4):
-            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-        # img4, labels4 = replicate(img4, labels4)  # replicate
-
-        # Augment
-        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
-        img4, labels4 = random_perspective(img4,
-                                           labels4,
-                                           segments4,
-                                           degrees=self.hyp['degrees'],
-                                           translate=self.hyp['translate'],
-                                           scale=self.hyp['scale'],
-                                           shear=self.hyp['shear'],
-                                           perspective=self.hyp['perspective'],
-                                           border=self.mosaic_border)  # border to remove
-
-        return img4, labels4
-
-    def load_mosaic9(self, index):
-        # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
-        labels9, segments9 = [], []
-        s = self.img_size
-        indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
-        random.shuffle(indices)
-        hp, wp = -1, -1  # height, width previous
-        for i, index in enumerate(indices):
-            # Load image
-            img, _, (h, w) = self.load_image(index)
-
-            # place img in img9
-            if i == 0:  # center
-                img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-                h0, w0 = h, w
-                c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
-            elif i == 1:  # top
-                c = s, s - h, s + w, s
-            elif i == 2:  # top right
-                c = s + wp, s - h, s + wp + w, s
-            elif i == 3:  # right
-                c = s + w0, s, s + w0 + w, s + h
-            elif i == 4:  # bottom right
-                c = s + w0, s + hp, s + w0 + w, s + hp + h
-            elif i == 5:  # bottom
-                c = s + w0 - w, s + h0, s + w0, s + h0 + h
-            elif i == 6:  # bottom left
-                c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
-            elif i == 7:  # left
-                c = s - w, s + h0 - h, s, s + h0
-            elif i == 8:  # top left
-                c = s - w, s + h0 - hp - h, s, s + h0 - hp
-
-            padx, pady = c[:2]
-            x1, y1, x2, y2 = (max(x, 0) for x in c)  # allocate coords
-
-            # Labels
-            labels, segments = self.labels[index].copy(), self.segments[index].copy()
-            if labels.size:
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
-                segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
-            labels9.append(labels)
-            segments9.extend(segments)
-
-            # Image
-            img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]
-            hp, wp = h, w  # height, width previous
-
-        # Offset
-        yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border)  # mosaic center x, y
-        img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
-
-        # Concat/clip labels
-        labels9 = np.concatenate(labels9, 0)
-        labels9[:, [1, 3]] -= xc
-        labels9[:, [2, 4]] -= yc
-        c = np.array([xc, yc])  # centers
-        segments9 = [x - c for x in segments9]
-
-        for x in (labels9[:, 1:], *segments9):
-            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-        # img9, labels9 = replicate(img9, labels9)  # replicate
-
-        # Augment
-        img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste'])
-        img9, labels9 = random_perspective(img9,
-                                           labels9,
-                                           segments9,
-                                           degrees=self.hyp['degrees'],
-                                           translate=self.hyp['translate'],
-                                           scale=self.hyp['scale'],
-                                           shear=self.hyp['shear'],
-                                           perspective=self.hyp['perspective'],
-                                           border=self.mosaic_border)  # border to remove
-
-        return img9, labels9
-
-    @staticmethod
-    def collate_fn(batch):
-        im, label, path, shapes = zip(*batch)  # transposed
-        for i, lb in enumerate(label):
-            lb[:, 0] = i  # add target image index for build_targets()
-        return torch.stack(im, 0), torch.cat(label, 0), path, shapes
-
-    @staticmethod
-    def collate_fn4(batch):
-        im, label, path, shapes = zip(*batch)  # transposed
-        n = len(shapes) // 4
-        im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
-
-        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
-        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
-        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # scale
-        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
-            i *= 4
-            if random.random() < 0.5:
-                im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear',
-                                    align_corners=False)[0].type(im[i].type())
-                lb = label[i]
-            else:
-                im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2)
-                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
-            im4.append(im1)
-            label4.append(lb)
-
-        for i, lb in enumerate(label4):
-            lb[:, 0] = i  # add target image index for build_targets()
-
-        return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4
-
-
-# Ancillary functions --------------------------------------------------------------------------------------------------
-def flatten_recursive(path=DATASETS_DIR / 'coco128'):
-    # Flatten a recursive directory by bringing all files to top level
-    new_path = Path(f'{str(path)}_flat')
-    if os.path.exists(new_path):
-        shutil.rmtree(new_path)  # delete output folder
-    os.makedirs(new_path)  # make new output folder
-    for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)):
-        shutil.copyfile(file, new_path / Path(file).name)
-
-
-def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders import *; extract_boxes()
-    # Convert detection dataset into classification dataset, with one directory per class
-    path = Path(path)  # images dir
-    shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None  # remove existing
-    files = list(path.rglob('*.*'))
-    n = len(files)  # number of files
-    for im_file in tqdm(files, total=n):
-        if im_file.suffix[1:] in IMG_FORMATS:
-            # image
-            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
-            h, w = im.shape[:2]
-
-            # labels
-            lb_file = Path(img2label_paths([str(im_file)])[0])
-            if Path(lb_file).exists():
-                with open(lb_file) as f:
-                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels
-
-                for j, x in enumerate(lb):
-                    c = int(x[0])  # class
-                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename
-                    if not f.parent.is_dir():
-                        f.parent.mkdir(parents=True)
-
-                    b = x[1:] * [w, h, w, h]  # box
-                    # b[2:] = b[2:].max()  # rectangle to square
-                    b[2:] = b[2:] * 1.2 + 3  # pad
-                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
-
-                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
-                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
-                    assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
-
-
-def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
-    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
-    Usage: from utils.dataloaders import *; autosplit()
-    Arguments
-        path:            Path to images directory
-        weights:         Train, val, test weights (list, tuple)
-        annotated_only:  Only use images with an annotated txt file
-    """
-    path = Path(path)  # images dir
-    files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
-    n = len(files)  # number of files
-    random.seed(0)  # for reproducibility
-    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
-
-    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
-    for x in txt:
-        if (path.parent / x).exists():
-            (path.parent / x).unlink()  # remove existing
-
-    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
-    for i, img in tqdm(zip(indices, files), total=n):
-        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
-            with open(path.parent / txt[i], 'a') as f:
-                f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n')  # add image to txt file
-
-
-def verify_image_label(args):
-    # Verify one image-label pair
-    im_file, lb_file, prefix = args
-    nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', []  # number (missing, found, empty, corrupt), message, segments
-    try:
-        # verify images
-        im = Image.open(im_file)
-        im.verify()  # PIL verify
-        shape = exif_size(im)  # image size
-        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
-        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
-        if im.format.lower() in ('jpg', 'jpeg'):
-            with open(im_file, 'rb') as f:
-                f.seek(-2, 2)
-                if f.read() != b'\xff\xd9':  # corrupt JPEG
-                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
-                    msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
-
-        # verify labels
-        if os.path.isfile(lb_file):
-            nf = 1  # label found
-            with open(lb_file) as f:
-                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
-                if any(len(x) > 6 for x in lb):  # is segment
-                    classes = np.array([x[0] for x in lb], dtype=np.float32)
-                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
-                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
-                lb = np.array(lb, dtype=np.float32)
-            nl = len(lb)
-            if nl:
-                assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
-                assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
-                assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
-                _, i = np.unique(lb, axis=0, return_index=True)
-                if len(i) < nl:  # duplicate row check
-                    lb = lb[i]  # remove duplicates
-                    if segments:
-                        segments = [segments[x] for x in i]
-                    msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
-            else:
-                ne = 1  # label empty
-                lb = np.zeros((0, 5), dtype=np.float32)
-        else:
-            nm = 1  # label missing
-            lb = np.zeros((0, 5), dtype=np.float32)
-        return im_file, lb, shape, segments, nm, nf, ne, nc, msg
-    except Exception as e:
-        nc = 1
-        msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
-        return [None, None, None, None, nm, nf, ne, nc, msg]
-
-
-class HUBDatasetStats():
-    """ Class for generating HUB dataset JSON and `-hub` dataset directory
-
-    Arguments
-        path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
-        autodownload:   Attempt to download dataset if not found locally
-
-    Usage
-        from utils.dataloaders import HUBDatasetStats
-        stats = HUBDatasetStats('coco128.yaml', autodownload=True)  # usage 1
-        stats = HUBDatasetStats('path/to/coco128.zip')  # usage 2
-        stats.get_json(save=False)
-        stats.process_images()
-    """
-
-    def __init__(self, path='coco128.yaml', autodownload=False):
-        # Initialize class
-        zipped, data_dir, yaml_path = self._unzip(Path(path))
-        try:
-            with open(check_yaml(yaml_path), errors='ignore') as f:
-                data = yaml.safe_load(f)  # data dict
-                if zipped:
-                    data['path'] = data_dir
-        except Exception as e:
-            raise Exception("error/HUB/dataset_stats/yaml_load") from e
-
-        check_dataset(data, autodownload)  # download dataset if missing
-        self.hub_dir = Path(data['path'] + '-hub')
-        self.im_dir = self.hub_dir / 'images'
-        self.im_dir.mkdir(parents=True, exist_ok=True)  # makes /images
-        self.stats = {'nc': data['nc'], 'names': list(data['names'].values())}  # statistics dictionary
-        self.data = data
-
-    @staticmethod
-    def _find_yaml(dir):
-        # Return data.yaml file
-        files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml'))  # try root level first and then recursive
-        assert files, f'No *.yaml file found in {dir}'
-        if len(files) > 1:
-            files = [f for f in files if f.stem == dir.stem]  # prefer *.yaml files that match dir name
-            assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed'
-        assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}'
-        return files[0]
-
-    def _unzip(self, path):
-        # Unzip data.zip
-        if not str(path).endswith('.zip'):  # path is data.yaml
-            return False, None, path
-        assert Path(path).is_file(), f'Error unzipping {path}, file not found'
-        unzip_file(path, path=path.parent)
-        dir = path.with_suffix('')  # dataset directory == zip name
-        assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
-        return True, str(dir), self._find_yaml(dir)  # zipped, data_dir, yaml_path
-
-    def _hub_ops(self, f, max_dim=1920):
-        # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing
-        f_new = self.im_dir / Path(f).name  # dataset-hub image filename
-        try:  # use PIL
-            im = Image.open(f)
-            r = max_dim / max(im.height, im.width)  # ratio
-            if r < 1.0:  # image too large
-                im = im.resize((int(im.width * r), int(im.height * r)))
-            im.save(f_new, 'JPEG', quality=50, optimize=True)  # save
-        except Exception as e:  # use OpenCV
-            LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
-            im = cv2.imread(f)
-            im_height, im_width = im.shape[:2]
-            r = max_dim / max(im_height, im_width)  # ratio
-            if r < 1.0:  # image too large
-                im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
-            cv2.imwrite(str(f_new), im)
-
-    def get_json(self, save=False, verbose=False):
-        # Return dataset JSON for Ultralytics HUB
-        def _round(labels):
-            # Update labels to integer class and 6 decimal place floats
-            return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]
-
-        for split in 'train', 'val', 'test':
-            if self.data.get(split) is None:
-                self.stats[split] = None  # i.e. no test set
-                continue
-            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
-            x = np.array([
-                np.bincount(label[:, 0].astype(int), minlength=self.data['nc'])
-                for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')])  # shape(128x80)
-            self.stats[split] = {
-                'instance_stats': {
-                    'total': int(x.sum()),
-                    'per_class': x.sum(0).tolist()},
-                'image_stats': {
-                    'total': dataset.n,
-                    'unlabelled': int(np.all(x == 0, 1).sum()),
-                    'per_class': (x > 0).sum(0).tolist()},
-                'labels': [{
-                    str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]}
-
-        # Save, print and return
-        if save:
-            stats_path = self.hub_dir / 'stats.json'
-            print(f'Saving {stats_path.resolve()}...')
-            with open(stats_path, 'w') as f:
-                json.dump(self.stats, f)  # save stats.json
-        if verbose:
-            print(json.dumps(self.stats, indent=2, sort_keys=False))
-        return self.stats
-
-    def process_images(self):
-        # Compress images for Ultralytics HUB
-        for split in 'train', 'val', 'test':
-            if self.data.get(split) is None:
-                continue
-            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
-            desc = f'{split} images'
-            for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc):
-                pass
-        print(f'Done. All images saved to {self.im_dir}')
-        return self.im_dir
-
-
-# Classification dataloaders -------------------------------------------------------------------------------------------
-class ClassificationDataset(torchvision.datasets.ImageFolder):
-    """
-    YOLOv5 Classification Dataset.
-    Arguments
-        root:  Dataset path
-        transform:  torchvision transforms, used by default
-        album_transform: Albumentations transforms, used if installed
-    """
-
-    def __init__(self, root, augment, imgsz, cache=False):
-        super().__init__(root=root)
-        self.torch_transforms = classify_transforms(imgsz)
-        self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
-        self.cache_ram = cache is True or cache == 'ram'
-        self.cache_disk = cache == 'disk'
-        self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples]  # file, index, npy, im
-
-    def __getitem__(self, i):
-        f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
-        if self.cache_ram and im is None:
-            im = self.samples[i][3] = cv2.imread(f)
-        elif self.cache_disk:
-            if not fn.exists():  # load npy
-                np.save(fn.as_posix(), cv2.imread(f))
-            im = np.load(fn)
-        else:  # read image
-            im = cv2.imread(f)  # BGR
-        if self.album_transforms:
-            sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
-        else:
-            sample = self.torch_transforms(im)
-        return sample, j
-
-
-def create_classification_dataloader(path,
-                                     imgsz=224,
-                                     batch_size=16,
-                                     augment=True,
-                                     cache=False,
-                                     rank=-1,
-                                     workers=8,
-                                     shuffle=True):
-    # Returns Dataloader object to be used with YOLOv5 Classifier
-    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
-        dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
-    batch_size = min(batch_size, len(dataset))
-    nd = torch.cuda.device_count()
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
-    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    generator = torch.Generator()
-    generator.manual_seed(6148914691236517205 + RANK)
-    return InfiniteDataLoader(dataset,
-                              batch_size=batch_size,
-                              shuffle=shuffle and sampler is None,
-                              num_workers=nw,
-                              sampler=sampler,
-                              pin_memory=PIN_MEMORY,
-                              worker_init_fn=seed_worker,
-                              generator=generator)  # or DataLoader(persistent_workers=True)
diff --git a/spaces/andfanilo/streamlit-drawable-canvas-demo/README.md b/spaces/andfanilo/streamlit-drawable-canvas-demo/README.md
deleted file mode 100644
index 154265de6ef947c8fd3801b935cbe4fbaf9798c4..0000000000000000000000000000000000000000
--- a/spaces/andfanilo/streamlit-drawable-canvas-demo/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Streamlit Drawable Canvas Demo
-emoji: 🏃
-colorFrom: gray
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/andresgtn/find-the-next-james-bond/app.py b/spaces/andresgtn/find-the-next-james-bond/app.py
deleted file mode 100644
index e1618ccd345118b005b3bdf42a5948b0b50a3da3..0000000000000000000000000000000000000000
--- a/spaces/andresgtn/find-the-next-james-bond/app.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from diffusers import StableDiffusionPipeline
-import gradio as gr
-import requests
-import base64
-from PIL import Image, PngImagePlugin
-from io import BytesIO
-import torch
-import os
-
-# required for stable difussion
-auth_token = os.environ.get("auth_token") #in secret space
-hf_writer = gr.HuggingFaceDatasetSaver(auth_token, "flagged-find-the-next-james-bond")
-
-pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=auth_token)
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-pipe = pipe.to(device)
-
-def encode_pil_to_base64(pil_image):
-  ''' From: https://github.com/gradio-app/gradio/blob/main/gradio/processing_utils.py'''
-  with BytesIO() as output_bytes:
-
-      # Copy any text-only metadata
-      use_metadata = False
-      metadata = PngImagePlugin.PngInfo()
-      for key, value in pil_image.info.items():
-          if isinstance(key, str) and isinstance(value, str):
-              metadata.add_text(key, value)
-              use_metadata = True
-
-      pil_image.save(
-          output_bytes, "PNG", pnginfo=(metadata if use_metadata else None)
-      )
-      bytes_data = output_bytes.getvalue()
-  base64_str = str(base64.b64encode(bytes_data), "utf-8")
-  return "data:image/png;base64," + base64_str
-
-def decode_base64_to_image(encoding):
-  ''' From: https://github.com/gradio-app/gradio/blob/main/gradio/processing_utils.py'''
-  content = encoding.split(";")[1]
-  image_encoded = content.split(",")[1]
-  return Image.open(BytesIO(base64.b64decode(image_encoded)))
-
-def improve_image(img, scale=2):
-  ''' Improves an input image using GFP-GAN
-  Inputs
-  img (PIL): image to improve
-  scale (int): scale factor for new image
-  Output
-  Improved image. If the request to GFPGAN is unsuccesful, it returns
-  a black image.
-  '''
-  url = "https://hf.space/embed/NotFungibleIO/GFPGAN/+/api/predict"
-  request_objt = {"data":[encode_pil_to_base64(img),'v1.3', scale]}
-  try:
-    imp_img = decode_base64_to_image(requests.post(url, json=request_objt).json()['data'][0])
-  except AttributeError:
-    return Image.new('RGB', size=(512, 512))
-  return imp_img
-
-def generate(celebrity, movie, guidance, improve_flag, scale): # add scale as var
-  prompt = f"A movie poster of {celebrity} in {movie}."
-  image = pipe(prompt, guidance=guidance).images[0]
-  if improve_flag:
-    image = improve_image(image, scale=scale)
-  return image
-
-movie_options = movie_options = ["Casino Royale (2006)",
-                "Quantum of Solace (2008)",
-                "Skyfall (2012)",
-                "Spectre (2015)",
-                "No Time To Die (2021)"]
-title = "Find the next James Bond"
-description = "Write the name of a celebrity, and pick a Bond movie from the dropdown menu.\
-               This will generate a new movie poster (inspired by the chosen movie)\
-               with the new celebrity in it. See below for explanation of the\
-               input variables (guidance, improve and scale). Some candidates are Tom Hardy, Idris Elba, Henry Cavill, Suranne Jones, Lashana Lynch, and Robert Pattinson."
-article= "Inputs explained: \n Guidance: a lower value will show more random results, but with better image quality. Improve and scale: if selected, the image resolution will be increased and faces will be higher quality with the eyes and facial features better defined), also the image size (originally 512x512) will be scaled by the chosen factor. Uses [GFP-GAN](https://github.com/TencentARC/GFPGAN)."
-
-demo = gr.Interface(
-    fn=generate,
-    inputs=[gr.Textbox(value="Tom Hardy"), 
-            gr.Dropdown(movie_options, value="Skyfall (2012)"), 
-            gr.Slider(1, 20, value=7.5, step=0.5),
-            gr.Checkbox(label="Improve and scale? (takes longer to process)"),
-            gr.Slider(1, 3, value=1, step=0.5)
-            ],
-    outputs='image',
-    title=title,
-    description=description,
-    article=article,
-    allow_flagging="manual",
-    flagging_options=["wrong celebrity", "wrong movie", "face not shown", "nonsensical output"],
-    flagging_callback=hf_writer
-) 
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/aodianyun/stable-diffusion-webui/modules/sd_vae.py b/spaces/aodianyun/stable-diffusion-webui/modules/sd_vae.py
deleted file mode 100644
index 9b00f76e9c62c794b3a27b36bae0f168ff4f5ab8..0000000000000000000000000000000000000000
--- a/spaces/aodianyun/stable-diffusion-webui/modules/sd_vae.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import torch
-import safetensors.torch
-import os
-import collections
-from collections import namedtuple
-from modules import paths, shared, devices, script_callbacks, sd_models
-import glob
-from copy import deepcopy
-
-
-vae_path = os.path.abspath(os.path.join(paths.models_path, "VAE"))
-vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
-vae_dict = {}
-
-
-base_vae = None
-loaded_vae_file = None
-checkpoint_info = None
-
-checkpoints_loaded = collections.OrderedDict()
-
-def get_base_vae(model):
-    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model:
-        return base_vae
-    return None
-
-
-def store_base_vae(model):
-    global base_vae, checkpoint_info
-    if checkpoint_info != model.sd_checkpoint_info:
-        assert not loaded_vae_file, "Trying to store non-base VAE!"
-        base_vae = deepcopy(model.first_stage_model.state_dict())
-        checkpoint_info = model.sd_checkpoint_info
-
-
-def delete_base_vae():
-    global base_vae, checkpoint_info
-    base_vae = None
-    checkpoint_info = None
-
-
-def restore_base_vae(model):
-    global loaded_vae_file
-    if base_vae is not None and checkpoint_info == model.sd_checkpoint_info:
-        print("Restoring base VAE")
-        _load_vae_dict(model, base_vae)
-        loaded_vae_file = None
-    delete_base_vae()
-
-
-def get_filename(filepath):
-    return os.path.basename(filepath)
-
-
-def refresh_vae_list():
-    vae_dict.clear()
-
-    paths = [
-        os.path.join(sd_models.model_path, '**/*.vae.ckpt'),
-        os.path.join(sd_models.model_path, '**/*.vae.pt'),
-        os.path.join(sd_models.model_path, '**/*.vae.safetensors'),
-        os.path.join(vae_path, '**/*.ckpt'),
-        os.path.join(vae_path, '**/*.pt'),
-        os.path.join(vae_path, '**/*.safetensors'),
-    ]
-
-    if shared.cmd_opts.ckpt_dir is not None and os.path.isdir(shared.cmd_opts.ckpt_dir):
-        paths += [
-            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.ckpt'),
-            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.pt'),
-            os.path.join(shared.cmd_opts.ckpt_dir, '**/*.vae.safetensors'),
-        ]
-
-    if shared.cmd_opts.vae_dir is not None and os.path.isdir(shared.cmd_opts.vae_dir):
-        paths += [
-            os.path.join(shared.cmd_opts.vae_dir, '**/*.ckpt'),
-            os.path.join(shared.cmd_opts.vae_dir, '**/*.pt'),
-            os.path.join(shared.cmd_opts.vae_dir, '**/*.safetensors'),
-        ]
-
-    candidates = []
-    for path in paths:
-        candidates += glob.iglob(path, recursive=True)
-
-    for filepath in candidates:
-        name = get_filename(filepath)
-        vae_dict[name] = filepath
-
-
-def find_vae_near_checkpoint(checkpoint_file):
-    checkpoint_path = os.path.splitext(checkpoint_file)[0]
-    for vae_location in [checkpoint_path + ".vae.pt", checkpoint_path + ".vae.ckpt", checkpoint_path + ".vae.safetensors"]:
-        if os.path.isfile(vae_location):
-            return vae_location
-
-    return None
-
-
-def resolve_vae(checkpoint_file):
-    if shared.cmd_opts.vae_path is not None:
-        return shared.cmd_opts.vae_path, 'from commandline argument'
-
-    is_automatic = shared.opts.sd_vae in {"Automatic", "auto"}  # "auto" for people with old config
-
-    vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
-    if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or is_automatic):
-        return vae_near_checkpoint, 'found near the checkpoint'
-
-    if shared.opts.sd_vae == "None":
-        return None, None
-
-    vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
-    if vae_from_options is not None:
-        return vae_from_options, 'specified in settings'
-
-    if not is_automatic:
-        print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
-
-    return None, None
-
-
-def load_vae_dict(filename, map_location):
-    vae_ckpt = sd_models.read_state_dict(filename, map_location=map_location)
-    vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
-    return vae_dict_1
-
-
-def load_vae(model, vae_file=None, vae_source="from unknown source"):
-    global vae_dict, loaded_vae_file
-    # save_settings = False
-
-    cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0
-
-    if vae_file:
-        if cache_enabled and vae_file in checkpoints_loaded:
-            # use vae checkpoint cache
-            print(f"Loading VAE weights {vae_source}: cached {get_filename(vae_file)}")
-            store_base_vae(model)
-            _load_vae_dict(model, checkpoints_loaded[vae_file])
-        else:
-            assert os.path.isfile(vae_file), f"VAE {vae_source} doesn't exist: {vae_file}"
-            print(f"Loading VAE weights {vae_source}: {vae_file}")
-            store_base_vae(model)
-
-            vae_dict_1 = load_vae_dict(vae_file, map_location=shared.weight_load_location)
-            _load_vae_dict(model, vae_dict_1)
-
-            if cache_enabled:
-                # cache newly loaded vae
-                checkpoints_loaded[vae_file] = vae_dict_1.copy()
-
-        # clean up cache if limit is reached
-        if cache_enabled:
-            while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model
-                checkpoints_loaded.popitem(last=False)  # LRU
-
-        # If vae used is not in dict, update it
-        # It will be removed on refresh though
-        vae_opt = get_filename(vae_file)
-        if vae_opt not in vae_dict:
-            vae_dict[vae_opt] = vae_file
-
-    elif loaded_vae_file:
-        restore_base_vae(model)
-
-    loaded_vae_file = vae_file
-
-
-# don't call this from outside
-def _load_vae_dict(model, vae_dict_1):
-    model.first_stage_model.load_state_dict(vae_dict_1)
-    model.first_stage_model.to(devices.dtype_vae)
-
-
-def clear_loaded_vae():
-    global loaded_vae_file
-    loaded_vae_file = None
-
-
-unspecified = object()
-
-
-def reload_vae_weights(sd_model=None, vae_file=unspecified):
-    from modules import lowvram, devices, sd_hijack
-
-    if not sd_model:
-        sd_model = shared.sd_model
-
-    checkpoint_info = sd_model.sd_checkpoint_info
-    checkpoint_file = checkpoint_info.filename
-
-    if vae_file == unspecified:
-        vae_file, vae_source = resolve_vae(checkpoint_file)
-    else:
-        vae_source = "from function argument"
-
-    if loaded_vae_file == vae_file:
-        return
-
-    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
-        lowvram.send_everything_to_cpu()
-    else:
-        sd_model.to(devices.cpu)
-
-    sd_hijack.model_hijack.undo_hijack(sd_model)
-
-    load_vae(sd_model, vae_file, vae_source)
-
-    sd_hijack.model_hijack.hijack(sd_model)
-    script_callbacks.model_loaded_callback(sd_model)
-
-    if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
-        sd_model.to(devices.device)
-
-    print("VAE weights loaded.")
-    return sd_model
diff --git a/spaces/applsisujsus/qiangbing/README.md b/spaces/applsisujsus/qiangbing/README.md
deleted file mode 100644
index 81f9fb598a7f7472d93664f390425dfb57e618b1..0000000000000000000000000000000000000000
--- a/spaces/applsisujsus/qiangbing/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Go Proxy Bingai
-emoji: 📉
-colorFrom: gray
-colorTo: red
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
-duplicated_from: laogou717/bing
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/arampacha/chat-with-simpsons/app_api.py b/spaces/arampacha/chat-with-simpsons/app_api.py
deleted file mode 100644
index c0d51e0773ad29f39ef46ca169ceb3e1f3bc8a79..0000000000000000000000000000000000000000
--- a/spaces/arampacha/chat-with-simpsons/app_api.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import streamlit as st
-import json
-import requests
-
-import time
-
-
-API_TOKEN = st.secrets["hf_api_token"]
-headers = {"Authorization": f"Bearer {API_TOKEN}"}
-API_URL = "https://api-inference.huggingface.co/models/arampacha/DialoGPT-medium-simpsons"
-
-def query(payload):
-    data = json.dumps(payload)
-    response = requests.request("POST", API_URL, headers=headers, data=data)
-    return json.loads(response.content.decode("utf-8"))
-
-def fake_query(payload):
-    user_input = payload["inputs"]["text"]
-    time.sleep(1)
-    res = {
-        "generated_text": user_input[::-1],
-        "conversation":{
-            "past_user_inputs": st.session_state.past_user_inputs + [user_input],
-            "generated_responses": st.session_state.generated_responses + [user_input[::-1]],
-        },
-    }
-    return res
-
-parameters = {
-    "min_length":None,
-    "max_length":100,
-    "top_p":0.92,
-    "temperature":1.0,
-    "repetition_penalty":None,
-}
-
-options = {
-    "use_cache":False,
-    "wait_for_model":False
-}
-
-def on_input():
-    # st.write("Input changed")
-    if st.session_state.count > 0:
-        user_input = st.session_state.user_input
-        st.session_state.full_text += f"_User_  >>> {user_input}\n\n"
-        dialog_output.markdown(st.session_state.full_text)
-        st.session_state.user_input = ""
-
-        payload = {
-            "inputs": {
-                "text": user_input,
-                "past_user_inputs": st.session_state.past_user_inputs,
-                "generated_responses": st.session_state.generated_responses,
-            },
-            "parameters": parameters,
-            "options":options,
-        }
-        # result = fake_query(payload)
-        result = query(payload)
-        try:
-            st.session_state.update(result["conversation"])
-            st.session_state.full_text += f'_Chatbot_ > {result["generated_text"]}\n\n'
-        except:
-            st.write("D'oh! Something went wrong. Try to rerun the app.")
-            st.write(result)
-    st.session_state.count += 1
-
-
-
-# init session state
-if "past_user_inputs" not in st.session_state:
-    st.session_state["past_user_inputs"] = []
-if "generated_responses" not in st.session_state:
-    st.session_state["generated_responses"] = []
-if "full_text" not in st.session_state:
-    st.session_state["full_text"] = ""
-if "user_input" not in st.session_state:
-    st.session_state["user_input"] = ""
-if "count" not in st.session_state:
-    st.session_state["count"] = 0
-
-# body
-st.title("Chat with Simpsons")
-
-st.image(
-    "https://raw.githubusercontent.com/arampacha/chat-with-simpsons/main/the-simpsons.png",
-    caption="(c) 20th Century Fox Television",
-)
-if st.session_state.count == 0:
-    st.write("Start dialog by inputing some text:")
-
-dialog_output = st.empty()
-
-if st.session_state.count > 0:
-    dialog_output.markdown(st.session_state.full_text)
-
-user_input = st.text_input(
-    "> User: ",
-    # value="Hey Homer! How is it going?",
-    on_change=on_input(),
-    key="user_input",
-)
-
-dialog_text = st.session_state.full_text
-dialog_output.markdown(dialog_text)
-
-def restart():
-    st.session_state.clear()
-    
-st.button("Restart", on_click=st.session_state.clear)
diff --git a/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/test_melgan_train.py b/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/test_melgan_train.py
deleted file mode 100644
index 6ef9cd495b022f8d01d4c2ed6cd2667e1b1894ce..0000000000000000000000000000000000000000
--- a/spaces/artificialguybr/video-dubbing/TTS/tests/vocoder_tests/test_melgan_train.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import glob
-import os
-import shutil
-
-from tests import get_device_id, get_tests_output_path, run_cli
-from TTS.vocoder.configs import MelganConfig
-
-config_path = os.path.join(get_tests_output_path(), "test_vocoder_config.json")
-output_path = os.path.join(get_tests_output_path(), "train_outputs")
-
-config = MelganConfig(
-    batch_size=4,
-    eval_batch_size=4,
-    num_loader_workers=0,
-    num_eval_loader_workers=0,
-    run_eval=True,
-    test_delay_epochs=-1,
-    epochs=1,
-    seq_len=2048,
-    eval_split_size=1,
-    print_step=1,
-    discriminator_model_params={"base_channels": 16, "max_channels": 64, "downsample_factors": [4, 4, 4]},
-    print_eval=True,
-    data_path="tests/data/ljspeech",
-    output_path=output_path,
-)
-config.audio.do_trim_silence = True
-config.audio.trim_db = 60
-config.save_json(config_path)
-
-# train the model for one epoch
-command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --config_path {config_path} "
-run_cli(command_train)
-
-# Find latest folder
-continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
-
-# restore the model and continue training for one more epoch
-command_train = (
-    f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_vocoder.py --continue_path {continue_path} "
-)
-run_cli(command_train)
-shutil.rmtree(continue_path)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Build/Inline.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Build/Inline.py
deleted file mode 100644
index db6d2640a5edaa56efbc8f563fb6dab015126477..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Build/Inline.py
+++ /dev/null
@@ -1,376 +0,0 @@
-from __future__ import absolute_import
-
-import hashlib
-import inspect
-import os
-import re
-import sys
-
-from distutils.core import Distribution, Extension
-from distutils.command.build_ext import build_ext
-
-import Cython
-from ..Compiler.Main import Context, default_options
-
-from ..Compiler.Visitor import CythonTransform, EnvTransform
-from ..Compiler.ParseTreeTransforms import SkipDeclarations
-from ..Compiler.TreeFragment import parse_from_strings
-from ..Compiler.StringEncoding import _unicode
-from .Dependencies import strip_string_literals, cythonize, cached_function
-from ..Compiler import Pipeline
-from ..Utils import get_cython_cache_dir
-import cython as cython_module
-
-
-IS_PY3 = sys.version_info >= (3,)
-
-# A utility function to convert user-supplied ASCII strings to unicode.
-if not IS_PY3:
-    def to_unicode(s):
-        if isinstance(s, bytes):
-            return s.decode('ascii')
-        else:
-            return s
-else:
-    to_unicode = lambda x: x
-
-if sys.version_info < (3, 5):
-    import imp
-    def load_dynamic(name, module_path):
-        return imp.load_dynamic(name, module_path)
-else:
-    import importlib.util as _importlib_util
-    def load_dynamic(name, module_path):
-        spec = _importlib_util.spec_from_file_location(name, module_path)
-        module = _importlib_util.module_from_spec(spec)
-        # sys.modules[name] = module
-        spec.loader.exec_module(module)
-        return module
-
-class UnboundSymbols(EnvTransform, SkipDeclarations):
-    def __init__(self):
-        CythonTransform.__init__(self, None)
-        self.unbound = set()
-    def visit_NameNode(self, node):
-        if not self.current_env().lookup(node.name):
-            self.unbound.add(node.name)
-        return node
-    def __call__(self, node):
-        super(UnboundSymbols, self).__call__(node)
-        return self.unbound
-
-
-@cached_function
-def unbound_symbols(code, context=None):
-    code = to_unicode(code)
-    if context is None:
-        context = Context([], default_options)
-    from ..Compiler.ParseTreeTransforms import AnalyseDeclarationsTransform
-    tree = parse_from_strings('(tree fragment)', code)
-    for phase in Pipeline.create_pipeline(context, 'pyx'):
-        if phase is None:
-            continue
-        tree = phase(tree)
-        if isinstance(phase, AnalyseDeclarationsTransform):
-            break
-    try:
-        import builtins
-    except ImportError:
-        import __builtin__ as builtins
-    return tuple(UnboundSymbols()(tree) - set(dir(builtins)))
-
-
-def unsafe_type(arg, context=None):
-    py_type = type(arg)
-    if py_type is int:
-        return 'long'
-    else:
-        return safe_type(arg, context)
-
-
-def safe_type(arg, context=None):
-    py_type = type(arg)
-    if py_type in (list, tuple, dict, str):
-        return py_type.__name__
-    elif py_type is complex:
-        return 'double complex'
-    elif py_type is float:
-        return 'double'
-    elif py_type is bool:
-        return 'bint'
-    elif 'numpy' in sys.modules and isinstance(arg, sys.modules['numpy'].ndarray):
-        return 'numpy.ndarray[numpy.%s_t, ndim=%s]' % (arg.dtype.name, arg.ndim)
-    else:
-        for base_type in py_type.__mro__:
-            if base_type.__module__ in ('__builtin__', 'builtins'):
-                return 'object'
-            module = context.find_module(base_type.__module__, need_pxd=False)
-            if module:
-                entry = module.lookup(base_type.__name__)
-                if entry.is_type:
-                    return '%s.%s' % (base_type.__module__, base_type.__name__)
-        return 'object'
-
-
-def _get_build_extension():
-    dist = Distribution()
-    # Ensure the build respects distutils configuration by parsing
-    # the configuration files
-    config_files = dist.find_config_files()
-    dist.parse_config_files(config_files)
-    build_extension = build_ext(dist)
-    build_extension.finalize_options()
-    return build_extension
-
-
-@cached_function
-def _create_context(cython_include_dirs):
-    return Context(list(cython_include_dirs), default_options)
-
-
-_cython_inline_cache = {}
-_cython_inline_default_context = _create_context(('.',))
-
-
-def _populate_unbound(kwds, unbound_symbols, locals=None, globals=None):
-    for symbol in unbound_symbols:
-        if symbol not in kwds:
-            if locals is None or globals is None:
-                calling_frame = inspect.currentframe().f_back.f_back.f_back
-                if locals is None:
-                    locals = calling_frame.f_locals
-                if globals is None:
-                    globals = calling_frame.f_globals
-            if symbol in locals:
-                kwds[symbol] = locals[symbol]
-            elif symbol in globals:
-                kwds[symbol] = globals[symbol]
-            else:
-                print("Couldn't find %r" % symbol)
-
-
-def _inline_key(orig_code, arg_sigs, language_level):
-    key = orig_code, arg_sigs, sys.version_info, sys.executable, language_level, Cython.__version__
-    return hashlib.sha1(_unicode(key).encode('utf-8')).hexdigest()
-
-
-def cython_inline(code, get_type=unsafe_type,
-                  lib_dir=os.path.join(get_cython_cache_dir(), 'inline'),
-                  cython_include_dirs=None, cython_compiler_directives=None,
-                  force=False, quiet=False, locals=None, globals=None, language_level=None, **kwds):
-
-    if get_type is None:
-        get_type = lambda x: 'object'
-    ctx = _create_context(tuple(cython_include_dirs)) if cython_include_dirs else _cython_inline_default_context
-
-    cython_compiler_directives = dict(cython_compiler_directives) if cython_compiler_directives else {}
-    if language_level is None and 'language_level' not in cython_compiler_directives:
-        language_level = '3str'
-    if language_level is not None:
-        cython_compiler_directives['language_level'] = language_level
-
-    # Fast path if this has been called in this session.
-    _unbound_symbols = _cython_inline_cache.get(code)
-    if _unbound_symbols is not None:
-        _populate_unbound(kwds, _unbound_symbols, locals, globals)
-        args = sorted(kwds.items())
-        arg_sigs = tuple([(get_type(value, ctx), arg) for arg, value in args])
-        key_hash = _inline_key(code, arg_sigs, language_level)
-        invoke = _cython_inline_cache.get((code, arg_sigs, key_hash))
-        if invoke is not None:
-            arg_list = [arg[1] for arg in args]
-            return invoke(*arg_list)
-
-    orig_code = code
-    code = to_unicode(code)
-    code, literals = strip_string_literals(code)
-    code = strip_common_indent(code)
-    if locals is None:
-        locals = inspect.currentframe().f_back.f_back.f_locals
-    if globals is None:
-        globals = inspect.currentframe().f_back.f_back.f_globals
-    try:
-        _cython_inline_cache[orig_code] = _unbound_symbols = unbound_symbols(code)
-        _populate_unbound(kwds, _unbound_symbols, locals, globals)
-    except AssertionError:
-        if not quiet:
-            # Parsing from strings not fully supported (e.g. cimports).
-            print("Could not parse code as a string (to extract unbound symbols).")
-
-    cimports = []
-    for name, arg in list(kwds.items()):
-        if arg is cython_module:
-            cimports.append('\ncimport cython as %s' % name)
-            del kwds[name]
-    arg_names = sorted(kwds)
-    arg_sigs = tuple([(get_type(kwds[arg], ctx), arg) for arg in arg_names])
-    key_hash = _inline_key(orig_code, arg_sigs, language_level)
-    module_name = "_cython_inline_" + key_hash
-
-    if module_name in sys.modules:
-        module = sys.modules[module_name]
-
-    else:
-        build_extension = None
-        if cython_inline.so_ext is None:
-            # Figure out and cache current extension suffix
-            build_extension = _get_build_extension()
-            cython_inline.so_ext = build_extension.get_ext_filename('')
-
-        module_path = os.path.join(lib_dir, module_name + cython_inline.so_ext)
-
-        if not os.path.exists(lib_dir):
-            os.makedirs(lib_dir)
-        if force or not os.path.isfile(module_path):
-            cflags = []
-            c_include_dirs = []
-            qualified = re.compile(r'([.\w]+)[.]')
-            for type, _ in arg_sigs:
-                m = qualified.match(type)
-                if m:
-                    cimports.append('\ncimport %s' % m.groups()[0])
-                    # one special case
-                    if m.groups()[0] == 'numpy':
-                        import numpy
-                        c_include_dirs.append(numpy.get_include())
-                        # cflags.append('-Wno-unused')
-            module_body, func_body = extract_func_code(code)
-            params = ', '.join(['%s %s' % a for a in arg_sigs])
-            module_code = """
-%(module_body)s
-%(cimports)s
-def __invoke(%(params)s):
-%(func_body)s
-    return locals()
-            """ % {'cimports': '\n'.join(cimports),
-                   'module_body': module_body,
-                   'params': params,
-                   'func_body': func_body }
-            for key, value in literals.items():
-                module_code = module_code.replace(key, value)
-            pyx_file = os.path.join(lib_dir, module_name + '.pyx')
-            fh = open(pyx_file, 'w')
-            try:
-                fh.write(module_code)
-            finally:
-                fh.close()
-            extension = Extension(
-                name = module_name,
-                sources = [pyx_file],
-                include_dirs = c_include_dirs,
-                extra_compile_args = cflags)
-            if build_extension is None:
-                build_extension = _get_build_extension()
-            build_extension.extensions = cythonize(
-                [extension],
-                include_path=cython_include_dirs or ['.'],
-                compiler_directives=cython_compiler_directives,
-                quiet=quiet)
-            build_extension.build_temp = os.path.dirname(pyx_file)
-            build_extension.build_lib  = lib_dir
-            build_extension.run()
-
-        module = load_dynamic(module_name, module_path)
-
-    _cython_inline_cache[orig_code, arg_sigs, key_hash] = module.__invoke
-    arg_list = [kwds[arg] for arg in arg_names]
-    return module.__invoke(*arg_list)
-
-
-# Cached suffix used by cython_inline above.  None should get
-# overridden with actual value upon the first cython_inline invocation
-cython_inline.so_ext = None
-
-_find_non_space = re.compile('[^ ]').search
-
-
-def strip_common_indent(code):
-    min_indent = None
-    lines = code.splitlines()
-    for line in lines:
-        match = _find_non_space(line)
-        if not match:
-            continue  # blank
-        indent = match.start()
-        if line[indent] == '#':
-            continue  # comment
-        if min_indent is None or min_indent > indent:
-            min_indent = indent
-    for ix, line in enumerate(lines):
-        match = _find_non_space(line)
-        if not match or not line or line[indent:indent+1] == '#':
-            continue
-        lines[ix] = line[min_indent:]
-    return '\n'.join(lines)
-
-
-module_statement = re.compile(r'^((cdef +(extern|class))|cimport|(from .+ cimport)|(from .+ import +[*]))')
-def extract_func_code(code):
-    module = []
-    function = []
-    current = function
-    code = code.replace('\t', ' ')
-    lines = code.split('\n')
-    for line in lines:
-        if not line.startswith(' '):
-            if module_statement.match(line):
-                current = module
-            else:
-                current = function
-        current.append(line)
-    return '\n'.join(module), '    ' + '\n    '.join(function)
-
-
-try:
-    from inspect import getcallargs
-except ImportError:
-    def getcallargs(func, *arg_values, **kwd_values):
-        all = {}
-        args, varargs, kwds, defaults = inspect.getargspec(func)
-        if varargs is not None:
-            all[varargs] = arg_values[len(args):]
-        for name, value in zip(args, arg_values):
-            all[name] = value
-        for name, value in list(kwd_values.items()):
-            if name in args:
-                if name in all:
-                    raise TypeError("Duplicate argument %s" % name)
-                all[name] = kwd_values.pop(name)
-        if kwds is not None:
-            all[kwds] = kwd_values
-        elif kwd_values:
-            raise TypeError("Unexpected keyword arguments: %s" % list(kwd_values))
-        if defaults is None:
-            defaults = ()
-        first_default = len(args) - len(defaults)
-        for ix, name in enumerate(args):
-            if name not in all:
-                if ix >= first_default:
-                    all[name] = defaults[ix - first_default]
-                else:
-                    raise TypeError("Missing argument: %s" % name)
-        return all
-
-
-def get_body(source):
-    ix = source.index(':')
-    if source[:5] == 'lambda':
-        return "return %s" % source[ix+1:]
-    else:
-        return source[ix+1:]
-
-
-# Lots to be done here... It would be especially cool if compiled functions
-# could invoke each other quickly.
-class RuntimeCompiledFunction(object):
-
-    def __init__(self, f):
-        self._f = f
-        self._body = get_body(inspect.getsource(f))
-
-    def __call__(self, *args, **kwds):
-        all = getcallargs(self._f, *args, **kwds)
-        if IS_PY3:
-            return cython_inline(self._body, locals=self._f.__globals__, globals=self._f.__globals__, **all)
-        else:
-            return cython_inline(self._body, locals=self._f.func_globals, globals=self._f.func_globals, **all)
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Utility/Optimize.c b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Utility/Optimize.c
deleted file mode 100644
index 35f3a67c962d1542021ddcb9c0c1df92fff5c089..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/Cython/Utility/Optimize.c
+++ /dev/null
@@ -1,1195 +0,0 @@
-/*
- * Optional optimisations of built-in functions and methods.
- *
- * Required replacements of builtins are in Builtins.c.
- *
- * General object operations and protocols are in ObjectHandling.c.
- */
-
-/////////////// append.proto ///////////////
-
-static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x); /*proto*/
-
-/////////////// append ///////////////
-//@requires: ListAppend
-//@requires: ObjectHandling.c::PyObjectCallMethod1
-
-static CYTHON_INLINE int __Pyx_PyObject_Append(PyObject* L, PyObject* x) {
-    if (likely(PyList_CheckExact(L))) {
-        if (unlikely(__Pyx_PyList_Append(L, x) < 0)) return -1;
-    } else {
-        PyObject* retval = __Pyx_PyObject_CallMethod1(L, PYIDENT("append"), x);
-        if (unlikely(!retval))
-            return -1;
-        Py_DECREF(retval);
-    }
-    return 0;
-}
-
-/////////////// ListAppend.proto ///////////////
-
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
-#endif
-
-/////////////// ListCompAppend.proto ///////////////
-
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
-    PyListObject* L = (PyListObject*) list;
-    Py_ssize_t len = Py_SIZE(list);
-    if (likely(L->allocated > len)) {
-        Py_INCREF(x);
-        PyList_SET_ITEM(list, len, x);
-        __Pyx_SET_SIZE(list, len + 1);
-        return 0;
-    }
-    return PyList_Append(list, x);
-}
-#else
-#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x)
-#endif
-
-//////////////////// ListExtend.proto ////////////////////
-
-static CYTHON_INLINE int __Pyx_PyList_Extend(PyObject* L, PyObject* v) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject* none = _PyList_Extend((PyListObject*)L, v);
-    if (unlikely(!none))
-        return -1;
-    Py_DECREF(none);
-    return 0;
-#else
-    return PyList_SetSlice(L, PY_SSIZE_T_MAX, PY_SSIZE_T_MAX, v);
-#endif
-}
-
-/////////////// pop.proto ///////////////
-
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L); /*proto*/
-
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L); /*proto*/
-#define __Pyx_PyObject_Pop(L) (likely(PyList_CheckExact(L)) ? \
-    __Pyx_PyList_Pop(L) : __Pyx__PyObject_Pop(L))
-
-#else
-#define __Pyx_PyList_Pop(L)  __Pyx__PyObject_Pop(L)
-#define __Pyx_PyObject_Pop(L)  __Pyx__PyObject_Pop(L)
-#endif
-
-/////////////// pop ///////////////
-//@requires: ObjectHandling.c::PyObjectCallMethod0
-
-static CYTHON_INLINE PyObject* __Pyx__PyObject_Pop(PyObject* L) {
-    if (Py_TYPE(L) == &PySet_Type) {
-        return PySet_Pop(L);
-    }
-    return __Pyx_PyObject_CallMethod0(L, PYIDENT("pop"));
-}
-
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static CYTHON_INLINE PyObject* __Pyx_PyList_Pop(PyObject* L) {
-    /* Check that both the size is positive and no reallocation shrinking needs to be done. */
-    if (likely(PyList_GET_SIZE(L) > (((PyListObject*)L)->allocated >> 1))) {
-        __Pyx_SET_SIZE(L, Py_SIZE(L) - 1);
-        return PyList_GET_ITEM(L, PyList_GET_SIZE(L));
-    }
-    return CALL_UNBOUND_METHOD(PyList_Type, "pop", L);
-}
-#endif
-
-
-/////////////// pop_index.proto ///////////////
-
-static PyObject* __Pyx__PyObject_PopNewIndex(PyObject* L, PyObject* py_ix); /*proto*/
-static PyObject* __Pyx__PyObject_PopIndex(PyObject* L, PyObject* py_ix); /*proto*/
-
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static PyObject* __Pyx__PyList_PopIndex(PyObject* L, PyObject* py_ix, Py_ssize_t ix); /*proto*/
-
-#define __Pyx_PyObject_PopIndex(L, py_ix, ix, is_signed, type, to_py_func) ( \
-    (likely(PyList_CheckExact(L) && __Pyx_fits_Py_ssize_t(ix, type, is_signed))) ? \
-        __Pyx__PyList_PopIndex(L, py_ix, ix) : ( \
-        (unlikely((py_ix) == Py_None)) ? __Pyx__PyObject_PopNewIndex(L, to_py_func(ix)) : \
-            __Pyx__PyObject_PopIndex(L, py_ix)))
-
-#define __Pyx_PyList_PopIndex(L, py_ix, ix, is_signed, type, to_py_func) ( \
-    __Pyx_fits_Py_ssize_t(ix, type, is_signed) ? \
-        __Pyx__PyList_PopIndex(L, py_ix, ix) : ( \
-        (unlikely((py_ix) == Py_None)) ? __Pyx__PyObject_PopNewIndex(L, to_py_func(ix)) : \
-            __Pyx__PyObject_PopIndex(L, py_ix)))
-
-#else
-
-#define __Pyx_PyList_PopIndex(L, py_ix, ix, is_signed, type, to_py_func) \
-    __Pyx_PyObject_PopIndex(L, py_ix, ix, is_signed, type, to_py_func)
-
-#define __Pyx_PyObject_PopIndex(L, py_ix, ix, is_signed, type, to_py_func) ( \
-    (unlikely((py_ix) == Py_None)) ? __Pyx__PyObject_PopNewIndex(L, to_py_func(ix)) : \
-        __Pyx__PyObject_PopIndex(L, py_ix))
-#endif
-
-/////////////// pop_index ///////////////
-//@requires: ObjectHandling.c::PyObjectCallMethod1
-
-static PyObject* __Pyx__PyObject_PopNewIndex(PyObject* L, PyObject* py_ix) {
-    PyObject *r;
-    if (unlikely(!py_ix)) return NULL;
-    r = __Pyx__PyObject_PopIndex(L, py_ix);
-    Py_DECREF(py_ix);
-    return r;
-}
-
-static PyObject* __Pyx__PyObject_PopIndex(PyObject* L, PyObject* py_ix) {
-    return __Pyx_PyObject_CallMethod1(L, PYIDENT("pop"), py_ix);
-}
-
-#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
-static PyObject* __Pyx__PyList_PopIndex(PyObject* L, PyObject* py_ix, Py_ssize_t ix) {
-    Py_ssize_t size = PyList_GET_SIZE(L);
-    if (likely(size > (((PyListObject*)L)->allocated >> 1))) {
-        Py_ssize_t cix = ix;
-        if (cix < 0) {
-            cix += size;
-        }
-        if (likely(__Pyx_is_valid_index(cix, size))) {
-            PyObject* v = PyList_GET_ITEM(L, cix);
-            __Pyx_SET_SIZE(L, Py_SIZE(L) - 1);
-            size -= 1;
-            memmove(&PyList_GET_ITEM(L, cix), &PyList_GET_ITEM(L, cix+1), (size_t)(size-cix)*sizeof(PyObject*));
-            return v;
-        }
-    }
-    if (py_ix == Py_None) {
-        return __Pyx__PyObject_PopNewIndex(L, PyInt_FromSsize_t(ix));
-    } else {
-        return __Pyx__PyObject_PopIndex(L, py_ix);
-    }
-}
-#endif
-
-
-/////////////// dict_getitem_default.proto ///////////////
-
-static PyObject* __Pyx_PyDict_GetItemDefault(PyObject* d, PyObject* key, PyObject* default_value); /*proto*/
-
-/////////////// dict_getitem_default ///////////////
-
-static PyObject* __Pyx_PyDict_GetItemDefault(PyObject* d, PyObject* key, PyObject* default_value) {
-    PyObject* value;
-#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-    value = PyDict_GetItemWithError(d, key);
-    if (unlikely(!value)) {
-        if (unlikely(PyErr_Occurred()))
-            return NULL;
-        value = default_value;
-    }
-    Py_INCREF(value);
-    // avoid C compiler warning about unused utility functions
-    if ((1));
-#else
-    if (PyString_CheckExact(key) || PyUnicode_CheckExact(key) || PyInt_CheckExact(key)) {
-        /* these presumably have safe hash functions */
-        value = PyDict_GetItem(d, key);
-        if (unlikely(!value)) {
-            value = default_value;
-        }
-        Py_INCREF(value);
-    }
-#endif
-    else {
-        if (default_value == Py_None)
-            value = CALL_UNBOUND_METHOD(PyDict_Type, "get", d, key);
-        else
-            value = CALL_UNBOUND_METHOD(PyDict_Type, "get", d, key, default_value);
-    }
-    return value;
-}
-
-
-/////////////// dict_setdefault.proto ///////////////
-
-static CYTHON_INLINE PyObject *__Pyx_PyDict_SetDefault(PyObject *d, PyObject *key, PyObject *default_value, int is_safe_type); /*proto*/
-
-/////////////// dict_setdefault ///////////////
-
-static CYTHON_INLINE PyObject *__Pyx_PyDict_SetDefault(PyObject *d, PyObject *key, PyObject *default_value,
-                                                       CYTHON_UNUSED int is_safe_type) {
-    PyObject* value;
-#if PY_VERSION_HEX >= 0x030400A0
-    // we keep the method call at the end to avoid "unused" C compiler warnings
-    if ((1)) {
-        value = PyDict_SetDefault(d, key, default_value);
-        if (unlikely(!value)) return NULL;
-        Py_INCREF(value);
-#else
-    if (is_safe_type == 1 || (is_safe_type == -1 &&
-        /* the following builtins presumably have repeatably safe and fast hash functions */
-#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY
-            (PyUnicode_CheckExact(key) || PyString_CheckExact(key) || PyLong_CheckExact(key)))) {
-        value = PyDict_GetItemWithError(d, key);
-        if (unlikely(!value)) {
-            if (unlikely(PyErr_Occurred()))
-                return NULL;
-            if (unlikely(PyDict_SetItem(d, key, default_value) == -1))
-                return NULL;
-            value = default_value;
-        }
-        Py_INCREF(value);
-#else
-            (PyString_CheckExact(key) || PyUnicode_CheckExact(key) || PyInt_CheckExact(key) || PyLong_CheckExact(key)))) {
-        value = PyDict_GetItem(d, key);
-        if (unlikely(!value)) {
-            if (unlikely(PyDict_SetItem(d, key, default_value) == -1))
-                return NULL;
-            value = default_value;
-        }
-        Py_INCREF(value);
-#endif
-#endif
-    } else {
-        value = CALL_UNBOUND_METHOD(PyDict_Type, "setdefault", d, key, default_value);
-    }
-    return value;
-}
-
-
-/////////////// py_dict_clear.proto ///////////////
-
-#define __Pyx_PyDict_Clear(d) (PyDict_Clear(d), 0)
-
-
-/////////////// py_dict_pop.proto ///////////////
-
-static CYTHON_INLINE PyObject *__Pyx_PyDict_Pop(PyObject *d, PyObject *key, PyObject *default_value); /*proto*/
-
-/////////////// py_dict_pop ///////////////
-
-static CYTHON_INLINE PyObject *__Pyx_PyDict_Pop(PyObject *d, PyObject *key, PyObject *default_value) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B3
-    if ((1)) {
-        return _PyDict_Pop(d, key, default_value);
-    } else
-    // avoid "function unused" warnings
-#endif
-    if (default_value) {
-        return CALL_UNBOUND_METHOD(PyDict_Type, "pop", d, key, default_value);
-    } else {
-        return CALL_UNBOUND_METHOD(PyDict_Type, "pop", d, key);
-    }
-}
-
-
-/////////////// dict_iter.proto ///////////////
-
-static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* dict, int is_dict, PyObject* method_name,
-                                                   Py_ssize_t* p_orig_length, int* p_is_dict);
-static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* dict_or_iter, Py_ssize_t orig_length, Py_ssize_t* ppos,
-                                              PyObject** pkey, PyObject** pvalue, PyObject** pitem, int is_dict);
-
-/////////////// dict_iter ///////////////
-//@requires: ObjectHandling.c::UnpackTuple2
-//@requires: ObjectHandling.c::IterFinish
-//@requires: ObjectHandling.c::PyObjectCallMethod0
-
-static CYTHON_INLINE PyObject* __Pyx_dict_iterator(PyObject* iterable, int is_dict, PyObject* method_name,
-                                                   Py_ssize_t* p_orig_length, int* p_source_is_dict) {
-    is_dict = is_dict || likely(PyDict_CheckExact(iterable));
-    *p_source_is_dict = is_dict;
-    if (is_dict) {
-#if !CYTHON_COMPILING_IN_PYPY
-        *p_orig_length = PyDict_Size(iterable);
-        Py_INCREF(iterable);
-        return iterable;
-#elif PY_MAJOR_VERSION >= 3
-        // On PyPy3, we need to translate manually a few method names.
-        // This logic is not needed on CPython thanks to the fast case above.
-        static PyObject *py_items = NULL, *py_keys = NULL, *py_values = NULL;
-        PyObject **pp = NULL;
-        if (method_name) {
-            const char *name = PyUnicode_AsUTF8(method_name);
-            if (strcmp(name, "iteritems") == 0) pp = &py_items;
-            else if (strcmp(name, "iterkeys") == 0) pp = &py_keys;
-            else if (strcmp(name, "itervalues") == 0) pp = &py_values;
-            if (pp) {
-                if (!*pp) {
-                    *pp = PyUnicode_FromString(name + 4);
-                    if (!*pp)
-                        return NULL;
-                }
-                method_name = *pp;
-            }
-        }
-#endif
-    }
-    *p_orig_length = 0;
-    if (method_name) {
-        PyObject* iter;
-        iterable = __Pyx_PyObject_CallMethod0(iterable, method_name);
-        if (!iterable)
-            return NULL;
-#if !CYTHON_COMPILING_IN_PYPY
-        if (PyTuple_CheckExact(iterable) || PyList_CheckExact(iterable))
-            return iterable;
-#endif
-        iter = PyObject_GetIter(iterable);
-        Py_DECREF(iterable);
-        return iter;
-    }
-    return PyObject_GetIter(iterable);
-}
-
-static CYTHON_INLINE int __Pyx_dict_iter_next(
-        PyObject* iter_obj, CYTHON_NCP_UNUSED Py_ssize_t orig_length, CYTHON_NCP_UNUSED Py_ssize_t* ppos,
-        PyObject** pkey, PyObject** pvalue, PyObject** pitem, int source_is_dict) {
-    PyObject* next_item;
-#if !CYTHON_COMPILING_IN_PYPY
-    if (source_is_dict) {
-        PyObject *key, *value;
-        if (unlikely(orig_length != PyDict_Size(iter_obj))) {
-            PyErr_SetString(PyExc_RuntimeError, "dictionary changed size during iteration");
-            return -1;
-        }
-        if (unlikely(!PyDict_Next(iter_obj, ppos, &key, &value))) {
-            return 0;
-        }
-        if (pitem) {
-            PyObject* tuple = PyTuple_New(2);
-            if (unlikely(!tuple)) {
-                return -1;
-            }
-            Py_INCREF(key);
-            Py_INCREF(value);
-            PyTuple_SET_ITEM(tuple, 0, key);
-            PyTuple_SET_ITEM(tuple, 1, value);
-            *pitem = tuple;
-        } else {
-            if (pkey) {
-                Py_INCREF(key);
-                *pkey = key;
-            }
-            if (pvalue) {
-                Py_INCREF(value);
-                *pvalue = value;
-            }
-        }
-        return 1;
-    } else if (PyTuple_CheckExact(iter_obj)) {
-        Py_ssize_t pos = *ppos;
-        if (unlikely(pos >= PyTuple_GET_SIZE(iter_obj))) return 0;
-        *ppos = pos + 1;
-        next_item = PyTuple_GET_ITEM(iter_obj, pos);
-        Py_INCREF(next_item);
-    } else if (PyList_CheckExact(iter_obj)) {
-        Py_ssize_t pos = *ppos;
-        if (unlikely(pos >= PyList_GET_SIZE(iter_obj))) return 0;
-        *ppos = pos + 1;
-        next_item = PyList_GET_ITEM(iter_obj, pos);
-        Py_INCREF(next_item);
-    } else
-#endif
-    {
-        next_item = PyIter_Next(iter_obj);
-        if (unlikely(!next_item)) {
-            return __Pyx_IterFinish();
-        }
-    }
-    if (pitem) {
-        *pitem = next_item;
-    } else if (pkey && pvalue) {
-        if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1))
-            return -1;
-    } else if (pkey) {
-        *pkey = next_item;
-    } else {
-        *pvalue = next_item;
-    }
-    return 1;
-}
-
-
-/////////////// set_iter.proto ///////////////
-
-static CYTHON_INLINE PyObject* __Pyx_set_iterator(PyObject* iterable, int is_set,
-                                                  Py_ssize_t* p_orig_length, int* p_source_is_set); /*proto*/
-static CYTHON_INLINE int __Pyx_set_iter_next(
-        PyObject* iter_obj, Py_ssize_t orig_length,
-        Py_ssize_t* ppos, PyObject **value,
-        int source_is_set); /*proto*/
-
-/////////////// set_iter ///////////////
-//@requires: ObjectHandling.c::IterFinish
-
-static CYTHON_INLINE PyObject* __Pyx_set_iterator(PyObject* iterable, int is_set,
-                                                  Py_ssize_t* p_orig_length, int* p_source_is_set) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    is_set = is_set || likely(PySet_CheckExact(iterable) || PyFrozenSet_CheckExact(iterable));
-    *p_source_is_set = is_set;
-    if (likely(is_set)) {
-        *p_orig_length = PySet_Size(iterable);
-        Py_INCREF(iterable);
-        return iterable;
-    }
-#else
-    (void)is_set;
-    *p_source_is_set = 0;
-#endif
-    *p_orig_length = 0;
-    return PyObject_GetIter(iterable);
-}
-
-static CYTHON_INLINE int __Pyx_set_iter_next(
-        PyObject* iter_obj, Py_ssize_t orig_length,
-        Py_ssize_t* ppos, PyObject **value,
-        int source_is_set) {
-    if (!CYTHON_COMPILING_IN_CPYTHON || unlikely(!source_is_set)) {
-        *value = PyIter_Next(iter_obj);
-        if (unlikely(!*value)) {
-            return __Pyx_IterFinish();
-        }
-        (void)orig_length;
-        (void)ppos;
-        return 1;
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (unlikely(PySet_GET_SIZE(iter_obj) != orig_length)) {
-        PyErr_SetString(
-            PyExc_RuntimeError,
-            "set changed size during iteration");
-        return -1;
-    }
-    {
-        Py_hash_t hash;
-        int ret = _PySet_NextEntry(iter_obj, ppos, value, &hash);
-        // CPython does not raise errors here, only if !isinstance(iter_obj, set/frozenset)
-        assert (ret != -1);
-        if (likely(ret)) {
-            Py_INCREF(*value);
-            return 1;
-        }
-    }
-#endif
-    return 0;
-}
-
-/////////////// py_set_discard_unhashable ///////////////
-//@requires: Builtins.c::pyfrozenset_new
-
-static int __Pyx_PySet_DiscardUnhashable(PyObject *set, PyObject *key) {
-    PyObject *tmpkey;
-    int rv;
-
-    if (likely(!PySet_Check(key) || !PyErr_ExceptionMatches(PyExc_TypeError)))
-        return -1;
-    PyErr_Clear();
-    tmpkey = __Pyx_PyFrozenSet_New(key);
-    if (tmpkey == NULL)
-        return -1;
-    rv = PySet_Discard(set, tmpkey);
-    Py_DECREF(tmpkey);
-    return rv;
-}
-
-
-/////////////// py_set_discard.proto ///////////////
-
-static CYTHON_INLINE int __Pyx_PySet_Discard(PyObject *set, PyObject *key); /*proto*/
-
-/////////////// py_set_discard ///////////////
-//@requires: py_set_discard_unhashable
-
-static CYTHON_INLINE int __Pyx_PySet_Discard(PyObject *set, PyObject *key) {
-    int found = PySet_Discard(set, key);
-    // Convert *key* to frozenset if necessary
-    if (unlikely(found < 0)) {
-        found = __Pyx_PySet_DiscardUnhashable(set, key);
-    }
-    // note: returns -1 on error, 0 (not found) or 1 (found) otherwise => error check for -1 or < 0 works
-    return found;
-}
-
-
-/////////////// py_set_remove.proto ///////////////
-
-static CYTHON_INLINE int __Pyx_PySet_Remove(PyObject *set, PyObject *key); /*proto*/
-
-/////////////// py_set_remove ///////////////
-//@requires: py_set_discard_unhashable
-
-static int __Pyx_PySet_RemoveNotFound(PyObject *set, PyObject *key, int found) {
-    // Convert *key* to frozenset if necessary
-    if (unlikely(found < 0)) {
-        found = __Pyx_PySet_DiscardUnhashable(set, key);
-    }
-    if (likely(found == 0)) {
-        // Not found
-        PyObject *tup;
-        tup = PyTuple_Pack(1, key);
-        if (!tup)
-            return -1;
-        PyErr_SetObject(PyExc_KeyError, tup);
-        Py_DECREF(tup);
-        return -1;
-    }
-    // note: returns -1 on error, 0 (not found) or 1 (found) otherwise => error check for -1 or < 0 works
-    return found;
-}
-
-static CYTHON_INLINE int __Pyx_PySet_Remove(PyObject *set, PyObject *key) {
-    int found = PySet_Discard(set, key);
-    if (unlikely(found != 1)) {
-        // note: returns -1 on error, 0 (not found) or 1 (found) otherwise => error check for -1 or < 0 works
-        return __Pyx_PySet_RemoveNotFound(set, key, found);
-    }
-    return 0;
-}
-
-
-/////////////// unicode_iter.proto ///////////////
-
-static CYTHON_INLINE int __Pyx_init_unicode_iteration(
-    PyObject* ustring, Py_ssize_t *length, void** data, int *kind); /* proto */
-
-/////////////// unicode_iter ///////////////
-
-static CYTHON_INLINE int __Pyx_init_unicode_iteration(
-    PyObject* ustring, Py_ssize_t *length, void** data, int *kind) {
-#if CYTHON_PEP393_ENABLED
-    if (unlikely(__Pyx_PyUnicode_READY(ustring) < 0)) return -1;
-    *kind   = PyUnicode_KIND(ustring);
-    *length = PyUnicode_GET_LENGTH(ustring);
-    *data   = PyUnicode_DATA(ustring);
-#else
-    *kind   = 0;
-    *length = PyUnicode_GET_SIZE(ustring);
-    *data   = (void*)PyUnicode_AS_UNICODE(ustring);
-#endif
-    return 0;
-}
-
-/////////////// pyobject_as_double.proto ///////////////
-
-static double __Pyx__PyObject_AsDouble(PyObject* obj); /* proto */
-
-#if CYTHON_COMPILING_IN_PYPY
-#define __Pyx_PyObject_AsDouble(obj) \
-(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \
- likely(PyInt_CheckExact(obj)) ? \
- PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
-#else
-#define __Pyx_PyObject_AsDouble(obj) \
-((likely(PyFloat_CheckExact(obj))) ? \
- PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj))
-#endif
-
-/////////////// pyobject_as_double ///////////////
-
-static double __Pyx__PyObject_AsDouble(PyObject* obj) {
-    PyObject* float_value;
-#if !CYTHON_USE_TYPE_SLOTS
-    float_value = PyNumber_Float(obj);  if ((0)) goto bad;
-#else
-    PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
-    if (likely(nb) && likely(nb->nb_float)) {
-        float_value = nb->nb_float(obj);
-        if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
-            PyErr_Format(PyExc_TypeError,
-                "__float__ returned non-float (type %.200s)",
-                Py_TYPE(float_value)->tp_name);
-            Py_DECREF(float_value);
-            goto bad;
-        }
-    } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
-#if PY_MAJOR_VERSION >= 3
-        float_value = PyFloat_FromString(obj);
-#else
-        float_value = PyFloat_FromString(obj, 0);
-#endif
-    } else {
-        PyObject* args = PyTuple_New(1);
-        if (unlikely(!args)) goto bad;
-        PyTuple_SET_ITEM(args, 0, obj);
-        float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
-        PyTuple_SET_ITEM(args, 0, 0);
-        Py_DECREF(args);
-    }
-#endif
-    if (likely(float_value)) {
-        double value = PyFloat_AS_DOUBLE(float_value);
-        Py_DECREF(float_value);
-        return value;
-    }
-bad:
-    return (double)-1;
-}
-
-
-/////////////// PyNumberPow2.proto ///////////////
-
-#define __Pyx_PyNumber_InPlacePowerOf2(a, b, c) __Pyx__PyNumber_PowerOf2(a, b, c, 1)
-#define __Pyx_PyNumber_PowerOf2(a, b, c) __Pyx__PyNumber_PowerOf2(a, b, c, 0)
-
-static PyObject* __Pyx__PyNumber_PowerOf2(PyObject *two, PyObject *exp, PyObject *none, int inplace); /*proto*/
-
-/////////////// PyNumberPow2 ///////////////
-
-static PyObject* __Pyx__PyNumber_PowerOf2(PyObject *two, PyObject *exp, PyObject *none, int inplace) {
-// in CPython, 1<<N is substantially faster than 2**N
-// see http://bugs.python.org/issue21420
-#if !CYTHON_COMPILING_IN_PYPY
-    Py_ssize_t shiftby;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact(exp))) {
-        shiftby = PyInt_AS_LONG(exp);
-    } else
-#endif
-    if (likely(PyLong_CheckExact(exp))) {
-        #if CYTHON_USE_PYLONG_INTERNALS
-        const Py_ssize_t size = Py_SIZE(exp);
-        // tuned to optimise branch prediction
-        if (likely(size == 1)) {
-            shiftby = ((PyLongObject*)exp)->ob_digit[0];
-        } else if (size == 0) {
-            return PyInt_FromLong(1L);
-        } else if (unlikely(size < 0)) {
-            goto fallback;
-        } else {
-            shiftby = PyLong_AsSsize_t(exp);
-        }
-        #else
-        shiftby = PyLong_AsSsize_t(exp);
-        #endif
-    } else {
-        goto fallback;
-    }
-    if (likely(shiftby >= 0)) {
-        if ((size_t)shiftby <= sizeof(long) * 8 - 2) {
-            long value = 1L << shiftby;
-            return PyInt_FromLong(value);
-#ifdef HAVE_LONG_LONG
-        } else if ((size_t)shiftby <= sizeof(unsigned PY_LONG_LONG) * 8 - 1) {
-            unsigned PY_LONG_LONG value = ((unsigned PY_LONG_LONG)1) << shiftby;
-            return PyLong_FromUnsignedLongLong(value);
-#endif
-        } else {
-            PyObject *result, *one = PyInt_FromLong(1L);
-            if (unlikely(!one)) return NULL;
-            result = PyNumber_Lshift(one, exp);
-            Py_DECREF(one);
-            return result;
-        }
-    } else if (shiftby == -1 && PyErr_Occurred()) {
-        PyErr_Clear();
-    }
-fallback:
-#endif
-    return (inplace ? PyNumber_InPlacePower : PyNumber_Power)(two, exp, none);
-}
-
-
-/////////////// PyIntCompare.proto ///////////////
-
-{{py: c_ret_type = 'PyObject*' if ret_type.is_pyobject else 'int'}}
-static CYTHON_INLINE {{c_ret_type}} __Pyx_PyInt_{{'' if ret_type.is_pyobject else 'Bool'}}{{op}}{{order}}(PyObject *op1, PyObject *op2, long intval, long inplace); /*proto*/
-
-/////////////// PyIntCompare ///////////////
-
-{{py: pyval, ival = ('op2', 'b') if order == 'CObj' else ('op1', 'a') }}
-{{py: c_ret_type = 'PyObject*' if ret_type.is_pyobject else 'int'}}
-{{py: return_true = 'Py_RETURN_TRUE' if ret_type.is_pyobject else 'return 1'}}
-{{py: return_false = 'Py_RETURN_FALSE' if ret_type.is_pyobject else 'return 0'}}
-{{py: slot_name = op.lower() }}
-{{py: c_op = {'Eq': '==', 'Ne': '!='}[op] }}
-{{py:
-return_compare = (
-    (lambda a,b,c_op, return_true=return_true, return_false=return_false: "if ({a} {c_op} {b}) {return_true}; else {return_false};".format(
-        a=a, b=b, c_op=c_op, return_true=return_true, return_false=return_false))
-    if ret_type.is_pyobject else
-    (lambda a,b,c_op: "return ({a} {c_op} {b});".format(a=a, b=b, c_op=c_op))
-    )
-}}
-
-static CYTHON_INLINE {{c_ret_type}} __Pyx_PyInt_{{'' if ret_type.is_pyobject else 'Bool'}}{{op}}{{order}}(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED long inplace) {
-    if (op1 == op2) {
-        {{return_true if op == 'Eq' else return_false}};
-    }
-
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact({{pyval}}))) {
-        const long {{'a' if order == 'CObj' else 'b'}} = intval;
-        long {{ival}} = PyInt_AS_LONG({{pyval}});
-        {{return_compare('a', 'b', c_op)}}
-    }
-    #endif
-
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact({{pyval}}))) {
-        int unequal;
-        unsigned long uintval;
-        Py_ssize_t size = Py_SIZE({{pyval}});
-        const digit* digits = ((PyLongObject*){{pyval}})->ob_digit;
-        if (intval == 0) {
-            // == 0  =>  Py_SIZE(pyval) == 0
-            {{return_compare('size', '0', c_op)}}
-        } else if (intval < 0) {
-            // < 0  =>  Py_SIZE(pyval) < 0
-            if (size >= 0)
-                {{return_false if op == 'Eq' else return_true}};
-            // both are negative => can use absolute values now.
-            intval = -intval;
-            size = -size;
-        } else {
-            // > 0  =>  Py_SIZE(pyval) > 0
-            if (size <= 0)
-                {{return_false if op == 'Eq' else return_true}};
-        }
-        // After checking that the sign is the same (and excluding 0), now compare the absolute values.
-        // When inlining, the C compiler should select exactly one line from this unrolled loop.
-        uintval = (unsigned long) intval;
-        {{for _size in range(4, 0, -1)}}
-#if PyLong_SHIFT * {{_size}} < SIZEOF_LONG*8
-        if (uintval >> (PyLong_SHIFT * {{_size}})) {
-            // The C integer value is between (PyLong_BASE ** _size) and MIN(PyLong_BASE ** _size, LONG_MAX).
-            unequal = (size != {{_size+1}}) || (digits[0] != (uintval & (unsigned long) PyLong_MASK))
-                {{for _i in range(1, _size+1)}} | (digits[{{_i}}] != ((uintval >> ({{_i}} * PyLong_SHIFT)) & (unsigned long) PyLong_MASK)){{endfor}};
-        } else
-#endif
-        {{endfor}}
-            unequal = (size != 1) || (((unsigned long) digits[0]) != (uintval & (unsigned long) PyLong_MASK));
-
-        {{return_compare('unequal', '0', c_op)}}
-    }
-    #endif
-
-    if (PyFloat_CheckExact({{pyval}})) {
-        const long {{'a' if order == 'CObj' else 'b'}} = intval;
-        double {{ival}} = PyFloat_AS_DOUBLE({{pyval}});
-        {{return_compare('(double)a', '(double)b', c_op)}}
-    }
-
-    return {{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(
-        PyObject_RichCompare(op1, op2, Py_{{op.upper()}}));
-}
-
-
-/////////////// PyIntBinop.proto ///////////////
-
-{{py: c_ret_type = 'PyObject*' if ret_type.is_pyobject else 'int'}}
-#if !CYTHON_COMPILING_IN_PYPY
-static {{c_ret_type}} __Pyx_PyInt_{{'' if ret_type.is_pyobject else 'Bool'}}{{op}}{{order}}(PyObject *op1, PyObject *op2, long intval, int inplace, int zerodivision_check); /*proto*/
-#else
-#define __Pyx_PyInt_{{'' if ret_type.is_pyobject else 'Bool'}}{{op}}{{order}}(op1, op2, intval, inplace, zerodivision_check) \
-    {{if op in ('Eq', 'Ne')}}{{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(PyObject_RichCompare(op1, op2, Py_{{op.upper()}}))
-    {{else}}(inplace ? PyNumber_InPlace{{op}}(op1, op2) : PyNumber_{{op}}(op1, op2))
-    {{endif}}
-#endif
-
-/////////////// PyIntBinop ///////////////
-
-#if !CYTHON_COMPILING_IN_PYPY
-{{py: from Cython.Utility import pylong_join }}
-{{py: pyval, ival = ('op2', 'b') if order == 'CObj' else ('op1', 'a') }}
-{{py: c_ret_type = 'PyObject*' if ret_type.is_pyobject else 'int'}}
-{{py: return_true = 'Py_RETURN_TRUE' if ret_type.is_pyobject else 'return 1'}}
-{{py: return_false = 'Py_RETURN_FALSE' if ret_type.is_pyobject else 'return 0'}}
-{{py: slot_name = {'TrueDivide': 'true_divide', 'FloorDivide': 'floor_divide'}.get(op, op.lower()) }}
-{{py: cfunc_name = '__Pyx_PyInt_%s%s%s' % ('' if ret_type.is_pyobject else 'Bool', op, order)}}
-{{py: zerodiv_check = lambda operand, _cfunc_name=cfunc_name: '%s_ZeroDivisionError(%s)' % (_cfunc_name, operand)}}
-{{py:
-c_op = {
-    'Add': '+', 'Subtract': '-', 'Remainder': '%', 'TrueDivide': '/', 'FloorDivide': '/',
-    'Or': '|', 'Xor': '^', 'And': '&', 'Rshift': '>>', 'Lshift': '<<',
-    'Eq': '==', 'Ne': '!=',
-    }[op]
-}}
-
-{{if op in ('TrueDivide', 'FloorDivide', 'Remainder')}}
-#if PY_MAJOR_VERSION < 3 || CYTHON_USE_PYLONG_INTERNALS
-#define {{zerodiv_check('operand')}} \
-    if (unlikely(zerodivision_check && ((operand) == 0))) { \
-        PyErr_SetString(PyExc_ZeroDivisionError, "integer division{{if op == 'Remainder'}} or modulo{{endif}} by zero"); \
-        return NULL; \
-    }
-#endif
-{{endif}}
-
-static {{c_ret_type}} {{cfunc_name}}(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, int inplace, int zerodivision_check) {
-    // Prevent "unused" warnings.
-    (void)inplace;
-    (void)zerodivision_check;
-
-    {{if op in ('Eq', 'Ne')}}
-    if (op1 == op2) {
-        {{return_true if op == 'Eq' else return_false}};
-    }
-    {{endif}}
-
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact({{pyval}}))) {
-        const long {{'a' if order == 'CObj' else 'b'}} = intval;
-        {{if c_op in '+-%' or op == 'FloorDivide'}}
-        long x;
-        {{endif}}
-        long {{ival}} = PyInt_AS_LONG({{pyval}});
-
-        {{if op in ('Eq', 'Ne')}}
-        if (a {{c_op}} b) {
-            {{return_true}};
-        } else {
-            {{return_false}};
-        }
-        {{elif c_op in '+-'}}
-            // adapted from intobject.c in Py2.7:
-            // casts in the line below avoid undefined behaviour on overflow
-            x = (long)((unsigned long)a {{c_op}} b);
-            if (likely((x^a) >= 0 || (x^{{ '~' if op == 'Subtract' else '' }}b) >= 0))
-                return PyInt_FromLong(x);
-            return PyLong_Type.tp_as_number->nb_{{slot_name}}(op1, op2);
-        {{elif c_op == '%'}}
-            {{zerodiv_check('b')}}
-            // see ExprNodes.py :: mod_int_utility_code
-            x = a % b;
-            x += ((x != 0) & ((x ^ b) < 0)) * b;
-            return PyInt_FromLong(x);
-        {{elif op == 'TrueDivide'}}
-            {{zerodiv_check('b')}}
-            if (8 * sizeof(long) <= 53 || likely(labs({{ival}}) <= ((PY_LONG_LONG)1 << 53))) {
-                return PyFloat_FromDouble((double)a / (double)b);
-            }
-            // let Python do the rounding
-            return PyInt_Type.tp_as_number->nb_{{slot_name}}(op1, op2);
-        {{elif op == 'FloorDivide'}}
-            // INT_MIN / -1  is the only case that overflows, b == 0 is an error case
-            {{zerodiv_check('b')}}
-            if (unlikely(b == -1 && ((unsigned long)a) == 0-(unsigned long)a))
-                return PyInt_Type.tp_as_number->nb_{{slot_name}}(op1, op2);
-            else {
-                long q, r;
-                // see ExprNodes.py :: div_int_utility_code
-                q = a / b;
-                r = a - q*b;
-                q -= ((r != 0) & ((r ^ b) < 0));
-                x = q;
-            }
-            return PyInt_FromLong(x);
-        {{elif op == 'Lshift'}}
-            if (likely(b < (long) (sizeof(long)*8) && a == (a << b) >> b) || !a) {
-                return PyInt_FromLong(a {{c_op}} b);
-            }
-        {{else}}
-            // other operations are safe, no overflow
-            return PyInt_FromLong(a {{c_op}} b);
-        {{endif}}
-    }
-    #endif
-
-    #if CYTHON_USE_PYLONG_INTERNALS
-    if (likely(PyLong_CheckExact({{pyval}}))) {
-        const long {{'a' if order == 'CObj' else 'b'}} = intval;
-        long {{ival}}{{if op not in ('Eq', 'Ne')}}, x{{endif}};
-        {{if op not in ('Eq', 'Ne', 'TrueDivide')}}
-#ifdef HAVE_LONG_LONG
-        const PY_LONG_LONG ll{{'a' if order == 'CObj' else 'b'}} = intval;
-        PY_LONG_LONG ll{{ival}}, llx;
-#endif
-        {{endif}}
-        const digit* digits = ((PyLongObject*){{pyval}})->ob_digit;
-        const Py_ssize_t size = Py_SIZE({{pyval}});
-        // handle most common case first to avoid indirect branch and optimise branch prediction
-        if (likely(__Pyx_sst_abs(size) <= 1)) {
-            {{ival}} = likely(size) ? digits[0] : 0;
-            if (size == -1) {{ival}} = -{{ival}};
-        } else {
-            switch (size) {
-                {{for _size in range(2, 5)}}
-                {{for _case in (-_size, _size)}}
-                case {{_case}}:
-                    if (8 * sizeof(long) - 1 > {{_size}} * PyLong_SHIFT{{if op == 'TrueDivide'}} && {{_size-1}} * PyLong_SHIFT < 53{{endif}}) {
-                        {{ival}} = {{'-' if _case < 0 else ''}}(long) {{pylong_join(_size, 'digits')}};
-                        break;
-                    {{if op not in ('Eq', 'Ne', 'TrueDivide')}}
-#ifdef HAVE_LONG_LONG
-                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > {{_size}} * PyLong_SHIFT) {
-                        ll{{ival}} = {{'-' if _case < 0 else ''}}(PY_LONG_LONG) {{pylong_join(_size, 'digits', 'unsigned PY_LONG_LONG')}};
-                        goto long_long;
-#endif
-                    {{endif}}
-                    }
-                    // if size doesn't fit into a long or PY_LONG_LONG anymore, fall through to default
-                    CYTHON_FALLTHROUGH;
-                {{endfor}}
-                {{endfor}}
-
-                {{if op in ('Eq', 'Ne')}}
-                #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15
-                // unusual setup - your fault
-                default: return {{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(
-                    PyLong_Type.tp_richcompare({{'op1, op2' if order == 'ObjC' else 'op2, op1'}}, Py_{{op.upper()}}));
-                #else
-                // too large for the long values we allow => definitely not equal
-                default: {{return_false if op == 'Eq' else return_true}};
-                #endif
-                {{else}}
-                default: return PyLong_Type.tp_as_number->nb_{{slot_name}}(op1, op2);
-                {{endif}}
-            }
-        }
-        {{if op in ('Eq', 'Ne')}}
-            if (a {{c_op}} b) {
-                {{return_true}};
-            } else {
-                {{return_false}};
-            }
-        {{else}}
-            {{if c_op == '%'}}
-                {{zerodiv_check('b')}}
-                // see ExprNodes.py :: mod_int_utility_code
-                x = a % b;
-                x += ((x != 0) & ((x ^ b) < 0)) * b;
-            {{elif op == 'TrueDivide'}}
-                {{zerodiv_check('b')}}
-                if ((8 * sizeof(long) <= 53 || likely(labs({{ival}}) <= ((PY_LONG_LONG)1 << 53)))
-                        || __Pyx_sst_abs(size) <= 52 / PyLong_SHIFT) {
-                    return PyFloat_FromDouble((double)a / (double)b);
-                }
-                return PyLong_Type.tp_as_number->nb_{{slot_name}}(op1, op2);
-            {{elif op == 'FloorDivide'}}
-                {{zerodiv_check('b')}}
-                {
-                    long q, r;
-                    // see ExprNodes.py :: div_int_utility_code
-                    q = a / b;
-                    r = a - q*b;
-                    q -= ((r != 0) & ((r ^ b) < 0));
-                    x = q;
-                }
-            {{else}}
-                x = a {{c_op}} b;
-                {{if op == 'Lshift'}}
-#ifdef HAVE_LONG_LONG
-                if (unlikely(!(b < (long) (sizeof(long)*8) && a == x >> b)) && a) {
-                    ll{{ival}} = {{ival}};
-                    goto long_long;
-                }
-#else
-                if (likely(b < (long) (sizeof(long)*8) && a == x >> b) || !a) /* execute return statement below */
-#endif
-                {{endif}}
-            {{endif}}
-            return PyLong_FromLong(x);
-
-        {{if op != 'TrueDivide'}}
-#ifdef HAVE_LONG_LONG
-        long_long:
-            {{if c_op == '%'}}
-                // see ExprNodes.py :: mod_int_utility_code
-                llx = lla % llb;
-                llx += ((llx != 0) & ((llx ^ llb) < 0)) * llb;
-            {{elif op == 'FloorDivide'}}
-                {
-                    PY_LONG_LONG q, r;
-                    // see ExprNodes.py :: div_int_utility_code
-                    q = lla / llb;
-                    r = lla - q*llb;
-                    q -= ((r != 0) & ((r ^ llb) < 0));
-                    llx = q;
-                }
-            {{else}}
-                llx = lla {{c_op}} llb;
-                {{if op == 'Lshift'}}
-                if (likely(lla == llx >> llb)) /* then execute 'return' below */
-                {{endif}}
-            {{endif}}
-            return PyLong_FromLongLong(llx);
-#endif
-        {{endif}}{{# if op != 'TrueDivide' #}}
-        {{endif}}{{# if op in ('Eq', 'Ne') #}}
-    }
-    #endif
-
-    {{if c_op in '+-' or op in ('TrueDivide', 'Eq', 'Ne')}}
-    if (PyFloat_CheckExact({{pyval}})) {
-        const long {{'a' if order == 'CObj' else 'b'}} = intval;
-        double {{ival}} = PyFloat_AS_DOUBLE({{pyval}});
-        {{if op in ('Eq', 'Ne')}}
-            if ((double)a {{c_op}} (double)b) {
-                {{return_true}};
-            } else {
-                {{return_false}};
-            }
-        {{else}}
-            double result;
-            {{if op == 'TrueDivide'}}
-            if (unlikely(zerodivision_check && b == 0)) {
-                PyErr_SetString(PyExc_ZeroDivisionError, "float division by zero");
-                return NULL;
-            }
-            {{endif}}
-            // copied from floatobject.c in Py3.5:
-            PyFPE_START_PROTECT("{{op.lower() if not op.endswith('Divide') else 'divide'}}", return NULL)
-            result = ((double)a) {{c_op}} (double)b;
-            PyFPE_END_PROTECT(result)
-            return PyFloat_FromDouble(result);
-        {{endif}}
-    }
-    {{endif}}
-
-    {{if op in ('Eq', 'Ne')}}
-    return {{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(
-        PyObject_RichCompare(op1, op2, Py_{{op.upper()}}));
-    {{else}}
-    return (inplace ? PyNumber_InPlace{{op}} : PyNumber_{{op}})(op1, op2);
-    {{endif}}
-}
-#endif
-
-/////////////// PyFloatBinop.proto ///////////////
-
-{{py: c_ret_type = 'PyObject*' if ret_type.is_pyobject else 'int'}}
-#if !CYTHON_COMPILING_IN_PYPY
-static {{c_ret_type}} __Pyx_PyFloat_{{'' if ret_type.is_pyobject else 'Bool'}}{{op}}{{order}}(PyObject *op1, PyObject *op2, double floatval, int inplace, int zerodivision_check); /*proto*/
-#else
-#define __Pyx_PyFloat_{{'' if ret_type.is_pyobject else 'Bool'}}{{op}}{{order}}(op1, op2, floatval, inplace, zerodivision_check) \
-    {{if op in ('Eq', 'Ne')}}{{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(PyObject_RichCompare(op1, op2, Py_{{op.upper()}}))
-    {{elif op == 'Divide'}}((inplace ? __Pyx_PyNumber_InPlaceDivide(op1, op2) : __Pyx_PyNumber_Divide(op1, op2)))
-    {{else}}(inplace ? PyNumber_InPlace{{op}}(op1, op2) : PyNumber_{{op}}(op1, op2))
-    {{endif}}
-#endif
-
-/////////////// PyFloatBinop ///////////////
-
-#if !CYTHON_COMPILING_IN_PYPY
-{{py: from Cython.Utility import pylong_join }}
-{{py: c_ret_type = 'PyObject*' if ret_type.is_pyobject else 'int'}}
-{{py: return_true = 'Py_RETURN_TRUE' if ret_type.is_pyobject else 'return 1'}}
-{{py: return_false = 'Py_RETURN_FALSE' if ret_type.is_pyobject else 'return 0'}}
-{{py: pyval, fval = ('op2', 'b') if order == 'CObj' else ('op1', 'a') }}
-{{py: cfunc_name = '__Pyx_PyFloat_%s%s%s' % ('' if ret_type.is_pyobject else 'Bool', op, order) }}
-{{py: zerodiv_check = lambda operand, _cfunc_name=cfunc_name: '%s_ZeroDivisionError(%s)' % (_cfunc_name, operand)}}
-{{py:
-c_op = {
-    'Add': '+', 'Subtract': '-', 'TrueDivide': '/', 'Divide': '/', 'Remainder': '%',
-    'Eq': '==', 'Ne': '!=',
-    }[op]
-}}
-
-{{if order == 'CObj' and c_op in '%/'}}
-#define {{zerodiv_check('operand')}} if (unlikely(zerodivision_check && ((operand) == 0))) { \
-    PyErr_SetString(PyExc_ZeroDivisionError, "float division{{if op == 'Remainder'}} or modulo{{endif}} by zero"); \
-    return NULL; \
-}
-{{endif}}
-
-static {{c_ret_type}} {{cfunc_name}}(PyObject *op1, PyObject *op2, double floatval, int inplace, int zerodivision_check) {
-    const double {{'a' if order == 'CObj' else 'b'}} = floatval;
-    double {{fval}}{{if op not in ('Eq', 'Ne')}}, result{{endif}};
-    // Prevent "unused" warnings.
-    (void)inplace;
-    (void)zerodivision_check;
-
-    {{if op in ('Eq', 'Ne')}}
-    if (op1 == op2) {
-        {{return_true if op == 'Eq' else return_false}};
-    }
-    {{endif}}
-
-    if (likely(PyFloat_CheckExact({{pyval}}))) {
-        {{fval}} = PyFloat_AS_DOUBLE({{pyval}});
-        {{if order == 'CObj' and c_op in '%/'}}{{zerodiv_check(fval)}}{{endif}}
-    } else
-
-    #if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_CheckExact({{pyval}}))) {
-        {{fval}} = (double) PyInt_AS_LONG({{pyval}});
-        {{if order == 'CObj' and c_op in '%/'}}{{zerodiv_check(fval)}}{{endif}}
-    } else
-    #endif
-
-    if (likely(PyLong_CheckExact({{pyval}}))) {
-        #if CYTHON_USE_PYLONG_INTERNALS
-        const digit* digits = ((PyLongObject*){{pyval}})->ob_digit;
-        const Py_ssize_t size = Py_SIZE({{pyval}});
-        switch (size) {
-            case  0: {{if order == 'CObj' and c_op in '%/'}}{{zerodiv_check('0')}}{{else}}{{fval}} = 0.0;{{endif}} break;
-            case -1: {{fval}} = -(double) digits[0]; break;
-            case  1: {{fval}} = (double) digits[0]; break;
-            {{for _size in (2, 3, 4)}}
-            case -{{_size}}:
-            case {{_size}}:
-                if (8 * sizeof(unsigned long) > {{_size}} * PyLong_SHIFT && ((8 * sizeof(unsigned long) < 53) || ({{_size-1}} * PyLong_SHIFT < 53))) {
-                    {{fval}} = (double) {{pylong_join(_size, 'digits')}};
-                    // let CPython do its own float rounding from 2**53 on (max. consecutive integer in double float)
-                    if ((8 * sizeof(unsigned long) < 53) || ({{_size}} * PyLong_SHIFT < 53) || ({{fval}} < (double) ((PY_LONG_LONG)1 << 53))) {
-                        if (size == {{-_size}})
-                            {{fval}} = -{{fval}};
-                        break;
-                    }
-                }
-                // Fall through if size doesn't fit safely into a double anymore.
-                // It may not be obvious that this is a safe fall-through given the "fval < 2**53"
-                // check above.  However, the number of digits that CPython uses for a given PyLong
-                // value is minimal, and together with the "(size-1) * SHIFT < 53" check above,
-                // this should make it safe.
-                CYTHON_FALLTHROUGH;
-            {{endfor}}
-            default:
-        #else
-        {
-        #endif
-        {{if op in ('Eq', 'Ne')}}
-            return {{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(
-                PyFloat_Type.tp_richcompare({{'op1, op2' if order == 'CObj' else 'op2, op1'}}, Py_{{op.upper()}}));
-        {{else}}
-            {{fval}} = PyLong_AsDouble({{pyval}});
-            if (unlikely({{fval}} == -1.0 && PyErr_Occurred())) return NULL;
-            {{if order == 'CObj' and c_op in '%/'}}{{zerodiv_check(fval)}}{{endif}}
-        {{endif}}
-        }
-    } else {
-        {{if op in ('Eq', 'Ne')}}
-        return {{'' if ret_type.is_pyobject else '__Pyx_PyObject_IsTrueAndDecref'}}(
-            PyObject_RichCompare(op1, op2, Py_{{op.upper()}}));
-        {{elif op == 'Divide'}}
-        return (inplace ? __Pyx_PyNumber_InPlaceDivide(op1, op2) : __Pyx_PyNumber_Divide(op1, op2));
-        {{else}}
-        return (inplace ? PyNumber_InPlace{{op}} : PyNumber_{{op}})(op1, op2);
-        {{endif}}
-    }
-
-    {{if op in ('Eq', 'Ne')}}
-        if (a {{c_op}} b) {
-            {{return_true}};
-        } else {
-            {{return_false}};
-        }
-    {{else}}
-        // copied from floatobject.c in Py3.5:
-        {{if order == 'CObj' and c_op in '%/'}}{{zerodiv_check('b')}}{{endif}}
-        PyFPE_START_PROTECT("{{op.lower() if not op.endswith('Divide') else 'divide'}}", return NULL)
-        {{if c_op == '%'}}
-        result = fmod(a, b);
-        if (result)
-            result += ((result < 0) ^ (b < 0)) * b;
-        else
-            result = copysign(0.0, b);
-        {{else}}
-        result = a {{c_op}} b;
-        {{endif}}
-        PyFPE_END_PROTECT(result)
-        return PyFloat_FromDouble(result);
-    {{endif}}
-}
-#endif
diff --git a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/cross_entropy.py b/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/cross_entropy.py
deleted file mode 100644
index fe461064716b38ecf2eb610daddbb609a1884e6b..0000000000000000000000000000000000000000
--- a/spaces/arxify/RVC-beta-v2-0618/runtime/Lib/site-packages/fairseq/criterions/cross_entropy.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import math
-from dataclasses import dataclass
-
-import torch.nn.functional as F
-from fairseq import metrics, utils
-from fairseq.criterions import FairseqCriterion, register_criterion
-from fairseq.dataclass import FairseqDataclass
-from omegaconf import II
-
-
-@dataclass
-class CrossEntropyCriterionConfig(FairseqDataclass):
-    sentence_avg: bool = II("optimization.sentence_avg")
-
-
-@register_criterion("cross_entropy", dataclass=CrossEntropyCriterionConfig)
-class CrossEntropyCriterion(FairseqCriterion):
-    def __init__(self, task, sentence_avg):
-        super().__init__(task)
-        self.sentence_avg = sentence_avg
-
-    def forward(self, model, sample, reduce=True):
-        """Compute the loss for the given sample.
-
-        Returns a tuple with three elements:
-        1) the loss
-        2) the sample size, which is used as the denominator for the gradient
-        3) logging outputs to display while training
-        """
-        net_output = model(**sample["net_input"])
-        loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce)
-        sample_size = (
-            sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
-        )
-        logging_output = {
-            "loss": loss.data,
-            "ntokens": sample["ntokens"],
-            "nsentences": sample["target"].size(0),
-            "sample_size": sample_size,
-        }
-        return loss, sample_size, logging_output
-
-    def compute_loss(self, model, net_output, sample, reduce=True):
-        lprobs = model.get_normalized_probs(net_output, log_probs=True)
-        lprobs = lprobs.view(-1, lprobs.size(-1))
-        target = model.get_targets(sample, net_output).view(-1)
-        loss = F.nll_loss(
-            lprobs,
-            target,
-            ignore_index=self.padding_idx,
-            reduction="sum" if reduce else "none",
-        )
-        return loss, loss
-
-    @staticmethod
-    def reduce_metrics(logging_outputs) -> None:
-        """Aggregate logging outputs from data parallel training."""
-        loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
-        ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
-        sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
-
-        # we divide by log(2) to convert the loss from base e to base 2
-        metrics.log_scalar(
-            "loss", loss_sum / sample_size / math.log(2), sample_size, round=3
-        )
-        if sample_size != ntokens:
-            metrics.log_scalar(
-                "nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
-            )
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
-            )
-        else:
-            metrics.log_derived(
-                "ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
-            )
-
-    @staticmethod
-    def logging_outputs_can_be_summed() -> bool:
-        """
-        Whether the logging outputs returned by `forward` can be summed
-        across workers prior to calling `reduce_metrics`. Setting this
-        to True will improves distributed training speed.
-        """
-        return True
diff --git a/spaces/aseifert/ExplaiNER/src/__init__.py b/spaces/aseifert/ExplaiNER/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ashzzf/vits-uma-genshin-honkai/transforms.py b/spaces/ashzzf/vits-uma-genshin-honkai/transforms.py
deleted file mode 100644
index 4793d67ca5a5630e0ffe0f9fb29445c949e64dae..0000000000000000000000000000000000000000
--- a/spaces/ashzzf/vits-uma-genshin-honkai/transforms.py
+++ /dev/null
@@ -1,193 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(inputs, 
-                                           unnormalized_widths,
-                                           unnormalized_heights,
-                                           unnormalized_derivatives,
-                                           inverse=False,
-                                           tails=None, 
-                                           tail_bound=1.,
-                                           min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                           min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                           min_derivative=DEFAULT_MIN_DERIVATIVE):
-
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {
-            'tails': tails,
-            'tail_bound': tail_bound
-        }
-
-    outputs, logabsdet = spline_fn(
-            inputs=inputs,
-            unnormalized_widths=unnormalized_widths,
-            unnormalized_heights=unnormalized_heights,
-            unnormalized_derivatives=unnormalized_derivatives,
-            inverse=inverse,
-            min_bin_width=min_bin_width,
-            min_bin_height=min_bin_height,
-            min_derivative=min_derivative,
-            **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(
-        inputs[..., None] >= bin_locations,
-        dim=-1
-    ) - 1
-
-
-def unconstrained_rational_quadratic_spline(inputs,
-                                            unnormalized_widths,
-                                            unnormalized_heights,
-                                            unnormalized_derivatives,
-                                            inverse=False,
-                                            tails='linear',
-                                            tail_bound=1.,
-                                            min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                                            min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                                            min_derivative=DEFAULT_MIN_DERIVATIVE):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == 'linear':
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError('{} tails are not implemented.'.format(tails))
-
-    outputs[inside_interval_mask], logabsdet[inside_interval_mask] = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound, right=tail_bound, bottom=-tail_bound, top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative
-    )
-
-    return outputs, logabsdet
-
-def rational_quadratic_spline(inputs,
-                              unnormalized_widths,
-                              unnormalized_heights,
-                              unnormalized_derivatives,
-                              inverse=False,
-                              left=0., right=1., bottom=0., top=1.,
-                              min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-                              min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-                              min_derivative=DEFAULT_MIN_DERIVATIVE):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError('Input to a transform is not within its domain')
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError('Minimal bin width too large for the number of bins')
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError('Minimal bin height too large for the number of bins')
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode='constant', value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode='constant', value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (((inputs - input_cumheights) * (input_derivatives
-                                             + input_derivatives_plus_one
-                                             - 2 * input_delta)
-              + input_heights * (input_delta - input_derivatives)))
-        b = (input_heights * input_derivatives
-             - (inputs - input_cumheights) * (input_derivatives
-                                              + input_derivatives_plus_one
-                                              - 2 * input_delta))
-        c = - input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * root.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - root).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (input_delta * theta.pow(2)
-                                     + input_derivatives * theta_one_minus_theta)
-        denominator = input_delta + ((input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-                                     * theta_one_minus_theta)
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (input_derivatives_plus_one * theta.pow(2)
-                                                     + 2 * input_delta * theta_one_minus_theta
-                                                     + input_derivatives * (1 - theta).pow(2))
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/awacke1/MemoryEmbeddingsChatGPT-1/README.md b/spaces/awacke1/MemoryEmbeddingsChatGPT-1/README.md
deleted file mode 100644
index 974ceb99462bac146a9e4973a2f2b981dfc7dd4b..0000000000000000000000000000000000000000
--- a/spaces/awacke1/MemoryEmbeddingsChatGPT-1/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: MemoryEmbeddingsChatGPT 1
-emoji: 🏢
-colorFrom: purple
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/awacke1/Streamlit-Data-Synthesis-Example/README.md b/spaces/awacke1/Streamlit-Data-Synthesis-Example/README.md
deleted file mode 100644
index fdf188941402d07a54c6794af150a518a5ae8e6a..0000000000000000000000000000000000000000
--- a/spaces/awacke1/Streamlit-Data-Synthesis-Example/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Streamlit Data Synthesis Example
-emoji: 🐨
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/CurveExtras.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/CurveExtras.js
deleted file mode 100644
index 5ffd804e05a4e4834c689218ac3b3897741a2628..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/CurveExtras.js
+++ /dev/null
@@ -1,425 +0,0 @@
-/*
- * A bunch of parametric curves
- * @author zz85
- *
- * Formulas collected from various sources
- * http://mathworld.wolfram.com/HeartCurve.html
- * http://mathdl.maa.org/images/upload_library/23/stemkoski/knots/page6.html
- * http://en.wikipedia.org/wiki/Viviani%27s_curve
- * http://mathdl.maa.org/images/upload_library/23/stemkoski/knots/page4.html
- * http://www.mi.sanu.ac.rs/vismath/taylorapril2011/Taylor.pdf
- * http://prideout.net/blog/?p=44
- */
-
-( function ( Curves ) {
-
-	// GrannyKnot
-
-	function GrannyKnot() {
-
-		THREE.Curve.call( this );
-
-	}
-
-	GrannyKnot.prototype = Object.create( THREE.Curve.prototype );
-	GrannyKnot.prototype.constructor = GrannyKnot;
-
-	GrannyKnot.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t = 2 * Math.PI * t;
-
-		var x = - 0.22 * Math.cos( t ) - 1.28 * Math.sin( t ) - 0.44 * Math.cos( 3 * t ) - 0.78 * Math.sin( 3 * t );
-		var y = - 0.1 * Math.cos( 2 * t ) - 0.27 * Math.sin( 2 * t ) + 0.38 * Math.cos( 4 * t ) + 0.46 * Math.sin( 4 * t );
-		var z = 0.7 * Math.cos( 3 * t ) - 0.4 * Math.sin( 3 * t );
-
-		return point.set( x, y, z ).multiplyScalar( 20 );
-
-	};
-
-	// HeartCurve
-
-	function HeartCurve( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 5 : scale;
-
-	}
-
-	HeartCurve.prototype = Object.create( THREE.Curve.prototype );
-	HeartCurve.prototype.constructor = HeartCurve;
-
-	HeartCurve.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t *= 2 * Math.PI;
-
-		var x = 16 * Math.pow( Math.sin( t ), 3 );
-		var y = 13 * Math.cos( t ) - 5 * Math.cos( 2 * t ) - 2 * Math.cos( 3 * t ) - Math.cos( 4 * t );
-		var z = 0;
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// Viviani's Curve
-
-	function VivianiCurve( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 70 : scale;
-
-	}
-
-	VivianiCurve.prototype = Object.create( THREE.Curve.prototype );
-	VivianiCurve.prototype.constructor = VivianiCurve;
-
-	VivianiCurve.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t = t * 4 * Math.PI; // normalized to 0..1
-		var a = this.scale / 2;
-
-		var x = a * ( 1 + Math.cos( t ) );
-		var y = a * Math.sin( t );
-		var z = 2 * a * Math.sin( t / 2 );
-
-		return point.set( x, y, z );
-
-	};
-
-	// KnotCurve
-
-	function KnotCurve() {
-
-		THREE.Curve.call( this );
-
-	}
-
-	KnotCurve.prototype = Object.create( THREE.Curve.prototype );
-	KnotCurve.prototype.constructor = KnotCurve;
-
-	KnotCurve.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t *= 2 * Math.PI;
-
-		var R = 10;
-		var s = 50;
-
-		var x = s * Math.sin( t );
-		var y = Math.cos( t ) * ( R + s * Math.cos( t ) );
-		var z = Math.sin( t ) * ( R + s * Math.cos( t ) );
-
-		return point.set( x, y, z );
-
-	};
-
-	// HelixCurve
-
-	function HelixCurve() {
-
-		THREE.Curve.call( this );
-
-	}
-
-	HelixCurve.prototype = Object.create( THREE.Curve.prototype );
-	HelixCurve.prototype.constructor = HelixCurve;
-
-	HelixCurve.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		var a = 30; // radius
-		var b = 150; // height
-
-		var t2 = 2 * Math.PI * t * b / 30;
-
-		var x = Math.cos( t2 ) * a;
-		var y = Math.sin( t2 ) * a;
-		var z = b * t;
-
-		return point.set( x, y, z );
-
-	};
-
-	// TrefoilKnot
-
-	function TrefoilKnot( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 10 : scale;
-
-	}
-
-	TrefoilKnot.prototype = Object.create( THREE.Curve.prototype );
-	TrefoilKnot.prototype.constructor = TrefoilKnot;
-
-	TrefoilKnot.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t *= Math.PI * 2;
-
-		var x = ( 2 + Math.cos( 3 * t ) ) * Math.cos( 2 * t );
-		var y = ( 2 + Math.cos( 3 * t ) ) * Math.sin( 2 * t );
-		var z = Math.sin( 3 * t );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// TorusKnot
-
-	function TorusKnot( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 10 : scale;
-
-	}
-
-	TorusKnot.prototype = Object.create( THREE.Curve.prototype );
-	TorusKnot.prototype.constructor = TorusKnot;
-
-	TorusKnot.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		var p = 3;
-		var q = 4;
-
-		t *= Math.PI * 2;
-
-		var x = ( 2 + Math.cos( q * t ) ) * Math.cos( p * t );
-		var y = ( 2 + Math.cos( q * t ) ) * Math.sin( p * t );
-		var z = Math.sin( q * t );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// CinquefoilKnot
-
-	function CinquefoilKnot( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 10 : scale;
-
-	}
-
-	CinquefoilKnot.prototype = Object.create( THREE.Curve.prototype );
-	CinquefoilKnot.prototype.constructor = CinquefoilKnot;
-
-	CinquefoilKnot.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		var p = 2;
-		var q = 5;
-
-		t *= Math.PI * 2;
-
-		var x = ( 2 + Math.cos( q * t ) ) * Math.cos( p * t );
-		var y = ( 2 + Math.cos( q * t ) ) * Math.sin( p * t );
-		var z = Math.sin( q * t );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// TrefoilPolynomialKnot
-
-	function TrefoilPolynomialKnot( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 10 : scale;
-
-	}
-
-	TrefoilPolynomialKnot.prototype = Object.create( THREE.Curve.prototype );
-	TrefoilPolynomialKnot.prototype.constructor = TrefoilPolynomialKnot;
-
-	TrefoilPolynomialKnot.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t = t * 4 - 2;
-
-		var x = Math.pow( t, 3 ) - 3 * t;
-		var y = Math.pow( t, 4 ) - 4 * t * t;
-		var z = 1 / 5 * Math.pow( t, 5 ) - 2 * t;
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	var scaleTo = function ( x, y, t ) {
-
-		var r = y - x;
-		return t * r + x;
-
-	};
-
-	// FigureEightPolynomialKnot
-
-	function FigureEightPolynomialKnot( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 1 : scale;
-
-	}
-
-	FigureEightPolynomialKnot.prototype = Object.create( THREE.Curve.prototype );
-	FigureEightPolynomialKnot.prototype.constructor = FigureEightPolynomialKnot;
-
-	FigureEightPolynomialKnot.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t = scaleTo( - 4, 4, t );
-
-		var x = 2 / 5 * t * ( t * t - 7 ) * ( t * t - 10 );
-		var y = Math.pow( t, 4 ) - 13 * t * t;
-		var z = 1 / 10 * t * ( t * t - 4 ) * ( t * t - 9 ) * ( t * t - 12 );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// DecoratedTorusKnot4a
-
-	function DecoratedTorusKnot4a( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 40 : scale;
-
-	}
-
-	DecoratedTorusKnot4a.prototype = Object.create( THREE.Curve.prototype );
-	DecoratedTorusKnot4a.prototype.constructor = DecoratedTorusKnot4a;
-
-	DecoratedTorusKnot4a.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		t *= Math.PI * 2;
-
-		var x = Math.cos( 2 * t ) * ( 1 + 0.6 * ( Math.cos( 5 * t ) + 0.75 * Math.cos( 10 * t ) ) );
-		var y = Math.sin( 2 * t ) * ( 1 + 0.6 * ( Math.cos( 5 * t ) + 0.75 * Math.cos( 10 * t ) ) );
-		var z = 0.35 * Math.sin( 5 * t );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// DecoratedTorusKnot4b
-
-	function DecoratedTorusKnot4b( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 40 : scale;
-
-	}
-
-	DecoratedTorusKnot4b.prototype = Object.create( THREE.Curve.prototype );
-	DecoratedTorusKnot4b.prototype.constructor = DecoratedTorusKnot4b;
-
-	DecoratedTorusKnot4b.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		var fi = t * Math.PI * 2;
-
-		var x = Math.cos( 2 * fi ) * ( 1 + 0.45 * Math.cos( 3 * fi ) + 0.4 * Math.cos( 9 * fi ) );
-		var y = Math.sin( 2 * fi ) * ( 1 + 0.45 * Math.cos( 3 * fi ) + 0.4 * Math.cos( 9 * fi ) );
-		var z = 0.2 * Math.sin( 9 * fi );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// DecoratedTorusKnot5a
-
-	function DecoratedTorusKnot5a( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 40 : scale;
-
-	}
-
-	DecoratedTorusKnot5a.prototype = Object.create( THREE.Curve.prototype );
-	DecoratedTorusKnot5a.prototype.constructor = DecoratedTorusKnot5a;
-
-	DecoratedTorusKnot5a.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		var fi = t * Math.PI * 2;
-
-		var x = Math.cos( 3 * fi ) * ( 1 + 0.3 * Math.cos( 5 * fi ) + 0.5 * Math.cos( 10 * fi ) );
-		var y = Math.sin( 3 * fi ) * ( 1 + 0.3 * Math.cos( 5 * fi ) + 0.5 * Math.cos( 10 * fi ) );
-		var z = 0.2 * Math.sin( 20 * fi );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// DecoratedTorusKnot5c
-
-	function DecoratedTorusKnot5c( scale ) {
-
-		THREE.Curve.call( this );
-
-		this.scale = ( scale === undefined ) ? 40 : scale;
-
-	}
-
-	DecoratedTorusKnot5c.prototype = Object.create( THREE.Curve.prototype );
-	DecoratedTorusKnot5c.prototype.constructor = DecoratedTorusKnot5c;
-
-	DecoratedTorusKnot5c.prototype.getPoint = function ( t, optionalTarget ) {
-
-		var point = optionalTarget || new THREE.Vector3();
-
-		var fi = t * Math.PI * 2;
-
-		var x = Math.cos( 4 * fi ) * ( 1 + 0.5 * ( Math.cos( 5 * fi ) + 0.4 * Math.cos( 20 * fi ) ) );
-		var y = Math.sin( 4 * fi ) * ( 1 + 0.5 * ( Math.cos( 5 * fi ) + 0.4 * Math.cos( 20 * fi ) ) );
-		var z = 0.35 * Math.sin( 15 * fi );
-
-		return point.set( x, y, z ).multiplyScalar( this.scale );
-
-	};
-
-	// export
-
-	Curves.GrannyKnot = GrannyKnot;
-	Curves.HeartCurve = HeartCurve;
-	Curves.VivianiCurve = VivianiCurve;
-	Curves.KnotCurve = KnotCurve;
-	Curves.HelixCurve = HelixCurve;
-	Curves.TrefoilKnot = TrefoilKnot;
-	Curves.TorusKnot = TorusKnot;
-	Curves.CinquefoilKnot = CinquefoilKnot;
-	Curves.TrefoilPolynomialKnot = TrefoilPolynomialKnot;
-	Curves.FigureEightPolynomialKnot = FigureEightPolynomialKnot;
-	Curves.DecoratedTorusKnot4a = DecoratedTorusKnot4a;
-	Curves.DecoratedTorusKnot4b = DecoratedTorusKnot4b;
-	Curves.DecoratedTorusKnot5a = DecoratedTorusKnot5a;
-	Curves.DecoratedTorusKnot5c = DecoratedTorusKnot5c;
-
-} )( THREE.Curves = THREE.Curves || {} );
diff --git a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/NodeMaterialLoader.js b/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/NodeMaterialLoader.js
deleted file mode 100644
index db29336b1b677908b6bc48b0bd12cdb6584cd53d..0000000000000000000000000000000000000000
--- a/spaces/banana-projects/web3d/node_modules/three/examples/js/loaders/NodeMaterialLoader.js
+++ /dev/null
@@ -1,265 +0,0 @@
-/**
- * @author sunag / http://www.sunag.com.br/
- */
-
-THREE.NodeMaterialLoader = function ( manager, library ) {
-
-	this.manager = ( manager !== undefined ) ? manager : THREE.DefaultLoadingManager;
-
-	this.nodes = {};
-	this.materials = {};
-	this.passes = {};
-	this.names = {};
-	this.library = library || {};
-
-};
-
-THREE.NodeMaterialLoaderUtils = {
-
-	replaceUUIDObject: function ( object, uuid, value, recursive ) {
-
-		recursive = recursive !== undefined ? recursive : true;
-
-		if ( typeof uuid === "object" ) uuid = uuid.uuid;
-
-		if ( typeof object === "object" ) {
-
-			var keys = Object.keys( object );
-
-			for ( var i = 0; i < keys.length; i ++ ) {
-
-				var key = keys[ i ];
-
-				if ( recursive ) {
-
-					object[ key ] = this.replaceUUIDObject( object[ key ], uuid, value );
-
-				}
-
-				if ( key === uuid ) {
-
-					object[ uuid ] = object[ key ];
-
-					delete object[ key ];
-
-				}
-
-			}
-
-		}
-
-		return object === uuid ? value : object;
-
-	},
-
-	replaceUUID: function ( json, uuid, value ) {
-
-		this.replaceUUIDObject( json, uuid, value, false );
-		this.replaceUUIDObject( json.nodes, uuid, value );
-		this.replaceUUIDObject( json.materials, uuid, value );
-		this.replaceUUIDObject( json.passes, uuid, value );
-		this.replaceUUIDObject( json.library, uuid, value, false );
-
-		return json;
-
-	}
-
-};
-
-Object.assign( THREE.NodeMaterialLoader.prototype, {
-
-	load: function ( url, onLoad, onProgress, onError ) {
-
-		var scope = this;
-
-		var loader = new THREE.FileLoader( scope.manager );
-		loader.setPath( scope.path );
-		loader.load( url, function ( text ) {
-
-			onLoad( scope.parse( JSON.parse( text ) ) );
-
-		}, onProgress, onError );
-
-		return this;
-
-	},
-
-	setPath: function ( value ) {
-
-		this.path = value;
-		return this;
-
-	},
-
-	getObjectByName: function ( uuid ) {
-
-		return this.names[ uuid ];
-
-	},
-
-	getObjectById: function ( uuid ) {
-
-		return this.library[ uuid ] ||
-			this.nodes[ uuid ] ||
-			this.materials[ uuid ] ||
-			this.passes[ uuid ] ||
-			this.names[ uuid ];
-
-	},
-
-	getNode: function ( uuid ) {
-
-		var object = this.getObjectById( uuid );
-
-		if ( ! object ) {
-
-			console.warn( "Node \"" + uuid + "\" not found." );
-
-		}
-
-		return object;
-
-	},
-
-	resolve: function( json ) {
-
-		switch( typeof json ) {
-
-			case "boolean":
-			case "number":
-
-				return json;
-
-			case "string":
-
-				if (/^\w{8}-\w{4}-\w{4}-\w{4}-\w{12}$/i.test(json) || this.library[ json ]) {
-
-					return this.getNode( json );
-
-				}
-
-				return json;
-
-			default:
-
-				if ( Array.isArray( json ) ) {
-
-					for(var i = 0; i < json.length; i++) {
-
-						json[i] = this.resolve( json[i] );
-
-					}
-
-				} else {
-
-					for ( var prop in json ) {
-
-						if (prop === "uuid") continue;
-
-						json[ prop ] = this.resolve( json[ prop ] );
-
-					}
-
-				}
-
-		}
-
-		return json;
-
-	},
-
-	declare: function( json ) {
-
-		var uuid, node, object;
-
-		for ( uuid in json.nodes ) {
-
-			node = json.nodes[ uuid ];
-
-			object = new THREE[ node.nodeType + "Node" ]();
-
-			if ( node.name ) {
-
-				object.name = node.name;
-
-				this.names[ object.name ] = object;
-
-			}
-
-			this.nodes[ uuid ] = object;
-
-		}
-
-		for ( uuid in json.materials ) {
-
-			node = json.materials[ uuid ];
-
-			object = new THREE[ node.type ]();
-
-			if ( node.name ) {
-
-				object.name = node.name;
-
-				this.names[ object.name ] = object;
-
-			}
-
-			this.materials[ uuid ] = object;
-
-		}
-
-		for ( uuid in json.passes ) {
-
-			node = json.passes[ uuid ];
-
-			object = new THREE[ node.type ]();
-
-			if ( node.name ) {
-
-				object.name = node.name;
-
-				this.names[ object.name ] = object;
-
-			}
-
-			this.passes[ uuid ] = object;
-
-		}
-
-		if ( json.material ) this.material = this.materials[ json.material ];
-
-		if ( json.pass ) this.pass = this.passes[ json.pass ];
-
-		return json;
-
-	},
-
-	parse: function ( json ) {
-
-		var uuid;
-
-		json = this.resolve( this.declare( json ) );
-
-		for ( uuid in json.nodes ) {
-
-			this.nodes[ uuid ].copy( json.nodes[ uuid ] );
-
-		}
-
-		for ( uuid in json.materials ) {
-
-			this.materials[ uuid ].copy( json.materials[ uuid ] );
-
-		}
-
-		for ( uuid in json.passes ) {
-
-			this.passes[ uuid ].copy( json.passes[ uuid ] );
-
-		}
-
-		return this.material || this.pass || this;
-
-	}
-
-} );
diff --git a/spaces/batmac/captioner/app2.py b/spaces/batmac/captioner/app2.py
deleted file mode 100644
index d2349688d4dfad1d2bb6b572275ce5f529e0883a..0000000000000000000000000000000000000000
--- a/spaces/batmac/captioner/app2.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-os.environ.setdefault("GRADIO_ANALYTICS_ENABLED", "False")
-import logging
-import gradio as gr
-from pillow_heif import register_heif_opener
-
-register_heif_opener()
-
-from transformers import pipeline
-
-
-LOG_LEVEL = os.getenv("LOG_LEVEL", "DEBUG")
-MAX_NEW_TOKENS = int(os.getenv("MAX_NEW_TOKENS", 200))
-# https://huggingface.co/models?pipeline_tag=image-to-text&sort=likes
-MODEL = os.getenv("MODEL", "Salesforce/blip-image-captioning-large")
-
-logging.basicConfig(level=LOG_LEVEL)
-logger = logging.getLogger(__name__)
-
-
-logger.info("Loading model...")
-# simpler model: "ydshieh/vit-gpt2-coco-en"
-captioner = pipeline(
-  "image-to-text",
-  model=MODEL,
-  max_new_tokens=MAX_NEW_TOKENS,
-)
-logger.info("Done loading model.")
-
-
-
-def graptioner(img):
-  result = captioner(img) 
-  caption = result[0]["generated_text"]
-  return caption
-
-iface = gr.Interface(
-  fn=graptioner,
-  inputs=gr.components.Image(type="pil"),
-  outputs=["text"],
-  allow_flagging="never",
-  # analytics_enabled=False
-)
-
-iface.launch()
diff --git a/spaces/bigcode/bigcode-playground/app.py b/spaces/bigcode/bigcode-playground/app.py
deleted file mode 100644
index 45ce73d1760d6ed008bc5daf189a05f673ebfaf4..0000000000000000000000000000000000000000
--- a/spaces/bigcode/bigcode-playground/app.py
+++ /dev/null
@@ -1,271 +0,0 @@
-import json
-import os
-import shutil
-import requests
-
-import gradio as gr
-from huggingface_hub import Repository
-from text_generation import Client
-
-from share_btn import community_icon_html, loading_icon_html, share_js, share_btn_css
-
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-API_URL = "https://api-inference.huggingface.co/models/bigcode/starcoder"
-API_URL_BASE ="https://api-inference.huggingface.co/models/bigcode/starcoderbase"
-API_URL_PLUS = "https://api-inference.huggingface.co/models/bigcode/starcoderplus"
-
-FIM_PREFIX = "<fim_prefix>"
-FIM_MIDDLE = "<fim_middle>"
-FIM_SUFFIX = "<fim_suffix>"
-
-FIM_INDICATOR = "<FILL_HERE>"
-
-FORMATS = """## Model Formats
-
-The model is pretrained on code and is formatted with special tokens in addition to the pure code data,\
-such as prefixes specifying the source of the file or tokens separating code from a commit message.\
-Use these templates to explore the model's capacities:
-
-### 1. Prefixes 🏷️
-For pure code files, use any combination of the following prefixes:
-
-```
-<reponame>REPONAME<filename>FILENAME<gh_stars>STARS\ncode<|endoftext|>
-```
-STARS can be one of: 0, 1-10, 10-100, 100-1000, 1000+
-
-### 2. Commits 💾
-The commits data is formatted as follows:
-
-```
-<commit_before>code<commit_msg>text<commit_after>code<|endoftext|>
-```
-
-### 3. Jupyter Notebooks 📓
-The model is trained on Jupyter notebooks as Python scripts and structured formats like:
-
-```
-<start_jupyter><jupyter_text>text<jupyter_code>code<jupyter_output>output<jupyter_text>
-```
-
-### 4. Issues 🐛
-We also trained on GitHub issues using the following formatting:
-```
-<issue_start><issue_comment>text<issue_comment>...<issue_closed>
-```
-
-### 5. Fill-in-the-middle 🧩
-Fill in the middle requires rearranging the model inputs. The playground handles this for you - all you need is to specify where to fill:
-```
-code before<FILL_HERE>code after
-```
-"""
-
-theme = gr.themes.Monochrome(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
-    radius_size=gr.themes.sizes.radius_sm,
-    font=[
-        gr.themes.GoogleFont("Open Sans"),
-        "ui-sans-serif",
-        "system-ui",
-        "sans-serif",
-    ],
-)
-
-client = Client(
-    API_URL,
-    headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-client_base = Client(
-    API_URL_BASE, headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-client_plus = Client(
-    API_URL_PLUS, headers={"Authorization": f"Bearer {HF_TOKEN}"},
-)
-
-def generate(
-    prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, version="StarCoder",
-):
-
-    temperature = float(temperature)
-    if temperature < 1e-2:
-        temperature = 1e-2
-    top_p = float(top_p)
-    fim_mode = False
-
-    generate_kwargs = dict(
-        temperature=temperature,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        repetition_penalty=repetition_penalty,
-        do_sample=True,
-        seed=42,
-    )
-
-    if FIM_INDICATOR in prompt:
-        fim_mode = True
-        try:
-            prefix, suffix = prompt.split(FIM_INDICATOR)
-        except:
-            raise ValueError(f"Only one {FIM_INDICATOR} allowed in prompt!")
-        prompt = f"{FIM_PREFIX}{prefix}{FIM_SUFFIX}{suffix}{FIM_MIDDLE}"
-
-    if version == "StarCoder":
-        stream = client.generate_stream(prompt, **generate_kwargs)
-    elif version == "StarCoderPlus":
-        stream = client_plus.generate_stream(prompt, **generate_kwargs)
-    else:
-        stream = client_base.generate_stream(prompt, **generate_kwargs)
-
-    if fim_mode:
-        output = prefix
-    else:
-        output = prompt
-
-    previous_token = ""
-    for response in stream:
-        if response.token.text == "<|endoftext|>":
-            if fim_mode:
-                output += suffix
-            else:
-                return output
-        else:
-            output += response.token.text
-        previous_token = response.token.text
-        yield output
-    return output
-
-
-examples = [
-    "X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.1)\n\n# Train a logistic regression model, predict the labels on the test set and compute the accuracy score",
-    "// Returns every other value in the array as a new array.\nfunction everyOther(arr) {",
-    "Poor English: She no went to the market. Corrected English:",
-    "def alternating(list1, list2):\n   results = []\n   for i in range(min(len(list1), len(list2))):\n       results.append(list1[i])\n       results.append(list2[i])\n   if len(list1) > len(list2):\n       <FILL_HERE>\n   else:\n       results.extend(list2[i+1:])\n   return results",
-]
-
-
-def process_example(args):
-    for x in generate(args):
-        pass
-    return x
-
-
-css = ".generating {visibility: hidden}"
-
-monospace_css = """
-#q-input textarea {
-    font-family: monospace, 'Consolas', Courier, monospace;
-}
-"""
-
-
-css += share_btn_css + monospace_css + ".gradio-container {color: black}"
-
-
-description = """
-<div style="text-align: center;">
-    <h1> ⭐ StarCoder <span style='color: #e6b800;'>Models</span> Playground</h1>
-</div>
-<div style="text-align: left;">
-    <p>This is a demo to generate text and code with the following StarCoder models:</p>
-    <ul>
-        <li><a href="https://huggingface.co/bigcode/starcoderplus" style='color: #e6b800;'>StarCoderPlus</a>: A finetuned version of StarCoderBase on English web data, making it strong in both English text and code generation.</li>
-        <li><a href="https://huggingface.co/bigcode/starcoderbase" style='color: #e6b800;'>StarCoderBase</a>: A code generation model trained on 80+ programming languages, providing broad language coverage for code generation tasks.</li>
-        <li><a href="https://huggingface.co/bigcode/starcoder" style='color: #e6b800;'>StarCoder</a>: A finetuned version of StarCoderBase specifically focused on Python, while also maintaining strong performance on other programming languages.</li>
-    </ul>
-    <p><b>Please note:</b> These models are not designed for instruction purposes. If you're looking for instruction or want to chat with a fine-tuned model, you can visit the <a href="https://huggingface.co/spaces/HuggingFaceH4/starchat-playground">StarChat Playground</a>.</p>
-</div>
-"""
-disclaimer = """⚠️<b>Any use or sharing of this demo constitues your acceptance of the BigCode [OpenRAIL-M](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) License Agreement and the use restrictions included within.</b>\
- <br>**Intended Use**: this app and its [supporting model](https://huggingface.co/bigcode) are provided for demonstration purposes; not to serve as replacement for human expertise. For more details on the model's limitations in terms of factuality and biases, see the [model card.](hf.co/bigcode)"""
-
-with gr.Blocks(theme=theme, analytics_enabled=False, css=css) as demo:
-    with gr.Column():
-        gr.Markdown(description)
-        with gr.Row():
-            version = gr.Dropdown(
-                        ["StarCoderPlus", "StarCoderBase", "StarCoder"],
-                        value="StarCoder",
-                        label="Model",
-                        info="Choose a model from the list",
-                        )
-        with gr.Row():
-            with gr.Column():
-                instruction = gr.Textbox(
-                    placeholder="Enter your code here",
-                    lines=5,
-                    label="Input",
-                    elem_id="q-input",
-                )
-                submit = gr.Button("Generate", variant="primary")
-                output = gr.Code(elem_id="q-output", lines=30, label="Output")
-                with gr.Row():
-                    with gr.Column():
-                        with gr.Accordion("Advanced settings", open=False):
-                            with gr.Row():
-                                column_1, column_2 = gr.Column(), gr.Column()
-                                with column_1:
-                                    temperature = gr.Slider(
-                                        label="Temperature",
-                                        value=0.2,
-                                        minimum=0.0,
-                                        maximum=1.0,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Higher values produce more diverse outputs",
-                                    )
-                                    max_new_tokens = gr.Slider(
-                                        label="Max new tokens",
-                                        value=256,
-                                        minimum=0,
-                                        maximum=8192,
-                                        step=64,
-                                        interactive=True,
-                                        info="The maximum numbers of new tokens",
-                                    )
-                                with column_2:
-                                    top_p = gr.Slider(
-                                        label="Top-p (nucleus sampling)",
-                                        value=0.90,
-                                        minimum=0.0,
-                                        maximum=1,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Higher values sample more low-probability tokens",
-                                    )
-                                    repetition_penalty = gr.Slider(
-                                        label="Repetition penalty",
-                                        value=1.2,
-                                        minimum=1.0,
-                                        maximum=2.0,
-                                        step=0.05,
-                                        interactive=True,
-                                        info="Penalize repeated tokens",
-                                    )
-                                    
-                gr.Markdown(disclaimer)
-                with gr.Group(elem_id="share-btn-container"):
-                    community_icon = gr.HTML(community_icon_html, visible=True)
-                    loading_icon = gr.HTML(loading_icon_html, visible=True)
-                    share_button = gr.Button(
-                        "Share to community", elem_id="share-btn", visible=True
-                    )
-                gr.Examples(
-                    examples=examples,
-                    inputs=[instruction],
-                    cache_examples=False,
-                    fn=process_example,
-                    outputs=[output],
-                )
-                gr.Markdown(FORMATS)
-
-    submit.click(
-        generate,
-        inputs=[instruction, temperature, max_new_tokens, top_p, repetition_penalty, version],
-        outputs=[output],
-    )
-    share_button.click(None, [], [], _js=share_js)
-demo.queue(concurrency_count=16).launch(debug=True)
\ No newline at end of file
diff --git a/spaces/bingbing520/ChatGPT2/modules/base_model.py b/spaces/bingbing520/ChatGPT2/modules/base_model.py
deleted file mode 100644
index 2b55623f6b0989f60d818be6e0e77f5948484b82..0000000000000000000000000000000000000000
--- a/spaces/bingbing520/ChatGPT2/modules/base_model.py
+++ /dev/null
@@ -1,561 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, List
-
-import logging
-import json
-import commentjson as cjson
-import os
-import sys
-import requests
-import urllib3
-import traceback
-
-from tqdm import tqdm
-import colorama
-from duckduckgo_search import ddg
-import asyncio
-import aiohttp
-from enum import Enum
-
-from .presets import *
-from .llama_func import *
-from .utils import *
-from . import shared
-from .config import retrieve_proxy
-
-
-class ModelType(Enum):
-    Unknown = -1
-    OpenAI = 0
-    ChatGLM = 1
-    LLaMA = 2
-    XMChat = 3
-
-    @classmethod
-    def get_type(cls, model_name: str):
-        model_type = None
-        model_name_lower = model_name.lower()
-        if "gpt" in model_name_lower:
-            model_type = ModelType.OpenAI
-        elif "chatglm" in model_name_lower:
-            model_type = ModelType.ChatGLM
-        elif "llama" in model_name_lower or "alpaca" in model_name_lower:
-            model_type = ModelType.LLaMA
-        elif "xmchat" in model_name_lower:
-            model_type = ModelType.XMChat
-        else:
-            model_type = ModelType.Unknown
-        return model_type
-
-
-class BaseLLMModel:
-    def __init__(
-        self,
-        model_name,
-        system_prompt="",
-        temperature=1.0,
-        top_p=1.0,
-        n_choices=1,
-        stop=None,
-        max_generation_token=None,
-        presence_penalty=0,
-        frequency_penalty=0,
-        logit_bias=None,
-        user="",
-    ) -> None:
-        self.history = []
-        self.all_token_counts = []
-        self.model_name = model_name
-        self.model_type = ModelType.get_type(model_name)
-        try:
-            self.token_upper_limit = MODEL_TOKEN_LIMIT[model_name]
-        except KeyError:
-            self.token_upper_limit = DEFAULT_TOKEN_LIMIT
-        self.interrupted = False
-        self.system_prompt = system_prompt
-        self.api_key = None
-        self.need_api_key = False
-        self.single_turn = False
-
-        self.temperature = temperature
-        self.top_p = top_p
-        self.n_choices = n_choices
-        self.stop_sequence = stop
-        self.max_generation_token = None
-        self.presence_penalty = presence_penalty
-        self.frequency_penalty = frequency_penalty
-        self.logit_bias = logit_bias
-        self.user_identifier = user
-
-    def get_answer_stream_iter(self):
-        """stream predict, need to be implemented
-        conversations are stored in self.history, with the most recent question, in OpenAI format
-        should return a generator, each time give the next word (str) in the answer
-        """
-        logging.warning("stream predict not implemented, using at once predict instead")
-        response, _ = self.get_answer_at_once()
-        yield response
-
-    def get_answer_at_once(self):
-        """predict at once, need to be implemented
-        conversations are stored in self.history, with the most recent question, in OpenAI format
-        Should return:
-        the answer (str)
-        total token count (int)
-        """
-        logging.warning("at once predict not implemented, using stream predict instead")
-        response_iter = self.get_answer_stream_iter()
-        count = 0
-        for response in response_iter:
-            count += 1
-        return response, sum(self.all_token_counts) + count
-
-    def billing_info(self):
-        """get billing infomation, inplement if needed"""
-        logging.warning("billing info not implemented, using default")
-        return BILLING_NOT_APPLICABLE_MSG
-
-    def count_token(self, user_input):
-        """get token count from input, implement if needed"""
-        logging.warning("token count not implemented, using default")
-        return len(user_input)
-
-    def stream_next_chatbot(self, inputs, chatbot, fake_input=None, display_append=""):
-        def get_return_value():
-            return chatbot, status_text
-
-        status_text = i18n("开始实时传输回答……")
-        if fake_input:
-            chatbot.append((fake_input, ""))
-        else:
-            chatbot.append((inputs, ""))
-
-        user_token_count = self.count_token(inputs)
-        self.all_token_counts.append(user_token_count)
-        logging.debug(f"输入token计数: {user_token_count}")
-
-        stream_iter = self.get_answer_stream_iter()
-
-        for partial_text in stream_iter:
-            chatbot[-1] = (chatbot[-1][0], partial_text + display_append)
-            self.all_token_counts[-1] += 1
-            status_text = self.token_message()
-            yield get_return_value()
-            if self.interrupted:
-                self.recover()
-                break
-        self.history.append(construct_assistant(partial_text))
-
-    def next_chatbot_at_once(self, inputs, chatbot, fake_input=None, display_append=""):
-        if fake_input:
-            chatbot.append((fake_input, ""))
-        else:
-            chatbot.append((inputs, ""))
-        if fake_input is not None:
-            user_token_count = self.count_token(fake_input)
-        else:
-            user_token_count = self.count_token(inputs)
-        self.all_token_counts.append(user_token_count)
-        ai_reply, total_token_count = self.get_answer_at_once()
-        self.history.append(construct_assistant(ai_reply))
-        if fake_input is not None:
-            self.history[-2] = construct_user(fake_input)
-        chatbot[-1] = (chatbot[-1][0], ai_reply + display_append)
-        if fake_input is not None:
-            self.all_token_counts[-1] += count_token(construct_assistant(ai_reply))
-        else:
-            self.all_token_counts[-1] = total_token_count - sum(self.all_token_counts)
-        status_text = self.token_message()
-        return chatbot, status_text
-
-    def handle_file_upload(self, files, chatbot):
-        """if the model accepts multi modal input, implement this function"""
-        status = gr.Markdown.update()
-        if files:
-            construct_index(self.api_key, file_src=files)
-            status = "索引构建完成"
-        return gr.Files.update(), chatbot, status
-
-    def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot):
-        fake_inputs = None
-        display_append = []
-        limited_context = False
-        fake_inputs = real_inputs
-        if files:
-            from llama_index.indices.vector_store.base_query import GPTVectorStoreIndexQuery
-            from llama_index.indices.query.schema import QueryBundle
-            from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-            from langchain.chat_models import ChatOpenAI
-            from llama_index import (
-                GPTSimpleVectorIndex,
-                ServiceContext,
-                LangchainEmbedding,
-                OpenAIEmbedding,
-            )
-            limited_context = True
-            msg = "加载索引中……"
-            logging.info(msg)
-            # yield chatbot + [(inputs, "")], msg
-            index = construct_index(self.api_key, file_src=files)
-            assert index is not None, "获取索引失败"
-            msg = "索引获取成功，生成回答中……"
-            logging.info(msg)
-            if local_embedding or self.model_type != ModelType.OpenAI:
-                embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2"))
-            else:
-                embed_model = OpenAIEmbedding()
-            # yield chatbot + [(inputs, "")], msg
-            with retrieve_proxy():
-                prompt_helper = PromptHelper(
-                    max_input_size=4096,
-                    num_output=5,
-                    max_chunk_overlap=20,
-                    chunk_size_limit=600,
-                )
-                from llama_index import ServiceContext
-
-                service_context = ServiceContext.from_defaults(
-                    prompt_helper=prompt_helper, embed_model=embed_model
-                )
-                query_object = GPTVectorStoreIndexQuery(
-                    index.index_struct,
-                    service_context=service_context,
-                    similarity_top_k=5,
-                    vector_store=index._vector_store,
-                    docstore=index._docstore,
-                )
-                query_bundle = QueryBundle(real_inputs)
-                nodes = query_object.retrieve(query_bundle)
-            reference_results = [n.node.text for n in nodes]
-            reference_results = add_source_numbers(reference_results, use_source=False)
-            display_append = add_details(reference_results)
-            display_append = "\n\n" + "".join(display_append)
-            real_inputs = (
-                replace_today(PROMPT_TEMPLATE)
-                .replace("{query_str}", real_inputs)
-                .replace("{context_str}", "\n\n".join(reference_results))
-                .replace("{reply_language}", reply_language)
-            )
-        elif use_websearch:
-            limited_context = True
-            search_results = ddg(real_inputs, max_results=5)
-            reference_results = []
-            for idx, result in enumerate(search_results):
-                logging.debug(f"搜索结果{idx + 1}：{result}")
-                domain_name = urllib3.util.parse_url(result["href"]).host
-                reference_results.append([result["body"], result["href"]])
-                display_append.append(
-                    # f"{idx+1}. [{domain_name}]({result['href']})\n"
-                    f"<li><a href=\"{result['href']}\" target=\"_blank\">{domain_name}</a></li>\n"
-                )
-            reference_results = add_source_numbers(reference_results)
-            display_append = "<ol>\n\n" + "".join(display_append) + "</ol>"
-            real_inputs = (
-                replace_today(WEBSEARCH_PTOMPT_TEMPLATE)
-                .replace("{query}", real_inputs)
-                .replace("{web_results}", "\n\n".join(reference_results))
-                .replace("{reply_language}", reply_language)
-            )
-        else:
-            display_append = ""
-        return limited_context, fake_inputs, display_append, real_inputs, chatbot
-
-    def predict(
-        self,
-        inputs,
-        chatbot,
-        stream=False,
-        use_websearch=False,
-        files=None,
-        reply_language="中文",
-        should_check_token_count=True,
-    ):  # repetition_penalty, top_k
-
-        status_text = "开始生成回答……"
-        logging.info(
-            "输入为：" + colorama.Fore.BLUE + f"{inputs}" + colorama.Style.RESET_ALL
-        )
-        if should_check_token_count:
-            yield chatbot + [(inputs, "")], status_text
-        if reply_language == "跟随问题语言（不稳定）":
-            reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
-
-        limited_context, fake_inputs, display_append, inputs, chatbot = self.prepare_inputs(real_inputs=inputs, use_websearch=use_websearch, files=files, reply_language=reply_language, chatbot=chatbot)
-        yield chatbot + [(fake_inputs, "")], status_text
-
-        if (
-            self.need_api_key and
-            self.api_key is None
-            and not shared.state.multi_api_key
-        ):
-            status_text = STANDARD_ERROR_MSG + NO_APIKEY_MSG
-            logging.info(status_text)
-            chatbot.append((inputs, ""))
-            if len(self.history) == 0:
-                self.history.append(construct_user(inputs))
-                self.history.append("")
-                self.all_token_counts.append(0)
-            else:
-                self.history[-2] = construct_user(inputs)
-            yield chatbot + [(inputs, "")], status_text
-            return
-        elif len(inputs.strip()) == 0:
-            status_text = STANDARD_ERROR_MSG + NO_INPUT_MSG
-            logging.info(status_text)
-            yield chatbot + [(inputs, "")], status_text
-            return
-
-        if self.single_turn:
-            self.history = []
-            self.all_token_counts = []
-        self.history.append(construct_user(inputs))
-
-        try:
-            if stream:
-                logging.debug("使用流式传输")
-                iter = self.stream_next_chatbot(
-                    inputs,
-                    chatbot,
-                    fake_input=fake_inputs,
-                    display_append=display_append,
-                )
-                for chatbot, status_text in iter:
-                    yield chatbot, status_text
-            else:
-                logging.debug("不使用流式传输")
-                chatbot, status_text = self.next_chatbot_at_once(
-                    inputs,
-                    chatbot,
-                    fake_input=fake_inputs,
-                    display_append=display_append,
-                )
-                yield chatbot, status_text
-        except Exception as e:
-            traceback.print_exc()
-            status_text = STANDARD_ERROR_MSG + str(e)
-            yield chatbot, status_text
-
-        if len(self.history) > 1 and self.history[-1]["content"] != inputs:
-            logging.info(
-                "回答为："
-                + colorama.Fore.BLUE
-                + f"{self.history[-1]['content']}"
-                + colorama.Style.RESET_ALL
-            )
-
-        if limited_context:
-            # self.history = self.history[-4:]
-            # self.all_token_counts = self.all_token_counts[-2:]
-            self.history = []
-            self.all_token_counts = []
-
-        max_token = self.token_upper_limit - TOKEN_OFFSET
-
-        if sum(self.all_token_counts) > max_token and should_check_token_count:
-            count = 0
-            while (
-                sum(self.all_token_counts)
-                > self.token_upper_limit * REDUCE_TOKEN_FACTOR
-                and sum(self.all_token_counts) > 0
-            ):
-                count += 1
-                del self.all_token_counts[0]
-                del self.history[:2]
-            logging.info(status_text)
-            status_text = f"为了防止token超限，模型忘记了早期的 {count} 轮对话"
-            yield chatbot, status_text
-
-    def retry(
-        self,
-        chatbot,
-        stream=False,
-        use_websearch=False,
-        files=None,
-        reply_language="中文",
-    ):
-        logging.debug("重试中……")
-        if len(self.history) > 0:
-            inputs = self.history[-2]["content"]
-            del self.history[-2:]
-            self.all_token_counts.pop()
-        elif len(chatbot) > 0:
-            inputs = chatbot[-1][0]
-        else:
-            yield chatbot, f"{STANDARD_ERROR_MSG}上下文是空的"
-            return
-
-        iter = self.predict(
-            inputs,
-            chatbot,
-            stream=stream,
-            use_websearch=use_websearch,
-            files=files,
-            reply_language=reply_language,
-        )
-        for x in iter:
-            yield x
-        logging.debug("重试完毕")
-
-    # def reduce_token_size(self, chatbot):
-    #     logging.info("开始减少token数量……")
-    #     chatbot, status_text = self.next_chatbot_at_once(
-    #         summarize_prompt,
-    #         chatbot
-    #     )
-    #     max_token_count = self.token_upper_limit * REDUCE_TOKEN_FACTOR
-    #     num_chat = find_n(self.all_token_counts, max_token_count)
-    #     logging.info(f"previous_token_count: {self.all_token_counts}, keeping {num_chat} chats")
-    #     chatbot = chatbot[:-1]
-    #     self.history = self.history[-2*num_chat:] if num_chat > 0 else []
-    #     self.all_token_counts = self.all_token_counts[-num_chat:] if num_chat > 0 else []
-    #     msg = f"保留了最近{num_chat}轮对话"
-    #     logging.info(msg)
-    #     logging.info("减少token数量完毕")
-    #     return chatbot, msg + "，" + self.token_message(self.all_token_counts if len(self.all_token_counts) > 0 else [0])
-
-    def interrupt(self):
-        self.interrupted = True
-
-    def recover(self):
-        self.interrupted = False
-
-    def set_token_upper_limit(self, new_upper_limit):
-        self.token_upper_limit = new_upper_limit
-        print(f"token上限设置为{new_upper_limit}")
-
-    def set_temperature(self, new_temperature):
-        self.temperature = new_temperature
-
-    def set_top_p(self, new_top_p):
-        self.top_p = new_top_p
-
-    def set_n_choices(self, new_n_choices):
-        self.n_choices = new_n_choices
-
-    def set_stop_sequence(self, new_stop_sequence: str):
-        new_stop_sequence = new_stop_sequence.split(",")
-        self.stop_sequence = new_stop_sequence
-
-    def set_max_tokens(self, new_max_tokens):
-        self.max_generation_token = new_max_tokens
-
-    def set_presence_penalty(self, new_presence_penalty):
-        self.presence_penalty = new_presence_penalty
-
-    def set_frequency_penalty(self, new_frequency_penalty):
-        self.frequency_penalty = new_frequency_penalty
-
-    def set_logit_bias(self, logit_bias):
-        logit_bias = logit_bias.split()
-        bias_map = {}
-        encoding = tiktoken.get_encoding("cl100k_base")
-        for line in logit_bias:
-            word, bias_amount = line.split(":")
-            if word:
-                for token in encoding.encode(word):
-                    bias_map[token] = float(bias_amount)
-        self.logit_bias = bias_map
-
-    def set_user_identifier(self, new_user_identifier):
-        self.user_identifier = new_user_identifier
-
-    def set_system_prompt(self, new_system_prompt):
-        self.system_prompt = new_system_prompt
-
-    def set_key(self, new_access_key):
-        self.api_key = new_access_key.strip()
-        msg = i18n("API密钥更改为了") + hide_middle_chars(self.api_key)
-        logging.info(msg)
-        return self.api_key, msg
-
-    def set_single_turn(self, new_single_turn):
-        self.single_turn = new_single_turn
-
-    def reset(self):
-        self.history = []
-        self.all_token_counts = []
-        self.interrupted = False
-        return [], self.token_message([0])
-
-    def delete_first_conversation(self):
-        if self.history:
-            del self.history[:2]
-            del self.all_token_counts[0]
-        return self.token_message()
-
-    def delete_last_conversation(self, chatbot):
-        if len(chatbot) > 0 and STANDARD_ERROR_MSG in chatbot[-1][1]:
-            msg = "由于包含报错信息，只删除chatbot记录"
-            chatbot.pop()
-            return chatbot, self.history
-        if len(self.history) > 0:
-            self.history.pop()
-            self.history.pop()
-        if len(chatbot) > 0:
-            msg = "删除了一组chatbot对话"
-            chatbot.pop()
-        if len(self.all_token_counts) > 0:
-            msg = "删除了一组对话的token计数记录"
-            self.all_token_counts.pop()
-        msg = "删除了一组对话"
-        return chatbot, msg
-
-    def token_message(self, token_lst=None):
-        if token_lst is None:
-            token_lst = self.all_token_counts
-        token_sum = 0
-        for i in range(len(token_lst)):
-            token_sum += sum(token_lst[: i + 1])
-        return i18n("Token 计数: ") + f"{sum(token_lst)}" + i18n("，本次对话累计消耗了 ") + f"{token_sum} tokens"
-
-    def save_chat_history(self, filename, chatbot, user_name):
-        if filename == "":
-            return
-        if not filename.endswith(".json"):
-            filename += ".json"
-        return save_file(filename, self.system_prompt, self.history, chatbot, user_name)
-
-    def export_markdown(self, filename, chatbot, user_name):
-        if filename == "":
-            return
-        if not filename.endswith(".md"):
-            filename += ".md"
-        return save_file(filename, self.system_prompt, self.history, chatbot, user_name)
-
-    def load_chat_history(self, filename, chatbot, user_name):
-        logging.debug(f"{user_name} 加载对话历史中……")
-        if type(filename) != str:
-            filename = filename.name
-        try:
-            with open(os.path.join(HISTORY_DIR, user_name, filename), "r") as f:
-                json_s = json.load(f)
-            try:
-                if type(json_s["history"][0]) == str:
-                    logging.info("历史记录格式为旧版，正在转换……")
-                    new_history = []
-                    for index, item in enumerate(json_s["history"]):
-                        if index % 2 == 0:
-                            new_history.append(construct_user(item))
-                        else:
-                            new_history.append(construct_assistant(item))
-                    json_s["history"] = new_history
-                    logging.info(new_history)
-            except:
-                # 没有对话历史
-                pass
-            logging.debug(f"{user_name} 加载对话历史完毕")
-            self.history = json_s["history"]
-            return filename, json_s["system"], json_s["chatbot"]
-        except FileNotFoundError:
-            logging.warning(f"{user_name} 没有找到对话历史文件，不执行任何操作")
-            return filename, self.system_prompt, chatbot
-
-    def like(self):
-        """like the last response, implement if needed
-        """
-        return gr.update()
-
-    def dislike(self):
-        """dislike the last response, implement if needed
-        """
-        return gr.update()
diff --git a/spaces/bohmian/simple_streamlit_app/app.py b/spaces/bohmian/simple_streamlit_app/app.py
deleted file mode 100644
index 857b0ce63125148173568bd84de198f8661a5b77..0000000000000000000000000000000000000000
--- a/spaces/bohmian/simple_streamlit_app/app.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-st.title("A Simple Streamlit Web App")
-
-name = st.text_input("Enter your name", '')
-
-st.write(f"Hello {name}!")
-
-x = st.slider("Select an integer x", 0, 10, 1)
-y = st.slider("Select an integer y", 0, 10, 1)
-
-df = pd.DataFrame({"x": [x], "y": [y] , "x + y": [x + y]}, index = ["addition row"])
-st.write(df)
\ No newline at end of file
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/english.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/english.py
deleted file mode 100644
index 6817392ba8a9eb830351de89fb7afc5ad72f5e42..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/english.py
+++ /dev/null
@@ -1,188 +0,0 @@
-""" from https://github.com/keithito/tacotron """
-
-'''
-Cleaners are transformations that run over the input text at both training and eval time.
-
-Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
-hyperparameter. Some cleaners are English-specific. You'll typically want to use:
-  1. "english_cleaners" for English text
-  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
-     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
-  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
-     the symbols in symbols.py to match your data).
-'''
-
-
-# Regular expression matching whitespace:
-
-
-import re
-import inflect
-from unidecode import unidecode
-import eng_to_ipa as ipa
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
-_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
-_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
-_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
-_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
-_number_re = re.compile(r'[0-9]+')
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
-    ('mrs', 'misess'),
-    ('mr', 'mister'),
-    ('dr', 'doctor'),
-    ('st', 'saint'),
-    ('co', 'company'),
-    ('jr', 'junior'),
-    ('maj', 'major'),
-    ('gen', 'general'),
-    ('drs', 'doctors'),
-    ('rev', 'reverend'),
-    ('lt', 'lieutenant'),
-    ('hon', 'honorable'),
-    ('sgt', 'sergeant'),
-    ('capt', 'captain'),
-    ('esq', 'esquire'),
-    ('ltd', 'limited'),
-    ('col', 'colonel'),
-    ('ft', 'fort'),
-]]
-
-
-# List of (ipa, lazy ipa) pairs:
-_lazy_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('æ', 'e'),
-    ('ɑ', 'a'),
-    ('ɔ', 'o'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ɛ', 'e'),
-    ('ɪ', 'i'),
-    ('ʊ', 'u'),
-    ('ʒ', 'ʥ'),
-    ('ʤ', 'ʥ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, lazy ipa2) pairs:
-_lazy_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ð', 'z'),
-    ('θ', 's'),
-    ('ʒ', 'ʑ'),
-    ('ʤ', 'dʑ'),
-    ('ˈ', '↓'),
-]]
-
-# List of (ipa, ipa2) pairs
-_ipa_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('r', 'ɹ'),
-    ('ʤ', 'dʒ'),
-    ('ʧ', 'tʃ')
-]]
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def collapse_whitespace(text):
-    return re.sub(r'\s+', ' ', text)
-
-
-def _remove_commas(m):
-    return m.group(1).replace(',', '')
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace('.', ' point ')
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split('.')
-    if len(parts) > 2:
-        return match + ' dollars'  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = 'dollar' if dollars == 1 else 'dollars'
-        return '%s %s' % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = 'cent' if cents == 1 else 'cents'
-        return '%s %s' % (cents, cent_unit)
-    else:
-        return 'zero dollars'
-
-
-def _expand_ordinal(m):
-    return _inflect.number_to_words(m.group(0))
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return 'two thousand'
-        elif num > 2000 and num < 2010:
-            return 'two thousand ' + _inflect.number_to_words(num % 100)
-        elif num % 100 == 0:
-            return _inflect.number_to_words(num // 100) + ' hundred'
-        else:
-            return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
-    else:
-        return _inflect.number_to_words(num, andword='')
-
-
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r'\1 pounds', text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
-
-
-def mark_dark_l(text):
-    return re.sub(r'l([^aeiouæɑɔəɛɪʊ ]*(?: |$))', lambda x: 'ɫ'+x.group(1), text)
-
-
-def english_to_ipa(text):
-    text = unidecode(text).lower()
-    text = expand_abbreviations(text)
-    text = normalize_numbers(text)
-    phonemes = ipa.convert(text)
-    phonemes = collapse_whitespace(phonemes)
-    return phonemes
-
-
-def english_to_lazy_ipa(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def english_to_ipa2(text):
-    text = english_to_ipa(text)
-    text = mark_dark_l(text)
-    for regex, replacement in _ipa_to_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text.replace('...', '…')
-
-
-def english_to_lazy_ipa2(text):
-    text = english_to_ipa(text)
-    for regex, replacement in _lazy_ipa2:
-        text = re.sub(regex, replacement, text)
-    return text
diff --git a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/shanghainese.py b/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/shanghainese.py
deleted file mode 100644
index cb29c24a08d2e406e8399cf7bc9fe5cb43cb9c61..0000000000000000000000000000000000000000
--- a/spaces/camenduru-com/VITS-Umamusume-voice-synthesizer/text/shanghainese.py
+++ /dev/null
@@ -1,64 +0,0 @@
-import re
-import cn2an
-import opencc
-
-
-converter = opencc.OpenCC('zaonhe')
-
-# List of (Latin alphabet, ipa) pairs:
-_latin_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [
-    ('A', 'ᴇ'),
-    ('B', 'bi'),
-    ('C', 'si'),
-    ('D', 'di'),
-    ('E', 'i'),
-    ('F', 'ᴇf'),
-    ('G', 'dʑi'),
-    ('H', 'ᴇtɕʰ'),
-    ('I', 'ᴀi'),
-    ('J', 'dʑᴇ'),
-    ('K', 'kʰᴇ'),
-    ('L', 'ᴇl'),
-    ('M', 'ᴇm'),
-    ('N', 'ᴇn'),
-    ('O', 'o'),
-    ('P', 'pʰi'),
-    ('Q', 'kʰiu'),
-    ('R', 'ᴀl'),
-    ('S', 'ᴇs'),
-    ('T', 'tʰi'),
-    ('U', 'ɦiu'),
-    ('V', 'vi'),
-    ('W', 'dᴀbɤliu'),
-    ('X', 'ᴇks'),
-    ('Y', 'uᴀi'),
-    ('Z', 'zᴇ')
-]]
-
-
-def _number_to_shanghainese(num):
-    num = cn2an.an2cn(num).replace('一十','十').replace('二十', '廿').replace('二', '两')
-    return re.sub(r'((?:^|[^三四五六七八九])十|廿)两', r'\1二', num)
-
-
-def number_to_shanghainese(text):
-    return re.sub(r'\d+(?:\.?\d+)?', lambda x: _number_to_shanghainese(x.group()), text)
-
-
-def latin_to_ipa(text):
-    for regex, replacement in _latin_to_ipa:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def shanghainese_to_ipa(text):
-    text = number_to_shanghainese(text.upper())
-    text = converter.convert(text).replace('-','').replace('$',' ')
-    text = re.sub(r'[A-Z]', lambda x: latin_to_ipa(x.group())+' ', text)
-    text = re.sub(r'[、；：]', '，', text)
-    text = re.sub(r'\s*，\s*', ', ', text)
-    text = re.sub(r'\s*。\s*', '. ', text)
-    text = re.sub(r'\s*？\s*', '? ', text)
-    text = re.sub(r'\s*！\s*', '! ', text)
-    text = re.sub(r'\s*$', '', text)
-    return text
diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/quick_schedules/README.md b/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/quick_schedules/README.md
deleted file mode 100644
index 4e6c82ef3f75a73c7006f33d7c850a0d4781a58f..0000000000000000000000000000000000000000
--- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/configs/quick_schedules/README.md
+++ /dev/null
@@ -1,8 +0,0 @@
-These are quick configs for performance or accuracy regression tracking purposes.
-
-* `*instance_test.yaml`: can train on 2 GPUs. They are used to test whether the training can
-  successfully finish. They are not expected to produce reasonable training results.
-* `*inference_acc_test.yaml`: They should be run using `--eval-only`. They run inference using pre-trained models and verify
-  the results are as expected.
-* `*training_acc_test.yaml`: They should be trained on 8 GPUs. They finish in about an hour and verify the training accuracy
-  is within the normal range.
diff --git a/spaces/chaitanya9/emotion_recognizer/utils.py b/spaces/chaitanya9/emotion_recognizer/utils.py
deleted file mode 100644
index 82ac6fba26834d92e70ff87c47d44f6547c7e51c..0000000000000000000000000000000000000000
--- a/spaces/chaitanya9/emotion_recognizer/utils.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import soundfile
-import librosa
-import numpy as np
-import pickle
-import os
-from convert_wavs import convert_audio
-
-
-AVAILABLE_EMOTIONS = {
-    "neutral",
-    "calm",
-    "happy",
-    "sad",
-    "angry",
-    "fear",
-    "disgust",
-    "ps", # pleasant surprised
-    "boredom"
-}
-
-
-def get_label(audio_config):
-    """Returns label corresponding to which features are to be extracted
-        e.g:
-    audio_config = {'mfcc': True, 'chroma': True, 'contrast': False, 'tonnetz': False, 'mel': False}
-    get_label(audio_config): 'mfcc-chroma'
-    """
-    features = ["mfcc", "chroma", "mel", "contrast", "tonnetz"]
-    label = ""
-    for feature in features:
-        if audio_config[feature]:
-            label += f"{feature}-"
-    return label.rstrip("-")
-
-
-def get_dropout_str(dropout, n_layers=3):
-    if isinstance(dropout, list):
-        return "_".join([ str(d) for d in dropout])
-    elif isinstance(dropout, float):
-        return "_".join([ str(dropout) for i in range(n_layers) ])
-
-
-def get_first_letters(emotions):
-    return "".join(sorted([ e[0].upper() for e in emotions ]))
-
-
-def extract_feature(file_name, **kwargs):
-    """
-    Extract feature from audio file `file_name`
-        Features supported:
-            - MFCC (mfcc)
-            - Chroma (chroma)
-            - MEL Spectrogram Frequency (mel)
-            - Contrast (contrast)
-            - Tonnetz (tonnetz)
-        e.g:
-        `features = extract_feature(path, mel=True, mfcc=True)`
-    """
-    mfcc = kwargs.get("mfcc")
-    chroma = kwargs.get("chroma")
-    mel = kwargs.get("mel")
-    contrast = kwargs.get("contrast")
-    tonnetz = kwargs.get("tonnetz")
-    # try:
-    #     with soundfile.SoundFile(file_name) as sound_file:
-    #         pass
-    # except RuntimeError:
-    #     # not properly formated, convert to 16000 sample rate & mono channel using ffmpeg
-    #     # get the basename
-    #     basename = os.path.basename(file_name)
-    #     dirname  = os.path.dirname(file_name)
-    #     name, ext = os.path.splitext(basename)
-    #     new_basename = f"{name}_c.wav"
-    #     new_filename = os.path.join(dirname, new_basename)
-    #     v = convert_audio(file_name, new_filename)
-    #     if v:
-    #         raise NotImplementedError("Converting the audio files failed, make sure `ffmpeg` is installed in your machine and added to PATH.")
-    # else:
-    #     new_filename = file_name
-    # with soundfile.SoundFile(new_filename) as sound_file:
-    X = file_name[1].astype("float32")
-        #X = sound_file.read(dtype="float32")
-    sample_rate = file_name[0] #sound_file.samplerate
-    #sample_rate = sound_file.samplerate
-    if chroma or contrast:
-        stft = np.abs(librosa.stft(X))
-    result = np.array([])
-    if mfcc:
-        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
-        result = np.hstack((result, mfccs))
-    if chroma:
-        chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
-        result = np.hstack((result, chroma))
-    if mel:
-        mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)
-        result = np.hstack((result, mel))
-    if contrast:
-        contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)
-        result = np.hstack((result, contrast))
-    if tonnetz:
-        tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)
-        result = np.hstack((result, tonnetz))
-    return result
-
-
-def get_best_estimators(classification):
-    """
-    Loads the estimators that are pickled in `grid` folder
-    Note that if you want to use different or more estimators,
-    you can fine tune the parameters in `grid_search.py` script
-    and run it again ( may take hours )
-    """
-    if classification:
-        return pickle.load(open("grid/best_classifiers.pickle", "rb"))
-    else:
-        return pickle.load(open("grid/best_regressors.pickle", "rb"))
-
-
-def get_audio_config(features_list):
-    """
-    Converts a list of features into a dictionary understandable by
-    `data_extractor.AudioExtractor` class
-    """
-    audio_config = {'mfcc': False, 'chroma': False, 'mel': False, 'contrast': False, 'tonnetz': False}
-    for feature in features_list:
-        if feature not in audio_config:
-            raise TypeError(f"Feature passed: {feature} is not recognized.")
-        audio_config[feature] = True
-    return audio_config
-    
\ No newline at end of file
diff --git a/spaces/chansung/zero2story/run.sh b/spaces/chansung/zero2story/run.sh
deleted file mode 100644
index ab2eb2b837ac7ae2c6691ad8d75833346a7c6f93..0000000000000000000000000000000000000000
--- a/spaces/chansung/zero2story/run.sh
+++ /dev/null
@@ -1,19 +0,0 @@
-#!/bin/bash
-
-PID=./gradio.pid
-if [[ -f "$PID" ]]; then
-    kill -15 `cat $PID` || kill -9 `cat $PID`
-fi
-
-mkdir -p ./logs
-rm -rf ./logs/app.log
-
-CONFIDENTIAL=./.palm_api_key.txt
-if [[ ! -f "$CONFIDENTIAL" ]]; then
-    echo "Error: PaLM API file not found. To continue, please create a .palm_api_key.txt file in the current directory."
-    exit 1
-fi
-
-export PALM_API_KEY=`cat .palm_api_key.txt`
-nohup python -u app.py > ./logs/app.log 2>&1 &
-echo $! > $PID
diff --git a/spaces/chasemcdo/hf_localai/api/prediction.go b/spaces/chasemcdo/hf_localai/api/prediction.go
deleted file mode 100644
index bc23d8681a29656f92e1fd0e08c210ef3eef11e9..0000000000000000000000000000000000000000
--- a/spaces/chasemcdo/hf_localai/api/prediction.go
+++ /dev/null
@@ -1,647 +0,0 @@
-package api
-
-import (
-	"fmt"
-	"os"
-	"path/filepath"
-	"regexp"
-	"strings"
-	"sync"
-
-	"github.com/donomii/go-rwkv.cpp"
-	"github.com/go-skynet/LocalAI/pkg/langchain"
-	model "github.com/go-skynet/LocalAI/pkg/model"
-	"github.com/go-skynet/LocalAI/pkg/stablediffusion"
-	"github.com/go-skynet/bloomz.cpp"
-	bert "github.com/go-skynet/go-bert.cpp"
-	transformers "github.com/go-skynet/go-ggml-transformers.cpp"
-	llama "github.com/go-skynet/go-llama.cpp"
-	gpt4all "github.com/nomic-ai/gpt4all/gpt4all-bindings/golang"
-)
-
-// mutex still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
-var mutexMap sync.Mutex
-var mutexes map[string]*sync.Mutex = make(map[string]*sync.Mutex)
-
-func defaultLLamaOpts(c Config) []llama.ModelOption {
-	llamaOpts := []llama.ModelOption{}
-	if c.ContextSize != 0 {
-		llamaOpts = append(llamaOpts, llama.SetContext(c.ContextSize))
-	}
-	if c.F16 {
-		llamaOpts = append(llamaOpts, llama.EnableF16Memory)
-	}
-	if c.Embeddings {
-		llamaOpts = append(llamaOpts, llama.EnableEmbeddings)
-	}
-
-	if c.NGPULayers != 0 {
-		llamaOpts = append(llamaOpts, llama.SetGPULayers(c.NGPULayers))
-	}
-
-	llamaOpts = append(llamaOpts, llama.SetMMap(c.MMap))
-	llamaOpts = append(llamaOpts, llama.SetMainGPU(c.MainGPU))
-	llamaOpts = append(llamaOpts, llama.SetTensorSplit(c.TensorSplit))
-	if c.Batch != 0 {
-		llamaOpts = append(llamaOpts, llama.SetNBatch(c.Batch))
-	} else {
-		llamaOpts = append(llamaOpts, llama.SetNBatch(512))
-	}
-
-	if c.NUMA {
-		llamaOpts = append(llamaOpts, llama.EnableNUMA)
-	}
-
-	if c.LowVRAM {
-		llamaOpts = append(llamaOpts, llama.EnabelLowVRAM)
-	}
-
-	return llamaOpts
-}
-
-func ImageGeneration(height, width, mode, step, seed int, positive_prompt, negative_prompt, dst string, loader *model.ModelLoader, c Config, o *Option) (func() error, error) {
-	if c.Backend != model.StableDiffusionBackend {
-		return nil, fmt.Errorf("endpoint only working with stablediffusion models")
-	}
-	inferenceModel, err := loader.BackendLoader(c.Backend, c.ImageGenerationAssets, []llama.ModelOption{}, uint32(c.Threads), o.assetsDestination)
-	if err != nil {
-		return nil, err
-	}
-
-	var fn func() error
-	switch model := inferenceModel.(type) {
-	case *stablediffusion.StableDiffusion:
-		fn = func() error {
-			return model.GenerateImage(height, width, mode, step, seed, positive_prompt, negative_prompt, dst)
-		}
-
-	default:
-		fn = func() error {
-			return fmt.Errorf("creation of images not supported by the backend")
-		}
-	}
-
-	return func() error {
-		// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
-		mutexMap.Lock()
-		l, ok := mutexes[c.Backend]
-		if !ok {
-			m := &sync.Mutex{}
-			mutexes[c.Backend] = m
-			l = m
-		}
-		mutexMap.Unlock()
-		l.Lock()
-		defer l.Unlock()
-
-		return fn()
-	}, nil
-}
-
-func ModelEmbedding(s string, tokens []int, loader *model.ModelLoader, c Config, o *Option) (func() ([]float32, error), error) {
-	if !c.Embeddings {
-		return nil, fmt.Errorf("endpoint disabled for this model by API configuration")
-	}
-
-	modelFile := c.Model
-
-	llamaOpts := defaultLLamaOpts(c)
-
-	var inferenceModel interface{}
-	var err error
-	if c.Backend == "" {
-		inferenceModel, err = loader.GreedyLoader(modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
-	} else {
-		inferenceModel, err = loader.BackendLoader(c.Backend, modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
-	}
-	if err != nil {
-		return nil, err
-	}
-
-	var fn func() ([]float32, error)
-	switch model := inferenceModel.(type) {
-	case *llama.LLama:
-		fn = func() ([]float32, error) {
-			predictOptions := buildLLamaPredictOptions(c, loader.ModelPath)
-			if len(tokens) > 0 {
-				return model.TokenEmbeddings(tokens, predictOptions...)
-			}
-			return model.Embeddings(s, predictOptions...)
-		}
-	// bert embeddings
-	case *bert.Bert:
-		fn = func() ([]float32, error) {
-			if len(tokens) > 0 {
-				return model.TokenEmbeddings(tokens, bert.SetThreads(c.Threads))
-			}
-			return model.Embeddings(s, bert.SetThreads(c.Threads))
-		}
-	default:
-		fn = func() ([]float32, error) {
-			return nil, fmt.Errorf("embeddings not supported by the backend")
-		}
-	}
-
-	return func() ([]float32, error) {
-		// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
-		mutexMap.Lock()
-		l, ok := mutexes[modelFile]
-		if !ok {
-			m := &sync.Mutex{}
-			mutexes[modelFile] = m
-			l = m
-		}
-		mutexMap.Unlock()
-		l.Lock()
-		defer l.Unlock()
-
-		embeds, err := fn()
-		if err != nil {
-			return embeds, err
-		}
-		// Remove trailing 0s
-		for i := len(embeds) - 1; i >= 0; i-- {
-			if embeds[i] == 0.0 {
-				embeds = embeds[:i]
-			} else {
-				break
-			}
-		}
-		return embeds, nil
-	}, nil
-}
-
-func buildLLamaPredictOptions(c Config, modelPath string) []llama.PredictOption {
-	// Generate the prediction using the language model
-	predictOptions := []llama.PredictOption{
-		llama.SetTemperature(c.Temperature),
-		llama.SetTopP(c.TopP),
-		llama.SetTopK(c.TopK),
-		llama.SetTokens(c.Maxtokens),
-		llama.SetThreads(c.Threads),
-	}
-
-	if c.PromptCacheAll {
-		predictOptions = append(predictOptions, llama.EnablePromptCacheAll)
-	}
-
-	if c.PromptCacheRO {
-		predictOptions = append(predictOptions, llama.EnablePromptCacheRO)
-	}
-
-	if c.PromptCachePath != "" {
-		// Create parent directory
-		p := filepath.Join(modelPath, c.PromptCachePath)
-		os.MkdirAll(filepath.Dir(p), 0755)
-		predictOptions = append(predictOptions, llama.SetPathPromptCache(p))
-	}
-
-	if c.Mirostat != 0 {
-		predictOptions = append(predictOptions, llama.SetMirostat(c.Mirostat))
-	}
-
-	if c.MirostatETA != 0 {
-		predictOptions = append(predictOptions, llama.SetMirostatETA(c.MirostatETA))
-	}
-
-	if c.MirostatTAU != 0 {
-		predictOptions = append(predictOptions, llama.SetMirostatTAU(c.MirostatTAU))
-	}
-
-	if c.Debug {
-		predictOptions = append(predictOptions, llama.Debug)
-	}
-
-	predictOptions = append(predictOptions, llama.SetStopWords(c.StopWords...))
-
-	if c.RepeatPenalty != 0 {
-		predictOptions = append(predictOptions, llama.SetPenalty(c.RepeatPenalty))
-	}
-
-	if c.Keep != 0 {
-		predictOptions = append(predictOptions, llama.SetNKeep(c.Keep))
-	}
-
-	if c.Batch != 0 {
-		predictOptions = append(predictOptions, llama.SetBatch(c.Batch))
-	}
-
-	if c.F16 {
-		predictOptions = append(predictOptions, llama.EnableF16KV)
-	}
-
-	if c.IgnoreEOS {
-		predictOptions = append(predictOptions, llama.IgnoreEOS)
-	}
-
-	if c.Seed != 0 {
-		predictOptions = append(predictOptions, llama.SetSeed(c.Seed))
-	}
-
-	//predictOptions = append(predictOptions, llama.SetLogitBias(c.Seed))
-
-	predictOptions = append(predictOptions, llama.SetFrequencyPenalty(c.FrequencyPenalty))
-	predictOptions = append(predictOptions, llama.SetMlock(c.MMlock))
-	predictOptions = append(predictOptions, llama.SetMemoryMap(c.MMap))
-	predictOptions = append(predictOptions, llama.SetPredictionMainGPU(c.MainGPU))
-	predictOptions = append(predictOptions, llama.SetPredictionTensorSplit(c.TensorSplit))
-	predictOptions = append(predictOptions, llama.SetTailFreeSamplingZ(c.TFZ))
-	predictOptions = append(predictOptions, llama.SetTypicalP(c.TypicalP))
-
-	return predictOptions
-}
-
-func ModelInference(s string, loader *model.ModelLoader, c Config, o *Option, tokenCallback func(string) bool) (func() (string, error), error) {
-	supportStreams := false
-	modelFile := c.Model
-
-	llamaOpts := defaultLLamaOpts(c)
-
-	var inferenceModel interface{}
-	var err error
-	if c.Backend == "" {
-		inferenceModel, err = loader.GreedyLoader(modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
-	} else {
-		inferenceModel, err = loader.BackendLoader(c.Backend, modelFile, llamaOpts, uint32(c.Threads), o.assetsDestination)
-	}
-	if err != nil {
-		return nil, err
-	}
-
-	var fn func() (string, error)
-
-	switch model := inferenceModel.(type) {
-	case *rwkv.RwkvState:
-		supportStreams = true
-
-		fn = func() (string, error) {
-			stopWord := "\n"
-			if len(c.StopWords) > 0 {
-				stopWord = c.StopWords[0]
-			}
-
-			if err := model.ProcessInput(s); err != nil {
-				return "", err
-			}
-
-			response := model.GenerateResponse(c.Maxtokens, stopWord, float32(c.Temperature), float32(c.TopP), tokenCallback)
-
-			return response, nil
-		}
-	case *transformers.GPTNeoX:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.Replit:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.Starcoder:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.MPT:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *bloomz.Bloomz:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []bloomz.PredictOption{
-				bloomz.SetTemperature(c.Temperature),
-				bloomz.SetTopP(c.TopP),
-				bloomz.SetTopK(c.TopK),
-				bloomz.SetTokens(c.Maxtokens),
-				bloomz.SetThreads(c.Threads),
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, bloomz.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.Falcon:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.GPTJ:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.Dolly:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *transformers.GPT2:
-		fn = func() (string, error) {
-			// Generate the prediction using the language model
-			predictOptions := []transformers.PredictOption{
-				transformers.SetTemperature(c.Temperature),
-				transformers.SetTopP(c.TopP),
-				transformers.SetTopK(c.TopK),
-				transformers.SetTokens(c.Maxtokens),
-				transformers.SetThreads(c.Threads),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, transformers.SetBatch(c.Batch))
-			}
-
-			if c.Seed != 0 {
-				predictOptions = append(predictOptions, transformers.SetSeed(c.Seed))
-			}
-
-			return model.Predict(
-				s,
-				predictOptions...,
-			)
-		}
-	case *gpt4all.Model:
-		supportStreams = true
-
-		fn = func() (string, error) {
-			if tokenCallback != nil {
-				model.SetTokenCallback(tokenCallback)
-			}
-
-			// Generate the prediction using the language model
-			predictOptions := []gpt4all.PredictOption{
-				gpt4all.SetTemperature(c.Temperature),
-				gpt4all.SetTopP(c.TopP),
-				gpt4all.SetTopK(c.TopK),
-				gpt4all.SetTokens(c.Maxtokens),
-			}
-
-			if c.Batch != 0 {
-				predictOptions = append(predictOptions, gpt4all.SetBatch(c.Batch))
-			}
-
-			str, er := model.Predict(
-				s,
-				predictOptions...,
-			)
-			// Seems that if we don't free the callback explicitly we leave functions registered (that might try to send on closed channels)
-			// For instance otherwise the API returns: {"error":{"code":500,"message":"send on closed channel","type":""}}
-			// after a stream event has occurred
-			model.SetTokenCallback(nil)
-			return str, er
-		}
-	case *llama.LLama:
-		supportStreams = true
-		fn = func() (string, error) {
-
-			if tokenCallback != nil {
-				model.SetTokenCallback(tokenCallback)
-			}
-
-			predictOptions := buildLLamaPredictOptions(c, loader.ModelPath)
-
-			str, er := model.Predict(
-				s,
-				predictOptions...,
-			)
-			// Seems that if we don't free the callback explicitly we leave functions registered (that might try to send on closed channels)
-			// For instance otherwise the API returns: {"error":{"code":500,"message":"send on closed channel","type":""}}
-			// after a stream event has occurred
-			model.SetTokenCallback(nil)
-			return str, er
-		}
-	case *langchain.HuggingFace:
-		fn = func() (string, error) {
-
-			// Generate the prediction using the language model
-			predictOptions := []langchain.PredictOption{
-				langchain.SetModel(c.Model),
-				langchain.SetMaxTokens(c.Maxtokens),
-				langchain.SetTemperature(c.Temperature),
-				langchain.SetStopWords(c.StopWords),
-			}
-
-			pred, er := model.PredictHuggingFace(s, predictOptions...)
-			if er != nil {
-				return "", er
-			}
-			return pred.Completion, nil
-		}
-	}
-
-	return func() (string, error) {
-		// This is still needed, see: https://github.com/ggerganov/llama.cpp/discussions/784
-		mutexMap.Lock()
-		l, ok := mutexes[modelFile]
-		if !ok {
-			m := &sync.Mutex{}
-			mutexes[modelFile] = m
-			l = m
-		}
-		mutexMap.Unlock()
-		l.Lock()
-		defer l.Unlock()
-
-		res, err := fn()
-		if tokenCallback != nil && !supportStreams {
-			tokenCallback(res)
-		}
-		return res, err
-	}, nil
-}
-
-func ComputeChoices(predInput string, input *OpenAIRequest, config *Config, o *Option, loader *model.ModelLoader, cb func(string, *[]Choice), tokenCallback func(string) bool) ([]Choice, error) {
-	result := []Choice{}
-
-	n := input.N
-
-	if input.N == 0 {
-		n = 1
-	}
-
-	// get the model function to call for the result
-	predFunc, err := ModelInference(predInput, loader, *config, o, tokenCallback)
-	if err != nil {
-		return result, err
-	}
-
-	for i := 0; i < n; i++ {
-		prediction, err := predFunc()
-		if err != nil {
-			return result, err
-		}
-
-		prediction = Finetune(*config, predInput, prediction)
-		cb(prediction, &result)
-
-		//result = append(result, Choice{Text: prediction})
-
-	}
-	return result, err
-}
-
-var cutstrings map[string]*regexp.Regexp = make(map[string]*regexp.Regexp)
-var mu sync.Mutex = sync.Mutex{}
-
-func Finetune(config Config, input, prediction string) string {
-	if config.Echo {
-		prediction = input + prediction
-	}
-
-	for _, c := range config.Cutstrings {
-		mu.Lock()
-		reg, ok := cutstrings[c]
-		if !ok {
-			cutstrings[c] = regexp.MustCompile(c)
-			reg = cutstrings[c]
-		}
-		mu.Unlock()
-		prediction = reg.ReplaceAllString(prediction, "")
-	}
-
-	for _, c := range config.TrimSpace {
-		prediction = strings.TrimSpace(strings.TrimPrefix(prediction, c))
-	}
-	return prediction
-
-}
diff --git a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/utils/demo_utils.py b/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/utils/demo_utils.py
deleted file mode 100644
index 56dd33686f03c4ec1b82a79e3dadcd49fec6c0bb..0000000000000000000000000000000000000000
--- a/spaces/chendl/compositional_test/multimodal/YOLOX/yolox/utils/demo_utils.py
+++ /dev/null
@@ -1,159 +0,0 @@
-#!/usr/bin/env python3
-# Copyright (c) Megvii Inc. All rights reserved.
-
-import os
-import random
-
-import cv2
-import numpy as np
-
-__all__ = [
-    "mkdir", "nms", "multiclass_nms", "demo_postprocess", "random_color", "visualize_assign"
-]
-
-
-def random_color():
-    return random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)
-
-
-def visualize_assign(img, boxes, coords, match_results, save_name=None) -> np.ndarray:
-    """visualize label assign result.
-
-    Args:
-        img: img to visualize
-        boxes: gt boxes in xyxy format
-        coords: coords of matched anchors
-        match_results: match results of each gt box and coord.
-        save_name: name of save image, if None, image will not be saved. Default: None.
-    """
-    for box_id, box in enumerate(boxes):
-        x1, y1, x2, y2 = box
-        color = random_color()
-        assign_coords = coords[match_results == box_id]
-        if assign_coords.numel() == 0:
-            # unmatched boxes are red
-            color = (0, 0, 255)
-            cv2.putText(
-                img, "unmatched", (int(x1), int(y1) - 5),
-                cv2.FONT_HERSHEY_SIMPLEX, 0.6, color, 1
-            )
-        else:
-            for coord in assign_coords:
-                # draw assigned anchor
-                cv2.circle(img, (int(coord[0]), int(coord[1])), 3, color, -1)
-        cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), color, 2)
-
-    if save_name is not None:
-        cv2.imwrite(save_name, img)
-
-    return img
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-
-def nms(boxes, scores, nms_thr):
-    """Single class NMS implemented in Numpy."""
-    x1 = boxes[:, 0]
-    y1 = boxes[:, 1]
-    x2 = boxes[:, 2]
-    y2 = boxes[:, 3]
-
-    areas = (x2 - x1 + 1) * (y2 - y1 + 1)
-    order = scores.argsort()[::-1]
-
-    keep = []
-    while order.size > 0:
-        i = order[0]
-        keep.append(i)
-        xx1 = np.maximum(x1[i], x1[order[1:]])
-        yy1 = np.maximum(y1[i], y1[order[1:]])
-        xx2 = np.minimum(x2[i], x2[order[1:]])
-        yy2 = np.minimum(y2[i], y2[order[1:]])
-
-        w = np.maximum(0.0, xx2 - xx1 + 1)
-        h = np.maximum(0.0, yy2 - yy1 + 1)
-        inter = w * h
-        ovr = inter / (areas[i] + areas[order[1:]] - inter)
-
-        inds = np.where(ovr <= nms_thr)[0]
-        order = order[inds + 1]
-
-    return keep
-
-
-def multiclass_nms(boxes, scores, nms_thr, score_thr, class_agnostic=True):
-    """Multiclass NMS implemented in Numpy"""
-    if class_agnostic:
-        nms_method = multiclass_nms_class_agnostic
-    else:
-        nms_method = multiclass_nms_class_aware
-    return nms_method(boxes, scores, nms_thr, score_thr)
-
-
-def multiclass_nms_class_aware(boxes, scores, nms_thr, score_thr):
-    """Multiclass NMS implemented in Numpy. Class-aware version."""
-    final_dets = []
-    num_classes = scores.shape[1]
-    for cls_ind in range(num_classes):
-        cls_scores = scores[:, cls_ind]
-        valid_score_mask = cls_scores > score_thr
-        if valid_score_mask.sum() == 0:
-            continue
-        else:
-            valid_scores = cls_scores[valid_score_mask]
-            valid_boxes = boxes[valid_score_mask]
-            keep = nms(valid_boxes, valid_scores, nms_thr)
-            if len(keep) > 0:
-                cls_inds = np.ones((len(keep), 1)) * cls_ind
-                dets = np.concatenate(
-                    [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1
-                )
-                final_dets.append(dets)
-    if len(final_dets) == 0:
-        return None
-    return np.concatenate(final_dets, 0)
-
-
-def multiclass_nms_class_agnostic(boxes, scores, nms_thr, score_thr):
-    """Multiclass NMS implemented in Numpy. Class-agnostic version."""
-    cls_inds = scores.argmax(1)
-    cls_scores = scores[np.arange(len(cls_inds)), cls_inds]
-
-    valid_score_mask = cls_scores > score_thr
-    if valid_score_mask.sum() == 0:
-        return None
-    valid_scores = cls_scores[valid_score_mask]
-    valid_boxes = boxes[valid_score_mask]
-    valid_cls_inds = cls_inds[valid_score_mask]
-    keep = nms(valid_boxes, valid_scores, nms_thr)
-    if keep:
-        dets = np.concatenate(
-            [valid_boxes[keep], valid_scores[keep, None], valid_cls_inds[keep, None]], 1
-        )
-    return dets
-
-
-def demo_postprocess(outputs, img_size, p6=False):
-    grids = []
-    expanded_strides = []
-    strides = [8, 16, 32] if not p6 else [8, 16, 32, 64]
-
-    hsizes = [img_size[0] // stride for stride in strides]
-    wsizes = [img_size[1] // stride for stride in strides]
-
-    for hsize, wsize, stride in zip(hsizes, wsizes, strides):
-        xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
-        grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
-        grids.append(grid)
-        shape = grid.shape[:2]
-        expanded_strides.append(np.full((*shape, 1), stride))
-
-    grids = np.concatenate(grids, 1)
-    expanded_strides = np.concatenate(expanded_strides, 1)
-    outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
-    outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
-
-    return outputs
diff --git a/spaces/chronopt-research/ViTExCo/setup.py b/spaces/chronopt-research/ViTExCo/setup.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/WebPImagePlugin.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/WebPImagePlugin.py
deleted file mode 100644
index ce8e05fcbb1aec68a9ca48c2fd02b3c130029521..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/PIL/WebPImagePlugin.py
+++ /dev/null
@@ -1,366 +0,0 @@
-from io import BytesIO
-
-from . import Image, ImageFile
-
-try:
-    from . import _webp
-
-    SUPPORTED = True
-except ImportError:
-    SUPPORTED = False
-
-
-_VALID_WEBP_MODES = {"RGBX": True, "RGBA": True, "RGB": True}
-
-_VALID_WEBP_LEGACY_MODES = {"RGB": True, "RGBA": True}
-
-_VP8_MODES_BY_IDENTIFIER = {
-    b"VP8 ": "RGB",
-    b"VP8X": "RGBA",
-    b"VP8L": "RGBA",  # lossless
-}
-
-
-def _accept(prefix):
-    is_riff_file_format = prefix[:4] == b"RIFF"
-    is_webp_file = prefix[8:12] == b"WEBP"
-    is_valid_vp8_mode = prefix[12:16] in _VP8_MODES_BY_IDENTIFIER
-
-    if is_riff_file_format and is_webp_file and is_valid_vp8_mode:
-        if not SUPPORTED:
-            return (
-                "image file could not be identified because WEBP support not installed"
-            )
-        return True
-
-
-class WebPImageFile(ImageFile.ImageFile):
-    format = "WEBP"
-    format_description = "WebP image"
-    __loaded = 0
-    __logical_frame = 0
-
-    def _open(self):
-        if not _webp.HAVE_WEBPANIM:
-            # Legacy mode
-            data, width, height, self.mode, icc_profile, exif = _webp.WebPDecode(
-                self.fp.read()
-            )
-            if icc_profile:
-                self.info["icc_profile"] = icc_profile
-            if exif:
-                self.info["exif"] = exif
-            self._size = width, height
-            self.fp = BytesIO(data)
-            self.tile = [("raw", (0, 0) + self.size, 0, self.mode)]
-            self.n_frames = 1
-            self.is_animated = False
-            return
-
-        # Use the newer AnimDecoder API to parse the (possibly) animated file,
-        # and access muxed chunks like ICC/EXIF/XMP.
-        self._decoder = _webp.WebPAnimDecoder(self.fp.read())
-
-        # Get info from decoder
-        width, height, loop_count, bgcolor, frame_count, mode = self._decoder.get_info()
-        self._size = width, height
-        self.info["loop"] = loop_count
-        bg_a, bg_r, bg_g, bg_b = (
-            (bgcolor >> 24) & 0xFF,
-            (bgcolor >> 16) & 0xFF,
-            (bgcolor >> 8) & 0xFF,
-            bgcolor & 0xFF,
-        )
-        self.info["background"] = (bg_r, bg_g, bg_b, bg_a)
-        self.n_frames = frame_count
-        self.is_animated = self.n_frames > 1
-        self.mode = "RGB" if mode == "RGBX" else mode
-        self.rawmode = mode
-        self.tile = []
-
-        # Attempt to read ICC / EXIF / XMP chunks from file
-        icc_profile = self._decoder.get_chunk("ICCP")
-        exif = self._decoder.get_chunk("EXIF")
-        xmp = self._decoder.get_chunk("XMP ")
-        if icc_profile:
-            self.info["icc_profile"] = icc_profile
-        if exif:
-            self.info["exif"] = exif
-        if xmp:
-            self.info["xmp"] = xmp
-
-        # Initialize seek state
-        self._reset(reset=False)
-
-    def _getexif(self):
-        if "exif" not in self.info:
-            return None
-        return self.getexif()._get_merged_dict()
-
-    def getxmp(self):
-        """
-        Returns a dictionary containing the XMP tags.
-        Requires defusedxml to be installed.
-
-        :returns: XMP tags in a dictionary.
-        """
-        return self._getxmp(self.info["xmp"]) if "xmp" in self.info else {}
-
-    def seek(self, frame):
-        if not self._seek_check(frame):
-            return
-
-        # Set logical frame to requested position
-        self.__logical_frame = frame
-
-    def _reset(self, reset=True):
-        if reset:
-            self._decoder.reset()
-        self.__physical_frame = 0
-        self.__loaded = -1
-        self.__timestamp = 0
-
-    def _get_next(self):
-        # Get next frame
-        ret = self._decoder.get_next()
-        self.__physical_frame += 1
-
-        # Check if an error occurred
-        if ret is None:
-            self._reset()  # Reset just to be safe
-            self.seek(0)
-            msg = "failed to decode next frame in WebP file"
-            raise EOFError(msg)
-
-        # Compute duration
-        data, timestamp = ret
-        duration = timestamp - self.__timestamp
-        self.__timestamp = timestamp
-
-        # libwebp gives frame end, adjust to start of frame
-        timestamp -= duration
-        return data, timestamp, duration
-
-    def _seek(self, frame):
-        if self.__physical_frame == frame:
-            return  # Nothing to do
-        if frame < self.__physical_frame:
-            self._reset()  # Rewind to beginning
-        while self.__physical_frame < frame:
-            self._get_next()  # Advance to the requested frame
-
-    def load(self):
-        if _webp.HAVE_WEBPANIM:
-            if self.__loaded != self.__logical_frame:
-                self._seek(self.__logical_frame)
-
-                # We need to load the image data for this frame
-                data, timestamp, duration = self._get_next()
-                self.info["timestamp"] = timestamp
-                self.info["duration"] = duration
-                self.__loaded = self.__logical_frame
-
-                # Set tile
-                if self.fp and self._exclusive_fp:
-                    self.fp.close()
-                self.fp = BytesIO(data)
-                self.tile = [("raw", (0, 0) + self.size, 0, self.rawmode)]
-
-        return super().load()
-
-    def tell(self):
-        if not _webp.HAVE_WEBPANIM:
-            return super().tell()
-
-        return self.__logical_frame
-
-
-def _save_all(im, fp, filename):
-    encoderinfo = im.encoderinfo.copy()
-    append_images = list(encoderinfo.get("append_images", []))
-
-    # If total frame count is 1, then save using the legacy API, which
-    # will preserve non-alpha modes
-    total = 0
-    for ims in [im] + append_images:
-        total += getattr(ims, "n_frames", 1)
-    if total == 1:
-        _save(im, fp, filename)
-        return
-
-    background = (0, 0, 0, 0)
-    if "background" in encoderinfo:
-        background = encoderinfo["background"]
-    elif "background" in im.info:
-        background = im.info["background"]
-        if isinstance(background, int):
-            # GifImagePlugin stores a global color table index in
-            # info["background"]. So it must be converted to an RGBA value
-            palette = im.getpalette()
-            if palette:
-                r, g, b = palette[background * 3 : (background + 1) * 3]
-                background = (r, g, b, 255)
-            else:
-                background = (background, background, background, 255)
-
-    duration = im.encoderinfo.get("duration", im.info.get("duration", 0))
-    loop = im.encoderinfo.get("loop", 0)
-    minimize_size = im.encoderinfo.get("minimize_size", False)
-    kmin = im.encoderinfo.get("kmin", None)
-    kmax = im.encoderinfo.get("kmax", None)
-    allow_mixed = im.encoderinfo.get("allow_mixed", False)
-    verbose = False
-    lossless = im.encoderinfo.get("lossless", False)
-    quality = im.encoderinfo.get("quality", 80)
-    method = im.encoderinfo.get("method", 0)
-    icc_profile = im.encoderinfo.get("icc_profile") or ""
-    exif = im.encoderinfo.get("exif", "")
-    if isinstance(exif, Image.Exif):
-        exif = exif.tobytes()
-    xmp = im.encoderinfo.get("xmp", "")
-    if allow_mixed:
-        lossless = False
-
-    # Sensible keyframe defaults are from gif2webp.c script
-    if kmin is None:
-        kmin = 9 if lossless else 3
-    if kmax is None:
-        kmax = 17 if lossless else 5
-
-    # Validate background color
-    if (
-        not isinstance(background, (list, tuple))
-        or len(background) != 4
-        or not all(0 <= v < 256 for v in background)
-    ):
-        msg = f"Background color is not an RGBA tuple clamped to (0-255): {background}"
-        raise OSError(msg)
-
-    # Convert to packed uint
-    bg_r, bg_g, bg_b, bg_a = background
-    background = (bg_a << 24) | (bg_r << 16) | (bg_g << 8) | (bg_b << 0)
-
-    # Setup the WebP animation encoder
-    enc = _webp.WebPAnimEncoder(
-        im.size[0],
-        im.size[1],
-        background,
-        loop,
-        minimize_size,
-        kmin,
-        kmax,
-        allow_mixed,
-        verbose,
-    )
-
-    # Add each frame
-    frame_idx = 0
-    timestamp = 0
-    cur_idx = im.tell()
-    try:
-        for ims in [im] + append_images:
-            # Get # of frames in this image
-            nfr = getattr(ims, "n_frames", 1)
-
-            for idx in range(nfr):
-                ims.seek(idx)
-                ims.load()
-
-                # Make sure image mode is supported
-                frame = ims
-                rawmode = ims.mode
-                if ims.mode not in _VALID_WEBP_MODES:
-                    alpha = (
-                        "A" in ims.mode
-                        or "a" in ims.mode
-                        or (ims.mode == "P" and "A" in ims.im.getpalettemode())
-                    )
-                    rawmode = "RGBA" if alpha else "RGB"
-                    frame = ims.convert(rawmode)
-
-                if rawmode == "RGB":
-                    # For faster conversion, use RGBX
-                    rawmode = "RGBX"
-
-                # Append the frame to the animation encoder
-                enc.add(
-                    frame.tobytes("raw", rawmode),
-                    round(timestamp),
-                    frame.size[0],
-                    frame.size[1],
-                    rawmode,
-                    lossless,
-                    quality,
-                    method,
-                )
-
-                # Update timestamp and frame index
-                if isinstance(duration, (list, tuple)):
-                    timestamp += duration[frame_idx]
-                else:
-                    timestamp += duration
-                frame_idx += 1
-
-    finally:
-        im.seek(cur_idx)
-
-    # Force encoder to flush frames
-    enc.add(None, round(timestamp), 0, 0, "", lossless, quality, 0)
-
-    # Get the final output from the encoder
-    data = enc.assemble(icc_profile, exif, xmp)
-    if data is None:
-        msg = "cannot write file as WebP (encoder returned None)"
-        raise OSError(msg)
-
-    fp.write(data)
-
-
-def _save(im, fp, filename):
-    lossless = im.encoderinfo.get("lossless", False)
-    quality = im.encoderinfo.get("quality", 80)
-    icc_profile = im.encoderinfo.get("icc_profile") or ""
-    exif = im.encoderinfo.get("exif", b"")
-    if isinstance(exif, Image.Exif):
-        exif = exif.tobytes()
-    if exif.startswith(b"Exif\x00\x00"):
-        exif = exif[6:]
-    xmp = im.encoderinfo.get("xmp", "")
-    method = im.encoderinfo.get("method", 4)
-    exact = 1 if im.encoderinfo.get("exact") else 0
-
-    if im.mode not in _VALID_WEBP_LEGACY_MODES:
-        alpha = (
-            "A" in im.mode
-            or "a" in im.mode
-            or (im.mode == "P" and "transparency" in im.info)
-        )
-        im = im.convert("RGBA" if alpha else "RGB")
-
-    data = _webp.WebPEncode(
-        im.tobytes(),
-        im.size[0],
-        im.size[1],
-        lossless,
-        float(quality),
-        im.mode,
-        icc_profile,
-        method,
-        exact,
-        exif,
-        xmp,
-    )
-    if data is None:
-        msg = "cannot write file as WebP (encoder returned None)"
-        raise OSError(msg)
-
-    fp.write(data)
-
-
-Image.register_open(WebPImageFile.format, WebPImageFile, _accept)
-if SUPPORTED:
-    Image.register_save(WebPImageFile.format, _save)
-    if _webp.HAVE_WEBPANIM:
-        Image.register_save_all(WebPImageFile.format, _save_all)
-    Image.register_extension(WebPImageFile.format, ".webp")
-    Image.register_mime(WebPImageFile.format, "image/webp")
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/ttVisitor.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/ttVisitor.py
deleted file mode 100644
index 54db61b1e0b1be5e2d36fd72008230de7fc35401..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/fontTools/ttLib/ttVisitor.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""Specialization of fontTools.misc.visitor to work with TTFont."""
-
-from fontTools.misc.visitor import Visitor
-from fontTools.ttLib import TTFont
-
-
-class TTVisitor(Visitor):
-    def visitAttr(self, obj, attr, value, *args, **kwargs):
-        if isinstance(value, TTFont):
-            return False
-        super().visitAttr(obj, attr, value, *args, **kwargs)
-
-    def visit(self, obj, *args, **kwargs):
-        if hasattr(obj, "ensureDecompiled"):
-            obj.ensureDecompiled(recurse=False)
-        super().visit(obj, *args, **kwargs)
-
-
-@TTVisitor.register(TTFont)
-def visit(visitor, font, *args, **kwargs):
-    # Some objects have links back to TTFont; even though we
-    # have a check in visitAttr to stop them from recursing
-    # onto TTFont, sometimes they still do, for example when
-    # someone overrides visitAttr.
-    if hasattr(visitor, "font"):
-        return False
-
-    visitor.font = font
-    for tag in font.keys():
-        visitor.visit(font[tag], *args, **kwargs)
-    del visitor.font
-    return False
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/_src/__init__.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/functorch/_src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7df11078.js b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7df11078.js
deleted file mode 100644
index 36bc5f2d9404af6afc92db5dfcf6069049734b6f..0000000000000000000000000000000000000000
--- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/gradio/templates/cdn/assets/index-7df11078.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import{S as m,e as r,s as d,a9 as v,N as b,K as u,as as f,U as c,p as g,ab as h,ac as G,ad as j,z as k,v as w,A as C}from"./index-f877dfd5.js";function S(i){let s,n,t;const o=i[4].default,l=v(o,i,i[3],null);return{c(){s=b("div"),l&&l.c(),u(s,"id",i[0]),u(s,"class",n=f(i[1].join(" "))+" svelte-18kevw"),c(s,"hide",!i[2])},m(e,a){g(e,s,a),l&&l.m(s,null),t=!0},p(e,[a]){l&&l.p&&(!t||a&8)&&h(l,o,e,e[3],t?j(o,e[3],a,null):G(e[3]),null),(!t||a&1)&&u(s,"id",e[0]),(!t||a&2&&n!==(n=f(e[1].join(" "))+" svelte-18kevw"))&&u(s,"class",n),(!t||a&6)&&c(s,"hide",!e[2])},i(e){t||(k(l,e),t=!0)},o(e){w(l,e),t=!1},d(e){e&&C(s),l&&l.d(e)}}}function q(i,s,n){let{$$slots:t={},$$scope:o}=s,{elem_id:l=""}=s,{elem_classes:e=[]}=s,{visible:a=!0}=s;return i.$$set=_=>{"elem_id"in _&&n(0,l=_.elem_id),"elem_classes"in _&&n(1,e=_.elem_classes),"visible"in _&&n(2,a=_.visible),"$$scope"in _&&n(3,o=_.$$scope)},[l,e,a,o,t]}class z extends m{constructor(s){super(),r(this,s,q,S,d,{elem_id:0,elem_classes:1,visible:2})}}const K=z,N=["static"];export{K as Component,N as modes};
-//# sourceMappingURL=index-7df11078.js.map
diff --git a/spaces/cihyFjudo/fairness-paper-search/Shrimad Bhagwat Mahapuran In Hindi Free Download The Best Source for Spiritual Knowledge and Inspiration.md b/spaces/cihyFjudo/fairness-paper-search/Shrimad Bhagwat Mahapuran In Hindi Free Download The Best Source for Spiritual Knowledge and Inspiration.md
deleted file mode 100644
index 1a39308a9c0fba03fa0a4c1ff490c2769a34678b..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Shrimad Bhagwat Mahapuran In Hindi Free Download The Best Source for Spiritual Knowledge and Inspiration.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<br />
-<p>Download भागवत पुराण Shrimad Bhagwat Puran Bhagavata Purana by Veda Vyasa Book in Hindi PDF katha for free using the direct download link from pdf reader. shrimad bhagwat puran gita press pdf.</p>
-<p>bhagwat geeta, shrimad bhagwat, bhagawad geeta, shrimad bhagwat geeta, bhagwat gita, bhagwat geeta in hindi, bhagvat geeta book, shrimad bhagwat mahapuran, bhagavad gita in hindi, bhagwat path, bhagvat geeta book in hindi, bhagwad geeta in hindi, bhagwat geeta book, shrimadbhagwat geeta, shreemad bhagwat geeta, bhagwat geeta in hindi book, shrimad bhagwat geeta in hindi, bhagavad gita book in hindi, shrimad bhagwat puran, shreemad bhagvad geeta, srimad bhagwat geeta, shree bhagwat geeta, shrimad bhagwat gita, shri krishna bhagwat geeta, bhagwat puran in hindi, bhagavad gita book online free,<br />srimad bhagavad gita in hindi, Free PDF of Bhagwat Gita, Download Free PDF of Bhagwat Gita, Gyan Vigyan Yoga, Lord Krishana, Mahatmya</p>
-<h2>Shrimad Bhagwat Mahapuran In Hindi Free Download</h2><br /><p><b><b>DOWNLOAD</b> >>> <a href="https://tinurli.com/2uwjeK">https://tinurli.com/2uwjeK</a></b></p><br /><br /> aaccfb2cb3<br />
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\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/Singapore Online Dating Find Your Soulmate in the Land of Merlion.md b/spaces/cihyFjudo/fairness-paper-search/Singapore Online Dating Find Your Soulmate in the Land of Merlion.md
deleted file mode 100644
index 5d0a4c6c0d9bc788fcb1454b0c9c5c928970fb42..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/Singapore Online Dating Find Your Soulmate in the Land of Merlion.md	
+++ /dev/null
@@ -1,40 +0,0 @@
-<br />
-<p><strong>Talent pool</strong> It's a mess. On most dating apps, you can only message someone once you both are matched but on OkCupid, it's open season. And from the quality of messages, there is no talent here.</p>
-<p>A dating app for gay, bi, trans, and queer people, Grindr is the perfect online gateway for the LGBTQ community to socialise and connect. This geo-social networking app uses your phone's location to pick up fellow Grindr males in your area. It's safe space for those looking to come out of the closet, meet like-minded people and navigate through the queer world.</p>
-<h2>singapore online dating</h2><br /><p><b><b>Download File</b> &#9733;&#9733;&#9733; <a href="https://tinurli.com/2uwiiw">https://tinurli.com/2uwiiw</a></b></p><br /><br />
-<p>Blindfold masks or blurs profile pictures based on the premise that online dating can be embarrassing. In reality, hiding your face is not so much for discretion as it is about being unsure about how attractive the opposite sex will find you.</p>
-<p>For instance, I used to heavily suppress expressing my love for kids out of fear that guys on dating apps would think that I were some baby-obsessed psycho who expects them to fertilise me ASAP. But with my partner, it was one of the first things we bonded over and it allowed us to identify a common trait from the get-go.</p>
-<p>Have I been made to feel crummy by guys who found me unattractive based on their own ideals? Yes. Did I proceed to hate my own appearance and vow never to show my physical self on dating apps for fear of being forever alone? Nah, not really.</p>
-<p><i><br /></i>Facts. Also, a little ironic that the show that this quote is from was recommended to me by a guy I met through a dating app, who ended up smashing my heart into smithereens.<br />Image credit: SineHub</p>
-<p>The 2021 Marriage and Parenthood Survey, which polled more than 5,800 Singapore residents aged 21 to 45, also found that a limited social circle or lack of opportunities to meet potential partners were among the top reasons given by respondents for them not dating.</p>
-<p>Both told TODAY that the increasing receptiveness towards finding partners online has been seen from before the Covid-19 pandemic in tandem with the growing popularity of social media and online channels, though pandemic-related restrictions on face-to-face gatherings may have made such approaches even more pertinent.</p>
-<p>Having seemingly conquered the dating world overnight with brand messaging of female empowerment and the COVID-19 pandemic accelerating the rise of online dating, Bumble is also making plays into non-dating social and even business interactions. While this may work with an American audience, time will tell if its long-term strategy works in other countries as well.</p>
-<p></p>
-<p>Humans, unlike chimpanzees, have evolved over the years based on female sexual selection. This means that women generally make the call on whom to choose as a sexual partner. With financial independence offered to women today, the (online) dating scene may look skewed in the future, with an over-abundance of men joining dating apps only to be rejected or excluded from the dating pool unless they make in-app purchases.</p>
-<p>According to Statista, the distribution of Tinder users (ratio of male to female) is heavily skewed towards male users, with over 72% of users on Tinder in the US being men versus 28% of women. Not only that, but there is also a massive perceived difference in motivations in using the app, with a disproportionate amount of men in a recent study seeking sex compared to women, who look more for relationships on dating apps.</p>
-<p>The dating app, which has a presence in India, Australia, New Zealand and Southeast Asia, including Singapore and the Philippines, wants to normalise the idea of making romantic connections through an app, which still carries a stigma in several Asian cultures.</p>
-<p>We also want to break the stigma that still exists in some Asian cultures around finding a romantic partner online. When dating apps came on the market just a few years ago, they revolutionised how people can meet each other, but there is still work to be done to normalise the concept of using an app to meet people. At Bumble, we have done this through a rigorous focus on safety through the development of product features like photo verification, private detector, video chat and more.</p>
-<p>Bumble has taken a different approach in India, where the brand has produced a content series called Dating These Nights. The series, now in its third season, takes a chat-show-style approach and sees celebrities sit down together to discuss dating topics.</p>
-<p>At Bumble, our mission is to create a world where all relationships are healthy and equitable, with our core values of kindness, respect and equality at the centre of everything we do. At every level of our business, our team is informed and led by these values and our goal to empower women on their dating journeys.</p>
-<p>Since then, there have been a plethora of dating apps developed like Bumble, OkCupid, and Paktor. Even Facebook jumped on the dating app bandwagon and released Facebook Dating in 2018. Some apps even cater to specific target markets such as Minder which is similar to Tinder but for Muslims. It is estimated that the dating market is worth around US$12 billion globally.</p>
-<p>Nevertheless, in recent years, dating apps still managed to make their way to ASEAN, especially among busy executives who prefer a faster way to meet people than the time consuming traditional approach. In 2019, Singaporeans spent US$7.1 million on dating apps, an increase from US$5 million in 2018 and US$3.9 million in 2017 while Malaysians and Indonesians spent about US$5.8 million on dating apps.</p>
-<p>Besides, the only person who cares about any stigma is you. We all know people who have met and married from online dating. Do you judge your friends and find them lacking because they met online? Of course not. No one will judge you, either. Online dating is a great tool for meeting a potential mate, and more and more people are using it.</p>
-<p>When Andy first emailed me, he explained that his online subscription had been inactive when I messaged him. But after weeks of unsuccessful attempts to meet women elsewhere, he re-upped his subscription, and my picture and message were there waiting for him as soon as he logged on.</p>
-<p>It works the same online. Show interest. Have a thoughtful profile and good photos of yourself. This is applicable to men, too. The picture of you taking a picture of yourself in the bathroom mirror is sad. Get a friend to take a good picture to use for your profile.</p>
-<p>Story by Junhao*<br><br>Casey* and I met on an online dating platform. Though we did not have much in common, she was persistent in meeting up and I caved in. I invited her over to my house with the intention of simply playing games and getting to know each other better.</p>
-<p>Story by Geraldine*<br><br>I met Byron* on an online dating app and we hit it right off the bat. He had a sunshine personality and looked cute in his pictures. We texted back and forth for a few days, but I never had the time for a meetup. We were in the middle of a pandemic and as an insurance agent, it was a busy period and I met most of my clients through video calls.</p>
-<p><strong>Lesson: </strong>Dining-in may be suspended for now, but all it takes is a little spontaneity and creativity to still have a good time. Most of us try to show our best sides on dating apps, but just being yourself is also a sort of beauty that no others can replicate.</p>
-<p>Holler Date is a voice dating platform that aims to amplify personality over a person's looks. Users hear short voice introductions of other singles. Photos are hidden unless a user uses their match points to reveal the photo behind a voice intro.</p>
-<p>There are plenty of dating apps and sites to choose from, some of which you may already be familiar with. But knowing which ones are popular among the locals can help improve your chances of matching with one.</p>
-<p>Since Singapore is such a small country, not many Singaporeans opt to sign up for international dating sites and apps. Some Singaporeans even took matters into their own hands and created dating apps that cater mostly to locals and those who are located in Singapore (i.e., foreigners). Here are two platforms that you can try:</p>
-<p>Joshua Phua and Ng Jing Shen launched Paktor in Singapore in 2013. Since online dating was still new in Asia at the time, the app took off and is now available in Taiwan, Malaysia, Thailand, South Korea, Indonesia, and Vietnam.</p>
-<p>As Muzmatch claims to be a halal dating app, it has plenty of features that help users date respectfully. For example, you can add a Wali, or chaperone, to your profile if it makes you more comfortable.</p>
-<p>It was the first time we met. We connected on a dating app and arranged to meet at the food court at Marina Square (yes, I'm the sort who would go to a food court on a first date, especially one with such a nice view).</p>
-<p>Online dating scams continue to rise, costing unsuspecting victims millions of dollars each year. Rather than simply sending phishing emails, cybercriminals are playing the long game to cheat people out of their money.</p>
-<p>Scammers target people across different demographics on every dating platform possible. This means that regardless of gender, sexual orientation, age, or preferred platform, no one is off-bounds to a scammer.</p>
-<p>With the rise of dating apps, scammers cast an even wider net for potential targets and automate much of the process, using bots to lure victims into scams. It's one of the reasons that online dating sucks.</p>
-<p>A common line among online dating scammers is that they want to meet you, but when the time comes, there is always some unexpected issue. Since the scammer is not the person they claim to be, they don't want to meet in person.</p>
-<p>Another online dating scam doesn't request money from victims, but turns them into "money mules". Rather than trying to get money from the victim, these scammers make you an accomplice in money laundering.</p>
-<p>Sometimes, scammers may ask a victim to open a bank account for them. If your online suitor asks you to get involved in these types of financial transactions and exchanges, it's likely that they're a scammer trying to lure you into illegal activities.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cihyFjudo/fairness-paper-search/[API SPEC 13A - Drilling Fluids Materials  GlobalSpec - Engineering360](1).md b/spaces/cihyFjudo/fairness-paper-search/[API SPEC 13A - Drilling Fluids Materials  GlobalSpec - Engineering360](1).md
deleted file mode 100644
index 3732c805134b6d533fc6353026f991ae24faf701..0000000000000000000000000000000000000000
--- a/spaces/cihyFjudo/fairness-paper-search/[API SPEC 13A - Drilling Fluids Materials  GlobalSpec - Engineering360](1).md	
+++ /dev/null
@@ -1,16 +0,0 @@
-<br />
-<p>Search for standards that are not included in the Monogram Program by clicking Advanced Search Options > Registration Scope Included > [specification number]. Examples of Specification numbers include:</p>
-<h2>Api Specification 13a Free Download</h2><br /><p><b><b>Download Zip</b> &#10084; <a href="https://tinurli.com/2uwkoy">https://tinurli.com/2uwkoy</a></b></p><br /><br />
-<p>This part of ISO 10414 is based on API RP 13B-1 download PDF, third edition, December 2003[2] and ISO 10414 (all parts)[6]<br>.<br>Annexes A to H and K of this part of ISO 10414 are for information only. Annexes I and J are normative. In this part of ISO 10414, where practical, U.S. Customary (USC) units are included in brackets for information<li>Single Zone Completion2023-01-07<li>Open Hole Completion2023-01-03<li>Drilling Stick-Slip Problem & Mitigation2022-12-26<li>Drilling Subs Types & Applications2022-12-23<li>Oil Rig Hazards & Safety Awareness2022-12-22</ul>API RP 13B-1 ScopeThis part of API RP 13B-1 Download PDF provides standard procedures for determining the following characteristics of water-based drilling fluids:</p>
-<p>The Department of Defense (DoD) initiated the Unified Facilities Criteria Program to unify all technical criteria (UFCs) and guide specifications (UFGS) pertaining to planning, design, construction, and operation and maintenance of real property facilities. The program streamlines the military criteria system by eliminating duplication of information, increasing reliance on private-sector standards, and creating a more efficient criteria development and publishing process. Administered by the United States Army Corps of Engineers (HQUSACE), Naval Facilities Engineering Systems Command (NAVFAC), and the Air Force Civil Engineer Center (AFCEC), the UFC Program organizes numerous working groups with one or more experts from each participating organization to develop the technical publications. Further information on the UFC program can be found in MIL-STD-3007 and in the UFC program report </i> from the last fiscal year. UFC 1-300-01 and UFC 1-300-02 also provide information on developing UFC documents. Read More</p>
-<p>Using UE4 with Vulkan* APIs requires that the engine be rebuilt, and this must be done after the Vulkan* SDK has been downloaded, and installed. Rebuilding the Unreal Engine requires the engine source-code, which is freely available on GitHub* to registered users who have linked their git account with Epic Games. All required steps are covered here.</p>
-<p>SacreBLEU (Post, 2018) provides hassle-free computation of shareable, comparable, and reproducible <strong>BLEU</strong> scores.Inspired by Rico Sennrich's multi-bleu-detok.perl, it produces the official WMT scores but works with plain text.It also knows all the standard test sets and handles downloading, processing, and tokenization for you.</p>
-<p>Comparing BLEU scores is harder than it should be. Every decoder has its own implementation, often borrowed from Moses, but maybe with subtle changes.Moses itself has a number of implementations as standalone scripts, with little indication of how they differ (note: they mostly don't, but multi-bleu.pl expects tokenized input). Different flags passed to each of these scripts can produce wide swings in the final score. All of these may handle tokenization in different ways. On top of this, downloading and managing test sets is a moderate annoyance.</p>
-<p></p>
-<p><strong>SacreBLEU</strong> aims to solve these problems by wrapping the original reference implementation (Papineni et al., 2002) together with other useful features.The defaults are set the way that BLEU should be computed, and furthermore, the script outputs a short version string that allows others to know exactly what you did.As an added bonus, it automatically downloads and manages test sets for you, so that you can simply tell it to score against wmt14, without having to hunt down a path on your local file system.It is all designed to take BLEU a little more seriously.After all, even with all its problems, BLEU is the default and---admit it---well-loved metric of our entire research community.Sacre BLEU.</p>
-<p>");$('#stage').append(accessLink);$('#stage').append("<br/>");$('#stage').append(val2.description);$('#stage').append("");totalProcessed = totalProcessed+1; } // for each item has downloadURL }); //for objects in dataset} //if is dataset } //for each item ); if(totalItems>20) $('#stage').append("Back to Top..."); console.log("==>INFO: process completed, total items in datafile:"+totalItems+" total processed:"+totalProcessed); //+" totalNoReferences ignored:"+totalNoReferences);} //getJSON); }); //--> XML Version (XML, 2.5 MB) | JSON Version (JSON, 7.8 MB)</p>
-<p>The Canadian Export Reporting System (CERS) is a free, web-based, self-service portal that allows the exporting community to electronically submit export declarations, bulk data uploads and Summary Reporting Program monthly reports to the Canada Border Services Agency (CBSA).</p>
-<p>In this example, we are going to use the daily data. If we want, we can rename the download dataset definition. This will save the query definition in your account and is useful if you plan to return and run the same dataset again.</p>
-<p>When you click download, the Excel data will be retrieved to your local computer. If you have submitted multiple locations, Query Builder will create a Zip file for you containing individual Excel files, one for each location.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cm107/agv-demo/TemplateData/style.css b/spaces/cm107/agv-demo/TemplateData/style.css
deleted file mode 100644
index cdc3477fb8c1c824db96f451631bca7cde305923..0000000000000000000000000000000000000000
--- a/spaces/cm107/agv-demo/TemplateData/style.css
+++ /dev/null
@@ -1,105 +0,0 @@
-html {
-    box-sizing: border-box;
-}
-*, *:before, *:after {
-    box-sizing: inherit;
-}
-html, body {
-    height: 100%;
-}
-canvas {
-    display: block;
-}
-body {
-    margin: 0;
-}
-#unity-container {
-    width: 100%;
-    height: 100%;
-}
-#unity-canvas {
-    width: 100%;
-    height: 100%;
-    background: #231F20;
-}
-#loading-cover {
-    position: absolute;
-    top: 0;
-    left: 0;
-    width: 100%;
-    height: 100%;
-    display: flex;
-    justify-content: center;
-    align-items: center;
-}
-#unity-loading-bar {
-    flex: 1 1 auto;
-    display: flex;
-    flex-direction: column;
-    justify-content: center;
-    align-items: center;
-}
-#unity-logo {
-    text-align: center;
-}
-#unity-logo img {
-    max-width: 80%;
-}
-#unity-progress-bar-empty {
-    width: 80%;
-    height: 24px;
-    margin: 10px 20px 20px 10px;
-    text-align: left;
-    border: 1px solid white;
-    padding: 2px;
-}
-#unity-progress-bar-full {
-    width: 0%;
-    height: 100%;
-    background: #ffd21e;
-}
-.light #unity-progress-bar-empty {
-    border-color: black;
-}
-.light #unity-progress-bar-full {
-    background: black;
-}
-
-#unity-fullscreen-button {
-    position: absolute;
-    right: 10px;
-    bottom: 10px;
-    width: 38px;
-    height: 38px;
-    background: url('fullscreen-button.png') no-repeat center;
-    background-size: contain;
-}
-
-.spinner,
-.spinner:after {
-  border-radius: 50%;
-  width: 5em;
-  height: 5em;
-}
-.spinner {
-  margin: 10px;
-  font-size: 10px;
-  position: relative;
-  text-indent: -9999em;
-  border-top: 1.1em solid rgba(255, 255, 255, 0.2);
-  border-right: 1.1em solid rgba(255, 255, 255, 0.2);
-  border-bottom: 1.1em solid rgba(255, 255, 255, 0.2);
-  border-left: 1.1em solid #ffffff;
-  transform: translateZ(0);
-  animation: spinner-spin 1.1s infinite linear;
-}
-@keyframes spinner-spin {
-  0% {
-    transform: rotate(0deg);
-  }
-  100% {
-    transform: rotate(360deg);
-  }
-}
-
-
diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/wmv2dsp_mmi.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/wmv2dsp_mmi.c
deleted file mode 100644
index 1a6781ae77e4914ff62be22373850e96222b974d..0000000000000000000000000000000000000000
--- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/wmv2dsp_mmi.c
+++ /dev/null
@@ -1,278 +0,0 @@
-/*
- * WMV2 - DSP functions Loongson MMI-optimized
- *
- * Copyright (c) 2016 Zhou Xiaoyong <zhouxiaoyong@loongson.cn>
- *
- * This file is part of FFmpeg.
- *
- * FFmpeg 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.
- *
- * FFmpeg 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 FFmpeg; if not, write to the Free Software
- * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
- */
-
-#include "libavutil/avassert.h"
-#include "constants.h"
-#include "wmv2dsp_mips.h"
-#include "libavutil/mips/mmiutils.h"
-
-#define W0 2048
-#define W1 2841 /* 2048*sqrt (2)*cos (1*pi/16) */
-#define W2 2676 /* 2048*sqrt (2)*cos (2*pi/16) */
-#define W3 2408 /* 2048*sqrt (2)*cos (3*pi/16) */
-#define W4 2048 /* 2048*sqrt (2)*cos (4*pi/16) */
-#define W5 1609 /* 2048*sqrt (2)*cos (5*pi/16) */
-#define W6 1108 /* 2048*sqrt (2)*cos (6*pi/16) */
-#define W7 565  /* 2048*sqrt (2)*cos (7*pi/16) */
-
-static void wmv2_idct_row_mmi(short * b)
-{
-    int s1, s2;
-    int a0, a1, a2, a3, a4, a5, a6, a7;
-
-    /* step 1 */
-    a0 = W0 * b[0] + W0 * b[4];
-    a1 = W1 * b[1] + W7 * b[7];
-    a2 = W2 * b[2] + W6 * b[6];
-    a3 = W3 * b[5] - W5 * b[3];
-    a4 = W0 * b[0] - W0 * b[4];
-    a5 = W5 * b[5] + W3 * b[3];
-    a6 = W6 * b[2] - W2 * b[6];
-    a7 = W7 * b[1] - W1 * b[7];
-
-    /* step 2 */
-    s1 = (181 * (a1 - a5 + a7 - a3) + 128) >> 8; // 1, 3, 5, 7
-    s2 = (181 * (a1 - a5 - a7 + a3) + 128) >> 8;
-
-    /* step 3 */
-    b[0] = (a0 + a2 + a1 + a5 + 128) >> 8;
-    b[1] = (a4 + a6 + s1      + 128) >> 8;
-    b[2] = (a4 - a6 + s2      + 128) >> 8;
-    b[3] = (a0 - a2 + a7 + a3 + 128) >> 8;
-    b[4] = (a0 - a2 - a7 - a3 + 128) >> 8;
-    b[5] = (a4 - a6 - s2      + 128) >> 8;
-    b[6] = (a4 + a6 - s1      + 128) >> 8;
-    b[7] = (a0 + a2 - a1 - a5 + 128) >> 8;
-}
-
-static void wmv2_idct_col_mmi(short * b)
-{
-    int s1, s2;
-    int a0, a1, a2, a3, a4, a5, a6, a7;
-
-    /* step 1, with extended precision */
-    a0 = (W0 * b[ 0] + W0 * b[32]    ) >> 3;
-    a1 = (W1 * b[ 8] + W7 * b[56] + 4) >> 3;
-    a2 = (W2 * b[16] + W6 * b[48] + 4) >> 3;
-    a3 = (W3 * b[40] - W5 * b[24] + 4) >> 3;
-    a4 = (W0 * b[ 0] - W0 * b[32]    ) >> 3;
-    a5 = (W5 * b[40] + W3 * b[24] + 4) >> 3;
-    a6 = (W6 * b[16] - W2 * b[48] + 4) >> 3;
-    a7 = (W7 * b[ 8] - W1 * b[56] + 4) >> 3;
-
-    /* step 2 */
-    s1 = (181 * (a1 - a5 + a7 - a3) + 128) >> 8;
-    s2 = (181 * (a1 - a5 - a7 + a3) + 128) >> 8;
-
-    /* step 3 */
-    b[ 0] = (a0 + a2 + a1 + a5 + 8192) >> 14;
-    b[ 8] = (a4 + a6 + s1      + 8192) >> 14;
-    b[16] = (a4 - a6 + s2      + 8192) >> 14;
-    b[24] = (a0 - a2 + a7 + a3 + 8192) >> 14;
-
-    b[32] = (a0 - a2 - a7 - a3 + 8192) >> 14;
-    b[40] = (a4 - a6 - s2      + 8192) >> 14;
-    b[48] = (a4 + a6 - s1      + 8192) >> 14;
-    b[56] = (a0 + a2 - a1 - a5 + 8192) >> 14;
-}
-
-void ff_wmv2_idct_add_mmi(uint8_t *dest, ptrdiff_t line_size, int16_t *block)
-{
-    int i;
-    double ftmp[11];
-
-    for (i = 0; i < 64; i += 8)
-        wmv2_idct_row_mmi(block + i);
-    for (i = 0; i < 8; i++)
-        wmv2_idct_col_mmi(block + i);
-
-    __asm__ volatile (
-        "pxor       %[ftmp0],   %[ftmp0],   %[ftmp0]                    \n\t"
-
-        // low 4 loop
-        MMI_LDC1(%[ftmp1], %[block], 0x00)
-        MMI_LDC1(%[ftmp2], %[block], 0x08)
-        MMI_LDC1(%[ftmp3], %[block], 0x10)
-        MMI_LDC1(%[ftmp4], %[block], 0x18)
-        MMI_LDC1(%[ftmp5], %[block], 0x20)
-        MMI_LDC1(%[ftmp6], %[block], 0x28)
-        MMI_LDC1(%[ftmp7], %[block], 0x30)
-        MMI_LDC1(%[ftmp8], %[block], 0x38)
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp1],   %[ftmp1],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp2],   %[ftmp2],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp1],   %[ftmp1],   %[ftmp2]                    \n\t"
-        MMI_SDC1(%[ftmp1], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp3],   %[ftmp3],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp4],   %[ftmp4],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp3],   %[ftmp3],   %[ftmp4]                    \n\t"
-        MMI_SDC1(%[ftmp3], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp5],   %[ftmp5],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp6],   %[ftmp6],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp5],   %[ftmp5],   %[ftmp6]                    \n\t"
-        MMI_SDC1(%[ftmp5], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp7],   %[ftmp7],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp8],   %[ftmp8],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp7],   %[ftmp7],   %[ftmp8]                    \n\t"
-        MMI_SDC1(%[ftmp7], %[dest], 0x00)
-
-        PTR_ADDIU  "%[block],   %[block],   0x40                        \n\t"
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        // high 4 loop
-        MMI_LDC1(%[ftmp1], %[block], 0x00)
-        MMI_LDC1(%[ftmp2], %[block], 0x08)
-        MMI_LDC1(%[ftmp3], %[block], 0x10)
-        MMI_LDC1(%[ftmp4], %[block], 0x18)
-        MMI_LDC1(%[ftmp5], %[block], 0x20)
-        MMI_LDC1(%[ftmp6], %[block], 0x28)
-        MMI_LDC1(%[ftmp7], %[block], 0x30)
-        MMI_LDC1(%[ftmp8], %[block], 0x38)
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp1],   %[ftmp1],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp2],   %[ftmp2],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp1],   %[ftmp1],   %[ftmp2]                    \n\t"
-        MMI_SDC1(%[ftmp1], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp3],   %[ftmp3],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp4],   %[ftmp4],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp3],   %[ftmp3],   %[ftmp4]                    \n\t"
-        MMI_SDC1(%[ftmp3], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp5],   %[ftmp5],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp6],   %[ftmp6],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp5],   %[ftmp5],   %[ftmp6]                    \n\t"
-        MMI_SDC1(%[ftmp5], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        MMI_LDC1(%[ftmp9], %[dest], 0x00)
-        "punpckhbh  %[ftmp10],  %[ftmp9],   %[ftmp0]                    \n\t"
-        "punpcklbh  %[ftmp9],   %[ftmp9],   %[ftmp0]                    \n\t"
-        "paddh      %[ftmp7],   %[ftmp7],   %[ftmp9]                    \n\t"
-        "paddh      %[ftmp8],   %[ftmp8],   %[ftmp10]                   \n\t"
-        "packushb   %[ftmp7],   %[ftmp7],   %[ftmp8]                    \n\t"
-        MMI_SDC1(%[ftmp7], %[dest], 0x00)
-        : [ftmp0]"=&f"(ftmp[0]),            [ftmp1]"=&f"(ftmp[1]),
-          [ftmp2]"=&f"(ftmp[2]),            [ftmp3]"=&f"(ftmp[3]),
-          [ftmp4]"=&f"(ftmp[4]),            [ftmp5]"=&f"(ftmp[5]),
-          [ftmp6]"=&f"(ftmp[6]),            [ftmp7]"=&f"(ftmp[7]),
-          [ftmp8]"=&f"(ftmp[8]),            [ftmp9]"=&f"(ftmp[9]),
-          [ftmp10]"=&f"(ftmp[10]),
-          [block]"+&r"(block),              [dest]"+&r"(dest)
-        : [line_size]"r"((mips_reg)line_size)
-        : "memory"
-    );
-}
-
-void ff_wmv2_idct_put_mmi(uint8_t *dest, ptrdiff_t line_size, int16_t *block)
-{
-    int i;
-    double ftmp[8];
-
-    for (i = 0; i < 64; i += 8)
-        wmv2_idct_row_mmi(block + i);
-    for (i = 0; i < 8; i++)
-        wmv2_idct_col_mmi(block + i);
-
-    __asm__ volatile (
-        // low 4 loop
-        MMI_LDC1(%[ftmp0], %[block], 0x00)
-        MMI_LDC1(%[ftmp1], %[block], 0x08)
-        MMI_LDC1(%[ftmp2], %[block], 0x10)
-        MMI_LDC1(%[ftmp3], %[block], 0x18)
-        MMI_LDC1(%[ftmp4], %[block], 0x20)
-        MMI_LDC1(%[ftmp5], %[block], 0x28)
-        MMI_LDC1(%[ftmp6], %[block], 0x30)
-        MMI_LDC1(%[ftmp7], %[block], 0x38)
-        "packushb   %[ftmp0],   %[ftmp0],   %[ftmp1]                    \n\t"
-        "packushb   %[ftmp2],   %[ftmp2],   %[ftmp3]                    \n\t"
-        "packushb   %[ftmp4],   %[ftmp4],   %[ftmp5]                    \n\t"
-        "packushb   %[ftmp6],   %[ftmp6],   %[ftmp7]                    \n\t"
-        MMI_SDC1(%[ftmp0], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-        MMI_SDC1(%[ftmp2], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-        MMI_SDC1(%[ftmp4], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-        MMI_SDC1(%[ftmp6], %[dest], 0x00)
-
-        PTR_ADDIU  "%[block],   %[block],   0x40                        \n\t"
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-
-        // high 4 loop
-        MMI_LDC1(%[ftmp0], %[block], 0x00)
-        MMI_LDC1(%[ftmp1], %[block], 0x08)
-        MMI_LDC1(%[ftmp2], %[block], 0x10)
-        MMI_LDC1(%[ftmp3], %[block], 0x18)
-        MMI_LDC1(%[ftmp4], %[block], 0x20)
-        MMI_LDC1(%[ftmp5], %[block], 0x28)
-        MMI_LDC1(%[ftmp6], %[block], 0x30)
-        MMI_LDC1(%[ftmp7], %[block], 0x38)
-        "packushb   %[ftmp0],   %[ftmp0],   %[ftmp1]                    \n\t"
-        "packushb   %[ftmp2],   %[ftmp2],   %[ftmp3]                    \n\t"
-        "packushb   %[ftmp4],   %[ftmp4],   %[ftmp5]                    \n\t"
-        "packushb   %[ftmp6],   %[ftmp6],   %[ftmp7]                    \n\t"
-        MMI_SDC1(%[ftmp0], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-        MMI_SDC1(%[ftmp2], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-        MMI_SDC1(%[ftmp4], %[dest], 0x00)
-        PTR_ADDU   "%[dest],    %[dest],    %[line_size]                \n\t"
-        MMI_SDC1(%[ftmp6], %[dest], 0x00)
-        : [ftmp0]"=&f"(ftmp[0]),            [ftmp1]"=&f"(ftmp[1]),
-          [ftmp2]"=&f"(ftmp[2]),            [ftmp3]"=&f"(ftmp[3]),
-          [ftmp4]"=&f"(ftmp[4]),            [ftmp5]"=&f"(ftmp[5]),
-          [ftmp6]"=&f"(ftmp[6]),            [ftmp7]"=&f"(ftmp[7]),
-          [block]"+&r"(block),              [dest]"+&r"(dest)
-        : [line_size]"r"((mips_reg)line_size)
-        : "memory"
-    );
-}
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Eat All the Rainbow Gummies and Candies with ASMR Rainbow Jelly Mod APK.md b/spaces/congsaPfin/Manga-OCR/logs/Eat All the Rainbow Gummies and Candies with ASMR Rainbow Jelly Mod APK.md
deleted file mode 100644
index 3919d056cfcc1bad4d9ecc3c13835935038409ff..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Eat All the Rainbow Gummies and Candies with ASMR Rainbow Jelly Mod APK.md	
+++ /dev/null
@@ -1,113 +0,0 @@
-<br />
-<h1>ASMR Rainbow Jelly Mod APK: Enjoy a Relaxing and Colorful Game</h1>
-<p>Do you love eating sweets and listening to relaxing sounds? Do you want to experience a pleasant tingling sensation in your scalp and spine? If yes, then you should try ASMR Rainbow Jelly, a game that combines the fun of eating rainbow-colored food with the soothing effect of ASMR.</p>
-<h2>What is ASMR Rainbow Jelly?</h2>
-<p>ASMR Rainbow Jelly is an Android game that is filled with colorful sweets, such as gummies, candies, and jelly. The game is inspired by the phenomenon of ASMR, which stands for Autonomous Sensory Meridian Response. ASMR is a subjective experience of "low-grade euphoria" characterized by "a combination of positive feelings and a distinct static-like tingling sensation on the skin". It is most commonly triggered by specific auditory or visual stimuli, such as soft whispering, crinkling paper, or a gentle touch.</p>
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-<p>In ASMR Rainbow Jelly, you can eat all the delicious rainbow-colored food from gummies to candies. You can also turn on the sound to enjoy the oddly satisfying ASMR chewing sounds. The game is designed to make you feel relaxed and happy as you munch on the sweet treats.</p>
-<h3>Features of ASMR Rainbow Jelly</h3>
-<h4>Eat colorful sweets</h4>
-<p>The game offers a variety of sweets to choose from, such as gummy bears, jelly beans, marshmallows, lollipops, and more. You can also mix and match different colors and shapes to create your own rainbow food. The game has realistic graphics and animations that make the food look appetizing and fun.</p>
-<h4>Listen to soothing sounds</h4>
-<p>The game also has realistic sound effects that mimic the sound of chewing, biting, and swallowing. You can adjust the volume and the speed of the sound to suit your preference. The game also has relaxing background music that complements the ASMR sounds. The game aims to provide you with a sensory experience that can calm your nerves and ease your stress.</p>
-<h4>Unlock new items and levels</h4>
-<p>The game has different levels that challenge you to eat different types of sweets in different scenarios. For example, you can eat gummy worms in a garden, jelly cubes in a pool, or candy canes in a snowman. You can also unlock new items, such as plates, cutlery, glasses, and decorations, to customize your food and your environment. The game rewards you with coins and stars that you can use to buy more items and access more levels.</p>
-<h2>What is a Mod APK?</h2>
-<p>A Mod APK is a modified version of an official Android app that has been altered by a third-party developer to add or enable some features that are not available in the original app. For example, a Mod APK can unlock premium features, remove ads, provide unlimited resources, or bypass restrictions.</p>
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-<h3>Benefits of Installing Mod APK Files</h3>
-<h4>Access all the features of an application</h4>
-<p>Many Android games are quite challenging to play and win since they have complicated gameplay. Some games also require you to pay real money or watch ads to access certain features or abilities. However, with a Mod APK file, you can access all the features of an application without any limitations or costs. You can enjoy the full potential of the game without any hassle.</p>
-<h4>Enjoy premium features for free of cost</h4>
-<p>Some Android apps have premium features that are only available for users who pay a subscription fee or make an in-app purchase. These features can enhance the functionality or the quality of the app <p>Some Android apps have premium features that are only available for users who pay a subscription fee or make an in-app purchase. These features can enhance the functionality or the quality of the app, such as removing ads, unlocking more content, or providing better performance. However, with a Mod APK file, you can enjoy these premium features for free of cost. You can save your money and still get the best experience from the app.</p>
-<h4>Easy to download and install</h4>
-<p>Another benefit of installing Mod APK files is that they are easy to download and install on your Android device. You do not need to root your device or use any special tools to install a Mod APK file. You just need to find a reliable source that provides the Mod APK file that you want, download it, and follow the instructions to install it. You can also uninstall it anytime you want without affecting your device.</p>
-<h2>How to Download and Install ASMR Rainbow Jelly Mod APK?</h2>
-<p>If you are interested in playing ASMR Rainbow Jelly with all the features unlocked and unlimited resources, you can download and install the Mod APK file from this link: [ASMR Rainbow Jelly Mod APK]. However, before you do that, you should follow these steps and precautions to ensure a safe and successful installation.</p>
-<h3>Steps to follow</h3>
-<ol>
-<li>Go to the settings of your device and enable the option of "Unknown sources" under the security section. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Download the ASMR Rainbow Jelly Mod APK file from the link provided above. You can use any browser or download manager that you prefer.</li>
-<li>Once the download is complete, locate the file in your device's storage and tap on it to start the installation process.</li>
-<li>Follow the on-screen instructions and grant the required permissions to the app.</li>
-<li>Wait for the installation to finish and then launch the app from your home screen or app drawer.</li>
-<li>Enjoy playing ASMR Rainbow Jelly with all the features unlocked and unlimited resources.</li>
-</ol>
-<h3>Precautions to take</h3>
-<ul>
-<li>Make sure that you have enough storage space on your device before downloading and installing the Mod APK file.</li>
-<li>Make sure that you have a stable internet connection during the download and installation process.</li>
-<li>Make sure that you download the Mod APK file from a trusted and verified source. Do not download any files from unknown or suspicious websites that may contain malware or viruses.</li>
-<li>Make sure that you backup your data before installing the Mod APK file. Installing a Mod APK file may overwrite or delete some of your existing data, such as your progress, settings, or preferences.</li>
-<li>Make sure that you uninstall any previous versions of ASMR Rainbow Jelly before installing the Mod APK file. Installing a Mod APK file over an existing app may cause errors or conflicts.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>ASMR Rainbow Jelly is a relaxing and colorful game that lets you eat rainbow-colored sweets and listen to soothing sounds. It is inspired by the phenomenon of ASMR, which can make you feel calm and happy. If you want to enjoy all the features of this game without any limitations or costs, you can download and install the Mod APK file from this link: [ASMR Rainbow Jelly Mod APK]. However, you should follow the steps and precautions mentioned above to ensure a safe and successful installation. We hope that this article has helped you learn more about ASMR Rainbow Jelly and its Mod APK file. Have fun playing this game and experiencing ASMR!</p>
-<h3>FAQs</h3>
-<ul>
-<li><b>What is ASMR?</b></li>
-<p>ASMR stands for Autonomous Sensory Meridian Response, which is a subjective experience of "low-grade euphoria" characterized by "a combination of positive feelings and a distinct static-like tingling sensation on the skin". It is most commonly triggered by specific auditory or visual stimuli, such as soft whispering, crinkling paper, or a gentle touch.</p>
-<li><b>What is a Mod APK?</b></li>
-<p>A Mod APK is a modified version of an official Android app that has been altered by a third-party developer to add or enable some features that are not available in the original app. For example, a Mod APK can unlock premium features, remove ads, provide unlimited resources, or bypass restrictions.</p>
-<li><b>How can I download and install ASMR Rainbow Jelly Mod APK?</b></li>
-<p>You can download and install ASMR Rainbow Jelly Mod APK from this link: [ASMR Rainbow Jelly Mod APK]. However, before you do that, you should follow these steps and precautions:</p <p>You can download and install ASMR Rainbow Jelly Mod APK from this link: [ASMR Rainbow Jelly Mod APK]. However, before you do that, you should follow these steps and precautions:</p>
-<ol>
-<li>Go to the settings of your device and enable the option of "Unknown sources" under the security section. This will allow you to install apps from sources other than the Google Play Store.</li>
-<li>Download the ASMR Rainbow Jelly Mod APK file from the link provided above. You can use any browser or download manager that you prefer.</li>
-<li>Once the download is complete, locate the file in your device's storage and tap on it to start the installation process.</li>
-<li>Follow the on-screen instructions and grant the required permissions to the app.</li>
-<li>Wait for the installation to finish and then launch the app from your home screen or app drawer.</li>
-<li>Enjoy playing ASMR Rainbow Jelly with all the features unlocked and unlimited resources.</li>
-</ol>
-<ul>
-<li><b>Is ASMR Rainbow Jelly Mod APK safe to use?</b></li>
-<p>ASMR Rainbow Jelly Mod APK is safe to use as long as you download it from a trusted and verified source. However, you should always be careful when installing any Mod APK files on your device, as they may contain malware or viruses that can harm your device or compromise your privacy. You should also scan the file with an antivirus software before installing it.</p>
-<li><b>What are some alternatives to ASMR Rainbow Jelly?</b></li>
-<p>If you are looking for other games that can provide you with a relaxing and colorful experience, you can try these alternatives:</p>
-<ul>
-<li><b>Candy Crush Saga</b>: A popular puzzle game that involves matching candies of the same color and shape. The game has hundreds of levels, each with different goals and challenges. The game also has bright graphics and catchy music that can keep you entertained for hours.</li>
-<li><b>Zen Koi 2</b>: A relaxing game that lets you breed and collect colorful koi fish. The game has a soothing soundtrack and a minimalist design that can help you relax and unwind. The game also has a zen mode that allows you to enjoy the game without any distractions or goals.</li>
-<li><b>Colorfy</b>: A coloring book app that lets you color various images, such as animals, flowers, mandalas, and more. The app has a large collection of images and colors to choose from. The app also has a social feature that allows you to share your creations with other users and see their artworks.</li>
-</ul>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/congsaPfin/Manga-OCR/logs/Enjoy the Realistic Simulation of Internet Cafe Simulator APK Mod.md b/spaces/congsaPfin/Manga-OCR/logs/Enjoy the Realistic Simulation of Internet Cafe Simulator APK Mod.md
deleted file mode 100644
index 8d353cb43f7d01b6f0f5465687ada5ef580bd993..0000000000000000000000000000000000000000
--- a/spaces/congsaPfin/Manga-OCR/logs/Enjoy the Realistic Simulation of Internet Cafe Simulator APK Mod.md	
+++ /dev/null
@@ -1,97 +0,0 @@
-
-<h1>Internet Cafe Simulator APK Mod: A Fun and Realistic Game for Android Users</h1>
-<p>Do you love playing simulation games on your Android device? Do you want to experience what it's like to run your own internet cafe business? If you answered yes to these questions, then you should check out Internet Cafe Simulator, a popular virtual game that lets you create and manage your own internet cafe.</p>
-<h2>What is Internet Cafe Simulator?</h2>
-<p>Internet Cafe Simulator is an exciting virtual game designed to provide a first-hand experience of running a cafe on the internet. In this game, you serve customers, manage finances, and grow your business. You can also customize your cafe with different furniture, decorations, and equipment. You can even interact with other characters in the game world, such as hackers, robbers, police officers, celebrities, etc.</p>
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-<p>To play Internet Cafe Simulator on your Android device, you need to download and install the game from Google Play Store or any other trusted source. The game is free to download but it contains ads and in-app purchases. You also need a stable internet connection to play the game smoothly.</p>
-<h2>What is Internet Cafe Simulator APK Mod?</h2>
-<p>If you want to enjoy Internet Cafe Simulator without any limitations or restrictions, you can try out Internet Cafe Simulator APK Mod. This is a modified version of the game that gives you unlimited money, free purchases, no ads, and more features. With this modded version, you can buy anything you want in the game without worrying about your budget. You can also play the game without any annoying ads or interruptions.</p>
-<p>To download and install Internet Cafe Simulator APK Mod on your Android device, you need to follow these steps:</p>
-<ol>
-<li>Go to [this link](^1^) and download the modded APK file.</li>
-<li>Enable unknown sources on your device settings.</li>
-<li>Locate and install the downloaded APK file.</li>
-<li>Launch the game and enjoy!</li>
-</ol>
-<h2> <h2>How to Play Internet Cafe Simulator?</h2>
-<p>Playing Internet Cafe Simulator is easy and fun. You just need to follow these steps:</p>
-<ol>
-<li>Create your character and choose your name, gender, and appearance.</li>
-<li>Choose a location for your internet cafe and rent a space.</li>
-<li>Buy some computers, desks, chairs, and other equipment for your cafe.</li>
-<li>Set up your internet connection and install some games and software on your computers.</li>
-<li>Open your cafe and start serving customers. You can charge them for using the internet, playing games, or buying snacks and drinks.</li>
-<li>Manage your income and expenses. You need to pay your rent, bills, taxes, and employees. You also need to buy more supplies and upgrade your equipment.</li>
-<li>Expand your business and attract more customers. You can advertise your cafe, offer discounts, host events, or join competitions.</li>
-<li>Deal with various challenges and situations. You may encounter hackers, robbers, police officers, celebrities, or other characters in the game. You need to make decisions that will affect your reputation and success.</li>
-</ol>
-<h2>What are the Pros and Cons of Internet Cafe Simulator?</h2>
-<p>Internet Cafe Simulator is a fun and realistic game that offers many benefits and drawbacks. Here are some of them:</p>
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-<table>
-<tr><th>Pros</th><th>Cons</th></tr>
-<tr><td>- It is entertaining and addictive. You can spend hours playing the game and enjoy the different aspects of running a cafe.</td><td>- It is challenging and stressful. You need to balance your time, money, and resources. You also need to deal with various problems and risks.</td></tr>
-<tr><td>- It is educational and informative. You can learn about the basics of internet cafe business, such as marketing, accounting, customer service, etc.</td><td>- It is unrealistic and inaccurate. Some features of the game are not based on real-life situations, such as hacking, robbing, or bribing.</td></tr>
-<tr><td>- It is customizable and flexible. You can design your cafe according to your preferences and style. You can also choose how to play the game according to your goals and strategies.</td><td>- It is buggy and glitchy. Some users have reported that the game crashes, freezes, or lags. Some features of the game are also not working properly or missing.</td></tr>
-</table>
-<p>The user reviews and ratings of Internet Cafe Simulator are mixed. Some users love the game and praise its graphics, gameplay, and features. Others hate the game and criticize its bugs, ads, and in-app purchases. The average rating of the game on Google Play Store is 3.9 out of 5 stars.</p>
-<h2>Conclusion</h2>
-<p>Internet Cafe Simulator is a fun and realistic game that lets you create and manage your own internet cafe business on your Android device. You can download and install the game for free from Google Play Store or any other trusted source. You can also download and install the modded version of the game that gives you unlimited money, free purchases, no ads, and more features. If you love playing simulation games, you should give Internet Cafe Simulator a try!</p>
- <h3>Frequently Asked Questions</h3>
- <h4>Q: How do I get more money in Internet Cafe Simulator?</h4>
-<p>A: There are several ways to get more money in Internet Cafe Simulator. You can charge more for your services, offer more products, attract more customers, reduce your expenses, or use cheats or hacks. You can also download and install the modded version of the game that gives you unlimited money.</p>
- <h4>Q: How do I get rid of ads in Internet Cafe Simulator?</h4>
-<p>A: There are two ways to get rid of ads in Internet Cafe Simulator. You can either pay for the premium version of the game that removes all ads or you can download and install the modded version of the game that has no ads.</p>
- <h4>Q: How do I update Internet Cafe Simulator?</h4>
-<p>A: To update Internet Cafe Simulator on your Android device, you need to go to Google Play Store or any other trusted source where you downloaded the game from and check for updates. If there are any available updates, you need to download and install them on your device.</p>
- <h4>Q: How do I uninstall Internet Cafe Simulator?</h4>
-<p>A: To uninstall Internet Cafe Simulator on your Android device, you need to go to your device settings and find the app manager or applications section. Then you need to find Internet Cafe Simulator on the list of apps installed on your device and tap on it. Then you need to tap on the uninstall button and confirm your action.</p>
- <h4>Q: How do I Q: How do I contact the developer of Internet Cafe Simulator?</h4>
-<p>A: To contact the developer of Internet Cafe Simulator, you can either visit their official website [here] or send them an email at [this address]. You can also follow them on their social media accounts, such as Facebook, Twitter, Instagram, or YouTube.</p>
- <p>I hope you enjoyed reading this article and learned something new about Internet Cafe Simulator. If you have any questions, comments, or feedback, feel free to leave them below. Thank you for your time and attention!</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/How to Install and Use Cascadia Code a Modern Coding Font with Style.md b/spaces/congsaPfin/Manga-OCR/logs/How to Install and Use Cascadia Code a Modern Coding Font with Style.md
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+++ /dev/null
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-<br />
-<h1>Introduction</h1>
-<p>Cascadia Code is a fun, new monospaced font that includes programming ligatures and is designed to enhance the modern look and feel of the Windows Terminal, Visual Studio, and Visual Studio Code. It was developed by Microsoft alongside Windows Terminal, a new command-line interface for Windows that offers a rich user experience with tabs, panes, themes, and more. Cascadia Code is also the default font in Visual Studio 2022, the latest version of Microsoft's integrated development environment (IDE) for building applications.</p>
-<p>But what are programming ligatures and why are they useful? Programming ligatures are special glyphs that are created by combining two or more characters into one. For example, the characters <= can be merged into a single glyph ≤ that looks like a less-than-or-equal-to sign. This can help make code more readable and user-friendly for some people, as it reduces visual clutter and creates a more natural flow of symbols. Programming ligatures are supported by many popular languages such as C#, JavaScript, Python, and more.</p>
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-<p>In this article, you will learn how to download, install, use, and customize Cascadia Code for your coding needs. You will also get some feedback from other users who have tried this font and see some of its features and benefits.</p>
- <h1>Download and installation</h1>
-<p>The easiest way to get Cascadia Code is to download it from its official GitHub repository. There you can find the latest version of the font in various formats and variants. You can also browse the source code, report issues, or contribute to the project.</p>
-<p>To download Cascadia Code from GitHub, follow these steps:</p>
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-<ol>
-<li>Go to the <a href="(^1^)">Cascadia Code GitHub releases page</a>.</li>
-<li>Under the latest release, find the Assets section and click on the <strong>CascadiaCode.zip</strong> link.</li>
-<li>Save the zip file to your preferred location on your computer.</li>
-<li>Extract the zip file using your preferred tool (such as Windows Explorer or 7-Zip).</li>
-<li>Open the extracted folder and go to the <strong>ttf</strong> folder.</li>
-<li>Select all the font files (.ttf) that you want to install. You can choose from different variants of Cascadia Code such as:</li>
-<ul>
-<li><strong>Cascadia Code</strong>: The standard version of Cascadia that includes ligatures.</li>
-<li><strong>Cascadia Mono</strong>: A version of Cascadia that does not have ligatures.</li>
-<li><strong>Cascadia (Code|Mono) PL</strong>: A version of Cascadia that has embedded Powerline symbols. Powerline is a common command-line plugin that allows you to display additional information in your prompt using special glyphs.</li>
-<li><strong>Cascadia (Code|Mono) Italic</strong>: A version of Cascadia that has an italic style. There is also a cursive variant accessible via a stylistic set (see below).</li>
-</ul>
-<li>Right-click on the selected font files and choose <strong>Install for all users</strong>. This will install the fonts on your system and make them available for all applications.</li>
-</ol>
-<p>Note: If you have previously installed a version of Cascadia Code, please uninstall it before installing a new version. Not doing so can result in improper rendering. Also, we do not recommend installing more than one variant of Cascadia Code as it can cause naming issues within the OS.</p>
- <h1>Usage</h1>
-<p>Once you have installed Cascadia Code on your system, you can start using it in your favorite applications that support custom fonts. In this section, I will show you how to set Cascadia Code as the default font in Windows Terminal, Visual Studio, and Visual Studio Code. These are some of the most popular tools for developers and programmers, but you can also use Cascadia Code in other applications that allow you to change the font settings. <h2>Windows Terminal</h2>
-<p>Windows Terminal is a new command-line interface for Windows that offers a rich user experience with tabs, panes, themes, and more. It supports various shells such as PowerShell, Command Prompt, WSL, and Azure Cloud Shell. You can also customize the appearance and behavior of Windows Terminal using a JSON file called settings.json.</p>
-<p>To set Cascadia Code as the default font in Windows Terminal, follow these steps:</p>
-<ol>
-<li>Open Windows Terminal and click on the drop-down menu at the top right corner.</li>
-<li>Select <strong>Settings</strong> from the menu. This will open the settings.json file in your default text editor.</li>
-<li>Find the <strong>"defaults"</strong> section under the <strong>"profiles"</strong> section. This section contains the default settings for all profiles in Windows Terminal.</li>
-<li>Add or modify the following lines:</li>
-<pre><code>"fontFace": "Cascadia Code", "fontSize": 12 </code></pre>
-<li>Save the settings.json file and restart Windows Terminal.</li>
-</ol>
-<p>Note: You can also set Cascadia Code as the font for a specific profile by adding or modifying the same lines under the profile's section. For example, if you want to use Cascadia Code for PowerShell only, you can find the <strong>"PowerShell"</strong> section under the <strong>"list"</strong> section and add or modify the lines there.</p>
- <h2>Visual Studio</h2>
-<p>Visual Studio is Microsoft's integrated development environment (IDE) for building applications using various languages such as C#, Visual Basic, C++, Python, and more. It offers a powerful code editor, debugger, designer, testing tools, and extensions. You can also customize the appearance and behavior of Visual Studio using various options and settings.</p>
-<p>To set Cascadia Code as the default font in Visual Studio, follow these steps:</p>
-<ol>
-<li>Open Visual Studio and go to <strong>Tools</strong> > <strong>Options</strong>.</li>
-<li>In the Options dialog box, expand the <strong>Environment</strong> node and select <strong>Fonts and Colors</strong>.</li>
-<li>In the Show settings for drop-down list, select <strong>Text Editor</strong>.</li>
-<li>In the Font drop-down list, select <strong>Cascadia Code</strong>.</li>
-<li>In the Size drop-down list, select your preferred font size.</li>
-<li>Click <strong>OK</strong> to apply the changes.</li>
-</ol>
- <h2>Visual Studio Code</h2>
-<p>Visual Studio Code is a lightweight but powerful code editor that supports various languages and frameworks such as JavaScript, TypeScript, Node.js, React, Angular, and more. It offers features such as IntelliSense, debugging, Git integration, extensions, and themes. You can also customize the appearance and behavior of Visual Studio Code using various settings and preferences.</p>
-<p>To set Cascadia Code as the default font in Visual Studio Code, follow these steps:</p>
-<ol>
-<li>Open Visual Studio Code and go to <strong>File</strong> > <strong>Preferences</strong> > <strong>Settings</strong>.</li>
-<li>In the Settings tab, search for <strong>"font family"</strong>.</li>
-<li>In the Editor: Font Family setting, enter <strong>'Cascadia Code', monospace</strong>. Make sure to include the single quotes and the comma.</li>
-<li>In the Editor: Font Size setting, enter your preferred font size.</li>
-<li>You can also enable or disable font ligatures by checking or unchecking the Editor: Font Ligatures setting.</li>
-<li>The changes will take effect immediately without restarting Visual Studio Code.</li>
-</ol>
- <h1>Customization</h1>
-<p>Cascadia Code is not only a beautiful font but also a customizable one. You can enable or disable some of its features such as ligatures, powerline symbols, and stylistic sets according to your preferences. Stylistic sets are alternative glyphs that you can access using OpenType features. For example, you can switch between a normal italic style and a cursive italic style using a stylistic set.</p>
- <h2>Ligatures</h2>
-<p>Ligatures are special glyphs that are created by combining two or more characters into one. For example, the characters <= can be merged into a single glyph ≤ that looks like a less-than-or-equal-to sign. Ligatures can help make code more readable and user-friendly for some people, as it reduces visual clutter and creates a more natural flow of symbols. Programming ligatures are supported by many popular languages such as C#, JavaScript, Python, and more.</p>
-<p>Cascadia Code includes ligatures by default, but you can also disable them if you prefer. To do so, you can use the Cascadia Mono variant, which does not have ligatures. Alternatively, you can disable ligatures in your application settings. For example, in Visual Studio Code, you can uncheck the Editor: Font Ligatures setting.</p>
- <h2>Powerline symbols</h2>
-<p>Powerline symbols are special glyphs that are used by Powerline, a common command-line plugin that allows you to display additional information in your prompt using special glyphs. For example, you can show the current branch, status, and time of your Git repository using Powerline symbols.</p>
-<p>Cascadia Code has embedded Powerline symbols in its PL variants. To use them, you need to install the Cascadia (Code|Mono) PL font and set it as your font in your application. You also need to install and configure Powerline for your shell. For more information on how to do that, please refer to the <a href="">Powerline documentation</a>.</p>
- <h2>Stylistic sets</h2>
-<p>Stylistic sets are alternative glyphs that you can access using OpenType features. For example, you can switch between a normal italic style and a cursive italic style using a stylistic set. Stylistic sets are useful for changing the appearance of certain characters or symbols according to your preferences.</p>
-<p>Cascadia Code supports several stylistic sets that you can enable or disable using your application settings. For example, in Visual Studio Code, you can use the Editor: Font Feature Settings setting to specify the stylistic sets you want to use. The syntax is <strong>"ss01"</strong> for the first stylistic set, <strong>"ss02"</strong> for the second stylistic set, and so on.</p>
-<p>The following table shows some of the stylistic sets available in Cascadia Code and their effects:</p>
- | Stylistic set | Effect | | --- | --- | | ss01 | Enables cursive italic style | | ss02 | Enables slashed zero | | ss03 | Enables dotted zero | | ss04 | Enables lowercase L with tail | | ss05 | Enables straight quotes | | ss06 | Enables Fira Code-style ligatures | <p>Note: Not all applications support OpenType features or stylistic sets. Please check your application documentation for more details on how to use them.</p>
- <h1>Feedback</h1>
-<p>Cascadia Code is a relatively new font that has received a lot of positive feedback from users who have tried it. Here are some of the pros and cons of Cascadia Code according to other users:</p>
- <h2>Pros</h2>
-<ul>
-<li>Cascadia Code is modern, clean, and elegant. It has a balanced weight and height that makes it easy to read and pleasant to look at.</li>
-<li>Cascadia Code supports programming ligatures that can make code more readable and user-friendly for some people.</li>
-<li>Cascadia Code has embedded Powerline symbols that can enhance the functionality and appearance of your command-line interface.</li>
-<li>Cascadia Code supports stylistic sets that allow you to customize the appearance of certain characters or symbols according to your preferences.</li>
-<li>Cascadia Code is open source and free to use. You can also contribute to its development or report issues on GitHub.</li>
-</ul>
- <h2>Cons</h2>
-<ul>
-<li>Cascadia Code may not be compatible with some applications or systems that do not support custom fonts or OpenType features.</li>
-<li>Cascadia Code may not suit everyone's taste or preferences. Some people may find it too thin, too wide, or too fancy for their liking.</li>
-<li>Cascadia Code may have some bugs or issues that need to be fixed or improved. For example, some users have reported problems with rendering or alignment of certain glyphs or symbols.</li>
-</ul>
- <h1>Conclusion</h1>
-<p>Cascadia Code is a fun, new monospaced font that includes programming ligatures and is designed to enhance the modern look and feel of the Windows Terminal, Visual Studio, and Visual Studio Code. It is also customizable and supports various features such as Powerline symbols and stylistic sets. You can download Cascadia Code from GitHub and install it on your system easily. You can also use it in other applications that support custom fonts.</p>
-<p>If you are looking for a fresh and stylish font for your coding needs, why not give Cascadia Code a try? You might be surprised by how much it can improve your coding experience and productivity. And if you like it, you can also share your feedback or suggestions with the Cascadia Code community on GitHub.</p>
- <h1>FAQs</h1>
-<p>Here are some of the frequently asked questions about Cascadia Code and their answers:</p>
- <h2>What is the difference between Cascadia Code and Cascadia Mono?</h2>
-<p>Cascadia Code and Cascadia Mono are two variants of the same font. The main difference is that Cascadia Code has ligatures while Cascadia Mono does not. Ligatures are special glyphs that are created by combining two or more characters into one. For example, the characters <= can be merged into a single glyph ≤ that looks like a less-than-or-equal-to sign. Ligatures can help make code more readable and user-friendly for some people, as it reduces visual clutter and creates a more natural flow of symbols.</p>
- <h2>How can I use Cascadia Code with Powerline?</h2>
-<p>Powerline is a common command-line plugin that allows you to display additional information in your prompt using special glyphs. For example, you can show the current branch, status, and time of your Git repository using Powerline symbols.</p>
-<p>Cascadia Code has embedded Powerline symbols in its PL variants. To use them, you need to install the Cascadia (Code|Mono) PL font and set it as your font in your application. You also need to install and configure Powerline for your shell. For more information on how to do that, please refer to the <a href="">Powerline documentation</a>.</p>
- <h2>How can I customize Cascadia Code using stylistic sets?</h2>
-<p>Stylistic sets are alternative glyphs that you can access using OpenType features. For example, you can switch between a normal italic style and a cursive italic style using a stylistic set. Stylistic sets are useful for changing the appearance of certain characters or symbols according to your preferences.</p>
-<p>Cascadia Code supports several stylistic sets that you can enable or disable using your application settings. For example, in Visual Studio Code, you can use the Editor: Font Feature Settings setting to specify the stylistic sets you want to use. The syntax is <strong>"ss01"</strong> for the first stylistic set, <strong>"ss02"</strong> for the second stylistic set, and so on.</p>
-<p>The following table shows some of the stylistic sets available in Cascadia Code and their effects:</p>
- | Stylistic set | Effect | | --- | --- | | ss01 | Enables cursive italic style | | ss02 | Enables slashed zero | | ss03 | Enables dotted zero | | ss04 | Enables lowercase L with tail | | ss05 | Enables straight quotes | | ss06 | Enables Fira Code-style ligatures | <h2>Where can I download Cascadia Code?</h2>
-<p>The easiest way to download Cascadia Code is to get it from its official GitHub repository. There you can find the latest version of the font in various formats and variants. You can also browse the source code, report issues, or contribute to the project.</p>
-<p>To download Cascadia Code from GitHub, go to the <a href="">Cascadia Code GitHub releases page</a>, find the Assets section under the latest release, and click on the <strong>CascadiaCode.zip</strong> link. Save the zip file to your preferred location on your computer and extract it using your preferred tool (such as Windows Explorer or 7-Zip). Open the extracted folder and go to the <strong>ttf</strong> folder. Select all the font files (.ttf) that you want to install and right-click on them and choose <strong>Install for all users</strong>.</p>
- <h2>How can I contribute to Cascadia Code?</h2>
-<p>Cascadia Code is an open source project that welcomes contributions from anyone who is interested in improving it. You can contribute to Cascadia Code by reporting issues, suggesting features, fixing bugs, adding glyphs, or creating variants. To do so, you need to have a GitHub account and follow the guidelines provided by the Cascadia Code team.</p>
-<p>To contribute to Cascadia Code, go to the <a href="">Cascadia Code GitHub repository</a>, read the README.md file and the CONTRIBUTING.md file for more information on how to get started. You can also join the discussion on GitHub issues or pull requests.</p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Raft Survival Multiplayer - evrimii Sal na Etme ve Okyanus Kefi Oyunu APK ndir.md b/spaces/congsaPfin/Manga-OCR/logs/Raft Survival Multiplayer - evrimii Sal na Etme ve Okyanus Kefi Oyunu APK ndir.md
deleted file mode 100644
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--- a/spaces/congsaPfin/Manga-OCR/logs/Raft Survival Multiplayer - evrimii Sal na Etme ve Okyanus Kefi Oyunu APK ndir.md	
+++ /dev/null
@@ -1,149 +0,0 @@
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- <p>Have you ever wondered what it would be like to survive on a raft in the middle of the ocean with your friends? Do you want to experience the thrill of fighting against sharks, hunger, thirst, and other dangers? If you answered yes, then you should try Raft Survival Multiplayer, a game that will test your survival skills and teamwork. In this article, we will tell you everything you need to know about Raft Survival Multiplayer APK Indir, how to download and install it, how to play it with your friends online, and why you should play it.</p>
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-<li>Dynamic day and night cycle and weather system</li>
-<li>Randomly generated world and events</li>
-<li>Over 100 items to craft and use</li>
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-<li>Chat system that lets you communicate with other players</li>
-<li>Leaderboard system that ranks your survival time and achievements</li>
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- <p>If you want to download and install Raft Survival Multiplayer APK Indir, you need to follow some simple steps. But before that, you need to make sure that your device meets the requirements for the APK file.</p>
- <h3>The requirements for the APK file</h3>
- <p>The APK file is a compressed file that contains the installation package for the game. It is usually used when the game is not available on Google Play Store or when you want to install an older or modified version of the game. To install the APK file, you need:</p>
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-<li>An Android device that runs on Android 4.4 or higher</li>
-<li>At least 100 MB of free storage space on your device</li>
-<li>A stable internet connection</li>
-<li>A file manager app that can open ZIP files</li>
-<li>A permission to install apps from unknown sources on your device settings</li>
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- <h3>The steps to download and install the APK file</h3>
- <p>Once you have checked the requirements, you can proceed with the following steps:</ <p>Here is the continuation of the article:</p>
- <p>- Go to a trusted website that provides the Raft Survival Multiplayer APK Indir file. You can search for it on Google or use this link: [Raft Survival Multiplayer APK Indir]</p>
-<p>- Download the ZIP file that contains the APK file and the OBB file. The OBB file is a data file that contains the game assets and resources. You need both files to run the game properly.</p>
-<p>- Open the ZIP file using your file manager app and extract the APK file and the OBB file to your device storage.</p>
-<p>- Locate the APK file and tap on it to start the installation process. You may need to allow the installation from unknown sources if you haven't done it before.</p>
-<p>- Wait for the installation to finish and then locate the OBB file. It should have a name like com.unisoftgames.raftsurvivalmultiplayer.zip.</p>
-<p>- Move or copy the OBB file to the Android/OBB folder on your device storage. If you don't have this folder, you can create it manually.</p>
-<p>- Launch the game from your app drawer and enjoy playing Raft Survival Multiplayer with your friends online.</p>
- <h2>How to play Raft Survival Multiplayer with your friends online?</h2>
- <p>Raft Survival Multiplayer is more fun and challenging when you play it with your friends online. You can cooperate or compete with other players in different modes of multiplayer gameplay.</p>
- <h3>The modes of multiplayer gameplay</h3>
- <p>The game offers three modes of multiplayer gameplay:</p>
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-<li>Co-op mode: In this mode, you can join or create a room with up to 16 players and work together to survive on the same raft. You can share resources, items, and tasks with your teammates. You can also chat and voice chat with them. You can choose to play on a public or private room, depending on your preference.</li>
-<li>PvP mode: In this mode, you can join or create a room with up to 16 players and fight against each other on different rafts. You can attack, loot, and sabotage other players' rafts. You can also chat and voice chat with them. You can choose to play on a public or private room, depending on your preference.</li>
-<li>Solo mode: In this mode, you can play alone on your own raft and challenge yourself to survive as long as possible. You can still chat and voice chat with other players who are online, but you cannot interact with them in any other way.</li>
-</ul>
- <h3>The tips and tricks for surviving together</h3>
- <p>Surviving on a raft in the ocean is not easy, especially when you have to deal with other players. Here are some tips and tricks that can help you survive together:</p>
-<ul>
-<li>Collect as much debris as possible and expand your raft. A bigger raft means more space, more stability, and more protection.</li>
-<li>Craft a fishing rod, a water purifier, a grill, and a crop plot as soon as possible. These items will help you get food and water, which are essential for survival.</li>
-<li>Craft a spear, a bow, and arrows to defend yourself from sharks and other players. You can also craft traps, nets, and walls to protect your raft.</li>
-<li>Craft a sail, an anchor, a paddle, and a steering wheel to control your raft's movement and direction. You can also craft engines, batteries, antennas, and receivers to enhance your navigation.</li>
-<li>Craft a research table, a blueprint table, a workbench, and a forge to unlock new items and upgrade your existing ones. You can also craft decorations, furniture, and clothing to customize your raft.</li>
-<li>Explore islands, underwater caves, and shipwrecks to find rare resources, items, and secrets. You can also find animals and plants that you can tame or harvest.</li>
-<li>Communicate with your teammates or opponents using the chat or voice chat system. You can also use gestures and emoticons to express yourself.</li>
-<li>Be careful of storms, waves, currents, fog, nightfall, and other environmental factors that can affect your raft's condition and visibility.</li>
-<li>Be aware of your hunger, thirst, health, and oxygen levels. You can check them on the top left corner of your screen. You can also check your inventory, crafting menu, map, settings, leaderboard, achievements, and help menu on the top right corner of your screen.</li>
-<li>Have fun and enjoy the game!</li>
-</ul>
- <h2>Why should you play Raft Survival Multiplayer?</h2>
- <p>Raft Survival Multiplayer is a game that offers many benefits and challenges for its players. Here are some of <p>Here is the continuation of the article:</p>
- <p>them:</p>
- <h3>The benefits of playing Raft Survival Multiplayer</h3>
- <p>Playing Raft Survival Multiplayer can help you:</p>
-<ul>
-<li>Improve your creativity and problem-solving skills by crafting and using various items and strategies.</li>
-<li>Enhance your communication and cooperation skills by working with your teammates or negotiating with your opponents.</li>
-<li>Develop your survival and adaptation skills by facing different challenges and scenarios.</li>
-<li>Learn more about the ocean and its ecosystem by interacting with different animals and plants.</li>
-<li>Have fun and relax by escaping from reality and immersing yourself in a virtual world.</li>
-</ul>
- <h3>The challenges of playing Raft Survival Multiplayer</h3>
- <p>Playing Raft Survival Multiplayer can also pose some challenges for you:</p>
-<ul>
-<li>You may encounter technical issues such as bugs, glitches, crashes, or lag that can affect your gameplay.</li>
-<li>You may face ethical dilemmas such as whether to help or harm other players, whether to share or hoard resources, or whether to trust or betray others.</li>
-<li>You may experience emotional stress such as fear, anger, frustration, or sadness due to the game's difficulty, unpredictability, or outcome.</li>
-<li>You may become addicted to the game and neglect your real-life responsibilities, relationships, or health.</li>
-<li>You may encounter inappropriate or toxic behavior from other players such as bullying, harassment, cheating, or trolling.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Raft Survival Multiplayer is a game that simulates the scenario of being stranded on a raft in the ocean with limited resources and threats. You can download it from Google Play Store or from an APK file. You can play it with your friends online in co-op mode or PvP mode, or play it alone in solo mode. You can craft and use over 100 items, explore over 20 types of animals and plants, and discover secrets and clues about the raft's origin. You can also enjoy realistic graphics and physics, dynamic day and night cycle and weather system, randomly generated world and events, chat and voice chat system, leaderboard system, and more. Playing Raft Survival Multiplayer can help you improve your creativity, communication, cooperation, survival, adaptation, and learning skills. It can also challenge you with technical issues, ethical dilemmas, emotional stress, addiction risk, and inappropriate behavior. Raft Survival Multiplayer is a game that will test your survival skills and teamwork. Are you ready to take on the challenge?</p>
- <h3>A call to action for the readers</h3>
- <p>If you are interested in playing Raft Survival Multiplayer with your friends online, you can download the game from Google Play Store or from this link: [Raft Survival Multiplayer APK Indir]. You can also join our Facebook group [Raft Survival Multiplayer Community] to share your experiences, tips, feedbacks, suggestions, questions, and more with other players. You can also follow us on Twitter [@RaftSurvivalMP] to get the latest news, updates, events, contests, giveaways, and more about the game. We hope you enjoy playing Raft Survival Multiplayer and we look forward to hearing from you soon!</p>
- <h2>Frequently Asked Questions</h2>
- <p>Here are some frequently asked questions about Raft Survival Multiplayer:</p>
- <h4>Q: How much does Raft Survival Multiplayer cost?</h4>
-<p>A: Raft Survival Multiplayer is free to download and play. However, it contains in-app purchases that allow you to buy coins or gems that you can use to buy premium items or features in the game. You can also watch ads to earn free coins or gems.</p>
- <h4>Q: Is Raft Survival Multiplayer safe to play?</h4>
-<p>A: Raft Survival Multiplayer is safe to play as long as you follow some basic precautions. You should not download the game from untrusted sources or websites that may contain viruses or malware. You should not share your personal information or account details with anyone. You should not engage in any illegal or unethical activities in the game. You should also be respectful and polite to other players and report any inappropriate or toxic behavior.</p>
- <h4>Q: Is Raft Survival Multiplayer compatible with my device?</h4>
-<p>A: Raft Survival Multiplayer is compatible with most Android devices that run on Android 4.4 or higher. However, some devices may have different specifications or performance levels that may affect the game's quality or functionality. You can check the game's description on Google Play Store or on the website for more information about the game's requirements and compatibility.</p>
- <h4>Q: How can I contact the developers of Raft Survival Multiplayer?</h4>
-<p>A: You can contact the developers of Ra <p>Here is the continuation of the article:</p>
- <p>ft Survival Multiplayer by sending an email to support@unisoftgames.com or by filling out the contact form on their website [Unisoft Games]. You can also follow them on Facebook [Unisoft Games] or Instagram [@unisoftgames] to get in touch with them. They are always happy to hear from their players and to answer their questions, feedbacks, suggestions, or complaints.</p>
- <h4>Q: How can I support the development of Raft Survival Multiplayer?</h4>
-<p>A: You can support the development of Raft Survival Multiplayer by doing the following things:</p>
-<ul>
-<li>Rate and review the game on Google Play Store or on the website. Your ratings and reviews help the game to get more visibility and recognition.</li>
-<li>Share the game with your friends and family. The more players the game has, the more fun and exciting it becomes.</li>
-<li>Buy coins or gems in the game or watch ads to earn free coins or gems. Your purchases or views help the developers to generate revenue and to improve the game.</li>
-<li>Donate to the developers via PayPal or Patreon. Your donations help the developers to cover the costs of development and maintenance of the game.</li>
-</ul></p> 197e85843d<br />
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-<h1>How to Download MP3 of The Shade by Rex Orange County</h1>
-<p>If you are a fan of indie pop and jazz hip hop, you might have heard of Rex Orange County, a talented singer, songwriter, and multi-instrumentalist from England. His latest album, Who Cares?, features a catchy and emotional song called The Shade, which has gained millions of views on YouTube and other platforms. But what if you want to download MP3 of The Shade and listen to it offline? In this article, we will show you how to do that legally and safely, using different options and methods. We will also give you some background information about Rex Orange County and The Shade, so you can enjoy the song even more.</p>
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- <h2>Introduction</h2>
-<h3>Who is Rex Orange County?</h3>
-<p>Rex Orange County is the stage name of Alexander James O'Connor, who was born on May 4, 1998 in Hampshire, England. He started making music at a young age, playing drums, piano, guitar, saxophone, and other instruments. He attended the BRIT School, where he studied music and met other aspiring musicians. He released his debut album, Bcos U Will Never B Free, on SoundCloud and Bandcamp in 2015, when he was only 17 years old. The album caught the attention of Tyler, the Creator, who invited him to collaborate on his album Flower Boy in 2017. Rex Orange County also gained popularity after being featured on several tracks by Frank Ocean, Chance the Rapper, Randy Newman, and others. He has released three more albums since then: Apricot Princess (2017), Pony (2019), and Who Cares? (2022). He has been nominated for several awards, including the Brit Awards and the Grammy Awards. He is known for his unique blend of hip hop, jazz, and bedroom pop, as well as his honest and relatable lyrics.</p>
- <h3>What is The Shade about?</h3>
-<p>The Shade is the eighth track on Rex Orange County's fourth album, Who Cares?, which was released on March 11, 2022. The song is about the fear of losing someone you love and the lengths you'll go to keep them in your life. The protagonist in the song is doing everything in their power to make sure their loved one feels safe and secure. He closes all the blinds so they can sleep better, stays by their side to make sure they are okay, opens the door for them whenever they want to leave or come back, and paints all the walls in their favorite color. However, he also realizes that his efforts might not be enough to keep them from leaving him. He expresses his love for them and his desire to be stuck to their side, not with anybody else. He pleads with them not to go and to stay with him.</p>
- <h2>How to download MP3 of The Shade legally and safely</h2>
-<h3>Option 1: Buy the song from online music stores</h3>
-<h4>Pros and cons of buying music online</h4>
-<p>One of the easiest and most legal ways to download MP3 of The Shade is to buy it from online music stores such as Amazon Music , iTunes , or Google Play Music . These stores offer high-quality MP3 files that you can download to your computer or mobile device after paying a small fee. You can also access your purchased music from any device that supports these services.</p>
-<p>However, buying music online also has some drawbacks. First of all, you have to <p>pay for each song or album individually, which can add up quickly if you want to download a lot of music. Secondly, you might not be able to find all the songs or artists you want on these stores, especially if they are indie or underground. Thirdly, you might encounter some technical issues or errors when downloading or transferring your music files.</p>
- <h4>How to buy music from Amazon, iTunes, and Google Play</h4>
-<p>If you decide to buy music from online music stores, here are the steps you need to follow:</p>
-<ul>
-<li>Choose the store that works best for your device and preferences. For example, if you have an iPhone or iPad, you might want to use iTunes. If you have an Android device, you might want to use Google Play. If you have a Kindle or Echo device, you might want to use Amazon.</li>
-<li>Create an account on the store if you don't have one already. You will need to provide your email address, password, and payment information.</li>
-<li>Search for the song or album you want to buy on the store. For example, if you want to buy The Shade by Rex Orange County, type "The Shade Rex Orange County" in the search bar.</li>
-<li>Select the song or album from the search results and click on the buy button. You will see the price and the file size of the MP3 file.</li>
-<li>Confirm your purchase and download the MP3 file to your device. You can also choose to stream it online if you have an internet connection.</li>
-<li>Enjoy listening to your music offline or online.</li>
-</ul>
- <h3>Option 2: Stream the song from music streaming services</h3>
-<h4>Pros and cons of streaming music online</h4>
-<p>Another option to listen to The Shade by Rex Orange County is to stream it from music streaming services such as Spotify , YouTube Music , or SoundCloud . These services offer a large library of music that you can access online for free or for a monthly subscription fee. You can also create playlists, discover new music, and share your music with others.</p>
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-<p>However, streaming music online also has some disadvantages. First of all, you need a stable internet connection to stream music without interruptions or buffering. Secondly, you might not be able to download or save the music files for offline listening, unless you pay for a premium subscription. Thirdly, you might have to deal with ads or limited skips if you use the free version of these services.</p>
- <h4>How to stream music from Spotify, YouTube Music, and SoundCloud</h4>
-<p>If you choose to stream music from music streaming services, here are the steps you need to follow:</p>
-<ul>
-<li>Choose the service that suits your taste and budget. For example, if you want a wide variety of music genres and artists, you might want to use Spotify. If you want to watch music videos and live performances, you might want to use YouTube Music. If you want to support independent and emerging artists, you might want to use SoundCloud.</li>
-<li>Create an account on the service if you don't have one already. You will need to provide your email address, password, and some personal information.</li>
-<li>Search for the song or artist you want to stream on the service. For example, if you want to stream The Shade by Rex Orange County, type "The Shade Rex Orange County" in the search bar.</li>
-<li>Select the song from the search results and click on the play button. You will see the album art and the lyrics of the song.</li>
-<li>Enjoy listening to your music online. You can also add it to your playlist, like it, comment on it, or share it with others.</li>
-</ul>
- <h3>Option 3: Download the song from free music download sites</h3>
-<h4>Pros and cons of downloading music for free</h4>
-<p>A third option to download MP3 of The Shade by Rex Orange County is to use free music download sites such as SoundCloud , Last.fm , or NoiseTrade . These sites allow you to download MP3 files of songs that are uploaded by artists or users for free. You can also discover new music and support independent musicians.</p>
-<p>However, downloading music for free also has some risks and drawbacks. First of all, it might be illegal or unethical to download music without paying for it or getting permission from the artist or owner. You might violate their copyright or intellectual property rights and face legal consequences. Secondly, you might not be able <p>to find the song or artist you want on these sites, especially if they are popular or mainstream. Thirdly, you might download low-quality or corrupted files that might harm your device or contain viruses or malware.</p>
- <h4>How to download music from SoundCloud, Last.fm, and NoiseTrade</h4>
-<p>If you decide to download music from free music download sites, here are the steps you need to follow:</p>
-<ul>
-<li>Choose the site that has the song or artist you want to download. For example, if you want to download The Shade by Rex Orange County, you might want to use SoundCloud, as he has uploaded his songs there.</li>
-<li>Create an account on the site if you don't have one already. You will need to provide your email address, password, and some personal information.</li>
-<li>Search for the song or artist you want to download on the site. For example, if you want to download The Shade by Rex Orange County, type "The Shade Rex Orange County" in the search bar.</li>
-<li>Select the song from the search results and click on the download button. You might have to follow some instructions or complete some tasks to get the download link. For example, on SoundCloud, you might have to like or repost the song, or follow the artist. On Last.fm, you might have to sign up for a newsletter or a trial. On NoiseTrade, you might have to enter your email address and zip code.</li>
-<li>Download the MP3 file to your device and enjoy listening to your music offline.</li>
-</ul>
- <h2>Conclusion</h2>
-<h3>Summary of the main points</h3>
-<p>In this article, we have shown you how to download MP3 of The Shade by Rex Orange County legally and safely. We have also given you some background information about Rex Orange County and The Shade, so you can appreciate the song better. We have discussed three options to download MP3 of The Shade: buying it from online music stores, streaming it from music streaming services, and downloading it from free music download sites. We have also listed the pros and cons of each option and explained how to use them step by step.</p>
- <h3>Call to action and recommendations</h3>
-<p>We hope you have found this article helpful and informative. If you have any questions or feedback, please leave a comment below. We would love to hear from you. If you liked this article, please share it with your friends and family who might be interested in downloading MP3 of The Shade by Rex Orange County. If you want to learn more about Rex Orange County and his music, please visit his official website , his YouTube channel , or his Instagram account . You can also check out his other songs and albums on Spotify , iTunes , or Amazon . Thank you for reading and happy listening!</p>
- <h2>Frequently Asked Questions</h2>
-<h3>What is MP3?</h3>
-<p>MP3 is a type of audio file format that compresses sound data into a smaller size without losing much quality. MP3 stands for MPEG-1 Audio Layer 3, which is a standard developed by the Moving Picture Experts Group (MPEG). MP3 files can be played on most devices and platforms that support audio playback.</p>
- <h3>What is the difference between downloading and streaming music?</h3>
-<p>Downloading music means saving the music files onto your device so you can listen to them offline without an internet connection. Streaming music means playing the music files online without saving them onto your device. You need an internet connection to stream music.</p>
- <h3>Is it legal to download music for free?</h3>
-<p>It depends on the source and the permission of the music owner. Some artists or owners allow their music to be downloaded for free for personal use or promotion. Some sites offer free music downloads legally and ethically. However, some sites offer pirated or unauthorized music downloads that violate the rights of the artists or owners. Downloading music from these sites is illegal and unethical.</p>
- <h3>How can I support Rex Orange County?</h3>
-<p>You can support Rex Orange County by buying his music from online music stores, streaming his music from music streaming services, attending his concerts or events, following him on social media, subscribing to his newsletter or fan club, donating to his causes or charities, or sending him fan mail or messages.</p>
- <h3>What are some other songs by Rex Orange County that I might like?</h3>
-<p>If you like The Shade by Rex Orange County, you might also like some of his other songs such as Loving Is Easy , Best Friend , Sunflower , 10/10 , Pluto Projector , Happiness , Edition , Corduroy Dreams , Untitled , or Television / So Far So Good . You can find these songs on his albums Bcos U Will Never B Free , Apricot Princess , Pony , and Who Cares?. You can also check out his collaborations with other artists such as Tyler, the Creator, Frank Ocean, Chance the Rapper, Randy Newman, and others.</p> 197e85843d<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Sakhi Re Bar Paa Gayini MP3 Song - Indu Sonali Khesari Lal Yadav - Bhojpuri Music from Mehandi Laga Ke Rakhna - Download Now.md b/spaces/congsaPfin/Manga-OCR/logs/Sakhi Re Bar Paa Gayini MP3 Song - Indu Sonali Khesari Lal Yadav - Bhojpuri Music from Mehandi Laga Ke Rakhna - Download Now.md
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-<h1>Sakhi Re Bar Paa Gayini: A Bhojpuri Hit Song by Khesari Lal Yadav and Kajal Raghwani</h1>
- <p>If you are a fan of Bhojpuri music, you must have heard of Sakhi Re Bar Paa Gayini, a catchy and romantic song that has become one of the most popular songs in the genre. In this article, we will tell you everything you need to know about this song, including its lyrics, meaning, popularity, singers, actors, album, movie, and how to download it legally and safely. So, let's get started!</p>
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- <h2>Introduction</h2>
- <h3>What is the song about?</h3>
- <p>Sakhi Re Bar Paa Gayini is a Bhojpuri song that expresses the love and attraction between a young couple who are about to get married. The title of the song means "My friend, I have fallen in love" in English. The song is full of sweet and playful words that describe how the lovers feel about each other and how they tease each other. The song also has some references to the traditional rituals and customs of Bhojpuri weddings, such as applying mehndi (henna) on the bride's hands and feet, wearing sarso (mustard) flowers, and exchanging garlands.</p>
- <h3>Who are the singers and actors?</h3>
- <p>The song is sung by Indu Sonali, Khesari Lal Yadav, and Alok Kumar. Indu Sonali is a famous Bhojpuri singer who has sung more than 3000 songs in various languages. She is known for her versatile voice and her hit songs like Lollipop Lagelu, Chhalakata Hamro Jawaniya, and Raja Ji I Love You. Khesari Lal Yadav is a popular Bhojpuri singer, actor, producer, and politician. He has won several awards for his singing and acting skills, such as the Dadasaheb Phalke Academy Award, the Bhojpuri Film Award, and the International Bhojpuri Film Award. He is also a member of the Bharatiya Janata Party (BJP). Alok Kumar is another well-known Bhojpuri singer who has sung many songs for movies and albums. He is also a composer and a music director.</p>
- <p>The song features Khesari Lal Yadav and Kajal Raghwani as the lead actors. Kajal Raghwani is a beautiful and talented Bhojpuri actress who has worked in more than 50 movies. She has won several awards for her acting skills, such as the Bhojpuri Best Actress People's Choice Award, the Bhojpuri Best Actress Critics Award, and the International Bhojpuri Film Award. She is also a dancer and a model.</p>
- <h3>Which album and movie is the song from?</h3>
- <p>The song is from the album Mehandi Laga Ke Rakhna, which was released in 2016 by Worldwide Records. The album consists of 11 songs that are composed by Rajnish Mishra and written by Pyare Lal Yadav 'Kavi Ji' and Shyam Dehati. The album features various singers like Indu Sonali, Khesari Lal Yadav, Alok Kumar, Kalpana Patowary, Priyanka Singh, Khushboo Jain, Om Jha, Pamela Jain, Rajnish Mishra, Honey Bee, and Madhukar Anand. The album is a hit among the Bhojpuri music lovers and has received positive reviews from the critics and the audience.</p>
- <p>The song is also from the movie Mehandi Laga Ke Rakhna, which was released in 2017 by Rajnish Mishra Films. The movie is a romantic comedy that revolves around the love story of Khesari Lal Yadav and Kajal Raghwani, who face many challenges and obstacles before they can get married. The movie also stars Ritu Singh, Avdhesh Mishra, Sanjay Pandey, Anand Mohan, Karan Pandey, and Sanjay Mahanand. The movie is directed by Rajnish Mishra and produced by Ananjay Raghuraj. The movie is a blockbuster at the box office and has received rave reviews from the critics and the audience.</p>
- <h2>Main Body</h2>
- <h3>How popular is the song?</h3>
- <h4>Number of views and downloads</h4>
- <p>The song is one of the most popular Bhojpuri songs on YouTube and other platforms. The song has more than 200 million views on YouTube and more than 10 million downloads on various websites and apps. The song has also been shared and liked by millions of people on social media platforms like Facebook, Twitter, Instagram, and TikTok. The song has also been played on various radio stations and TV channels across India and abroad.</p>
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- <h4>Awards and nominations</h4>
- <p>The song has also won several awards and nominations for its singers, actors, composers, writers, and producers. Some of the awards and nominations that the song has received are: - Bhojpuri Film Award for Best Male Playback Singer (Khesari Lal Yadav) in 2017 - Bhojpuri Film Award for Best Female Playback Singer (Indu Sonali) in 2017 - Bhojpuri Film Award for Best Music Director (Rajnish Mishra) in 2017 - Bhojpuri Film Award for Best Lyricist (Pyare Lal Yadav 'Kavi Ji') in 2017 - International Bhojpuri Film Award for Best Male Playback Singer (Khesari Lal Yadav) in 2018 - International Bhojpuri Film Award for Best Female Playback Singer (Indu Sonali) in 2018 - International Bhojpuri Film Award for Best Music Director (Rajnish Mishra) in 2018 - International Bhojpuri Film Award for Best Lyricist (Pyare Lal Yadav 'Kavi Ji') in 2018</p>
- <h4>Fan reactions and comments</h4>
- <p>The song has also received a lot of love and appreciation from the fans and the listeners. Some of the fan reactions and comments that the song has received are: - "This song is so sweet and romantic. I love Khesari Lal Yadav and Kajal Raghwani's chemistry. They are the best couple in Bhojpuri cinema." - Pooja Kumari - "This song is very catchy and melodious. I can't stop humming it. Indu Sonali's voice is amazing. She is my favorite singer." - Ravi Kumar - "This song is very beautiful and meaningful. It shows the true culture and tradition of Bhojpuri weddings. I love the lyrics and the music." - Sunita Devi - "This song is very fun and entertaining. I love the dance moves and the expressions of Khesari Lal Yadav and Kajal Raghwani. They are very talented actors." - Rajesh Singh - "This song is very awesome and superb. I have downloaded it on my phone and I listen to it every day. It makes me happy and energetic." - Amit Kumar</p> <h3>What is the meaning of the song?</h3>
- <h4>Lyrics and translation</h4>
- <p>The song has very catchy and romantic lyrics that are written in Bhojpuri, a language that is spoken in parts of Bihar, Uttar Pradesh, Jharkhand, and Nepal. The lyrics are composed of four verses and a chorus that are repeated throughout the song. Here are the lyrics and their translation in English:</p>
- <table>
-<tr>
-<th>Bhojpuri</th>
-<th>English</th>
-</tr>
-<tr>
-<td>Sakhi re bar paa gayini<br>
-Sakhi re bar paa gayini<br>
-Sakhi re bar paa gayini<br>
-Sakhi re bar paa gayini</td>
-<td>My friend, I have fallen in love<br>
-My friend, I have fallen in love<br>
-My friend, I have fallen in love<br>
-My friend, I have fallen in love</td>
-</tr>
-<tr>
-<td>Mehandi laga ke rakhna<br>
-Doli saja ke rakhna<br>
-Mehandi laga ke rakhna<br>
-Doli saja ke rakhna</td>
-<td>Keep the mehndi on your hands<br>
-Keep the palanquin decorated<br>
-Keep the mehndi on your hands<br>
-Keep the palanquin decorated</td>
-</tr>
-<tr>
-<td>Lene tujhe o gori aayenge tere sajna<br>
-Lene tujhe o gori aayenge tere sajna<br>
-Lene tujhe o gori aayenge tere sajna<br>
-Lene tujhe o gori aayenge tere sajna</td>
-<td>To take you away, my fair one, your beloved will come<br>
-To take you away, my fair one, your beloved will come<br>
-To take you away, my fair one, your beloved will come<br>
-To take you away, my fair one, your beloved will come</td>
-</tr> <tr>
-<td>Chal gaila piya pardeshwa<br>
-Chal gaila piya pardeshwa<br>
-Chal gaila piya pardeshwa<br>
-Chal gaila piya pardeshwa</td>
-<td>My beloved has gone to a foreign land<br>
-My beloved has gone to a foreign land<br>
-My beloved has gone to a foreign land<br>
-My beloved has gone to a foreign land</td>
-</tr>
-<tr>
-<td>Ho gayi hoon main teri deewani<br>
-Ho gayi hoon main teri deewani<br>
-Ho gayi hoon main teri deewani<br>
-Ho gayi hoon main teri deewani</td>
-<td>I have become crazy for you<br>
-I have become crazy for you<br>
-I have become crazy for you<br>
-I have become crazy for you</td>
-</tr>
-<tr>
-<td>Ab na kisi se kuchh kehna<br>
-Ab na kisi se kuchh kehna<br>
-Ab na kisi se kuchh kehna<br>
-Ab na kisi se kuchh kehna</td>
-<td>Now don't say anything to anyone<br>
-Now don't say anything to anyone<br>
-Now don't say anything to anyone<br>
-Now don't say anything to anyone</td>
-</tr>
-<tr>
-<td>Bas tere hi intezaar mein rehna<br>
-Bas tere hi intezaar mein rehna<br>
-Bas tere hi intezaar mein rehna<br>
-Bas tere hi intezaar mein rehna</td>
-<td>Just wait for you only<br>
-Just wait for you only<br>
-Just wait for you only<br>
-Just wait for you only</td>
-</tr>
- </table>
- <h4>Cultural and social context</h4>
- <p>The song reflects the cultural and social context of Bhojpuri society, where love and marriage are celebrated with joy and enthusiasm. The song also shows the respect and affection that the lovers have for each other and their families. The song also portrays the challenges and difficulties that the lovers face due to the distance and separation that they have to endure. The song also expresses the hope and faith that the lovers have in their relationship and their future.</p>
- <h4>Message and theme</h4>
- <p>The song conveys a message and a theme of love, loyalty, devotion, and happiness. The song tells the story of how two people fall in love and decide to get married despite the obstacles and hardships that they face. The song also shows how the lovers cherish and value each other and their relationship. The song also inspires and motivates the listeners to follow their heart and pursue their dreams.</p>
- <h3>How to download the song?</h3>
- <h4>Legal and safe options</h4>
- <p>If you want to download the song legally and safely, you have several options to choose from. Some of the legal and safe options that you can use are: - YouTube: You can watch and listen to the song on YouTube, where it is uploaded by Worldwide Records. You can also use YouTube Premium, which allows you to download videos and songs offline on your device. You can subscribe to YouTube Premium for a monthly fee of $11.99. - Gaana: You can stream and download the song on Gaana, which is a music streaming app that offers a variety of songs in different languages and genres. You can use Gaana for free with ads, or you can upgrade to Gaana Plus, which allows you to download songs offline on your device. You can subscribe to Gaana Plus for a monthly fee of $3.99. - JioSaavn: You can stream and download the song on JioSaavn, which is another music streaming app that offers a wide range of songs in different languages and genres. You can use JioSaavn for free with ads, or you can upgrade to JioSaavn Pro, which allows you to download songs offline on your device. You can subscribe to JioSaavn Pro for a monthly fee of $3.99.</p>
- <h4>Steps and tips</h4>
- <p>If you want to download the song using any of these options, you need to follow these steps and tips: - Step 1: Download and install the app of your choice on your device from the Google Play Store or the Apple App Store. - Step 2: Open the app and sign up or log in with your email or phone number. - Step 3: Search for the song by typing its name or using voice search. - Step 4: Tap on the song and play it online or offline. - Step 5: If you want to download the song offline, tap on the download icon or button next to the song. - Step 6: Choose the quality and format of the song that you want - Step 7: Wait for the download to complete and enjoy the song offline on your device. - Tip 1: Make sure you have a stable and fast internet connection to stream or download the song online. - Tip 2: Make sure you have enough storage space on your device to download the song offline. - Tip 3: Make sure you have the permission and the license to download the song legally and safely. - Tip 4: Make sure you do not share or distribute the song illegally or without the consent of the owners or the creators. - Tip 5: Make sure you respect and appreciate the work and the effort of the singers, actors, composers, writers, and producers of the song.</p>
- <h4>Alternatives and recommendations</h4>
- <p>If you want to explore other options to listen to or download the song, you can also check out these alternatives and recommendations: - Spotify: You can stream and download the song on Spotify, which is a global music streaming app that offers millions of songs in different languages and genres. You can use Spotify for free with ads, or you can upgrade to Spotify Premium, which allows you to download songs offline on your device. You can subscribe to Spotify Premium for a monthly fee of $9.99. - Wynk Music: You can stream and download the song on Wynk Music, which is an Indian music streaming app that offers a variety of songs in different languages and genres. You can use Wynk Music for free with ads, or you can upgrade to Wynk Premium, which allows you to download songs offline on your device. You can subscribe to Wynk Premium for a monthly fee of $1.99. - Hungama Music: You can stream and download the song on Hungama Music, which is another Indian music streaming app that offers a wide range of songs in different languages and genres. You can use Hungama Music for free with ads, or you can upgrade to Hungama Pro, which allows you to download songs offline on your device. You can subscribe to Hungama Pro for a monthly fee of $2.99.</p>
- <h2>Conclusion</h2>
- <h3>Summary of the main points</h3>
- <p>In conclusion, Sakhi Re Bar Paa Gayini is a Bhojpuri hit song that has won the hearts of millions of people with its catchy and romantic lyrics, music, and video. The song is sung by Indu Sonali, Khesari Lal Yadav, and Alok Kumar, and features Khesari Lal Yadav and Kajal Raghwani as the lead actors. The song is from the album and the movie Mehandi Laga Ke Rakhna, which are both blockbuster hits in Bhojpuri cinema. The song is about the love and attraction between a young couple who are about to get married. The song has more than 200 million views on YouTube and more than 10 million downloads on various platforms. The song has also received several awards and nominations for its singers, actors, composers, writers, and producers. The song has also received a lot of love and appreciation from the fans and the listeners.</p>
- <h3>Personal opinion and rating</h3>
- <p>Personally, I love this song very much. I think it is one of the best Bhojpuri songs ever made. I like the lyrics, the music, the video, and everything about this song. I think it is very sweet and romantic, but also very fun and entertaining. I think it is very catchy and melodious, but also very meaningful and expressive. I think it is very beautiful and charming, but also very lively and energetic. I think it is a perfect song for any occasion, whether it is a wedding, a party, a date, or just a relaxing time at home. I would rate this song 5 out of 5 stars.</p>
- <h3>Call to action and feedback</h3>
- <p>If you have not listened to this song yet, I highly recommend you to do so. You will not regret it. You can find this song on YouTube or any of the legal and safe options that we have mentioned in this article. You can also share this article with your friends and family who might be interested in this song or Bhojpuri music in general. You can also leave your comments and feedback below this article. We would love to hear from you.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions (FAQs) that you might have about this song or Bhojpuri music in general:</p>
- <ol>
-<li>What is Bhojpuri?</li>
-<p>Bhojpuri is a language that belongs to the Indo-Aryan branch of the Indo-European language family. It is spoken by more than 150 million people in parts of Bihar, Uttar Pradesh, Jharkhand, and Nepal. It is also spoken by many people in other countries like Mauritius, Fiji, Suriname, Guyana, Trinidad and Tobago, and South Africa. Bhojpuri is also a rich and diverse culture that has its own literature, cinema, music, art, cuisine, and festivals.</p>
- <li>What are the characteristics of Bhojpuri music?</li>
-<p>Bhojpuri music is a genre of music that is based on the Bhojpuri language and culture. It is influenced by various musical traditions, such as folk, classical, devotional, filmi, pop, rock, and rap. Bhojpuri music is known for its catchy and lively tunes, its expressive and poetic lyrics, its colorful and vibrant videos, and its diverse and talented singers and musicians. Bhojpuri music is also very popular and influential in India and abroad.</p>
- <li>Who are some of the famous Bhojpuri singers and actors?</li>
-<p>Some of the famous Bhojpuri singers and actors are: - Manoj Tiwari: He is a singer, actor, producer, director, and politician. He is known for his songs like Rinkiya Ke Papa, Chat Deni Maar Deli, and Lollypop Lagelu. He is also a member of the BJP and a member of parliament. - Ravi Kishan: He is an actor, producer, and politician. He is known for his movies like Sasura Bada Paisawala, Devra Bada Satawela, and Crack Fighter. He is also a member of the BJP and a member of parliament. - Dinesh Lal Yadav 'Nirahua': He is a singer, actor, producer, and politician. He is known for his songs like Nirahua Satal Rahe, Nirahua Hindustani, and Nirahua Chalal London. He is also a member of the BJP and a former member of parliament. - Amrapali Dubey: She is an actress, singer, and producer. She is known for her movies like Nirahua Hindustani, Patna Se Pakistan, and Nirahua Chalal Sasural 2. She is also a dancer and a model. - Pawan Singh: He is a singer, actor, producer, and director. He is known for his songs like Lollypop Lagelu, Chhalakata Hamro Jawaniya, and Kamariya Hila Rahi Hai. He is also a composer and a music director.</p>
- <li>How can I learn more about Bhojpuri music?</li>
-<p>If you want to learn more about Bhojpuri music, you can do the following: - Listen to more Bhojpuri songs on YouTube or other platforms that we have mentioned in this article. - Watch more Bhojpuri movies on YouTube or other platforms that we have mentioned in this article. - Read more articles and blogs about Bhojpuri music on the internet or in magazines. - Follow more Bhojpuri singers and actors on social media platforms like Facebook, Twitter, Instagram, and TikTok. - Join more Bhojpuri music groups and communities on social media platforms or online forums. - Attend more Bhojpuri music events and festivals in your area or online.</p>
- <li>How can I contact the singers or actors of Sakhi Re Bar Paa Gayini?</li>
-<p>If you want to contact the singers or actors of Sakhi Re Bar Paa Gayini, you can do the following: - Send them an email or a message on their official website or social media accounts. - Call them or text them on their official phone number or WhatsApp number. - Write them a letter or a postcard on their official address or fan mail address. - Meet them in person at their concerts or shows or public appearances.</p>
-</ol></p> 197e85843d<br />
-<br />
-<br />
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-<li>Choose the right font mood and message. Finally, you should consider the mood and message that your font conveys in your design. You should use a font that matches the purpose and audience of your project, and that expresses the tone and personality that you want to communicate. You should also avoid using fonts that are inappropriate or offensive for your project.</li>
-</ul>
- <h3>The best examples and inspiration of using Univers Next 820 Condensed Black</h3>
- <p>If you need some examples and inspiration of how to use Univers Next 820 Condensed Black in your design projects, you can check out these websites:</p>
- <ul>
-<li <li><a href="(^1^)">Fonts.com</a> - This website offers a free trial for 30 days, after which you need to subscribe to access the font. You can also purchase the font individually or as part of the Univers Next font family.</li>
-<li><a href="(^2^)">FontShop.com</a> - This website also offers a free trial for 30 days, after which you need to buy the font. You can also browse and buy other fonts from the Univers Next font family or other typefaces.</li>
-<li><a href="(^3^)">MyFonts.com</a> - This website is similar to FontShop.com, offering a free trial for 30 days and then requiring a purchase. You can also find and buy other fonts from the Univers Next font family or other typefaces.</li>
-<li><a href="">FontSpace.com</a> - This website offers a free download of Univers Next 820 Condensed Black for personal use only, with attribution required. You can also find and download other free fonts from various categories and genres.</li>
-<li><a href="">Dafont.com</a> - This website also offers a free download of Univers Next 820 Condensed Black for personal use only, with donation appreciated. You can also find and download other free fonts from various categories and genres.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>Univers Next 820 Condensed Black is a modern and versatile font that can enhance your design projects with its high readability, clarity, and impact. It is part of the Univers Next font family, which is a reworked version of the classic Univers typeface designed by Adrian Frutiger in 1957. You can download Univers Next 820 Condensed Black for free from various websites, but you should always check the license terms and conditions before using it. You should also follow some best practices and tips for font design, such as choosing the right font size, color, contrast, alignment, hierarchy, combination, variation, mood, and message. You can also get some examples and inspiration from other websites that use Univers Next 820 Condensed Black in their design projects.</p>
- <p>If you are interested in learning more about Univers Next 820 Condensed Black or other fonts, you can visit the websites mentioned in this article or contact us for more information. We hope you enjoyed this article and found it useful for your design needs. Thank you for reading!</p>
- <h2>FAQs</h2>
- <h3>What is the difference between a font and a typeface?</h3>
- <p>A typeface is a design of a set of characters, such as letters, numbers, symbols, and punctuation marks. A font is a specific style or weight of a typeface, such as regular, italic, bold, or condensed. For example, Univers Next is a typeface, while Univers Next 820 Condensed Black is a font.</p>
- <h3>What are the advantages of using condensed fonts?</h3>
- <p>Condensed fonts are fonts that have a narrow width, which means they take up less horizontal space than normal or extended fonts. Some of the advantages of using condensed fonts are:</p>
- <ul>
-<li>They can fit more text in a limited space, which is useful for headlines, logos, labels, or captions.</li>
-<li>They can create a sense of urgency, dynamism, or importance, which is suitable for advertising, marketing, or news.</li>
-<li>They can contrast well with other fonts that have a normal or extended width, which can create visual interest and diversity.</li>
-</ul>
- <h3>How can I install Univers Next 820 Condensed Black on my computer?</h3>
- <p>To install Univers Next 820 Condensed Black on your computer, you need to follow these steps:</p>
- <ol>
-<li>Download the font file from one of the websites mentioned in this article.</li>
-<li>Unzip the file if it is compressed.</li>
-<li>Double-click on the font file to open it.</li>
-<li>Click on the Install button to install the font on your computer.</li>
-<li>Restart your computer or any applications that use fonts to apply the changes.</li>
-</ol>
- <h3>How can I optimize my content for SEO using fonts?</h3>
- <p>To optimize your content for SEO using fonts, you need to consider these factors:</p>
- <ul>
-<li>The readability and clarity of your fonts. You should use fonts that are easy to read and understand by both humans and search engines. You should also avoid using too many different fonts or styles that can confuse or distract your readers.</li>
-<li>The loading speed and performance of your fonts. You should use fonts that are fast and efficient to load and display on different devices and browsers. You should also avoid using too many or too large font files that can slow down your website or affect its functionality or usability.</li>
-<li>The relevance and consistency of your fonts. You should use fonts that match the topic and tone of your content, and that convey the message and personality that you want to communicate. You should also use fonts that are consistent with your brand identity and style guidelines.</li>
-</ul>
- <h3>Where can I find more information and resources on font design?</h3>
- <p>If you want to learn more about font design, you can check out these websites:</p>
- <ul>
-<li><a href="">Fonts.com</a> - This website offers a blog, a magazine, a newsletter, and a podcast that cover various topics and trends on font design, typography, and graphic design.</li>
-<li><a href="">FontShop.com</a> - This website offers a blog, a newsletter, and a font book that provide useful tips, insights, and inspiration on font design, typography, and graphic design.</li>
-<li><a href="">MyFonts.com</a> - This website offers a blog, a newsletter, and a font magazine that feature interviews, stories, and showcases on font design, typography, and graphic design.</li>
-<li><a href="">FontSpace.com</a> - This website offers a blog, a forum, and a gallery that share news, opinions, and examples on font design, typography, and graphic design.</li>
-<li><a href="">Dafont.com</a> - This website offers a forum and a comment section that allow users to interact and exchange feedback on font design, typography, and graphic design.</li>
-</ul></p> 401be4b1e0<br />
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diff --git a/spaces/congsaPfin/Manga-OCR/logs/Watch Love Aaj Kal (2020) Online or Download in 720p BluRay  Hindi Movie.md b/spaces/congsaPfin/Manga-OCR/logs/Watch Love Aaj Kal (2020) Online or Download in 720p BluRay  Hindi Movie.md
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-<h1>Love Aaj Kal Movie Download 720p Filmywap: A Guide for Movie Lovers</h1>
-<p>If you are a fan of romantic comedy-drama movies, you might have heard of Love Aaj Kal, a 2020 Hindi film directed by Imtiaz Ali and starring Kartik Aaryan and Sara Ali Khan. The movie is a spiritual successor to the 2009 film of the same name, which featured Saif Ali Khan and Deepika Padukone. The movie follows two parallel love stories, one set in the present day and one in the past, and explores how love has changed over time.</p>
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-<p>Love Aaj Kal was released on Valentine's Day 2020 and received mixed reviews from critics and audiences. However, it still managed to attract a lot of viewers who were curious about the chemistry between Kartik Aaryan and Sara Ali Khan, who were rumored to be dating in real life. The movie also featured some catchy songs composed by Pritam and sung by Arijit Singh, such as Shayad, Haan Main Galat, and Mehrama.</p>
-<p>If you missed watching Love Aaj Kal in theatres or want to watch it again at your convenience, you might be wondering how to download it in high-quality from the internet. One of the websites that claims to offer Love Aaj Kal movie download in 720p quality is Filmywap, a popular pirated movie site that provides free downloads of Bollywood, Hollywood, and regional movies. But is it safe and legal to use Filmywap? And how can you download Love Aaj Kal movie from Filmywap without any hassle? In this article, we will answer these questions and more.</p>
-<h2>What is Love Aaj Kal and why is it popular?</h2>
-<p>Love Aaj Kal is a romantic comedy-drama film that tells two stories of love in different eras. The first story is about Veer (Kartik Aaryan) and Zoe (Sara Ali Khan), who meet at a bar in Delhi and start a casual relationship. Veer is an engineer who wants to pursue his passion for coding, while Zoe is an ambitious career woman who wants to make it big in the corporate world. However, their professional goals clash with their personal feelings, and they struggle to balance their love life and work life.</p>
-<p>The second story is about Raghu (also played by Kartik Aaryan) and Leena (Arushi Sharma), who fall in love in Udaipur in the 1990s. Raghu is a medical student who runs away from his hometown to be with Leena, who belongs to a different caste. Their love story faces many challenges, such as family opposition, social stigma, and personal insecurities.</p>
-<p>The two stories are narrated by Raj (Randeep Hooda), who owns a café where Veer and Zoe often hang out. Raj is actually an older version of Raghu, who regrets leaving Leena for his big city dreams. He tries to help Veer and Zoe understand their true feelings and learn from their past mistakes.</p>
-<p>Love Aaj Kal is popular because it is a modern take on the classic theme of love and its complexities. The movie tries to show how love has evolved over the years, and how different generations have different expectations and challenges in their relationships. The movie also appeals to the fans of Imtiaz Ali, who is known for his realistic and relatable portrayal of romance and emotions. The movie also features some stunning visuals of Delhi and Udaipur, and some memorable performances by the lead actors.</p>
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-<h2>How to download Love Aaj Kal movie in 720p quality from Filmywap?</h2>
-<p>If you want to download Love Aaj Kal movie in 720p quality from Filmywap, you will need to follow these steps:</p>
-<ol>
-<li>Open your web browser and search for Filmywap on Google or any other search engine. You will find many links to the website, but be careful as some of them might be fake or malicious. The original website of Filmywap has the domain name filmywap.com, but it might change frequently due to legal issues.</li>
-<li>Once you find the correct website, click on it and you will see a homepage with various categories of movies, such as Bollywood, Hollywood, Punjabi, South Indian, etc. You can either browse through these categories or use the search bar to find Love Aaj Kal movie.</li>
-<li>When you find the movie, click on it and you will see a page with some information about the movie, such as genre, rating, release date, etc. You will also see some download links with different qualities and sizes, such as 480p, 720p, 1080p, etc. Choose the 720p link that suits your preference and device.</li>
-<li>After clicking on the link, you will be redirected to another page where you will have to verify that you are not a robot by completing a captcha or a survey. This is done to prevent spam and bots from accessing the website.</li>
-<li>Once you complete the verification, you will see another page with a download button. Click on it and your download will start automatically. Depending on your internet speed and connection, it might take some time to finish the download.</li>
-<li>After the download is complete, you can enjoy watching Love Aaj Kal movie in 720p quality on your device. However, make sure that you have a compatible media player that can play the file format of the movie.</li>
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-<p>Using Filmywap to download Love Aaj Kal movie in 720p quality might seem like a convenient and cost-effective option, but it also comes with some benefits and risks that you should be aware of before using it. Here are some of them:</p>
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-<ul>
-<li>You can download Love Aaj Kal movie in 720p quality for free without paying any subscription fee or registration fee.</li>
-<li>You can download the movie as soon as it is available on the website, without waiting for its official release on DVD or streaming platforms.</li>
-<li>You can download the movie in different formats and sizes according to your preference and device compatibility.</li>
-<li>You can access a large collection of movies from different genres, languages, and countries on Filmywap.</li>
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-</ul> <h2>The alternatives to Filmywap</h2>
-<p>If you want to watch Love Aaj Kal movie in 720p quality without using Filmywap, you have some other options that are safer and legal. Here are some of them:</p>
-<h3>Legal streaming services</h3>
-<p>You can watch Love Aaj Kal movie on some of the legal streaming platforms that have the rights to stream the movie online. Some of these platforms are Netflix, Amazon Prime Video, Disney+ Hotstar, Zee5, etc. However, you will need to pay a subscription fee or a rental fee to access these platforms and watch the movie. You will also need a stable internet connection and a compatible device to stream the movie.</p>
-<h3>Official websites</h3>
-<p>You can also watch Love Aaj Kal movie on the official websites of the filmmakers and producers, such as Maddock Films, Window Seat Films, Reliance Entertainment, etc. These websites might offer the movie for free or for a nominal charge. You will also get to see some behind-the-scenes footage, interviews, trailers, and other exclusive content related to the movie. However, you might have to wait for some time before the movie is available on these websites.</p>
-<h1>Conclusion</h1>
-<p>Love Aaj Kal is a romantic comedy-drama movie that explores the changing dynamics of love in different generations. The movie features Kartik Aaryan and Sara Ali Khan in the lead roles, and is directed by Imtiaz Ali. The movie was released on Valentine's Day 2020 and received mixed reviews from critics and audiences.</p>
-<p>If you want to download Love Aaj Kal movie in 720p quality from Filmywap, you will have to follow some steps and face some risks. Filmywap is a pirated movie website that offers free downloads of movies in various formats and sizes. However, using Filmywap is illegal, unethical, and unsafe, and could land you in trouble.</p>
-<p>Therefore, we recommend that you use some of the alternatives to Filmywap, such as legal streaming services or official websites. These options are safer and legal, and will also support the film industry and the people who work hard to make movies.</p>
-<p>We hope that this article has helped you understand how to download Love Aaj Kal movie in 720p quality from Filmywap. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!</p>
- <h2>FAQs</h2>
-<ol>
-<li>What is the meaning of Love Aaj Kal?</li>
-<p>Love Aaj Kal is a Hindi phrase that means "love nowadays" or "love in the present". It implies how love has changed over time and how different generations have different views on love.</p>
-<li>Who are the actors in Love Aaj Kal?</li>
-<p>The actors in Love Aaj Kal are Kartik Aaryan, Sara Ali Khan, Randeep Hooda, and Arushi Sharma. Kartik Aaryan plays two roles: Veer and Raghu. Sara Ali Khan plays Zoe, Veer's love interest. Randeep Hooda plays Raj, an older version of Raghu who narrates his story to Veer and Zoe. Arushi Sharma plays Leena, Raghu's love interest in the past.</p>
-<li>Who is the director of Love Aaj Kal?</li>
-<p>The director of Love Aaj Kal is Imtiaz Ali, who is known for his romantic movies such as Jab We Met, Rockstar, Highway, Tamasha, etc. He also directed the 2009 movie of the same name, which starred Saif Ali Khan and Deepika Padukone.</p>
-<li>What are the songs in Love Aaj Kal?</li>
-<p>The songs in Love Aaj Kal are composed by Pritam and sung by various artists such as Arijit Singh, Darshan Raval, Antara Mitra, Nikhita Gandhi, etc. Some of the popular songs are Shayad, Haan Main Galat, Mehrama, Aur Tanha, Yeh Dooriyan, etc.</p>
-<li>Where can I watch Love Aaj Kal legally?</li>
-<p>You can watch Love Aaj Kal legally on some of the streaming platforms that have the rights to stream the movie online. Some of these platforms are Netflix, Amazon Prime Video, Disney+ Hotstar, Zee5, etc. You can also watch the movie on the official websites of the filmmakers and producers.</p>
-</ol></p> 401be4b1e0<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/Download Adobe Illustrator Cs2 Mac Create Stunning Vector Graphics with Ease.md b/spaces/contluForse/HuggingGPT/assets/Download Adobe Illustrator Cs2 Mac Create Stunning Vector Graphics with Ease.md
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--- a/spaces/contluForse/HuggingGPT/assets/Download Adobe Illustrator Cs2 Mac Create Stunning Vector Graphics with Ease.md	
+++ /dev/null
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const CONVERSATION_PAGE='Community: conversationPage';//evar203 mapped variablesconst CARD_CREATED_DATE='digitalData.community.communityAttributes.cardCreatedDate';const COUNT_CORRECT_ANSWER='digitalData.community.communityAttributes.countCorrectAnswer';const COMMUNITY_FLAG='digitalData.community.communityInfo.communityFlag'; const COUNT_REPLY='digitalData.community.communityAttributes.countReply'; const RELATED_CONVERSATION_ACTION='relatedConversationClick';const COMMUNITY_DD_PROPERTY='digitalData.community';const CONVERSATION_REPORT='Community: conversationReportClick';const REPLY_REPORT='Community: repliesReportClick';const MARKED_CORRECT='Community: Marked as Correct';const UNMARKED_CORRECT='Community: UnMarked as Correct';const REPLY_MARKED_CORRECT='replyMarkedCorrect';const REPLY_UNMARKED_CORRECT='replyUnmarkedCorrect';const CONVERSATION_FOLLOW='Community: conversationFollowClick';const REPLY_FOLLOW='Community: repliesFollowClick';const CONVERSATION_UNFOLLOW='Community: conversationUnfollowClick';const REPLY_UNFOLLOW='Community: repliesUnfollowClick';const SOPHIA_EVENTS = 'digitalData.sophiaResponse.fromPage';const CC_LINK1 = 'Community: CCD_';const CC_LINK2 = 'Click';const CC_LINK_CLICK = 'ccdLinkClick';const CC_MANAGE_ACCOUNT_CLICK = 'manageAccountLinkClick'; const REC_CONVO_FEEDBACK_SHOWN='digitalData.community.communityAttributes.recConvoFeedbackShown';const CONVERSATION_EDIT='Community: conversationEditClick';const CONVERSATION_VIEW_HISTORY='Community: conversationViewHistoryClick';const CONVERSATION_MOVE_MERGE='Community: conversationMoveMergeClick';const CONVERSATION_SPAM='Community: conversationSpamClick';const CONVERSATION_DELETE='Community: conversationDeleteClick';const CONVERSATION_BAN_USER='Community: conversationBanUserClick';const REPLY_BAN_USER='Community: repliesBanUserClick';const REPLY_SPAM='Community: repliesSpamClick';const REPLY_DELETE='Community: repliesDeleteClick';const REPLY_MOVE_MERGE='Community: repliesMoveMergeClick';const REPLY_VIEW_HISTORY='Community: repliesViewHistoryClick';const REPLY_EDIT='Community: repliesEditClick';const REPLIES_IN_RESPONSE_TO ='Community: repliesInResponseToClick';$.when(promise1).done( function () userProfilePromise.then(trackConversationPageLoad);); function trackConversationPageLoad()  //Conversation Page Load Tracking const subject = $('.userStrip').attr('data-message-subject');let messageUid = '8165913';const tempDD = digitalData; let boardId = normalizeBoardId('download-and-install'); let community = normalizeCategoryBoardId(); let contentType = getBoardType(boardId); //track new post success trackNewPostSuccess(community, subject, messageUid); //track merge message success trackMergeSuccess(subject,community,'8165913',contentType); //recover digital data property digitalData = tempDD; const valArr = location.pathname.split('/'); let pageName; let layoutView = 'threaded'; if('ForumTopicPage' === 'IdeaPage') layoutView = 'linear'; //Ideas do not support threaded view so it will always be linear  let sortOrder = 'by_date_ascending'=="by_date_ascending"?"Earliest":"Latest"; if(PAGE_LANG!=='en') pageName = location.hostname + ':t5:' + boardId + ':' + 'conversationPage';  else if(valArr && valArr.length > 2)  pageName = location.hostname + ':' + valArr[1] + ':' + community + ':' + 'conversationPage';  if(pageName)  setDigitalDataProperty(PAGE_NAME, pageName);  if(messageUid)  setDigitalDataProperty(COMMUNITY_ID, messageUid);  setDigitalDataProperty(LANGUAGE, getLocale()); setDigitalDataProperty(SITE_SECTION, CONVERSATION_PAGE); setPrimaryEvent(CONVERSATION_PAGE, 'pageload');let replyCount = 0;if($('.reply-count__text').length > 0) replyCount = $('.reply-count__text').attr('data-reply-count');  let status = ''; let voteCount = 0; if($('.message-status-link').length > 0) status = $('.message-status-link')[0].innerText; if($('#messageKudosCount_').length > 0) voteCount = $('#messageKudosCount_')[0].getAttribute('data-upvote-count');  const correctAnswerCount = $('.correct-answer-div').attr('data-correct-answer-count'); const creationDate = $('.roleTimestamp').attr('data-post-time'); setDigitalDataProperty(CARD_CREATED_DATE, creationDate); //setDigitalDataProperty(COUNT_REPLY, replyCount?replyCount:'0'); setDigitalDataProperty(COUNT_CORRECT_ANSWER, correctAnswerCount?correctAnswerCount:'0'); setDigitalDataProperty(COMMUNITY_CONTENT_TYPE, contentType); setDigitalDataProperty(COMMUNITY_CATEGORY, community); setDigitalDataProperty(COMMUNITY_TITLE, subject); let solnType = $('.conversation-page-container').attr('data-solution-type'); if(parseInt(solnType) 0)  solnType = '1';  else if($('#special-reply-pinned').length > 0)  solnType = '4';   solnType = CONVERSATION_FLAG_TYPE[solnType]; let flag = solnType; if($('.body-outer-container').attr('data-pin-flag') === "true") if(flag != '') flag = flag + ';Pinned'; else flag = 'Pinned';   if(flag != '') setDigitalDataProperty(COMMUNITY_FLAG, flag);  if(document.getElementById('feedback_view_1'))  setDigitalDataProperty(REC_CONVO_FEEDBACK_SHOWN, 'true'); dnmsTrackConversationFeedback('render', 'feedback-answer', [messageUid, community, null, 'radio button']);  setDigitalDataProperty(FILTERS, [createGPSortInfoObj(sortOrder)]); setDigitalDataProperty(SOPHIA_EVENTS,['CampaignId': relatedConvCampaignId, 'ControlGroupId': relatedConvControlGroupId, 'VariationId': relatedConvVariationId, 'ActionBlockId': relatedConvActionBlockId, 'CampaignId': manageAccountCampaignId, 'ControlGroupId': manageAccountControlGroupId, 'VariationId': manageAccountVariationId, 'ActionBlockId': manageAccountActionBlockId]); captureSnapshot('state'); //dunamis api call dnmsConversationPageRender(community, replyCount, subject, getCommunityCurrentPageNum(), getConversationTags().toString(), messageUid, layoutView, flag, status, voteCount); cleanDigitalDataProperties([SOPHIA_EVENTS]); if ($('.promos-wrapper').length > 0) let promotype = $('.promos-wrapper').attr('data-promotype'); let promosubtype = $('.promos-wrapper').attr('data-promosubtype'); dnmsPromoRender(promotype, promosubtype, community, messageUid);  //Track related conversation clickdetectRelatedConversationsLoad(); //track status update success if(localStorage.hasOwnProperty('messageStatusUpdate')) trackStatusUpdateSuccess();  //Track reply post success trackReplyPostSuccess(); let lsCleanUpArr = ['gpEditMessageType', 'gpEditMessagePageNum', 'gpReportMessageDetails', 'gpReportMessageType'];clearStorage(lsCleanUpArr);cleanDigitalDataProperties(['digitalData.primaryEvent.eventInfo', FILTERS]); function getPayload(params) var sophiaPayload = []; try params = params.split("&"); var keyMapping = 'aid':'ActionBlockId','campid':'CampaignId', 'cid':'ContainerId','cgid':'ControlGroupId','tid':'TreatmentId','vid':'VariationId','sid':'SurfaceId'; var sophiaMap = ; for(let i=0;i 1 && (keys[0] in keyMapping)) sophiaMap[keyMapping[keys[0]]] = keys[1];   sophiaPayload.push(sophiaMap); catch(err) console.log(err);  return sophiaPayload;function trackNewPostSuccess(communityName, subject, messageUid)  const npsDD = localStorage.getItem('npsDigitalData'); if(npsDD) const ddVal = JSON.parse(npsDD);if(subject === ddVal.community.communityInfo.communityTitle)  digitalData = ddVal; setDigitalDataProperty(COMMUNITY_ID, messageUid); dnmsNewPostSuccess(communityName, subject, messageUid, JSON.parse(npsDD).sophiaResponse); captureSnapshot('event'); cleanDigitalDataProperties([SOPHIA_EVENTS]);  localStorage.removeItem('npsDigitalData');function trackMergeSuccess(subject,community,messageId,contentType)  try const mergeMsgDD = localStorage.getItem('mergeMsgDigitalData'); if(mergeMsgDD)  const ddVal = JSON.parse(mergeMsgDD); if(messageId === ddVal.community.communityInfo.communityId)  digitalData = ddVal; setDigitalDataProperty(COMMUNITY_CATEGORY, community); setDigitalDataProperty('digitalData.community.communityInfo.communityContentTab', contentType); setDigitalDataProperty(COMMUNITY_TITLE, subject); captureSnapshot('event'); let cnvrstnIds = []; let slctdCnvrstnArr = ddVal.community.attributes.selectedConversations; for(let i=0;i 4)  messages that got merged if(triggerBy === 'communityPage')  dnmsMoveMergeDeleteSuccessClick('Community','Community Controls', 'success', 'Merge', xArr);  else if(triggerBy === 'conversationPage')  dnmsMoveMergeDeleteSuccessClick('Conversation','Merge Conversation', 'click', 'Merge success', xArr);  localStorage.removeItem('moveMergeDeletetriggeredBy');   localStorage.removeItem('mergeMsgDigitalData');  catch(err) console.log(err); function clearStorage(items) for(let x=0; x 0) $('.related-conversations-card').on('click', function(e)  if(e.target.hasAttribute('data-related-content-type')) //section tab click events let destinationTab = e.target.getAttribute('data-related-content-type'); dnmsCPSectionTabClick(getDigitalDataProperty(COMMUNITY_CATEGORY), 'related conversation', destinationTab); setPrimaryEvent('Community: relatedConversationLabelClick', SECTION_TAB_ACTION); setDigitalDataProperty(COMMUNITY_CONTENT_TYPE, destinationTab); captureSnapshot('event');  else let subject = e.target.getAttribute('data-related-conversation-subject'); let boardId = e.target.getAttribute('data-related-conversation-board'); let relatedCommContentType = getBoardType(boardId); let community = normalizeCategoryBoardId(); let target_href = e.target.href; let convo_id = e.target.getAttribute('data-related-conversation-id'); let org_convo_id = getDigitalDataProperty(COMMUNITY_ID); dnmsRelatedConversationsClick(community, target_href, org_convo_id, convo_id, "", subject, relatedConvCampaignId, relatedConvControlGroupId, relatedConvVariationId, relatedCommContentType); setPrimaryEvent(RELATED_CONVERSATION_CLICK, RELATED_CONVERSATION_ACTION); cleanDigitalDataProperties([COMMUNITY_DD_PROPERTY]); setDigitalDataProperty(COMMUNITY_CATEGORY, community); setDigitalDataProperty(COMMUNITY_CONTENT_TYPE,relatedCommContentType); setDigitalDataProperty(COMMUNITY_ID, convo_id); setDigitalDataProperty(COMMUNITY_TITLE, subject); setDigitalDataProperty(SOPHIA_EVENTS,['CampaignId': relatedConvCampaignId, 'ControlGroupId': relatedConvControlGroupId, 'VariationId': relatedConvVariationId, 'ActionBlockId': relatedConvActionBlockId]); captureSnapshot('event'); cleanDigitalDataProperties([SOPHIA_EVENTS]);  ); //Track actions on conversation and repliesif($('.lia-quilt-column-main_content').length > 0)  $('.lia-quilt-column-main_content').on('click', function(e) let targetElement = $(e.target); //Track Report if(targetElement.hasClass('report__text'))  trackReportClick(targetElement);  //Track mark correct answer if(targetElement.hasClass('lia-component-solutions-action-mark-message-as-accepted-solution'))  trackMarkUnmarkCorrectAnswer('mark correct answer', targetElement);  //Track Unmark correct answer if(targetElement.hasClass('lia-component-solutions-action-unmark-message-as-accepted-solution'))  trackMarkUnmarkCorrectAnswer('unmark correct answer', targetElement);  //Track view history click if(targetElement.hasClass('view-message-history'))  trackViewHistoryClick(targetElement);  //Track move merge click if(targetElement.hasClass('move-message'))  trackMoveMergeClick(targetElement); if(getDigitalDataProperty(COMMUNITY_CONTENT_TYPE) !== 'Discussion' ) let authorId = $(targetElement).closest('.MessageView').find('.userStrip__link').attr('data-user-id'); if(authorId.length > 0) localStorage.setItem("mergeAuthor", authorId);   //Track delete conversation/reply click if(targetElement.hasClass('delete-message-and-replies') );//Track edit message clickif($('.edit-message').length > 0)  $('.edit-message').on('click', function(e) trackEditMessageClick($(e.target)); );//Track mark spam clickif($('.lia-component-spam-action-mark-message-as-spam').length > 0)  $('.lia-component-spam-action-mark-message-as-spam').on('click', function(e) trackMarkSpamClick($(e.target)); ); //Track conversation page CC clicksvar ccElements = document.querySelectorAll(".cc-links-cta-container__anchor, .cc-links-banner-p2 a button");for (let i = 0; i < ccElements.length; i++)  if($(ccElements[i]).length)  $(ccElements[i]).on('click', function(e)  let ccType = e.currentTarget.getAttribute('data-type'); let ccurl = e.currentTarget.getAttribute('href'); if(ccType && CC_LINKS_TYPE[ccType])  if (ccType == '4')  let primaryEvent = "Community: ManageAccountBtn_Click"; setPrimaryEvent(primaryEvent, CC_MANAGE_ACCOUNT_CLICK); setDigitalDataProperty(SOPHIA_EVENTS,['CampaignId': manageAccountCampaignId, 'ControlGroupId': manageAccountControlGroupId, 'VariationId': manageAccountVariationId, 'ActionBlockId': manageAccountActionBlockId]); captureSnapshot('event'); cleanDigitalDataProperties([SOPHIA_EVENTS]); dnmsManageAccountEvent(getDigitalDataProperty(COMMUNITY_CATEGORY), ccurl, 'ManageAccount', 'click', 'Conversation', manageAccountCampaignId, manageAccountVariationId, manageAccountControlGroupId);  else  let primaryEvent = CC_LINK1+CC_LINKS_TYPE[ccType]+CC_LINK2; setPrimaryEvent(primaryEvent, CC_LINK_CLICK); captureSnapshot('event'); dnmsCCLinkClick(getDigitalDataProperty(COMMUNITY_CATEGORY), ccurl, CC_LINKS_TYPE[ccType], 'Conversation');   );  function trackFollowUnfollowClick(tElement, action)  let isFollowAction = action==='follow'; if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(isFollowAction?CONVERSATION_FOLLOW:CONVERSATION_UNFOLLOW, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick(action, getConversationPageDetails());  else  setPrimaryEvent(isFollowAction?REPLY_FOLLOW:REPLY_UNFOLLOW, REPLY_ACTION); let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick(action, replyType, getConversationPageDetails());   cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackBanUserClick(tElement)  if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_BAN_USER, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('ban user', getConversationPageDetails());  else  let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick('ban user', replyType, getConversationPageDetails());  setPrimaryEvent(REPLY_BAN_USER, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackMarkSpamClick(tElement)  if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_SPAM, CONVERSATION_ACTION); //dunamis api call let convArray = getConversationPageDetails(); dnmsConversationActionsClick('mark as spam', convArray); if(convArray.length > 1) syncDataOnS3('Spam', convArray[1]);   else  let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick('mark as spam', replyType, getConversationPageDetails());  setPrimaryEvent(REPLY_SPAM, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackDeleteMessageClick(tElement)  if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_DELETE, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('delete the conversation', getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:originalPost'+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE));  else  let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick('delete the reply', replyType, getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:'+replyType+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE));  setPrimaryEvent(REPLY_DELETE, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackMoveMergeClick(tElement)  localStorage.setItem("movingConversationId", getDigitalDataProperty(COMMUNITY_ID)); if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_MOVE_MERGE, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('move/merge the conversation', getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:originalPost'+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE));  else  let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick('move/merge the conversation', replyType, getConversationPageDetails()); localStorage.setItem('moveMergeDeletetriggeredBy','conversationPage:'+replyType+':'+getConversationPageDetails().toString()+':'+getDigitalDataProperty(COMMUNITY_CONTENT_TYPE));  setPrimaryEvent(REPLY_MOVE_MERGE, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackViewHistoryClick(tElement)  if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_VIEW_HISTORY, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('view history', getConversationPageDetails());  else  let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick('view history', replyType, getConversationPageDetails());  setPrimaryEvent(REPLY_VIEW_HISTORY, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackEditMessageClick(tElement)  if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_EDIT, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('edit message', getConversationPageDetails()); localStorage.setItem('gpEditMessagePageNum', getCommunityCurrentPageNum());  else  let replyType = getReplyType(tElement); if(replyType)  localStorage.setItem('gpEditMessagePageNum', getCommunityCurrentPageNum()); dnmsConversationReplyActionsClick('edit message', replyType, getConversationPageDetails()); localStorage.setItem('gpEditMessageType', replyType);  setPrimaryEvent(REPLY_EDIT, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackReportClick(tElement)  let tempConversationPageDetails = getConversationPageDetails(); tempConversationPageDetails[2] = encodeURIComponent(tempConversationPageDetails[2]); localStorage.setItem('gpReportMessageDetails', tempConversationPageDetails); if(tElement.closest('.lia-thread-topic').length > 0)  setPrimaryEvent(CONVERSATION_REPORT, CONVERSATION_ACTION); //dunamis api call dnmsConversationActionsClick('report', getConversationPageDetails());  else  let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick('report', replyType, getConversationPageDetails()); localStorage.setItem('gpReportMessageType', replyType);  setPrimaryEvent(REPLY_REPORT, REPLY_ACTION);  cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); captureSnapshot('event');function trackMarkUnmarkCorrectAnswer(action, tElement)  let correctFlag = action==='mark correct answer'; setPrimaryEvent(correctFlag?MARKED_CORRECT:UNMARKED_CORRECT, correctFlag?REPLY_MARKED_CORRECT:REPLY_UNMARKED_CORRECT); cleanDigitalDataProperties([COMMUNITY_ATTRIBUTES]); convDetails = getConversationPageDetails(); if(correctFlag) convDetails = setSophiaPayload(convDetails);  captureSnapshot('event'); let replyType = getReplyType(tElement); if(replyType)  dnmsConversationReplyActionsClick(action, replyType, convDetails);  cleanDigitalDataProperties([SOPHIA_EVENTS]);function detectRelatedConversationsLoad() { if($('.personalised-related-conversations').length > 0) let targetNode = $('.personalised-related-conversations')[0]; let config =  childList: true ; let callback = function(mutationsList, observer)  for(let i=0; i 0)  status = $('.message-status-link')[0].innerText;  dnmsConversationStatusUpdate('success',getConversationPageDetails(), comment, status); setPrimaryEvent('Community: StatusChanged'+status.replace(' ',''),'conversationStatusUpdated'); setDigitalDataProperty(PRIMARY_FILTER, createGPFilterInfoObj(status, 'statusChange')); captureSnapshot('event'); localStorage.removeItem('messageStatusUpdate'); cleanDigitalDataProperties([PRIMARY_FILTER, FILTERS]);  catch(e) console.log(e); function isReplyBodyEmpty() { let result = false; let xNode;if($('.mce-edit-area').length > 0 && $('.mce-edit-area').children().length > 0) { let mceEditAreaiFrames = $('.mce-edit-area').children(); for(let i=0; i 0 && (content[0].hasAttribute('data-mce-bogus') || tinymce.innerHTML === '<br></p>
-<h2>Download Adobe Illustrator Cs2 Mac</h2><br /><p><b><b>Download File</b> &#10004; <a href="https://ssurll.com/2uzy9h">https://ssurll.com/2uzy9h</a></b></p><br /><br /> aaccfb2cb3<br />
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diff --git a/spaces/contluForse/HuggingGPT/assets/Enjoy Hours of Fun and Laughter with Birds On A Wire Game a Virtual Birds App with Talking Animals.md b/spaces/contluForse/HuggingGPT/assets/Enjoy Hours of Fun and Laughter with Birds On A Wire Game a Virtual Birds App with Talking Animals.md
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-<p>Each player determines their smallest grouping of identical birds. Each set of this size scores one point for each bird in those sets. Additionally, the player earns one point for each bird standing on one leg, so being zapped does have its benefits! Of course, the player with the most points is victorious.</p>
-<p>Birds on a Wire is quite likely the lightest game in the Gryphon bookshelf game series. Families with young children will likely find it to be a pleasant pastime. Serious gamers, however, will likely take flight quickly.</p>
-<h2>Birds On A Wire Game</h2><br /><p><b><b>Download Zip</b> ===== <a href="https://ssurll.com/2uzwKv">https://ssurll.com/2uzwKv</a></b></p><br /><br />
-<p><strong>Two Birds On A Wire</strong> refers to a viral video set to the song "Two Birds" by Regina Spektor featuring two birds from the Talking Larry application, one of which gets hit by lightning followed by the other screaming in horror. The video was originally uploaded to TikTok around November 2021. It was later pitched up and made into a Nightcore remix, inspiring a trend where users recreate the video.</p>
-<p>The original sound for the video went viral throughout December 2021 and January 2022, inspiring over 94,000 videos by March 2022. The majority of the videos are reenactments of the meme, where two people pretend to be birds on a wire, one dying and one screaming after (popular examples shown below, left and right).[4][5]</p>
-<p>The high voltage generator has gone bananas! The birds better watch out when landing on the telephone wires. Birds On A Wire® is a slot featuring Dropping symbols, High Voltage Multipliers, Inwinity Spin®, Bonus game with Free Spins, 17 paylines.</p>
-<p>Join acclaimed bird taxidermist and judge Glen Browning of British Columbia as he thoroughly explains all of the procedures in his groundbreaking wire frame method for mounting gamebirds. Using a hawk as an example, Glen narrates an outstanding PowerPoint presentation featuring his clear close-up photography and diagrams outlining every step. From skinning to final posing, Glen shares all of his secrets for creating a stable wire frame for an anatomically accurate mannikin which can be used for any pose or species. Recorded live at the 2005 World Taxidermy Championships. Approximate running time: 2:15.</p>
-<p>Birds on a Wire game may not feature a regular playing grid, but it maintains the combination payouts that are a defining factor in pokies. The set includes seventeen play lines that reward players when three or more birds of a kind sit on them. These clusters have to be from the leftmost column, and the birds have to be on adjacent reels. The low-value navy blue, pink, and orange birds appear the most while the other high-value ones are rare occurrences. The payout of each combination depends on the stake used. The paytable adjusts winnings according to placed wager, which makes it easy for punters to keep track of expected payouts.</p>
-<p>You start to play Birds on a Wire with the selection of coin value. The free pokie machine uses wagers between 0.10 and 100 credits placed on 17 fixed bet lines. Spin the reels using either the ordinary spin button or autoplay. The game generates a new set of tiles, and any winning combinations are rewarded. This procedure is used to launch free slot Birds on a Wire to play for fun or real money. The slot strategy does not allow one to influence the odds, but they can be exploited at their best by;</p>
-<p></p>
-<p>Birds on a Wire is a simple yet entertaining title. The game is structured using doddles that look like they were cooked up by a seven-year-old. The video slot abandons the use of the regular reels and places birds on electric cables. Nonetheless, the characters sit as they would in a standard five by three paytable. Thunderkick adds a comical touch to the setting by animating the birds. During each spin, the sitting set of fifteen birds flies off the screen and is replaced with a new one. When a winning combo is created, the birds are zapped before their skeleton comes into view, and they explode off the screen. New birds fly onto the screen to fill in the gaps left behind. The cycle continues until no more new combos are formed.</p>
-<p>Free Birds on a Wire slot incorporates a storyline the bonus character. When three of these icons land on the grid, one of the boxes reveals a cat underneath that forces the birds to fly off. After flying above the green mountainous terrain in the backdrop, they land on a second grid where the free spins play out. Once complete, the same cat appears to chase the birds off to the first set of cables where the base game continues to play.</p>
-<p>The concept of birds in wires as a slot theme introduced by Birds on a Wire no download slot is a unique storyline. It has inspired one other slot with a similar theme dubbed Birds! by Betsoft. Thunderkick has other attention-worthy slots including Esqueleto Explosivo, Babushkas, Pink Elephants, Fruit Warp, and Turning Totems. Other slot games that match the animal theme featured in Birds on a Wire include:</p>
-<p>These games are available to play for free and real money like Birds on a Wire. Their availability varies from one casino to the other, and they accommodate different bet limits and winnings. The free versions are accessible in affiliate websites and some casino with no registration demand. These games also run on both PC and mobile gadgets. They can be launched on Android, Windows, and other popular OS. Their accessibility on iOS depends on their use of HTML5 technology. If the set includes the tech, it can run on iPhones and iPads as well.</p>
-<p>Birds on a Wire free play and real money modes have Return to Player percentage of 96%. This rate is higher than the standard 95%, which makes the online pokie inclined to award higher than the average video slot. Even though the RTP percentage is encouraging, the metric remains theoretical. Therefore, significant wins and losses are not out of the ballpark. The RTP odds are best exploited when gameplay lasts a long period. Birds on a Wire slot game also boasts high variance. This means that the pokie is likely to award big payouts but in spaced-out intervals. The variance is hypothetical as well, and the payout results can swing in any direction. Its maximum potential is exploited over long-term play, which makes the Birds on a Wire game suitable for high rollers.</p>
-<p>"Larkwire is a simple, easy-to-learn, ingenious app that will have you identifying birds by ear in no time! There is no better way to learn birds' songs (other than actually being in the field surrounded by birds and more capable birders)."</p>
-<p>You'll have a chirping good time helping the birds escape from the evil witch's spell in this Zuma style arcade puzzler.<br> <br>The cute and colourful birds are walking in a trance to their doom and only you can halt their untimely end by shooting birds of the same colour into groups snapping them out of their trance and letting them fly to safety. With over 50 engaging levels, a multitude of bonuses to help your scores soar and 5 distinct game modes, Birds On A Wire is going 'cheep' on the App Store!<br><br>Game features:<br><br>- Battle your way through a whopping 50 levels of avian arcade adventure!<br>- Pit your wits against 5 distinct game modes<br>- Compete against your friends' scores via Twitter and Facebook integration<br>- Use the myriad bonuses wisely to clear the levels quickly<br>- Cute, not angry birds!<br>______________________________________<br> <br>Keep track of HeroCraft game news and updates!<br><br>twitter.com/Herocraft <br>youtube.com/herocraft <br>facebook.com/herocraft.games</p>
-<p><i><b>For the Birds</b></i> (stylized as <i><b>for the birds</b></i>) is a 2000 American computer animated short film produced by Pixar and written and directed by Ralph Eggleston. It won the Academy Award for Best Animated Short Film in 2001.[1] It premiered on June 5, 2000, at the Annecy International Animated Film Festival in France,[2] and was shown alongside the theatrical release of the 2001 Disney/Pixar feature film <i>Monsters, Inc.</i>[2]</p>
-<p>A small blue bird lands on a powerline and makes himself comfortable, only for a second bird to land close by. The two birds start squabbling, and are gradually joined by 13 others of the same species, all bickering for space.</p>
-<p>A large, awkward heron-like bird honks and waves at them from a nearby telephone pole, interrupting the squabble. The 15 little birds start imitating his plumage and honk, mockingly, until the bigger bird again calls to them, causing the little birds to scoot further down the wire and quietly gossip about it.</p>
-<p>Undeterred, the bigger bird flies and perches between the flock of smaller birds, but his weight causes the powerline to sag almost to the ground. The smaller birds slide down and are squished together against the big bird's sides, one angrily pecking the big bird and making him fall over backwards, hanging upside down. Egged on by the others, the two little birds in the middle start pecking the big bird's toes, which release one-by-one from the wire. One little bird realizes what is about to happen, and warns the others. They stop too late, and the last toe of the big bird releases, snapping the wire like a slingshot and flinging the little birds up in a cloud of feathers.</p>
-<p>The bigger bird lands unharmed on the ground, and plays with the floating feathers until one of the smaller birds, now naked, crashes beside him. He sees the naked little bird and laughs at him, offering a leaf for coverage. Later, the other little birds land nearby, also naked. The big bird laughs even harder at the little birds, who quickly hide behind him in shame. The film ends with a splat of bird droppings containing the words "the end" landing on the screen.</p>
-<p>You can download DCUO for free from the Microsoft Store on Xbox One (Xbox Live Gold subscription required). Once you start the game, choose your morality, mentor, and powers to use in battle. Then create and customize your unique character and start your adventures in the iconic DC Universe!</p>
-<p>The game was a 120-119 overtime road win at Moda Center in Portland, and on a lark the Celtic great decided to shoot heavily with his off hand, his left. Bird produced a ridiculous 47 points, 14 rebounds, 11 assists, a steal and 2 blocks in 49 minutes of floor time. Larry Legend hit 21 of his 34 field goal attempts, 10 of which he shot left-handed, and was a perfect 7-of-7 from the free-throw line.</p> aaccfb2cb3<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/cooelf/Multimodal-CoT/timm/loss/__init__.py b/spaces/cooelf/Multimodal-CoT/timm/loss/__init__.py
deleted file mode 100644
index 28a686ce896f4335dcf074717b52077b43c237d7..0000000000000000000000000000000000000000
--- a/spaces/cooelf/Multimodal-CoT/timm/loss/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from .cross_entropy import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
-from .jsd import JsdCrossEntropy
-from .asymmetric_loss import AsymmetricLossMultiLabel, AsymmetricLossSingleLabel
\ No newline at end of file
diff --git a/spaces/cyberspyde/chatbot-team4/utils/indexing.py b/spaces/cyberspyde/chatbot-team4/utils/indexing.py
deleted file mode 100644
index 1a0ca06c57701d464d0251c3b0be463b92d81c30..0000000000000000000000000000000000000000
--- a/spaces/cyberspyde/chatbot-team4/utils/indexing.py
+++ /dev/null
@@ -1,37 +0,0 @@
-from haystack.utils import fetch_archive_from_http
-from haystack.document_stores import ElasticsearchDocumentStore
-import os
-from haystack import Pipeline
-from haystack.nodes import TextConverter, PreProcessor
-doc_dir = "data/JBNU-FOCUS"
-
-host = os.environ.get("ELASTICSEARCH_HOST", "localhost")
-print(host)
-document_store = ElasticsearchDocumentStore(
-    host='121.186.58.11',
-    username="",
-    password="",
-    index="document"
-)
-
-
-indexing_pipeline = Pipeline()
-text_converter = TextConverter()
-preprocessor = PreProcessor(
-    clean_whitespace=True,
-    clean_header_footer=True,
-    clean_empty_lines=True,
-    split_by="word",
-    split_length=200,
-    split_overlap=20,
-    split_respect_sentence_boundary=True,
-)
-
-import os
-
-indexing_pipeline.add_node(component=text_converter, name="TextConverter", inputs=["File"])
-indexing_pipeline.add_node(component=preprocessor, name="PreProcessor", inputs=["TextConverter"])
-indexing_pipeline.add_node(component=document_store, name="DocumentStore", inputs=["PreProcessor"])
-
-files_to_index = [doc_dir + "/" + f for f in os.listdir(doc_dir)]
-indexing_pipeline.run_batch(file_paths=files_to_index)
\ No newline at end of file
diff --git a/spaces/cymic/VITS-Tokaiteio/modules.py b/spaces/cymic/VITS-Tokaiteio/modules.py
deleted file mode 100644
index 9c7fd9cd6eb8b7e0ec0e08957e970744a374a924..0000000000000000000000000000000000000000
--- a/spaces/cymic/VITS-Tokaiteio/modules.py
+++ /dev/null
@@ -1,390 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-class ConvReluNorm(nn.Module):
-  def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
-    super().__init__()
-    self.in_channels = in_channels
-    self.hidden_channels = hidden_channels
-    self.out_channels = out_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-    assert n_layers > 1, "Number of layers should be larger than 0."
-
-    self.conv_layers = nn.ModuleList()
-    self.norm_layers = nn.ModuleList()
-    self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-    self.norm_layers.append(LayerNorm(hidden_channels))
-    self.relu_drop = nn.Sequential(
-        nn.ReLU(),
-        nn.Dropout(p_dropout))
-    for _ in range(n_layers-1):
-      self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
-      self.norm_layers.append(LayerNorm(hidden_channels))
-    self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask):
-    x_org = x
-    for i in range(self.n_layers):
-      x = self.conv_layers[i](x * x_mask)
-      x = self.norm_layers[i](x)
-      x = self.relu_drop(x)
-    x = x_org + self.proj(x)
-    return x * x_mask
-
-
-class DDSConv(nn.Module):
-  """
-  Dialted and Depth-Separable Convolution
-  """
-  def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
-    super().__init__()
-    self.channels = channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.p_dropout = p_dropout
-
-    self.drop = nn.Dropout(p_dropout)
-    self.convs_sep = nn.ModuleList()
-    self.convs_1x1 = nn.ModuleList()
-    self.norms_1 = nn.ModuleList()
-    self.norms_2 = nn.ModuleList()
-    for i in range(n_layers):
-      dilation = kernel_size ** i
-      padding = (kernel_size * dilation - dilation) // 2
-      self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size, 
-          groups=channels, dilation=dilation, padding=padding
-      ))
-      self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-      self.norms_1.append(LayerNorm(channels))
-      self.norms_2.append(LayerNorm(channels))
-
-  def forward(self, x, x_mask, g=None):
-    if g is not None:
-      x = x + g
-    for i in range(self.n_layers):
-      y = self.convs_sep[i](x * x_mask)
-      y = self.norms_1[i](y)
-      y = F.gelu(y)
-      y = self.convs_1x1[i](y)
-      y = self.norms_2[i](y)
-      y = F.gelu(y)
-      y = self.drop(y)
-      x = x + y
-    return x * x_mask
-
-
-class WN(torch.nn.Module):
-  def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
-    super(WN, self).__init__()
-    assert(kernel_size % 2 == 1)
-    self.hidden_channels =hidden_channels
-    self.kernel_size = kernel_size,
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-    self.p_dropout = p_dropout
-
-    self.in_layers = torch.nn.ModuleList()
-    self.res_skip_layers = torch.nn.ModuleList()
-    self.drop = nn.Dropout(p_dropout)
-
-    if gin_channels != 0:
-      cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
-      self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
-
-    for i in range(n_layers):
-      dilation = dilation_rate ** i
-      padding = int((kernel_size * dilation - dilation) / 2)
-      in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
-                                 dilation=dilation, padding=padding)
-      in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
-      self.in_layers.append(in_layer)
-
-      # last one is not necessary
-      if i < n_layers - 1:
-        res_skip_channels = 2 * hidden_channels
-      else:
-        res_skip_channels = hidden_channels
-
-      res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-      res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
-      self.res_skip_layers.append(res_skip_layer)
-
-  def forward(self, x, x_mask, g=None, **kwargs):
-    output = torch.zeros_like(x)
-    n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-    if g is not None:
-      g = self.cond_layer(g)
-
-    for i in range(self.n_layers):
-      x_in = self.in_layers[i](x)
-      if g is not None:
-        cond_offset = i * 2 * self.hidden_channels
-        g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
-      else:
-        g_l = torch.zeros_like(x_in)
-
-      acts = commons.fused_add_tanh_sigmoid_multiply(
-          x_in,
-          g_l,
-          n_channels_tensor)
-      acts = self.drop(acts)
-
-      res_skip_acts = self.res_skip_layers[i](acts)
-      if i < self.n_layers - 1:
-        res_acts = res_skip_acts[:,:self.hidden_channels,:]
-        x = (x + res_acts) * x_mask
-        output = output + res_skip_acts[:,self.hidden_channels:,:]
-      else:
-        output = output + res_skip_acts
-    return output * x_mask
-
-  def remove_weight_norm(self):
-    if self.gin_channels != 0:
-      torch.nn.utils.remove_weight_norm(self.cond_layer)
-    for l in self.in_layers:
-      torch.nn.utils.remove_weight_norm(l)
-    for l in self.res_skip_layers:
-     torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
-                               padding=get_padding(kernel_size, dilation[2])))
-        ])
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
-                               padding=get_padding(kernel_size, 1)))
-        ])
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList([
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
-                               padding=get_padding(kernel_size, dilation[0]))),
-            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
-                               padding=get_padding(kernel_size, dilation[1])))
-        ])
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-      logdet = torch.sum(-y, [1, 2])
-      return y, logdet
-    else:
-      x = torch.exp(x) * x_mask
-      return x
-    
-
-class Flip(nn.Module):
-  def forward(self, x, *args, reverse=False, **kwargs):
-    x = torch.flip(x, [1])
-    if not reverse:
-      logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-      return x, logdet
-    else:
-      return x
-
-
-class ElementwiseAffine(nn.Module):
-  def __init__(self, channels):
-    super().__init__()
-    self.channels = channels
-    self.m = nn.Parameter(torch.zeros(channels,1))
-    self.logs = nn.Parameter(torch.zeros(channels,1))
-
-  def forward(self, x, x_mask, reverse=False, **kwargs):
-    if not reverse:
-      y = self.m + torch.exp(self.logs) * x
-      y = y * x_mask
-      logdet = torch.sum(self.logs * x_mask, [1,2])
-      return y, logdet
-    else:
-      x = (x - self.m) * torch.exp(-self.logs) * x_mask
-      return x
-
-
-class ResidualCouplingLayer(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      p_dropout=0,
-      gin_channels=0,
-      mean_only=False):
-    assert channels % 2 == 0, "channels should be divisible by 2"
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.half_channels = channels // 2
-    self.mean_only = mean_only
-
-    self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-    self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
-    self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-    self.post.weight.data.zero_()
-    self.post.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0) * x_mask
-    h = self.enc(h, x_mask, g=g)
-    stats = self.post(h) * x_mask
-    if not self.mean_only:
-      m, logs = torch.split(stats, [self.half_channels]*2, 1)
-    else:
-      m = stats
-      logs = torch.zeros_like(m)
-
-    if not reverse:
-      x1 = m + x1 * torch.exp(logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      logdet = torch.sum(logs, [1,2])
-      return x, logdet
-    else:
-      x1 = (x1 - m) * torch.exp(-logs) * x_mask
-      x = torch.cat([x0, x1], 1)
-      return x
-
-
-class ConvFlow(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, n_layers, num_bins=10, tail_bound=5.0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.n_layers = n_layers
-    self.num_bins = num_bins
-    self.tail_bound = tail_bound
-    self.half_channels = in_channels // 2
-
-    self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-    self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.)
-    self.proj = nn.Conv1d(filter_channels, self.half_channels * (num_bins * 3 - 1), 1)
-    self.proj.weight.data.zero_()
-    self.proj.bias.data.zero_()
-
-  def forward(self, x, x_mask, g=None, reverse=False):
-    x0, x1 = torch.split(x, [self.half_channels]*2, 1)
-    h = self.pre(x0)
-    h = self.convs(h, x_mask, g=g)
-    h = self.proj(h) * x_mask
-
-    b, c, t = x0.shape
-    h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2) # [b, cx?, t] -> [b, c, t, ?]
-
-    unnormalized_widths = h[..., :self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_heights = h[..., self.num_bins:2*self.num_bins] / math.sqrt(self.filter_channels)
-    unnormalized_derivatives = h[..., 2 * self.num_bins:]
-
-    x1, logabsdet = piecewise_rational_quadratic_transform(x1,
-        unnormalized_widths,
-        unnormalized_heights,
-        unnormalized_derivatives,
-        inverse=reverse,
-        tails='linear',
-        tail_bound=self.tail_bound
-    )
-
-    x = torch.cat([x0, x1], 1) * x_mask
-    logdet = torch.sum(logabsdet * x_mask, [1,2])
-    if not reverse:
-        return x, logdet
-    else:
-        return x
diff --git a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/options/inference_options.py b/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/options/inference_options.py
deleted file mode 100644
index c453965959ab4cfb31acbc424f994db68c3d4df5..0000000000000000000000000000000000000000
--- a/spaces/daddyjin/TalkingFaceGeneration/Demo_TFR_Pirenderer/src/face3d/options/inference_options.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from face3d.options.base_options import BaseOptions
-
-
-class InferenceOptions(BaseOptions):
-    """This class includes test options.
-
-    It also includes shared options defined in BaseOptions.
-    """
-
-    def initialize(self, parser):
-        parser = BaseOptions.initialize(self, parser)  # define shared options
-        parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
-        parser.add_argument('--dataset_mode', type=str, default=None, help='chooses how datasets are loaded. [None | flist]')
-
-        parser.add_argument('--input_dir', type=str, help='the folder of the input files')
-        parser.add_argument('--keypoint_dir', type=str, help='the folder of the keypoint files')
-        parser.add_argument('--output_dir', type=str, default='mp4', help='the output dir to save the extracted coefficients')
-        parser.add_argument('--save_split_files', action='store_true', help='save split files or not')
-        parser.add_argument('--inference_batch_size', type=int, default=8)
-        
-        # Dropout and Batchnorm has different behavior during training and test.
-        self.isTrain = False
-        return parser
diff --git a/spaces/dbis/AI_Doctor_Bot/README.md b/spaces/dbis/AI_Doctor_Bot/README.md
deleted file mode 100644
index 512fe120d21fab33e5cd1d0f49dfeb762e26b446..0000000000000000000000000000000000000000
--- a/spaces/dbis/AI_Doctor_Bot/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: AI Doctor Bot
-emoji: 🌖
-colorFrom: purple
-colorTo: red
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PcxImagePlugin.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PcxImagePlugin.py
deleted file mode 100644
index f42c2456b4b6c90700267b43cbfd4033ecc1370d..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/PIL/PcxImagePlugin.py
+++ /dev/null
@@ -1,221 +0,0 @@
-#
-# The Python Imaging Library.
-# $Id$
-#
-# PCX file handling
-#
-# This format was originally used by ZSoft's popular PaintBrush
-# program for the IBM PC.  It is also supported by many MS-DOS and
-# Windows applications, including the Windows PaintBrush program in
-# Windows 3.
-#
-# history:
-# 1995-09-01 fl   Created
-# 1996-05-20 fl   Fixed RGB support
-# 1997-01-03 fl   Fixed 2-bit and 4-bit support
-# 1999-02-03 fl   Fixed 8-bit support (broken in 1.0b1)
-# 1999-02-07 fl   Added write support
-# 2002-06-09 fl   Made 2-bit and 4-bit support a bit more robust
-# 2002-07-30 fl   Seek from to current position, not beginning of file
-# 2003-06-03 fl   Extract DPI settings (info["dpi"])
-#
-# Copyright (c) 1997-2003 by Secret Labs AB.
-# Copyright (c) 1995-2003 by Fredrik Lundh.
-#
-# See the README file for information on usage and redistribution.
-#
-
-import io
-import logging
-
-from . import Image, ImageFile, ImagePalette
-from ._binary import i16le as i16
-from ._binary import o8
-from ._binary import o16le as o16
-
-logger = logging.getLogger(__name__)
-
-
-def _accept(prefix):
-    return prefix[0] == 10 and prefix[1] in [0, 2, 3, 5]
-
-
-##
-# Image plugin for Paintbrush images.
-
-
-class PcxImageFile(ImageFile.ImageFile):
-    format = "PCX"
-    format_description = "Paintbrush"
-
-    def _open(self):
-        # header
-        s = self.fp.read(128)
-        if not _accept(s):
-            msg = "not a PCX file"
-            raise SyntaxError(msg)
-
-        # image
-        bbox = i16(s, 4), i16(s, 6), i16(s, 8) + 1, i16(s, 10) + 1
-        if bbox[2] <= bbox[0] or bbox[3] <= bbox[1]:
-            msg = "bad PCX image size"
-            raise SyntaxError(msg)
-        logger.debug("BBox: %s %s %s %s", *bbox)
-
-        # format
-        version = s[1]
-        bits = s[3]
-        planes = s[65]
-        provided_stride = i16(s, 66)
-        logger.debug(
-            "PCX version %s, bits %s, planes %s, stride %s",
-            version,
-            bits,
-            planes,
-            provided_stride,
-        )
-
-        self.info["dpi"] = i16(s, 12), i16(s, 14)
-
-        if bits == 1 and planes == 1:
-            mode = rawmode = "1"
-
-        elif bits == 1 and planes in (2, 4):
-            mode = "P"
-            rawmode = "P;%dL" % planes
-            self.palette = ImagePalette.raw("RGB", s[16:64])
-
-        elif version == 5 and bits == 8 and planes == 1:
-            mode = rawmode = "L"
-            # FIXME: hey, this doesn't work with the incremental loader !!!
-            self.fp.seek(-769, io.SEEK_END)
-            s = self.fp.read(769)
-            if len(s) == 769 and s[0] == 12:
-                # check if the palette is linear greyscale
-                for i in range(256):
-                    if s[i * 3 + 1 : i * 3 + 4] != o8(i) * 3:
-                        mode = rawmode = "P"
-                        break
-                if mode == "P":
-                    self.palette = ImagePalette.raw("RGB", s[1:])
-            self.fp.seek(128)
-
-        elif version == 5 and bits == 8 and planes == 3:
-            mode = "RGB"
-            rawmode = "RGB;L"
-
-        else:
-            msg = "unknown PCX mode"
-            raise OSError(msg)
-
-        self.mode = mode
-        self._size = bbox[2] - bbox[0], bbox[3] - bbox[1]
-
-        # Don't trust the passed in stride.
-        # Calculate the approximate position for ourselves.
-        # CVE-2020-35653
-        stride = (self._size[0] * bits + 7) // 8
-
-        # While the specification states that this must be even,
-        # not all images follow this
-        if provided_stride != stride:
-            stride += stride % 2
-
-        bbox = (0, 0) + self.size
-        logger.debug("size: %sx%s", *self.size)
-
-        self.tile = [("pcx", bbox, self.fp.tell(), (rawmode, planes * stride))]
-
-
-# --------------------------------------------------------------------
-# save PCX files
-
-
-SAVE = {
-    # mode: (version, bits, planes, raw mode)
-    "1": (2, 1, 1, "1"),
-    "L": (5, 8, 1, "L"),
-    "P": (5, 8, 1, "P"),
-    "RGB": (5, 8, 3, "RGB;L"),
-}
-
-
-def _save(im, fp, filename):
-    try:
-        version, bits, planes, rawmode = SAVE[im.mode]
-    except KeyError as e:
-        msg = f"Cannot save {im.mode} images as PCX"
-        raise ValueError(msg) from e
-
-    # bytes per plane
-    stride = (im.size[0] * bits + 7) // 8
-    # stride should be even
-    stride += stride % 2
-    # Stride needs to be kept in sync with the PcxEncode.c version.
-    # Ideally it should be passed in in the state, but the bytes value
-    # gets overwritten.
-
-    logger.debug(
-        "PcxImagePlugin._save: xwidth: %d, bits: %d, stride: %d",
-        im.size[0],
-        bits,
-        stride,
-    )
-
-    # under windows, we could determine the current screen size with
-    # "Image.core.display_mode()[1]", but I think that's overkill...
-
-    screen = im.size
-
-    dpi = 100, 100
-
-    # PCX header
-    fp.write(
-        o8(10)
-        + o8(version)
-        + o8(1)
-        + o8(bits)
-        + o16(0)
-        + o16(0)
-        + o16(im.size[0] - 1)
-        + o16(im.size[1] - 1)
-        + o16(dpi[0])
-        + o16(dpi[1])
-        + b"\0" * 24
-        + b"\xFF" * 24
-        + b"\0"
-        + o8(planes)
-        + o16(stride)
-        + o16(1)
-        + o16(screen[0])
-        + o16(screen[1])
-        + b"\0" * 54
-    )
-
-    assert fp.tell() == 128
-
-    ImageFile._save(im, fp, [("pcx", (0, 0) + im.size, 0, (rawmode, bits * planes))])
-
-    if im.mode == "P":
-        # colour palette
-        fp.write(o8(12))
-        palette = im.im.getpalette("RGB", "RGB")
-        palette += b"\x00" * (768 - len(palette))
-        fp.write(palette)  # 768 bytes
-    elif im.mode == "L":
-        # greyscale palette
-        fp.write(o8(12))
-        for i in range(256):
-            fp.write(o8(i) * 3)
-
-
-# --------------------------------------------------------------------
-# registry
-
-
-Image.register_open(PcxImageFile.format, PcxImageFile, _accept)
-Image.register_save(PcxImageFile.format, _save)
-
-Image.register_extension(PcxImageFile.format, ".pcx")
-
-Image.register_mime(PcxImageFile.format, "image/x-pcx")
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/__main__.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/__main__.py
deleted file mode 100644
index 7c74ad3c86e54cb7e9939ed2bf96aa59cc6dcd06..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/__main__.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import sys
-
-
-def main(args=None):
-    if args is None:
-        args = sys.argv[1:]
-
-    # TODO Handle library-wide options. Eg.:
-    # --unicodedata
-    # --verbose / other logging stuff
-
-    # TODO Allow a way to run arbitrary modules? Useful for setting
-    # library-wide options and calling another library. Eg.:
-    #
-    #   $ fonttools --unicodedata=... fontmake ...
-    #
-    # This allows for a git-like command where thirdparty commands
-    # can be added.  Should we just try importing the fonttools
-    # module first and try without if it fails?
-
-    if len(sys.argv) < 2:
-        sys.argv.append("help")
-    if sys.argv[1] == "-h" or sys.argv[1] == "--help":
-        sys.argv[1] = "help"
-    mod = "fontTools." + sys.argv[1]
-    sys.argv[1] = sys.argv[0] + " " + sys.argv[1]
-    del sys.argv[0]
-
-    import runpy
-
-    runpy.run_module(mod, run_name="__main__")
-
-
-if __name__ == "__main__":
-    sys.exit(main())
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/tables.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/tables.py
deleted file mode 100644
index 394541b8a4d3355784ef84a04aaaa7501e4dc201..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/merge/tables.py
+++ /dev/null
@@ -1,338 +0,0 @@
-# Copyright 2013 Google, Inc. All Rights Reserved.
-#
-# Google Author(s): Behdad Esfahbod, Roozbeh Pournader
-
-from fontTools import ttLib, cffLib
-from fontTools.misc.psCharStrings import T2WidthExtractor
-from fontTools.ttLib.tables.DefaultTable import DefaultTable
-from fontTools.merge.base import add_method, mergeObjects
-from fontTools.merge.cmap import computeMegaCmap
-from fontTools.merge.util import *
-import logging
-
-
-log = logging.getLogger("fontTools.merge")
-
-
-ttLib.getTableClass("maxp").mergeMap = {
-    "*": max,
-    "tableTag": equal,
-    "tableVersion": equal,
-    "numGlyphs": sum,
-    "maxStorage": first,
-    "maxFunctionDefs": first,
-    "maxInstructionDefs": first,
-    # TODO When we correctly merge hinting data, update these values:
-    # maxFunctionDefs, maxInstructionDefs, maxSizeOfInstructions
-}
-
-headFlagsMergeBitMap = {
-    "size": 16,
-    "*": bitwise_or,
-    1: bitwise_and,  # Baseline at y = 0
-    2: bitwise_and,  # lsb at x = 0
-    3: bitwise_and,  # Force ppem to integer values. FIXME?
-    5: bitwise_and,  # Font is vertical
-    6: lambda bit: 0,  # Always set to zero
-    11: bitwise_and,  # Font data is 'lossless'
-    13: bitwise_and,  # Optimized for ClearType
-    14: bitwise_and,  # Last resort font. FIXME? equal or first may be better
-    15: lambda bit: 0,  # Always set to zero
-}
-
-ttLib.getTableClass("head").mergeMap = {
-    "tableTag": equal,
-    "tableVersion": max,
-    "fontRevision": max,
-    "checkSumAdjustment": lambda lst: 0,  # We need *something* here
-    "magicNumber": equal,
-    "flags": mergeBits(headFlagsMergeBitMap),
-    "unitsPerEm": equal,
-    "created": current_time,
-    "modified": current_time,
-    "xMin": min,
-    "yMin": min,
-    "xMax": max,
-    "yMax": max,
-    "macStyle": first,
-    "lowestRecPPEM": max,
-    "fontDirectionHint": lambda lst: 2,
-    "indexToLocFormat": first,
-    "glyphDataFormat": equal,
-}
-
-ttLib.getTableClass("hhea").mergeMap = {
-    "*": equal,
-    "tableTag": equal,
-    "tableVersion": max,
-    "ascent": max,
-    "descent": min,
-    "lineGap": max,
-    "advanceWidthMax": max,
-    "minLeftSideBearing": min,
-    "minRightSideBearing": min,
-    "xMaxExtent": max,
-    "caretSlopeRise": first,
-    "caretSlopeRun": first,
-    "caretOffset": first,
-    "numberOfHMetrics": recalculate,
-}
-
-ttLib.getTableClass("vhea").mergeMap = {
-    "*": equal,
-    "tableTag": equal,
-    "tableVersion": max,
-    "ascent": max,
-    "descent": min,
-    "lineGap": max,
-    "advanceHeightMax": max,
-    "minTopSideBearing": min,
-    "minBottomSideBearing": min,
-    "yMaxExtent": max,
-    "caretSlopeRise": first,
-    "caretSlopeRun": first,
-    "caretOffset": first,
-    "numberOfVMetrics": recalculate,
-}
-
-os2FsTypeMergeBitMap = {
-    "size": 16,
-    "*": lambda bit: 0,
-    1: bitwise_or,  # no embedding permitted
-    2: bitwise_and,  # allow previewing and printing documents
-    3: bitwise_and,  # allow editing documents
-    8: bitwise_or,  # no subsetting permitted
-    9: bitwise_or,  # no embedding of outlines permitted
-}
-
-
-def mergeOs2FsType(lst):
-    lst = list(lst)
-    if all(item == 0 for item in lst):
-        return 0
-
-    # Compute least restrictive logic for each fsType value
-    for i in range(len(lst)):
-        # unset bit 1 (no embedding permitted) if either bit 2 or 3 is set
-        if lst[i] & 0x000C:
-            lst[i] &= ~0x0002
-        # set bit 2 (allow previewing) if bit 3 is set (allow editing)
-        elif lst[i] & 0x0008:
-            lst[i] |= 0x0004
-        # set bits 2 and 3 if everything is allowed
-        elif lst[i] == 0:
-            lst[i] = 0x000C
-
-    fsType = mergeBits(os2FsTypeMergeBitMap)(lst)
-    # unset bits 2 and 3 if bit 1 is set (some font is "no embedding")
-    if fsType & 0x0002:
-        fsType &= ~0x000C
-    return fsType
-
-
-ttLib.getTableClass("OS/2").mergeMap = {
-    "*": first,
-    "tableTag": equal,
-    "version": max,
-    "xAvgCharWidth": first,  # Will be recalculated at the end on the merged font
-    "fsType": mergeOs2FsType,  # Will be overwritten
-    "panose": first,  # FIXME: should really be the first Latin font
-    "ulUnicodeRange1": bitwise_or,
-    "ulUnicodeRange2": bitwise_or,
-    "ulUnicodeRange3": bitwise_or,
-    "ulUnicodeRange4": bitwise_or,
-    "fsFirstCharIndex": min,
-    "fsLastCharIndex": max,
-    "sTypoAscender": max,
-    "sTypoDescender": min,
-    "sTypoLineGap": max,
-    "usWinAscent": max,
-    "usWinDescent": max,
-    # Version 1
-    "ulCodePageRange1": onlyExisting(bitwise_or),
-    "ulCodePageRange2": onlyExisting(bitwise_or),
-    # Version 2, 3, 4
-    "sxHeight": onlyExisting(max),
-    "sCapHeight": onlyExisting(max),
-    "usDefaultChar": onlyExisting(first),
-    "usBreakChar": onlyExisting(first),
-    "usMaxContext": onlyExisting(max),
-    # version 5
-    "usLowerOpticalPointSize": onlyExisting(min),
-    "usUpperOpticalPointSize": onlyExisting(max),
-}
-
-
-@add_method(ttLib.getTableClass("OS/2"))
-def merge(self, m, tables):
-    DefaultTable.merge(self, m, tables)
-    if self.version < 2:
-        # bits 8 and 9 are reserved and should be set to zero
-        self.fsType &= ~0x0300
-    if self.version >= 3:
-        # Only one of bits 1, 2, and 3 may be set. We already take
-        # care of bit 1 implications in mergeOs2FsType. So unset
-        # bit 2 if bit 3 is already set.
-        if self.fsType & 0x0008:
-            self.fsType &= ~0x0004
-    return self
-
-
-ttLib.getTableClass("post").mergeMap = {
-    "*": first,
-    "tableTag": equal,
-    "formatType": max,
-    "isFixedPitch": min,
-    "minMemType42": max,
-    "maxMemType42": lambda lst: 0,
-    "minMemType1": max,
-    "maxMemType1": lambda lst: 0,
-    "mapping": onlyExisting(sumDicts),
-    "extraNames": lambda lst: [],
-}
-
-ttLib.getTableClass("vmtx").mergeMap = ttLib.getTableClass("hmtx").mergeMap = {
-    "tableTag": equal,
-    "metrics": sumDicts,
-}
-
-ttLib.getTableClass("name").mergeMap = {
-    "tableTag": equal,
-    "names": first,  # FIXME? Does mixing name records make sense?
-}
-
-ttLib.getTableClass("loca").mergeMap = {
-    "*": recalculate,
-    "tableTag": equal,
-}
-
-ttLib.getTableClass("glyf").mergeMap = {
-    "tableTag": equal,
-    "glyphs": sumDicts,
-    "glyphOrder": sumLists,
-    "axisTags": equal,
-}
-
-
-@add_method(ttLib.getTableClass("glyf"))
-def merge(self, m, tables):
-    for i, table in enumerate(tables):
-        for g in table.glyphs.values():
-            if i:
-                # Drop hints for all but first font, since
-                # we don't map functions / CVT values.
-                g.removeHinting()
-            # Expand composite glyphs to load their
-            # composite glyph names.
-            if g.isComposite() or g.isVarComposite():
-                g.expand(table)
-    return DefaultTable.merge(self, m, tables)
-
-
-ttLib.getTableClass("prep").mergeMap = lambda self, lst: first(lst)
-ttLib.getTableClass("fpgm").mergeMap = lambda self, lst: first(lst)
-ttLib.getTableClass("cvt ").mergeMap = lambda self, lst: first(lst)
-ttLib.getTableClass("gasp").mergeMap = lambda self, lst: first(
-    lst
-)  # FIXME? Appears irreconcilable
-
-
-@add_method(ttLib.getTableClass("CFF "))
-def merge(self, m, tables):
-    if any(hasattr(table.cff[0], "FDSelect") for table in tables):
-        raise NotImplementedError("Merging CID-keyed CFF tables is not supported yet")
-
-    for table in tables:
-        table.cff.desubroutinize()
-
-    newcff = tables[0]
-    newfont = newcff.cff[0]
-    private = newfont.Private
-    newDefaultWidthX, newNominalWidthX = private.defaultWidthX, private.nominalWidthX
-    storedNamesStrings = []
-    glyphOrderStrings = []
-    glyphOrder = set(newfont.getGlyphOrder())
-
-    for name in newfont.strings.strings:
-        if name not in glyphOrder:
-            storedNamesStrings.append(name)
-        else:
-            glyphOrderStrings.append(name)
-
-    chrset = list(newfont.charset)
-    newcs = newfont.CharStrings
-    log.debug("FONT 0 CharStrings: %d.", len(newcs))
-
-    for i, table in enumerate(tables[1:], start=1):
-        font = table.cff[0]
-        defaultWidthX, nominalWidthX = (
-            font.Private.defaultWidthX,
-            font.Private.nominalWidthX,
-        )
-        widthsDiffer = (
-            defaultWidthX != newDefaultWidthX or nominalWidthX != newNominalWidthX
-        )
-        font.Private = private
-        fontGlyphOrder = set(font.getGlyphOrder())
-        for name in font.strings.strings:
-            if name in fontGlyphOrder:
-                glyphOrderStrings.append(name)
-        cs = font.CharStrings
-        gs = table.cff.GlobalSubrs
-        log.debug("Font %d CharStrings: %d.", i, len(cs))
-        chrset.extend(font.charset)
-        if newcs.charStringsAreIndexed:
-            for i, name in enumerate(cs.charStrings, start=len(newcs)):
-                newcs.charStrings[name] = i
-                newcs.charStringsIndex.items.append(None)
-        for name in cs.charStrings:
-            if widthsDiffer:
-                c = cs[name]
-                defaultWidthXToken = object()
-                extractor = T2WidthExtractor([], [], nominalWidthX, defaultWidthXToken)
-                extractor.execute(c)
-                width = extractor.width
-                if width is not defaultWidthXToken:
-                    c.program.pop(0)
-                else:
-                    width = defaultWidthX
-                if width != newDefaultWidthX:
-                    c.program.insert(0, width - newNominalWidthX)
-            newcs[name] = cs[name]
-
-    newfont.charset = chrset
-    newfont.numGlyphs = len(chrset)
-    newfont.strings.strings = glyphOrderStrings + storedNamesStrings
-
-    return newcff
-
-
-@add_method(ttLib.getTableClass("cmap"))
-def merge(self, m, tables):
-    # TODO Handle format=14.
-    if not hasattr(m, "cmap"):
-        computeMegaCmap(m, tables)
-    cmap = m.cmap
-
-    cmapBmpOnly = {uni: gid for uni, gid in cmap.items() if uni <= 0xFFFF}
-    self.tables = []
-    module = ttLib.getTableModule("cmap")
-    if len(cmapBmpOnly) != len(cmap):
-        # format-12 required.
-        cmapTable = module.cmap_classes[12](12)
-        cmapTable.platformID = 3
-        cmapTable.platEncID = 10
-        cmapTable.language = 0
-        cmapTable.cmap = cmap
-        self.tables.append(cmapTable)
-    # always create format-4
-    cmapTable = module.cmap_classes[4](4)
-    cmapTable.platformID = 3
-    cmapTable.platEncID = 1
-    cmapTable.language = 0
-    cmapTable.cmap = cmapBmpOnly
-    # ordered by platform then encoding
-    self.tables.insert(0, cmapTable)
-    self.tableVersion = 0
-    self.numSubTables = len(self.tables)
-    return self
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_v_a_r.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_v_a_r.py
deleted file mode 100644
index 11485bf09aee04a15307d094fdead26e7e4572ea..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_g_v_a_r.py
+++ /dev/null
@@ -1,284 +0,0 @@
-from collections import UserDict, deque
-from functools import partial
-from fontTools.misc import sstruct
-from fontTools.misc.textTools import safeEval
-from . import DefaultTable
-import array
-import itertools
-import logging
-import struct
-import sys
-import fontTools.ttLib.tables.TupleVariation as tv
-
-
-log = logging.getLogger(__name__)
-TupleVariation = tv.TupleVariation
-
-
-# https://www.microsoft.com/typography/otspec/gvar.htm
-# https://www.microsoft.com/typography/otspec/otvarcommonformats.htm
-#
-# Apple's documentation of 'gvar':
-# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6gvar.html
-#
-# FreeType2 source code for parsing 'gvar':
-# http://git.savannah.gnu.org/cgit/freetype/freetype2.git/tree/src/truetype/ttgxvar.c
-
-GVAR_HEADER_FORMAT = """
-	> # big endian
-	version:			H
-	reserved:			H
-	axisCount:			H
-	sharedTupleCount:		H
-	offsetToSharedTuples:		I
-	glyphCount:			H
-	flags:				H
-	offsetToGlyphVariationData:	I
-"""
-
-GVAR_HEADER_SIZE = sstruct.calcsize(GVAR_HEADER_FORMAT)
-
-
-class _LazyDict(UserDict):
-    def __init__(self, data):
-        super().__init__()
-        self.data = data
-
-    def __getitem__(self, k):
-        v = self.data[k]
-        if callable(v):
-            v = v()
-            self.data[k] = v
-        return v
-
-
-class table__g_v_a_r(DefaultTable.DefaultTable):
-    dependencies = ["fvar", "glyf"]
-
-    def __init__(self, tag=None):
-        DefaultTable.DefaultTable.__init__(self, tag)
-        self.version, self.reserved = 1, 0
-        self.variations = {}
-
-    def compile(self, ttFont):
-        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
-        sharedTuples = tv.compileSharedTuples(
-            axisTags, itertools.chain(*self.variations.values())
-        )
-        sharedTupleIndices = {coord: i for i, coord in enumerate(sharedTuples)}
-        sharedTupleSize = sum([len(c) for c in sharedTuples])
-        compiledGlyphs = self.compileGlyphs_(ttFont, axisTags, sharedTupleIndices)
-        offset = 0
-        offsets = []
-        for glyph in compiledGlyphs:
-            offsets.append(offset)
-            offset += len(glyph)
-        offsets.append(offset)
-        compiledOffsets, tableFormat = self.compileOffsets_(offsets)
-
-        header = {}
-        header["version"] = self.version
-        header["reserved"] = self.reserved
-        header["axisCount"] = len(axisTags)
-        header["sharedTupleCount"] = len(sharedTuples)
-        header["offsetToSharedTuples"] = GVAR_HEADER_SIZE + len(compiledOffsets)
-        header["glyphCount"] = len(compiledGlyphs)
-        header["flags"] = tableFormat
-        header["offsetToGlyphVariationData"] = (
-            header["offsetToSharedTuples"] + sharedTupleSize
-        )
-        compiledHeader = sstruct.pack(GVAR_HEADER_FORMAT, header)
-
-        result = [compiledHeader, compiledOffsets]
-        result.extend(sharedTuples)
-        result.extend(compiledGlyphs)
-        return b"".join(result)
-
-    def compileGlyphs_(self, ttFont, axisTags, sharedCoordIndices):
-        result = []
-        glyf = ttFont["glyf"]
-        for glyphName in ttFont.getGlyphOrder():
-            variations = self.variations.get(glyphName, [])
-            if not variations:
-                result.append(b"")
-                continue
-            pointCountUnused = 0  # pointCount is actually unused by compileGlyph
-            result.append(
-                compileGlyph_(
-                    variations, pointCountUnused, axisTags, sharedCoordIndices
-                )
-            )
-        return result
-
-    def decompile(self, data, ttFont):
-        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
-        glyphs = ttFont.getGlyphOrder()
-        sstruct.unpack(GVAR_HEADER_FORMAT, data[0:GVAR_HEADER_SIZE], self)
-        assert len(glyphs) == self.glyphCount
-        assert len(axisTags) == self.axisCount
-        offsets = self.decompileOffsets_(
-            data[GVAR_HEADER_SIZE:],
-            tableFormat=(self.flags & 1),
-            glyphCount=self.glyphCount,
-        )
-        sharedCoords = tv.decompileSharedTuples(
-            axisTags, self.sharedTupleCount, data, self.offsetToSharedTuples
-        )
-        variations = {}
-        offsetToData = self.offsetToGlyphVariationData
-        glyf = ttFont["glyf"]
-
-        def decompileVarGlyph(glyphName, gid):
-            gvarData = data[
-                offsetToData + offsets[gid] : offsetToData + offsets[gid + 1]
-            ]
-            if not gvarData:
-                return []
-            glyph = glyf[glyphName]
-            numPointsInGlyph = self.getNumPoints_(glyph)
-            return decompileGlyph_(numPointsInGlyph, sharedCoords, axisTags, gvarData)
-
-        for gid in range(self.glyphCount):
-            glyphName = glyphs[gid]
-            variations[glyphName] = partial(decompileVarGlyph, glyphName, gid)
-        self.variations = _LazyDict(variations)
-
-        if ttFont.lazy is False:  # Be lazy for None and True
-            self.ensureDecompiled()
-
-    def ensureDecompiled(self, recurse=False):
-        # The recurse argument is unused, but part of the signature of
-        # ensureDecompiled across the library.
-        # Use a zero-length deque to consume the lazy dict
-        deque(self.variations.values(), maxlen=0)
-
-    @staticmethod
-    def decompileOffsets_(data, tableFormat, glyphCount):
-        if tableFormat == 0:
-            # Short format: array of UInt16
-            offsets = array.array("H")
-            offsetsSize = (glyphCount + 1) * 2
-        else:
-            # Long format: array of UInt32
-            offsets = array.array("I")
-            offsetsSize = (glyphCount + 1) * 4
-        offsets.frombytes(data[0:offsetsSize])
-        if sys.byteorder != "big":
-            offsets.byteswap()
-
-        # In the short format, offsets need to be multiplied by 2.
-        # This is not documented in Apple's TrueType specification,
-        # but can be inferred from the FreeType implementation, and
-        # we could verify it with two sample GX fonts.
-        if tableFormat == 0:
-            offsets = [off * 2 for off in offsets]
-
-        return offsets
-
-    @staticmethod
-    def compileOffsets_(offsets):
-        """Packs a list of offsets into a 'gvar' offset table.
-
-        Returns a pair (bytestring, tableFormat). Bytestring is the
-        packed offset table. Format indicates whether the table
-        uses short (tableFormat=0) or long (tableFormat=1) integers.
-        The returned tableFormat should get packed into the flags field
-        of the 'gvar' header.
-        """
-        assert len(offsets) >= 2
-        for i in range(1, len(offsets)):
-            assert offsets[i - 1] <= offsets[i]
-        if max(offsets) <= 0xFFFF * 2:
-            packed = array.array("H", [n >> 1 for n in offsets])
-            tableFormat = 0
-        else:
-            packed = array.array("I", offsets)
-            tableFormat = 1
-        if sys.byteorder != "big":
-            packed.byteswap()
-        return (packed.tobytes(), tableFormat)
-
-    def toXML(self, writer, ttFont):
-        writer.simpletag("version", value=self.version)
-        writer.newline()
-        writer.simpletag("reserved", value=self.reserved)
-        writer.newline()
-        axisTags = [axis.axisTag for axis in ttFont["fvar"].axes]
-        for glyphName in ttFont.getGlyphNames():
-            variations = self.variations.get(glyphName)
-            if not variations:
-                continue
-            writer.begintag("glyphVariations", glyph=glyphName)
-            writer.newline()
-            for gvar in variations:
-                gvar.toXML(writer, axisTags)
-            writer.endtag("glyphVariations")
-            writer.newline()
-
-    def fromXML(self, name, attrs, content, ttFont):
-        if name == "version":
-            self.version = safeEval(attrs["value"])
-        elif name == "reserved":
-            self.reserved = safeEval(attrs["value"])
-        elif name == "glyphVariations":
-            if not hasattr(self, "variations"):
-                self.variations = {}
-            glyphName = attrs["glyph"]
-            glyph = ttFont["glyf"][glyphName]
-            numPointsInGlyph = self.getNumPoints_(glyph)
-            glyphVariations = []
-            for element in content:
-                if isinstance(element, tuple):
-                    name, attrs, content = element
-                    if name == "tuple":
-                        gvar = TupleVariation({}, [None] * numPointsInGlyph)
-                        glyphVariations.append(gvar)
-                        for tupleElement in content:
-                            if isinstance(tupleElement, tuple):
-                                tupleName, tupleAttrs, tupleContent = tupleElement
-                                gvar.fromXML(tupleName, tupleAttrs, tupleContent)
-            self.variations[glyphName] = glyphVariations
-
-    @staticmethod
-    def getNumPoints_(glyph):
-        NUM_PHANTOM_POINTS = 4
-
-        if glyph.isComposite():
-            return len(glyph.components) + NUM_PHANTOM_POINTS
-        elif glyph.isVarComposite():
-            count = 0
-            for component in glyph.components:
-                count += component.getPointCount()
-            return count + NUM_PHANTOM_POINTS
-        else:
-            # Empty glyphs (eg. space, nonmarkingreturn) have no "coordinates" attribute.
-            return len(getattr(glyph, "coordinates", [])) + NUM_PHANTOM_POINTS
-
-
-def compileGlyph_(variations, pointCount, axisTags, sharedCoordIndices):
-    tupleVariationCount, tuples, data = tv.compileTupleVariationStore(
-        variations, pointCount, axisTags, sharedCoordIndices
-    )
-    if tupleVariationCount == 0:
-        return b""
-    result = [struct.pack(">HH", tupleVariationCount, 4 + len(tuples)), tuples, data]
-    if (len(tuples) + len(data)) % 2 != 0:
-        result.append(b"\0")  # padding
-    return b"".join(result)
-
-
-def decompileGlyph_(pointCount, sharedTuples, axisTags, data):
-    if len(data) < 4:
-        return []
-    tupleVariationCount, offsetToData = struct.unpack(">HH", data[:4])
-    dataPos = offsetToData
-    return tv.decompileTupleVariationStore(
-        "gvar",
-        axisTags,
-        tupleVariationCount,
-        pointCount,
-        sharedTuples,
-        data,
-        4,
-        offsetToData,
-    )
diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_fixes.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_fixes.py
deleted file mode 100644
index ff4f9e2d70e323e108fbd7bade2fbed3f5595cbe..0000000000000000000000000000000000000000
--- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_fixes.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# JSONDecodeError was introduced in requests=2.27 released in 2022.
-# This allows us to support older requests for users
-# More information: https://github.com/psf/requests/pull/5856
-try:
-    from requests import JSONDecodeError  # type: ignore  # noqa: F401
-except ImportError:
-    try:
-        from simplejson import JSONDecodeError  # type: ignore # noqa: F401
-    except ImportError:
-        from json import JSONDecodeError  # type: ignore  # noqa: F401
-
-import contextlib
-import os
-import shutil
-import stat
-import tempfile
-from functools import partial
-from pathlib import Path
-from typing import Callable, Generator, Optional, Union
-
-import yaml
-
-
-# Wrap `yaml.dump` to set `allow_unicode=True` by default.
-#
-# Example:
-# ```py
-# >>> yaml.dump({"emoji": "👀", "some unicode": "日本か"})
-# 'emoji: "\\U0001F440"\nsome unicode: "\\u65E5\\u672C\\u304B"\n'
-#
-# >>> yaml_dump({"emoji": "👀", "some unicode": "日本か"})
-# 'emoji: "👀"\nsome unicode: "日本か"\n'
-# ```
-yaml_dump: Callable[..., str] = partial(yaml.dump, stream=None, allow_unicode=True)  # type: ignore
-
-
-@contextlib.contextmanager
-def SoftTemporaryDirectory(
-    suffix: Optional[str] = None,
-    prefix: Optional[str] = None,
-    dir: Optional[Union[Path, str]] = None,
-    **kwargs,
-) -> Generator[str, None, None]:
-    """
-    Context manager to create a temporary directory and safely delete it.
-
-    If tmp directory cannot be deleted normally, we set the WRITE permission and retry.
-    If cleanup still fails, we give up but don't raise an exception. This is equivalent
-    to  `tempfile.TemporaryDirectory(..., ignore_cleanup_errors=True)` introduced in
-    Python 3.10.
-
-    See https://www.scivision.dev/python-tempfile-permission-error-windows/.
-    """
-    tmpdir = tempfile.TemporaryDirectory(prefix=prefix, suffix=suffix, dir=dir, **kwargs)
-    yield tmpdir.name
-
-    try:
-        # First once with normal cleanup
-        shutil.rmtree(tmpdir.name)
-    except Exception:
-        # If failed, try to set write permission and retry
-        try:
-            shutil.rmtree(tmpdir.name, onerror=_set_write_permission_and_retry)
-        except Exception:
-            pass
-
-    # And finally, cleanup the tmpdir.
-    # If it fails again, give up but do not throw error
-    try:
-        tmpdir.cleanup()
-    except Exception:
-        pass
-
-
-def _set_write_permission_and_retry(func, path, excinfo):
-    os.chmod(path, stat.S_IWRITE)
-    func(path)
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py b/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
deleted file mode 100644
index 2063238df27a0dd3c795ddc2e03a0ab7657662a0..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
+++ /dev/null
@@ -1,527 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import warnings
-from functools import partial
-from typing import Dict, List, Optional, Union
-
-import jax
-import jax.numpy as jnp
-import numpy as np
-from flax.core.frozen_dict import FrozenDict
-from flax.jax_utils import unreplicate
-from flax.training.common_utils import shard
-from PIL import Image
-from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel
-
-from ...models import FlaxAutoencoderKL, FlaxUNet2DConditionModel
-from ...schedulers import (
-    FlaxDDIMScheduler,
-    FlaxDPMSolverMultistepScheduler,
-    FlaxLMSDiscreteScheduler,
-    FlaxPNDMScheduler,
-)
-from ...utils import PIL_INTERPOLATION, logging, replace_example_docstring
-from ..pipeline_flax_utils import FlaxDiffusionPipeline
-from . import FlaxStableDiffusionPipelineOutput
-from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-# Set to True to use python for loop instead of jax.fori_loop for easier debugging
-DEBUG = False
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import jax
-        >>> import numpy as np
-        >>> import jax.numpy as jnp
-        >>> from flax.jax_utils import replicate
-        >>> from flax.training.common_utils import shard
-        >>> import requests
-        >>> from io import BytesIO
-        >>> from PIL import Image
-        >>> from diffusers import FlaxStableDiffusionImg2ImgPipeline
-
-
-        >>> def create_key(seed=0):
-        ...     return jax.random.PRNGKey(seed)
-
-
-        >>> rng = create_key(0)
-
-        >>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
-        >>> response = requests.get(url)
-        >>> init_img = Image.open(BytesIO(response.content)).convert("RGB")
-        >>> init_img = init_img.resize((768, 512))
-
-        >>> prompts = "A fantasy landscape, trending on artstation"
-
-        >>> pipeline, params = FlaxStableDiffusionImg2ImgPipeline.from_pretrained(
-        ...     "CompVis/stable-diffusion-v1-4",
-        ...     revision="flax",
-        ...     dtype=jnp.bfloat16,
-        ... )
-
-        >>> num_samples = jax.device_count()
-        >>> rng = jax.random.split(rng, jax.device_count())
-        >>> prompt_ids, processed_image = pipeline.prepare_inputs(
-        ...     prompt=[prompts] * num_samples, image=[init_img] * num_samples
-        ... )
-        >>> p_params = replicate(params)
-        >>> prompt_ids = shard(prompt_ids)
-        >>> processed_image = shard(processed_image)
-
-        >>> output = pipeline(
-        ...     prompt_ids=prompt_ids,
-        ...     image=processed_image,
-        ...     params=p_params,
-        ...     prng_seed=rng,
-        ...     strength=0.75,
-        ...     num_inference_steps=50,
-        ...     jit=True,
-        ...     height=512,
-        ...     width=768,
-        ... ).images
-
-        >>> output_images = pipeline.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
-        ```
-"""
-
-
-class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
-    r"""
-    Pipeline for image-to-image generation using Stable Diffusion.
-
-    This model inherits from [`FlaxDiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    Args:
-        vae ([`FlaxAutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`FlaxCLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.FlaxCLIPTextModel),
-            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`FlaxUNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`FlaxDDIMScheduler`], [`FlaxLMSDiscreteScheduler`], [`FlaxPNDMScheduler`], or
-            [`FlaxDPMSolverMultistepScheduler`].
-        safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    def __init__(
-        self,
-        vae: FlaxAutoencoderKL,
-        text_encoder: FlaxCLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: FlaxUNet2DConditionModel,
-        scheduler: Union[
-            FlaxDDIMScheduler, FlaxPNDMScheduler, FlaxLMSDiscreteScheduler, FlaxDPMSolverMultistepScheduler
-        ],
-        safety_checker: FlaxStableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        dtype: jnp.dtype = jnp.float32,
-    ):
-        super().__init__()
-        self.dtype = dtype
-
-        if safety_checker is None:
-            logger.warn(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-
-    def prepare_inputs(self, prompt: Union[str, List[str]], image: Union[Image.Image, List[Image.Image]]):
-        if not isinstance(prompt, (str, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if not isinstance(image, (Image.Image, list)):
-            raise ValueError(f"image has to be of type `PIL.Image.Image` or list but is {type(image)}")
-
-        if isinstance(image, Image.Image):
-            image = [image]
-
-        processed_images = jnp.concatenate([preprocess(img, jnp.float32) for img in image])
-
-        text_input = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            truncation=True,
-            return_tensors="np",
-        )
-        return text_input.input_ids, processed_images
-
-    def _get_has_nsfw_concepts(self, features, params):
-        has_nsfw_concepts = self.safety_checker(features, params)
-        return has_nsfw_concepts
-
-    def _run_safety_checker(self, images, safety_model_params, jit=False):
-        # safety_model_params should already be replicated when jit is True
-        pil_images = [Image.fromarray(image) for image in images]
-        features = self.feature_extractor(pil_images, return_tensors="np").pixel_values
-
-        if jit:
-            features = shard(features)
-            has_nsfw_concepts = _p_get_has_nsfw_concepts(self, features, safety_model_params)
-            has_nsfw_concepts = unshard(has_nsfw_concepts)
-            safety_model_params = unreplicate(safety_model_params)
-        else:
-            has_nsfw_concepts = self._get_has_nsfw_concepts(features, safety_model_params)
-
-        images_was_copied = False
-        for idx, has_nsfw_concept in enumerate(has_nsfw_concepts):
-            if has_nsfw_concept:
-                if not images_was_copied:
-                    images_was_copied = True
-                    images = images.copy()
-
-                images[idx] = np.zeros(images[idx].shape, dtype=np.uint8)  # black image
-
-            if any(has_nsfw_concepts):
-                warnings.warn(
-                    "Potential NSFW content was detected in one or more images. A black image will be returned"
-                    " instead. Try again with a different prompt and/or seed."
-                )
-
-        return images, has_nsfw_concepts
-
-    def get_timestep_start(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep, 0)
-
-        return t_start
-
-    def _generate(
-        self,
-        prompt_ids: jnp.array,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        start_timestep: int,
-        num_inference_steps: int,
-        height: int,
-        width: int,
-        guidance_scale: float,
-        noise: Optional[jnp.array] = None,
-        neg_prompt_ids: Optional[jnp.array] = None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # get prompt text embeddings
-        prompt_embeds = self.text_encoder(prompt_ids, params=params["text_encoder"])[0]
-
-        # TODO: currently it is assumed `do_classifier_free_guidance = guidance_scale > 1.0`
-        # implement this conditional `do_classifier_free_guidance = guidance_scale > 1.0`
-        batch_size = prompt_ids.shape[0]
-
-        max_length = prompt_ids.shape[-1]
-
-        if neg_prompt_ids is None:
-            uncond_input = self.tokenizer(
-                [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="np"
-            ).input_ids
-        else:
-            uncond_input = neg_prompt_ids
-        negative_prompt_embeds = self.text_encoder(uncond_input, params=params["text_encoder"])[0]
-        context = jnp.concatenate([negative_prompt_embeds, prompt_embeds])
-
-        latents_shape = (
-            batch_size,
-            self.unet.in_channels,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if noise is None:
-            noise = jax.random.normal(prng_seed, shape=latents_shape, dtype=jnp.float32)
-        else:
-            if noise.shape != latents_shape:
-                raise ValueError(f"Unexpected latents shape, got {noise.shape}, expected {latents_shape}")
-
-        # Create init_latents
-        init_latent_dist = self.vae.apply({"params": params["vae"]}, image, method=self.vae.encode).latent_dist
-        init_latents = init_latent_dist.sample(key=prng_seed).transpose((0, 3, 1, 2))
-        init_latents = self.vae.config.scaling_factor * init_latents
-
-        def loop_body(step, args):
-            latents, scheduler_state = args
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            latents_input = jnp.concatenate([latents] * 2)
-
-            t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
-            timestep = jnp.broadcast_to(t, latents_input.shape[0])
-
-            latents_input = self.scheduler.scale_model_input(scheduler_state, latents_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet.apply(
-                {"params": params["unet"]},
-                jnp.array(latents_input),
-                jnp.array(timestep, dtype=jnp.int32),
-                encoder_hidden_states=context,
-            ).sample
-            # perform guidance
-            noise_pred_uncond, noise_prediction_text = jnp.split(noise_pred, 2, axis=0)
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_prediction_text - noise_pred_uncond)
-
-            # compute the previous noisy sample x_t -> x_t-1
-            latents, scheduler_state = self.scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
-            return latents, scheduler_state
-
-        scheduler_state = self.scheduler.set_timesteps(
-            params["scheduler"], num_inference_steps=num_inference_steps, shape=latents_shape
-        )
-
-        latent_timestep = scheduler_state.timesteps[start_timestep : start_timestep + 1].repeat(batch_size)
-
-        latents = self.scheduler.add_noise(params["scheduler"], init_latents, noise, latent_timestep)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * params["scheduler"].init_noise_sigma
-
-        if DEBUG:
-            # run with python for loop
-            for i in range(start_timestep, num_inference_steps):
-                latents, scheduler_state = loop_body(i, (latents, scheduler_state))
-        else:
-            latents, _ = jax.lax.fori_loop(start_timestep, num_inference_steps, loop_body, (latents, scheduler_state))
-
-        # scale and decode the image latents with vae
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.apply({"params": params["vae"]}, latents, method=self.vae.decode).sample
-
-        image = (image / 2 + 0.5).clip(0, 1).transpose(0, 2, 3, 1)
-        return image
-
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt_ids: jnp.array,
-        image: jnp.array,
-        params: Union[Dict, FrozenDict],
-        prng_seed: jax.random.KeyArray,
-        strength: float = 0.8,
-        num_inference_steps: int = 50,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        guidance_scale: Union[float, jnp.array] = 7.5,
-        noise: jnp.array = None,
-        neg_prompt_ids: jnp.array = None,
-        return_dict: bool = True,
-        jit: bool = False,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt_ids (`jnp.array`):
-                The prompt or prompts to guide the image generation.
-            image (`jnp.array`):
-                Array representing an image batch, that will be used as the starting point for the process.
-            params (`Dict` or `FrozenDict`): Dictionary containing the model parameters/weights
-            prng_seed (`jax.random.KeyArray` or `jax.Array`): Array containing random number generator key
-            strength (`float`, *optional*, defaults to 0.8):
-                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
-                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
-                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
-                be maximum and the denoising process will run for the full number of iterations specified in
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            noise (`jnp.array`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. tensor will ge generated
-                by sampling using the supplied random `generator`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] instead of
-                a plain tuple.
-            jit (`bool`, defaults to `False`):
-                Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
-                exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.FlaxStableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple. When returning a tuple, the first element is a list with the generated images, and the second
-            element is a list of `bool`s denoting whether the corresponding generated image likely represents
-            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        if isinstance(guidance_scale, float):
-            # Convert to a tensor so each device gets a copy. Follow the prompt_ids for
-            # shape information, as they may be sharded (when `jit` is `True`), or not.
-            guidance_scale = jnp.array([guidance_scale] * prompt_ids.shape[0])
-            if len(prompt_ids.shape) > 2:
-                # Assume sharded
-                guidance_scale = guidance_scale[:, None]
-
-        start_timestep = self.get_timestep_start(num_inference_steps, strength)
-
-        if jit:
-            images = _p_generate(
-                self,
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                start_timestep,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                noise,
-                neg_prompt_ids,
-            )
-        else:
-            images = self._generate(
-                prompt_ids,
-                image,
-                params,
-                prng_seed,
-                start_timestep,
-                num_inference_steps,
-                height,
-                width,
-                guidance_scale,
-                noise,
-                neg_prompt_ids,
-            )
-
-        if self.safety_checker is not None:
-            safety_params = params["safety_checker"]
-            images_uint8_casted = (images * 255).round().astype("uint8")
-            num_devices, batch_size = images.shape[:2]
-
-            images_uint8_casted = np.asarray(images_uint8_casted).reshape(num_devices * batch_size, height, width, 3)
-            images_uint8_casted, has_nsfw_concept = self._run_safety_checker(images_uint8_casted, safety_params, jit)
-            images = np.asarray(images)
-
-            # block images
-            if any(has_nsfw_concept):
-                for i, is_nsfw in enumerate(has_nsfw_concept):
-                    if is_nsfw:
-                        images[i] = np.asarray(images_uint8_casted[i])
-
-            images = images.reshape(num_devices, batch_size, height, width, 3)
-        else:
-            images = np.asarray(images)
-            has_nsfw_concept = False
-
-        if not return_dict:
-            return (images, has_nsfw_concept)
-
-        return FlaxStableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept)
-
-
-# Static argnums are pipe, start_timestep, num_inference_steps, height, width. A change would trigger recompilation.
-# Non-static args are (sharded) input tensors mapped over their first dimension (hence, `0`).
-@partial(
-    jax.pmap,
-    in_axes=(None, 0, 0, 0, 0, None, None, None, None, 0, 0, 0),
-    static_broadcasted_argnums=(0, 5, 6, 7, 8),
-)
-def _p_generate(
-    pipe,
-    prompt_ids,
-    image,
-    params,
-    prng_seed,
-    start_timestep,
-    num_inference_steps,
-    height,
-    width,
-    guidance_scale,
-    noise,
-    neg_prompt_ids,
-):
-    return pipe._generate(
-        prompt_ids,
-        image,
-        params,
-        prng_seed,
-        start_timestep,
-        num_inference_steps,
-        height,
-        width,
-        guidance_scale,
-        noise,
-        neg_prompt_ids,
-    )
-
-
-@partial(jax.pmap, static_broadcasted_argnums=(0,))
-def _p_get_has_nsfw_concepts(pipe, features, params):
-    return pipe._get_has_nsfw_concepts(features, params)
-
-
-def unshard(x: jnp.ndarray):
-    # einops.rearrange(x, 'd b ... -> (d b) ...')
-    num_devices, batch_size = x.shape[:2]
-    rest = x.shape[2:]
-    return x.reshape(num_devices * batch_size, *rest)
-
-
-def preprocess(image, dtype):
-    w, h = image.size
-    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
-    image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
-    image = jnp.array(image).astype(dtype) / 255.0
-    image = image[None].transpose(0, 3, 1, 2)
-    return 2.0 * image - 1.0
diff --git a/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py b/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py
deleted file mode 100644
index b6240559fc88fa45e4612dc3005ba66e10d3269d..0000000000000000000000000000000000000000
--- a/spaces/declare-lab/tango/diffusers/src/diffusers/schedulers/scheduling_sde_ve_flax.py
+++ /dev/null
@@ -1,279 +0,0 @@
-# Copyright 2023 Google Brain and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union
-
-import flax
-import jax.numpy as jnp
-from jax import random
-
-from ..configuration_utils import ConfigMixin, register_to_config
-from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
-
-
-@flax.struct.dataclass
-class ScoreSdeVeSchedulerState:
-    # setable values
-    timesteps: Optional[jnp.ndarray] = None
-    discrete_sigmas: Optional[jnp.ndarray] = None
-    sigmas: Optional[jnp.ndarray] = None
-
-    @classmethod
-    def create(cls):
-        return cls()
-
-
-@dataclass
-class FlaxSdeVeOutput(FlaxSchedulerOutput):
-    """
-    Output class for the ScoreSdeVeScheduler's step function output.
-
-    Args:
-        state (`ScoreSdeVeSchedulerState`):
-        prev_sample (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
-            denoising loop.
-        prev_sample_mean (`jnp.ndarray` of shape `(batch_size, num_channels, height, width)` for images):
-            Mean averaged `prev_sample`. Same as `prev_sample`, only mean-averaged over previous timesteps.
-    """
-
-    state: ScoreSdeVeSchedulerState
-    prev_sample: jnp.ndarray
-    prev_sample_mean: Optional[jnp.ndarray] = None
-
-
-class FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin):
-    """
-    The variance exploding stochastic differential equation (SDE) scheduler.
-
-    For more information, see the original paper: https://arxiv.org/abs/2011.13456
-
-    [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
-    function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
-    [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
-    [`~SchedulerMixin.from_pretrained`] functions.
-
-    Args:
-        num_train_timesteps (`int`): number of diffusion steps used to train the model.
-        snr (`float`):
-            coefficient weighting the step from the model_output sample (from the network) to the random noise.
-        sigma_min (`float`):
-                initial noise scale for sigma sequence in sampling procedure. The minimum sigma should mirror the
-                distribution of the data.
-        sigma_max (`float`): maximum value used for the range of continuous timesteps passed into the model.
-        sampling_eps (`float`): the end value of sampling, where timesteps decrease progressively from 1 to
-        epsilon.
-        correct_steps (`int`): number of correction steps performed on a produced sample.
-    """
-
-    @property
-    def has_state(self):
-        return True
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 2000,
-        snr: float = 0.15,
-        sigma_min: float = 0.01,
-        sigma_max: float = 1348.0,
-        sampling_eps: float = 1e-5,
-        correct_steps: int = 1,
-    ):
-        pass
-
-    def create_state(self):
-        state = ScoreSdeVeSchedulerState.create()
-        return self.set_sigmas(
-            state,
-            self.config.num_train_timesteps,
-            self.config.sigma_min,
-            self.config.sigma_max,
-            self.config.sampling_eps,
-        )
-
-    def set_timesteps(
-        self, state: ScoreSdeVeSchedulerState, num_inference_steps: int, shape: Tuple = (), sampling_eps: float = None
-    ) -> ScoreSdeVeSchedulerState:
-        """
-        Sets the continuous timesteps used for the diffusion chain. Supporting function to be run before inference.
-
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            sampling_eps (`float`, optional):
-                final timestep value (overrides value given at Scheduler instantiation).
-
-        """
-        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
-
-        timesteps = jnp.linspace(1, sampling_eps, num_inference_steps)
-        return state.replace(timesteps=timesteps)
-
-    def set_sigmas(
-        self,
-        state: ScoreSdeVeSchedulerState,
-        num_inference_steps: int,
-        sigma_min: float = None,
-        sigma_max: float = None,
-        sampling_eps: float = None,
-    ) -> ScoreSdeVeSchedulerState:
-        """
-        Sets the noise scales used for the diffusion chain. Supporting function to be run before inference.
-
-        The sigmas control the weight of the `drift` and `diffusion` components of sample update.
-
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            num_inference_steps (`int`):
-                the number of diffusion steps used when generating samples with a pre-trained model.
-            sigma_min (`float`, optional):
-                initial noise scale value (overrides value given at Scheduler instantiation).
-            sigma_max (`float`, optional):
-                final noise scale value (overrides value given at Scheduler instantiation).
-            sampling_eps (`float`, optional):
-                final timestep value (overrides value given at Scheduler instantiation).
-        """
-        sigma_min = sigma_min if sigma_min is not None else self.config.sigma_min
-        sigma_max = sigma_max if sigma_max is not None else self.config.sigma_max
-        sampling_eps = sampling_eps if sampling_eps is not None else self.config.sampling_eps
-        if state.timesteps is None:
-            state = self.set_timesteps(state, num_inference_steps, sampling_eps)
-
-        discrete_sigmas = jnp.exp(jnp.linspace(jnp.log(sigma_min), jnp.log(sigma_max), num_inference_steps))
-        sigmas = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])
-
-        return state.replace(discrete_sigmas=discrete_sigmas, sigmas=sigmas)
-
-    def get_adjacent_sigma(self, state, timesteps, t):
-        return jnp.where(timesteps == 0, jnp.zeros_like(t), state.discrete_sigmas[timesteps - 1])
-
-    def step_pred(
-        self,
-        state: ScoreSdeVeSchedulerState,
-        model_output: jnp.ndarray,
-        timestep: int,
-        sample: jnp.ndarray,
-        key: random.KeyArray,
-        return_dict: bool = True,
-    ) -> Union[FlaxSdeVeOutput, Tuple]:
-        """
-        Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
-        process from the learned model outputs (most often the predicted noise).
-
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            timestep (`int`): current discrete timestep in the diffusion chain.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class
-
-        Returns:
-            [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        if state.timesteps is None:
-            raise ValueError(
-                "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        timestep = timestep * jnp.ones(
-            sample.shape[0],
-        )
-        timesteps = (timestep * (len(state.timesteps) - 1)).long()
-
-        sigma = state.discrete_sigmas[timesteps]
-        adjacent_sigma = self.get_adjacent_sigma(state, timesteps, timestep)
-        drift = jnp.zeros_like(sample)
-        diffusion = (sigma**2 - adjacent_sigma**2) ** 0.5
-
-        # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
-        # also equation 47 shows the analog from SDE models to ancestral sampling methods
-        diffusion = diffusion.flatten()
-        diffusion = broadcast_to_shape_from_left(diffusion, sample.shape)
-        drift = drift - diffusion**2 * model_output
-
-        #  equation 6: sample noise for the diffusion term of
-        key = random.split(key, num=1)
-        noise = random.normal(key=key, shape=sample.shape)
-        prev_sample_mean = sample - drift  # subtract because `dt` is a small negative timestep
-        # TODO is the variable diffusion the correct scaling term for the noise?
-        prev_sample = prev_sample_mean + diffusion * noise  # add impact of diffusion field g
-
-        if not return_dict:
-            return (prev_sample, prev_sample_mean, state)
-
-        return FlaxSdeVeOutput(prev_sample=prev_sample, prev_sample_mean=prev_sample_mean, state=state)
-
-    def step_correct(
-        self,
-        state: ScoreSdeVeSchedulerState,
-        model_output: jnp.ndarray,
-        sample: jnp.ndarray,
-        key: random.KeyArray,
-        return_dict: bool = True,
-    ) -> Union[FlaxSdeVeOutput, Tuple]:
-        """
-        Correct the predicted sample based on the output model_output of the network. This is often run repeatedly
-        after making the prediction for the previous timestep.
-
-        Args:
-            state (`ScoreSdeVeSchedulerState`): the `FlaxScoreSdeVeScheduler` state data class instance.
-            model_output (`jnp.ndarray`): direct output from learned diffusion model.
-            sample (`jnp.ndarray`):
-                current instance of sample being created by diffusion process.
-            generator: random number generator.
-            return_dict (`bool`): option for returning tuple rather than FlaxSdeVeOutput class
-
-        Returns:
-            [`FlaxSdeVeOutput`] or `tuple`: [`FlaxSdeVeOutput`] if `return_dict` is True, otherwise a `tuple`. When
-            returning a tuple, the first element is the sample tensor.
-
-        """
-        if state.timesteps is None:
-            raise ValueError(
-                "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"
-            )
-
-        # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
-        # sample noise for correction
-        key = random.split(key, num=1)
-        noise = random.normal(key=key, shape=sample.shape)
-
-        # compute step size from the model_output, the noise, and the snr
-        grad_norm = jnp.linalg.norm(model_output)
-        noise_norm = jnp.linalg.norm(noise)
-        step_size = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
-        step_size = step_size * jnp.ones(sample.shape[0])
-
-        # compute corrected sample: model_output term and noise term
-        step_size = step_size.flatten()
-        step_size = broadcast_to_shape_from_left(step_size, sample.shape)
-        prev_sample_mean = sample + step_size * model_output
-        prev_sample = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
-
-        if not return_dict:
-            return (prev_sample, state)
-
-        return FlaxSdeVeOutput(prev_sample=prev_sample, state=state)
-
-    def __len__(self):
-        return self.config.num_train_timesteps
diff --git a/spaces/declare-lab/tango/diffusers/tests/models/__init__.py b/spaces/declare-lab/tango/diffusers/tests/models/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/devoworm-group/Lineage_Population/README.md b/spaces/devoworm-group/Lineage_Population/README.md
deleted file mode 100644
index ab0cdabc7d08c6e0fc533ccbd01e2edb83664105..0000000000000000000000000000000000000000
--- a/spaces/devoworm-group/Lineage_Population/README.md
+++ /dev/null
@@ -1,41 +0,0 @@
----
-title: Lineage Population
-emoji: 💻
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.17.0
-app_file: app.py
-pinned: false
----
-
-# Devolearn_Lineag_population_huggingface_spaces
-# elegant-embryos
-
-This repository contains work surrounding DevoLearn models and metadata extraction from the _C. elegans_ embryo. 
-
-
-[![Binder](https://camo.githubusercontent.com/bfeb5472ee3df9b7c63ea3b260dc0c679be90b97/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f72656e6465722d6e627669657765722d6f72616e67652e7376673f636f6c6f72423d66333736323626636f6c6f72413d346434643464)](https://nbviewer.jupyter.org/github/Mainakdeb/elegant-embryos/)
-
-
-### [Exploring Spatial distribution of cells in C. elegans embryo Using 3D Interactive Plots](explore_cell_movement_data.ipynb)
-The EPIC dataset contains time series data surrounding the embryogenesis of the C. elegans embryo. I had to Extract the X, Y, Z coordinates of each cell (of all lineages), extract the timestamps of each cell in sequence, using which I generated the following plots.
-
-1. A plot showcasing cell positions through time. 
-
-<img src="/image/3d_plot_rotate_zoom_1.gif" width=300/> <img src="/image/3d_plot_timelapse_1.gif" width=285/>
-
-
-2. Visualizing single cell movement through time. This example shows the movement of the cell of lineage 'Dpa' through time.
-
-<img src="/image/Dpa_-9_track_compressed.gif" width=285/> <img src="/image/Dpa_-9_track_zoom_compressed.gif" width=285/>
-
-Gaining insights from static plots can be difficult at times, this is where these interactive plots can help by enabling users to rotate, zoom, animate plots (with respect to time), rather than being stuck with just one point of time and perspective.
-
-### [Post Processing outputs from the DevoLearn lineage population model](post_process_lineage_population_model.ipynb)
-Post processing algorithms modify the outputs from the neural-network in order to further increase the accuracy of the predictions. I implemented a post processing algorithm on the lineage population model from DevoLearn, which exploits an inherent pattern in the data and modifies the model outputs accordingly, resulting in better accuracy (about 30% less loss) as highlighted in figure below.
-
-<img src="/image/postprocess_lineage_population_1.png" width=800 />
-
-
-
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-Basically I have a NSFetchRequest that selects an object from a relationship, then I make an NSArray from that fetched data.  I then do an NSPredicate on this array to limit the returned objects to 2 per table.  
-
-So far so good.  The problem I'm having is that I can't find any info on how to limit the results of a query like this.  I'm assuming that the NSFetchRequest will somehow have this built in.  Am I right?
-
-Thanks in advance.
-
-A:
-
-The answer is YES.
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-I have a sort of example on my project.
-
-The thing is, the NSPredicate from the object you are fetching is a compound predicate. The simplest version is:
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index 296c8d92641ed1143b4f286585b11f0d293d33e4..0000000000000000000000000000000000000000
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diff --git a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/attentions.py b/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/attentions.py
deleted file mode 100644
index ecbdbc8be941a962046fc11fd6739b093112123e..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Longread-Dongmuchang-Bert-VITS2/attentions.py
+++ /dev/null
@@ -1,343 +0,0 @@
-import copy
-import math
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-from torch.nn.utils import weight_norm, remove_weight_norm
-class LayerNorm(nn.Module):
-  def __init__(self, channels, eps=1e-5):
-    super().__init__()
-    self.channels = channels
-    self.eps = eps
-
-    self.gamma = nn.Parameter(torch.ones(channels))
-    self.beta = nn.Parameter(torch.zeros(channels))
-
-  def forward(self, x):
-    x = x.transpose(1, -1)
-    x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-    return x.transpose(1, -1)
-
- 
-   
-@torch.jit.script
-def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
-  n_channels_int = n_channels[0]
-  in_act = input_a + input_b
-  t_act = torch.tanh(in_act[:, :n_channels_int, :])
-  s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
-  acts = t_act * s_act
-  return acts
-
-class Encoder(nn.Module):
-   def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, isflow = True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    if isflow:
-      cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
-      self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
-      self.cond_layer = weight_norm(cond_layer, name='weight')
-      self.gin_channels = 256
-    self.cond_layer_idx = self.n_layers
-    if 'gin_channels' in kwargs:
-      self.gin_channels = kwargs['gin_channels']
-      if self.gin_channels != 0:
-        self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
-        # vits2 says 3rd block, so idx is 2 by default
-        self.cond_layer_idx = kwargs['cond_layer_idx'] if 'cond_layer_idx' in kwargs else 2
-        print(self.gin_channels, self.cond_layer_idx)
-        assert self.cond_layer_idx < self.n_layers, 'cond_layer_idx should be less than n_layers'
-    self.drop = nn.Dropout(p_dropout)
-    self.attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-   def forward(self, x, x_mask, g=None):
-    attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      if i == self.cond_layer_idx and g is not None:
-        g = self.spk_emb_linear(g.transpose(1, 2))
-        g = g.transpose(1, 2)
-        x = x + g
-        x = x * x_mask
-      y = self.attn_layers[i](x, x, attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class Decoder(nn.Module):
-  def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
-    super().__init__()
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-
-    self.drop = nn.Dropout(p_dropout)
-    self.self_attn_layers = nn.ModuleList()
-    self.norm_layers_0 = nn.ModuleList()
-    self.encdec_attn_layers = nn.ModuleList()
-    self.norm_layers_1 = nn.ModuleList()
-    self.ffn_layers = nn.ModuleList()
-    self.norm_layers_2 = nn.ModuleList()
-    for i in range(self.n_layers):
-      self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
-      self.norm_layers_0.append(LayerNorm(hidden_channels))
-      self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
-      self.norm_layers_1.append(LayerNorm(hidden_channels))
-      self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
-      self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-  def forward(self, x, x_mask, h, h_mask):
-    """
-    x: decoder input
-    h: encoder output
-    """
-    self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
-    encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-    x = x * x_mask
-    for i in range(self.n_layers):
-      y = self.self_attn_layers[i](x, x, self_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_0[i](x + y)
-
-      y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
-      y = self.drop(y)
-      x = self.norm_layers_1[i](x + y)
-      
-      y = self.ffn_layers[i](x, x_mask)
-      y = self.drop(y)
-      x = self.norm_layers_2[i](x + y)
-    x = x * x_mask
-    return x
-
-
-class MultiHeadAttention(nn.Module):
-  def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
-    super().__init__()
-    assert channels % n_heads == 0
-
-    self.channels = channels
-    self.out_channels = out_channels
-    self.n_heads = n_heads
-    self.p_dropout = p_dropout
-    self.window_size = window_size
-    self.heads_share = heads_share
-    self.block_length = block_length
-    self.proximal_bias = proximal_bias
-    self.proximal_init = proximal_init
-    self.attn = None
-
-    self.k_channels = channels // n_heads
-    self.conv_q = nn.Conv1d(channels, channels, 1)
-    self.conv_k = nn.Conv1d(channels, channels, 1)
-    self.conv_v = nn.Conv1d(channels, channels, 1)
-    self.conv_o = nn.Conv1d(channels, out_channels, 1)
-    self.drop = nn.Dropout(p_dropout)
-
-    if window_size is not None:
-      n_heads_rel = 1 if heads_share else n_heads
-      rel_stddev = self.k_channels**-0.5
-      self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-      self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
-
-    nn.init.xavier_uniform_(self.conv_q.weight)
-    nn.init.xavier_uniform_(self.conv_k.weight)
-    nn.init.xavier_uniform_(self.conv_v.weight)
-    if proximal_init:
-      with torch.no_grad():
-        self.conv_k.weight.copy_(self.conv_q.weight)
-        self.conv_k.bias.copy_(self.conv_q.bias)
-      
-  def forward(self, x, c, attn_mask=None):
-    q = self.conv_q(x)
-    k = self.conv_k(c)
-    v = self.conv_v(c)
-    
-    x, self.attn = self.attention(q, k, v, mask=attn_mask)
-
-    x = self.conv_o(x)
-    return x
-
-  def attention(self, query, key, value, mask=None):
-    # reshape [b, d, t] -> [b, n_h, t, d_k]
-    b, d, t_s, t_t = (*key.size(), query.size(2))
-    query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
-    key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-    value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
-
-    scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
-    if self.window_size is not None:
-      assert t_s == t_t, "Relative attention is only available for self-attention."
-      key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
-      rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
-      scores_local = self._relative_position_to_absolute_position(rel_logits)
-      scores = scores + scores_local
-    if self.proximal_bias:
-      assert t_s == t_t, "Proximal bias is only available for self-attention."
-      scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
-    if mask is not None:
-      scores = scores.masked_fill(mask == 0, -1e4)
-      if self.block_length is not None:
-        assert t_s == t_t, "Local attention is only available for self-attention."
-        block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
-        scores = scores.masked_fill(block_mask == 0, -1e4)
-    p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
-    p_attn = self.drop(p_attn)
-    output = torch.matmul(p_attn, value)
-    if self.window_size is not None:
-      relative_weights = self._absolute_position_to_relative_position(p_attn)
-      value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
-      output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
-    output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
-    return output, p_attn
-
-  def _matmul_with_relative_values(self, x, y):
-    """
-    x: [b, h, l, m]
-    y: [h or 1, m, d]
-    ret: [b, h, l, d]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0))
-    return ret
-
-  def _matmul_with_relative_keys(self, x, y):
-    """
-    x: [b, h, l, d]
-    y: [h or 1, m, d]
-    ret: [b, h, l, m]
-    """
-    ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
-    return ret
-
-  def _get_relative_embeddings(self, relative_embeddings, length):
-    max_relative_position = 2 * self.window_size + 1
-    # Pad first before slice to avoid using cond ops.
-    pad_length = max(length - (self.window_size + 1), 0)
-    slice_start_position = max((self.window_size + 1) - length, 0)
-    slice_end_position = slice_start_position + 2 * length - 1
-    if pad_length > 0:
-      padded_relative_embeddings = F.pad(
-          relative_embeddings,
-          commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
-    else:
-      padded_relative_embeddings = relative_embeddings
-    used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
-    return used_relative_embeddings
-
-  def _relative_position_to_absolute_position(self, x):
-    """
-    x: [b, h, l, 2*l-1]
-    ret: [b, h, l, l]
-    """
-    batch, heads, length, _ = x.size()
-    # Concat columns of pad to shift from relative to absolute indexing.
-    x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
-
-    # Concat extra elements so to add up to shape (len+1, 2*len-1).
-    x_flat = x.view([batch, heads, length * 2 * length])
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
-
-    # Reshape and slice out the padded elements.
-    x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
-    return x_final
-
-  def _absolute_position_to_relative_position(self, x):
-    """
-    x: [b, h, l, l]
-    ret: [b, h, l, 2*l-1]
-    """
-    batch, heads, length, _ = x.size()
-    # padd along column
-    x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
-    x_flat = x.view([batch, heads, length**2 + length*(length -1)])
-    # add 0's in the beginning that will skew the elements after reshape
-    x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
-    x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
-    return x_final
-
-  def _attention_bias_proximal(self, length):
-    """Bias for self-attention to encourage attention to close positions.
-    Args:
-      length: an integer scalar.
-    Returns:
-      a Tensor with shape [1, 1, length, length]
-    """
-    r = torch.arange(length, dtype=torch.float32)
-    diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
-    return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
-
-
-class FFN(nn.Module):
-  def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.activation = activation
-    self.causal = causal
-
-    if causal:
-      self.padding = self._causal_padding
-    else:
-      self.padding = self._same_padding
-
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-    self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-    self.drop = nn.Dropout(p_dropout)
-
-  def forward(self, x, x_mask):
-    x = self.conv_1(self.padding(x * x_mask))
-    if self.activation == "gelu":
-      x = x * torch.sigmoid(1.702 * x)
-    else:
-      x = torch.relu(x)
-    x = self.drop(x)
-    x = self.conv_2(self.padding(x * x_mask))
-    return x * x_mask
-  
-  def _causal_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = self.kernel_size - 1
-    pad_r = 0
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
-
-  def _same_padding(self, x):
-    if self.kernel_size == 1:
-      return x
-    pad_l = (self.kernel_size - 1) // 2
-    pad_r = self.kernel_size // 2
-    padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-    x = F.pad(x, commons.convert_pad_shape(padding))
-    return x
diff --git a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/models.py b/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/models.py
deleted file mode 100644
index d4afe44d883691610c5903e602a3ca245fcb3a5c..0000000000000000000000000000000000000000
--- a/spaces/digitalxingtong/Xingtong-Read-Bert-VITS2/models.py
+++ /dev/null
@@ -1,707 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-
-from commons import init_weights, get_padding
-from text import symbols, num_tones, num_languages
-class DurationDiscriminator(nn.Module): #vits2
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-    super().__init__()
-
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.gin_channels = gin_channels
-
-    self.drop = nn.Dropout(p_dropout)
-    self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_1 = modules.LayerNorm(filter_channels)
-    self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.norm_2 = modules.LayerNorm(filter_channels)
-    self.dur_proj = nn.Conv1d(1, filter_channels, 1)
-
-    self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.pre_out_norm_1 = modules.LayerNorm(filter_channels)
-    self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
-    self.pre_out_norm_2 = modules.LayerNorm(filter_channels)
-
-    if gin_channels != 0:
-       self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-    self.output_layer = nn.Sequential(
-        nn.Linear(filter_channels, 1), 
-        nn.Sigmoid() 
-    )
-
-  def forward_probability(self, x, x_mask, dur, g=None):
-    dur = self.dur_proj(dur)
-    x = torch.cat([x, dur], dim=1)
-    x = self.pre_out_conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.pre_out_norm_1(x)
-    x = self.drop(x)
-    x = self.pre_out_conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.pre_out_norm_2(x)
-    x = self.drop(x)
-    x = x * x_mask
-    x = x.transpose(1, 2)
-    output_prob = self.output_layer(x)
-    return output_prob
-
-  def forward(self, x, x_mask, dur_r, dur_hat, g=None):
-    x = torch.detach(x)
-    if g is not None:
-       g = torch.detach(g)
-       x = x + self.cond(g)
-    x = self.conv_1(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_1(x)
-    x = self.drop(x)
-    x = self.conv_2(x * x_mask)
-    x = torch.relu(x)
-    x = self.norm_2(x)
-    x = self.drop(x)
-
-    output_probs = []
-    for dur in [dur_r, dur_hat]:
-      output_prob = self.forward_probability(x, x_mask, dur, g)
-      output_probs.append(output_prob)
-
-    return output_probs
-
-class TransformerCouplingBlock(nn.Module):
-    def __init__(self,
-                 channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 n_flows=4,
-                 gin_channels=0,
-                 share_parameter=False
-                 ):
-
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-
-        self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None
-
-        for i in range(n_flows):
-            self.flows.append(
-                modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels))
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-class StochasticDurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-        super().__init__()
-        filter_channels = in_channels  # it needs to be removed from future version.
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.log_flow = modules.Log()
-        self.flows = nn.ModuleList()
-        self.flows.append(modules.ElementwiseAffine(2))
-        for i in range(n_flows):
-            self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.flows.append(modules.Flip())
-
-        self.post_pre = nn.Conv1d(1, filter_channels, 1)
-        self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        self.post_flows = nn.ModuleList()
-        self.post_flows.append(modules.ElementwiseAffine(2))
-        for i in range(4):
-            self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-            self.post_flows.append(modules.Flip())
-
-        self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-        self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-        self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0):
-        x = torch.detach(x)
-        x = self.pre(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.convs(x, x_mask)
-        x = self.proj(x) * x_mask
-
-        if not reverse:
-            flows = self.flows
-            assert w is not None
-
-            logdet_tot_q = 0
-            h_w = self.post_pre(w)
-            h_w = self.post_convs(h_w, x_mask)
-            h_w = self.post_proj(h_w) * x_mask
-            e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-            z_q = e_q
-            for flow in self.post_flows:
-                z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-                logdet_tot_q += logdet_q
-            z_u, z1 = torch.split(z_q, [1, 1], 1)
-            u = torch.sigmoid(z_u) * x_mask
-            z0 = (w - u) * x_mask
-            logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2])
-            logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q
-
-            logdet_tot = 0
-            z0, logdet = self.log_flow(z0, x_mask)
-            logdet_tot += logdet
-            z = torch.cat([z0, z1], 1)
-            for flow in flows:
-                z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-                logdet_tot = logdet_tot + logdet
-            nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot
-            return nll + logq  # [b]
-        else:
-            flows = list(reversed(self.flows))
-            flows = flows[:-2] + [flows[-1]]  # remove a useless vflow
-            z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-            for flow in flows:
-                z = flow(z, x_mask, g=x, reverse=reverse)
-            z0, z1 = torch.split(z, [1, 1], 1)
-            logw = z0
-            return logw
-
-
-class DurationPredictor(nn.Module):
-    def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0):
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-
-        self.drop = nn.Dropout(p_dropout)
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_1 = modules.LayerNorm(filter_channels)
-        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2)
-        self.norm_2 = modules.LayerNorm(filter_channels)
-        self.proj = nn.Conv1d(filter_channels, 1, 1)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, in_channels, 1)
-
-    def forward(self, x, x_mask, g=None):
-        x = torch.detach(x)
-        if g is not None:
-            g = torch.detach(g)
-            x = x + self.cond(g)
-        x = self.conv_1(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_1(x)
-        x = self.drop(x)
-        x = self.conv_2(x * x_mask)
-        x = torch.relu(x)
-        x = self.norm_2(x)
-        x = self.drop(x)
-        x = self.proj(x * x_mask)
-        return x * x_mask
-
-
-class TextEncoder(nn.Module):
-    def __init__(self,
-                 n_vocab,
-                 out_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 gin_channels=0):
-        super().__init__()
-        self.n_vocab = n_vocab
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.gin_channels = gin_channels
-        self.emb = nn.Embedding(len(symbols), hidden_channels)
-        nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5)
-        self.tone_emb = nn.Embedding(num_tones, hidden_channels)
-        nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5)
-        self.language_emb = nn.Embedding(num_languages, hidden_channels)
-        nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5)
-        self.bert_proj = nn.Conv1d(1024, hidden_channels, 1)
-
-        self.encoder = attentions.Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            p_dropout,
-            gin_channels=self.gin_channels)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, tone, language, bert, g=None):
-        x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)+self.bert_proj(bert).transpose(1,2)) * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-        x = self.encoder(x * x_mask, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-    def __init__(self,
-                 channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 n_flows=4,
-                 gin_channels=0):
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.n_flows = n_flows
-        self.gin_channels = gin_channels
-
-        self.flows = nn.ModuleList()
-        for i in range(n_flows):
-            self.flows.append(
-                modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
-                                              gin_channels=gin_channels, mean_only=True))
-            self.flows.append(modules.Flip())
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        if not reverse:
-            for flow in self.flows:
-                x, _ = flow(x, x_mask, g=g, reverse=reverse)
-        else:
-            for flow in reversed(self.flows):
-                x = flow(x, x_mask, g=g, reverse=reverse)
-        return x
-
-
-class PosteriorEncoder(nn.Module):
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 hidden_channels,
-                 kernel_size,
-                 dilation_rate,
-                 n_layers,
-                 gin_channels=0):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-
-        self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-        self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, g=None):
-        x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-        x = self.pre(x) * x_mask
-        x = self.enc(x, x_mask, g=g)
-        stats = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-        return z, m, logs, x_mask
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                 upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)),
-                                k, u, padding=(k - u) // 2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-            x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i * self.num_kernels + j](x)
-                else:
-                    xs += self.resblocks[i * self.num_kernels + j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0:  # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2, 3, 5, 7, 11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-class ReferenceEncoder(nn.Module):
-    '''
-    inputs --- [N, Ty/r, n_mels*r]  mels
-    outputs --- [N, ref_enc_gru_size]
-    '''
-
-    def __init__(self, spec_channels, gin_channels=0):
-
-        super().__init__()
-        self.spec_channels = spec_channels
-        ref_enc_filters = [32, 32, 64, 64, 128, 128]
-        K = len(ref_enc_filters)
-        filters = [1] + ref_enc_filters
-        convs = [weight_norm(nn.Conv2d(in_channels=filters[i],
-                                       out_channels=filters[i + 1],
-                                       kernel_size=(3, 3),
-                                       stride=(2, 2),
-                                       padding=(1, 1))) for i in range(K)]
-        self.convs = nn.ModuleList(convs)
-        # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)])
-
-        out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K)
-        self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels,
-                          hidden_size=256 // 2,
-                          batch_first=True)
-        self.proj = nn.Linear(128, gin_channels)
-
-    def forward(self, inputs, mask=None):
-        N = inputs.size(0)
-        out = inputs.view(N, 1, -1, self.spec_channels)  # [N, 1, Ty, n_freqs]
-        for conv in self.convs:
-            out = conv(out)
-            # out = wn(out)
-            out = F.relu(out)  # [N, 128, Ty//2^K, n_mels//2^K]
-
-        out = out.transpose(1, 2)  # [N, Ty//2^K, 128, n_mels//2^K]
-        T = out.size(1)
-        N = out.size(0)
-        out = out.contiguous().view(N, T, -1)  # [N, Ty//2^K, 128*n_mels//2^K]
-
-        self.gru.flatten_parameters()
-        memory, out = self.gru(out)  # out --- [1, N, 128]
-
-        return self.proj(out.squeeze(0))
-
-    def calculate_channels(self, L, kernel_size, stride, pad, n_convs):
-        for i in range(n_convs):
-            L = (L - kernel_size + 2 * pad) // stride + 1
-        return L
-
-
-class SynthesizerTrn(nn.Module):
-    """
-  Synthesizer for Training
-  """
-
-    def __init__(self,
-                 n_vocab,
-                 spec_channels,
-                 segment_size,
-                 inter_channels,
-                 hidden_channels,
-                 filter_channels,
-                 n_heads,
-                 n_layers,
-                 kernel_size,
-                 p_dropout,
-                 resblock,
-                 resblock_kernel_sizes,
-                 resblock_dilation_sizes,
-                 upsample_rates,
-                 upsample_initial_channel,
-                 upsample_kernel_sizes,
-                 n_speakers=256,
-                 gin_channels=256,
-                 use_sdp=True,
-                 n_flow_layer = 4,
-                 n_layers_trans_flow = 3,
-                 flow_share_parameter = False,
-                 use_transformer_flow = True,
-                 **kwargs):
-
-        super().__init__()
-        self.n_vocab = n_vocab
-        self.spec_channels = spec_channels
-        self.inter_channels = inter_channels
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = n_layers
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.resblock = resblock
-        self.resblock_kernel_sizes = resblock_kernel_sizes
-        self.resblock_dilation_sizes = resblock_dilation_sizes
-        self.upsample_rates = upsample_rates
-        self.upsample_initial_channel = upsample_initial_channel
-        self.upsample_kernel_sizes = upsample_kernel_sizes
-        self.segment_size = segment_size
-        self.n_speakers = n_speakers
-        self.gin_channels = gin_channels
-        self.n_layers_trans_flow = n_layers_trans_flow
-        self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True)
-        self.use_sdp = use_sdp
-        self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False)
-        self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01)
-        self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6)
-        self.current_mas_noise_scale = self.mas_noise_scale_initial
-        if self.use_spk_conditioned_encoder and gin_channels > 0:
-            self.enc_gin_channels = gin_channels
-        self.enc_p = TextEncoder(n_vocab,
-                                 inter_channels,
-                                 hidden_channels,
-                                 filter_channels,
-                                 n_heads,
-                                 n_layers,
-                                 kernel_size,
-                                 p_dropout,
-                                 gin_channels=self.enc_gin_channels)
-        self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates,
-                             upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-        self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16,
-                                      gin_channels=gin_channels)
-        if use_transformer_flow:
-            self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels,share_parameter= flow_share_parameter)
-        else:
-            self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels)
-        self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-        self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels)
-        
-        if n_speakers >= 1:
-            self.emb_g = nn.Embedding(n_speakers, gin_channels)
-        else:
-            self.ref_enc = ReferenceEncoder(spec_channels, gin_channels)
-
-    def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert):
-        if self.n_speakers > 0:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
-        z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
-        z_p = self.flow(z, y_mask, g=g)
-
-        with torch.no_grad():
-            # negative cross-entropy
-            s_p_sq_r = torch.exp(-2 * logs_p)  # [b, d, t]
-            neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True)  # [b, 1, t_s]
-            neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2),
-                                     s_p_sq_r)  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-            neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r))  # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s]
-            neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True)  # [b, 1, t_s]
-            neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4
-            if self.use_noise_scaled_mas:
-               epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale
-               neg_cent = neg_cent + epsilon
-
-            attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-            attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach()
-
-        w = attn.sum(2)
-
-        l_length_sdp = self.sdp(x, x_mask, w, g=g)
-        l_length_sdp = l_length_sdp / torch.sum(x_mask)
-        
-        logw_ = torch.log(w + 1e-6) * x_mask
-        logw = self.dp(x, x_mask, g=g)
-        l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask)  # for averaging
-
-        l_length = l_length_dp + l_length_sdp
-
-        # expand prior
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2)
-
-        z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size)
-        o = self.dec(z_slice, g=g)
-        return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_)
-      
-    def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None):
-        #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert)
-        # g = self.gst(y)
-        if self.n_speakers > 0:
-            g = self.emb_g(sid).unsqueeze(-1)  # [b, h, 1]
-        else:
-            g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1)
-        x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g)
-        logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio)
-        w = torch.exp(logw) * x_mask * length_scale
-        w_ceil = torch.ceil(w)
-        y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-        y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-        attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-        attn = commons.generate_path(w_ceil, attn_mask)
-
-        m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-        logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1,
-                                                                                 2)  # [b, t', t], [b, t, d] -> [b, d, t']
-
-        z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-        z = self.flow(z_p, y_mask, g=g, reverse=True)
-        o = self.dec((z * y_mask)[:, :, :max_len], g=g)
-        return o, attn, y_mask, (z, z_p, m_p, logs_p)
diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/dense_test_mixins.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/dense_test_mixins.py
deleted file mode 100644
index dd81364dec90e97c30a6e2220a5e0fe96373c5bd..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/dense_test_mixins.py
+++ /dev/null
@@ -1,100 +0,0 @@
-from inspect import signature
-
-import torch
-
-from mmdet.core import bbox2result, bbox_mapping_back, multiclass_nms
-
-
-class BBoxTestMixin(object):
-    """Mixin class for test time augmentation of bboxes."""
-
-    def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):
-        """Merge augmented detection bboxes and scores.
-
-        Args:
-            aug_bboxes (list[Tensor]): shape (n, 4*#class)
-            aug_scores (list[Tensor] or None): shape (n, #class)
-            img_shapes (list[Tensor]): shape (3, ).
-
-        Returns:
-            tuple: (bboxes, scores)
-        """
-        recovered_bboxes = []
-        for bboxes, img_info in zip(aug_bboxes, img_metas):
-            img_shape = img_info[0]['img_shape']
-            scale_factor = img_info[0]['scale_factor']
-            flip = img_info[0]['flip']
-            flip_direction = img_info[0]['flip_direction']
-            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
-                                       flip_direction)
-            recovered_bboxes.append(bboxes)
-        bboxes = torch.cat(recovered_bboxes, dim=0)
-        if aug_scores is None:
-            return bboxes
-        else:
-            scores = torch.cat(aug_scores, dim=0)
-            return bboxes, scores
-
-    def aug_test_bboxes(self, feats, img_metas, rescale=False):
-        """Test det bboxes with test time augmentation.
-
-        Args:
-            feats (list[Tensor]): the outer list indicates test-time
-                augmentations and inner Tensor should have a shape NxCxHxW,
-                which contains features for all images in the batch.
-            img_metas (list[list[dict]]): the outer list indicates test-time
-                augs (multiscale, flip, etc.) and the inner list indicates
-                images in a batch. each dict has image information.
-            rescale (bool, optional): Whether to rescale the results.
-                Defaults to False.
-
-        Returns:
-            list[ndarray]: bbox results of each class
-        """
-        # check with_nms argument
-        gb_sig = signature(self.get_bboxes)
-        gb_args = [p.name for p in gb_sig.parameters.values()]
-        if hasattr(self, '_get_bboxes'):
-            gbs_sig = signature(self._get_bboxes)
-        else:
-            gbs_sig = signature(self._get_bboxes_single)
-        gbs_args = [p.name for p in gbs_sig.parameters.values()]
-        assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
-            f'{self.__class__.__name__}' \
-            ' does not support test-time augmentation'
-
-        aug_bboxes = []
-        aug_scores = []
-        aug_factors = []  # score_factors for NMS
-        for x, img_meta in zip(feats, img_metas):
-            # only one image in the batch
-            outs = self.forward(x)
-            bbox_inputs = outs + (img_meta, self.test_cfg, False, False)
-            bbox_outputs = self.get_bboxes(*bbox_inputs)[0]
-            aug_bboxes.append(bbox_outputs[0])
-            aug_scores.append(bbox_outputs[1])
-            # bbox_outputs of some detectors (e.g., ATSS, FCOS, YOLOv3)
-            # contains additional element to adjust scores before NMS
-            if len(bbox_outputs) >= 3:
-                aug_factors.append(bbox_outputs[2])
-
-        # after merging, bboxes will be rescaled to the original image size
-        merged_bboxes, merged_scores = self.merge_aug_bboxes(
-            aug_bboxes, aug_scores, img_metas)
-        merged_factors = torch.cat(aug_factors, dim=0) if aug_factors else None
-        det_bboxes, det_labels = multiclass_nms(
-            merged_bboxes,
-            merged_scores,
-            self.test_cfg.score_thr,
-            self.test_cfg.nms,
-            self.test_cfg.max_per_img,
-            score_factors=merged_factors)
-
-        if rescale:
-            _det_bboxes = det_bboxes
-        else:
-            _det_bboxes = det_bboxes.clone()
-            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
-                img_metas[0][0]['scale_factor'])
-        bbox_results = bbox2result(_det_bboxes, det_labels, self.num_classes)
-        return bbox_results
diff --git a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/__init__.py b/spaces/dineshreddy/WALT/mmdet/models/roi_heads/__init__.py
deleted file mode 100644
index 1a3e515e40ffa26f83381342952ea9a0e1ccc235..0000000000000000000000000000000000000000
--- a/spaces/dineshreddy/WALT/mmdet/models/roi_heads/__init__.py
+++ /dev/null
@@ -1,43 +0,0 @@
-'''
-from .base_roi_head import BaseRoIHead
-from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DoubleConvFCBBoxHead,
-                         SCNetBBoxHead, Shared2FCBBoxHead,
-                         Shared4Conv1FCBBoxHead)
-from .cascade_roi_head import CascadeRoIHead
-from .double_roi_head import DoubleHeadRoIHead
-from .dynamic_roi_head import DynamicRoIHead
-from .grid_roi_head import GridRoIHead
-from .htc_roi_head import HybridTaskCascadeRoIHead
-from .mask_heads import (CoarseMaskHead, FCNMaskHead, FeatureRelayHead,
-                         FusedSemanticHead, GlobalContextHead, GridHead,
-                         HTCMaskHead, MaskIoUHead, MaskPointHead,
-                         SCNetMaskHead, SCNetSemanticHead)
-from .mask_scoring_roi_head import MaskScoringRoIHead
-from .pisa_roi_head import PISARoIHead
-from .point_rend_roi_head import PointRendRoIHead
-from .roi_extractors import SingleRoIExtractor
-from .scnet_roi_head import SCNetRoIHead
-from .shared_heads import ResLayer
-from .sparse_roi_head import SparseRoIHead
-from .standard_roi_head import StandardRoIHead
-from .trident_roi_head import TridentRoIHead
-
-__all__ = [
-    'BaseRoIHead', 'CascadeRoIHead', 'DoubleHeadRoIHead', 'MaskScoringRoIHead',
-    'HybridTaskCascadeRoIHead', 'GridRoIHead', 'ResLayer', 'BBoxHead',
-    'ConvFCBBoxHead', 'Shared2FCBBoxHead', 'StandardRoIHead',
-    'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'FCNMaskHead',
-    'HTCMaskHead', 'FusedSemanticHead', 'GridHead', 'MaskIoUHead',
-    'SingleRoIExtractor', 'PISARoIHead', 'PointRendRoIHead', 'MaskPointHead',
-    'CoarseMaskHead', 'DynamicRoIHead', 'SparseRoIHead', 'TridentRoIHead',
-    'SCNetRoIHead', 'SCNetMaskHead', 'SCNetSemanticHead', 'SCNetBBoxHead',
-    'FeatureRelayHead', 'GlobalContextHead'
-]
-'''
-from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DoubleConvFCBBoxHead,
-                         SCNetBBoxHead, Shared2FCBBoxHead,
-                         Shared4Conv1FCBBoxHead)
-from .standard_roi_head import StandardRoIHead
-from .roi_extractors import SingleRoIExtractor
-from .mask_heads import FCNMaskHead
-__all__ = ['BBoxHead','StandardRoIHead','SingleRoIExtractor','Shared2FCBBoxHead','FCNMaskHead']
diff --git a/spaces/dongyi/MMFS/models/modules/stylegan2/op/upfirdn2d.cpp b/spaces/dongyi/MMFS/models/modules/stylegan2/op/upfirdn2d.cpp
deleted file mode 100644
index 73928ece8150f847d98af65a95685a29fcceecde..0000000000000000000000000000000000000000
--- a/spaces/dongyi/MMFS/models/modules/stylegan2/op/upfirdn2d.cpp
+++ /dev/null
@@ -1,31 +0,0 @@
-#include <ATen/ATen.h>
-#include <torch/extension.h>
-
-torch::Tensor upfirdn2d_op(const torch::Tensor &input,
-                           const torch::Tensor &kernel, int up_x, int up_y,
-                           int down_x, int down_y, int pad_x0, int pad_x1,
-                           int pad_y0, int pad_y1);
-
-#define CHECK_CUDA(x)                                                          \
-  TORCH_CHECK(x.type().is_cuda(), #x " must be a CUDA tensor")
-#define CHECK_CONTIGUOUS(x)                                                    \
-  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
-#define CHECK_INPUT(x)                                                         \
-  CHECK_CUDA(x);                                                               \
-  CHECK_CONTIGUOUS(x)
-
-torch::Tensor upfirdn2d(const torch::Tensor &input, const torch::Tensor &kernel,
-                        int up_x, int up_y, int down_x, int down_y, int pad_x0,
-                        int pad_x1, int pad_y0, int pad_y1) {
-  CHECK_INPUT(input);
-  CHECK_INPUT(kernel);
-
-  at::DeviceGuard guard(input.device());
-
-  return upfirdn2d_op(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
-                      pad_y0, pad_y1);
-}
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("upfirdn2d", &upfirdn2d, "upfirdn2d (CUDA)");
-}
\ No newline at end of file
diff --git a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py b/spaces/eIysia/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py
deleted file mode 100644
index acd00238895d57ba878fd0211d5654250fb10061..0000000000000000000000000000000000000000
--- a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/ONNXVITS_models.py
+++ /dev/null
@@ -1,509 +0,0 @@
-import copy
-import math
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import commons
-import ONNXVITS_modules as modules
-import attentions
-import monotonic_align
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
-from commons import init_weights, get_padding
-
-
-class StochasticDurationPredictor(nn.Module):
-  def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0):
-    super().__init__()
-    filter_channels = in_channels # it needs to be removed from future version.
-    self.in_channels = in_channels
-    self.filter_channels = filter_channels
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.log_flow = modules.Log()
-    self.flows = nn.ModuleList()
-    self.flows.append(modules.ElementwiseAffine(2))
-    for i in range(n_flows):
-      self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.flows.append(modules.Flip())
-
-    self.post_pre = nn.Conv1d(1, filter_channels, 1)
-    self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    self.post_flows = nn.ModuleList()
-    self.post_flows.append(modules.ElementwiseAffine(2))
-    for i in range(4):
-      self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3))
-      self.post_flows.append(modules.Flip())
-
-    self.pre = nn.Conv1d(in_channels, filter_channels, 1)
-    self.proj = nn.Conv1d(filter_channels, filter_channels, 1)
-    self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout)
-    if gin_channels != 0:
-      self.cond = nn.Conv1d(gin_channels, filter_channels, 1)
-
-    self.w = None
-    self.reverse = None
-    self.noise_scale = None
-  def forward(self, x, x_mask, g=None):
-    w = self.w
-    reverse = self.reverse
-    noise_scale = self.noise_scale
-
-    x = torch.detach(x)
-    x = self.pre(x)
-    if g is not None:
-      g = torch.detach(g)
-      x = x + self.cond(g)
-    x = self.convs(x, x_mask)
-    x = self.proj(x) * x_mask
-
-    if not reverse:
-      flows = self.flows
-      assert w is not None
-
-      logdet_tot_q = 0 
-      h_w = self.post_pre(w)
-      h_w = self.post_convs(h_w, x_mask)
-      h_w = self.post_proj(h_w) * x_mask
-      e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask
-      z_q = e_q
-      for flow in self.post_flows:
-        z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w))
-        logdet_tot_q += logdet_q
-      z_u, z1 = torch.split(z_q, [1, 1], 1) 
-      u = torch.sigmoid(z_u) * x_mask
-      z0 = (w - u) * x_mask
-      logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2])
-      logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q
-
-      logdet_tot = 0
-      z0, logdet = self.log_flow(z0, x_mask)
-      logdet_tot += logdet
-      z = torch.cat([z0, z1], 1)
-      for flow in flows:
-        z, logdet = flow(z, x_mask, g=x, reverse=reverse)
-        logdet_tot = logdet_tot + logdet
-      nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot
-      return nll + logq # [b]
-    else:
-      flows = list(reversed(self.flows))
-      flows = flows[:-2] + [flows[-1]] # remove a useless vflow
-      z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale
-      for flow in flows:
-        z = flow(z, x_mask, g=x, reverse=reverse)
-      z0, z1 = torch.split(z, [1, 1], 1)
-      logw = z0
-      return logw
-
-
-class TextEncoder(nn.Module):
-  def __init__(self,
-      n_vocab,
-      out_channels,
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout):
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-
-    self.emb = nn.Embedding(n_vocab, hidden_channels)
-    nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-    self.encoder = attentions.Encoder(
-      hidden_channels,
-      filter_channels,
-      n_heads,
-      n_layers,
-      kernel_size,
-      p_dropout)
-    self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths):
-    x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h]
-    x = torch.transpose(x, 1, -1) # [b, h, t]
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-
-    x = self.encoder(x * x_mask, x_mask)
-    stats = self.proj(x) * x_mask
-
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    return x, m, logs, x_mask
-
-
-class ResidualCouplingBlock(nn.Module):
-  def __init__(self,
-      channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      n_flows=4,
-      gin_channels=0):
-    super().__init__()
-    self.channels = channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.n_flows = n_flows
-    self.gin_channels = gin_channels
-
-    self.flows = nn.ModuleList()
-    for i in range(n_flows):
-      self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
-      self.flows.append(modules.Flip())
-
-    self.reverse = None
-  def forward(self, x, x_mask, g=None):
-    reverse = self.reverse 
-    if not reverse:
-      for flow in self.flows:
-        x, _ = flow(x, x_mask, g=g, reverse=reverse)
-    else:
-      for flow in reversed(self.flows):
-        x = flow(x, x_mask, g=g, reverse=reverse)
-    return x
-
-
-class PosteriorEncoder(nn.Module):
-  def __init__(self,
-      in_channels,
-      out_channels,
-      hidden_channels,
-      kernel_size,
-      dilation_rate,
-      n_layers,
-      gin_channels=0):
-    super().__init__()
-    self.in_channels = in_channels
-    self.out_channels = out_channels
-    self.hidden_channels = hidden_channels
-    self.kernel_size = kernel_size
-    self.dilation_rate = dilation_rate
-    self.n_layers = n_layers
-    self.gin_channels = gin_channels
-
-    self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
-    self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
-    self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-  def forward(self, x, x_lengths, g=None):
-    x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
-    x = self.pre(x) * x_mask # x_in : [b, c, t] -> [b, h, t] 
-    x = self.enc(x, x_mask, g=g) # x_in : [b, h, t], g : [b, h, 1],  x = x_in + g
-    stats = self.proj(x) * x_mask
-    m, logs = torch.split(stats, self.out_channels, dim=1)
-    z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
-    return z, m, logs, x_mask # z, m, logs : [b, h, t]
-
-
-class Generator(torch.nn.Module):
-    def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0):
-        super(Generator, self).__init__()
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_rates)
-        self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3)
-        resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2
-
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
-            self.ups.append(weight_norm(
-                ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)),
-                                k, u, padding=(k-u)//2)))
-
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel//(2**(i+1))
-            for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-
-        self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False)
-        self.ups.apply(init_weights)
-
-        if gin_channels != 0:
-            self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1)
-
-    def forward(self, x, g=None):
-        x = self.conv_pre(x)
-        if g is not None:
-          x = x + self.cond(g)
-
-        for i in range(self.num_upsamples):
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            x = self.ups[i](x)
-            xs = None
-            for j in range(self.num_kernels):
-                if xs is None:
-                    xs = self.resblocks[i*self.num_kernels+j](x)
-                else:
-                    xs += self.resblocks[i*self.num_kernels+j](x)
-            x = xs / self.num_kernels
-        x = F.leaky_relu(x)
-        x = self.conv_post(x)
-        x = torch.tanh(x)
-
-        return x
-
-    def remove_weight_norm(self):
-        print('Removing weight norm...')
-        for l in self.ups:
-            remove_weight_norm(l)
-        for l in self.resblocks:
-            l.remove_weight_norm()
-
-
-class DiscriminatorP(torch.nn.Module):
-    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
-        super(DiscriminatorP, self).__init__()
-        self.period = period
-        self.use_spectral_norm = use_spectral_norm
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
-            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
-        ])
-        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
-
-    def forward(self, x):
-        fmap = []
-
-        # 1d to 2d
-        b, c, t = x.shape
-        if t % self.period != 0: # pad first
-            n_pad = self.period - (t % self.period)
-            x = F.pad(x, (0, n_pad), "reflect")
-            t = t + n_pad
-        x = x.view(b, c, t // self.period, self.period)
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class DiscriminatorS(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(DiscriminatorS, self).__init__()
-        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
-        self.convs = nn.ModuleList([
-            norm_f(Conv1d(1, 16, 15, 1, padding=7)),
-            norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
-            norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
-            norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
-            norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
-            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
-        ])
-        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
-
-    def forward(self, x):
-        fmap = []
-
-        for l in self.convs:
-            x = l(x)
-            x = F.leaky_relu(x, modules.LRELU_SLOPE)
-            fmap.append(x)
-        x = self.conv_post(x)
-        fmap.append(x)
-        x = torch.flatten(x, 1, -1)
-
-        return x, fmap
-
-
-class MultiPeriodDiscriminator(torch.nn.Module):
-    def __init__(self, use_spectral_norm=False):
-        super(MultiPeriodDiscriminator, self).__init__()
-        periods = [2,3,5,7,11]
-
-        discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-        discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
-        self.discriminators = nn.ModuleList(discs)
-
-    def forward(self, y, y_hat):
-        y_d_rs = []
-        y_d_gs = []
-        fmap_rs = []
-        fmap_gs = []
-        for i, d in enumerate(self.discriminators):
-            y_d_r, fmap_r = d(y)
-            y_d_g, fmap_g = d(y_hat)
-            y_d_rs.append(y_d_r)
-            y_d_gs.append(y_d_g)
-            fmap_rs.append(fmap_r)
-            fmap_gs.append(fmap_g)
-
-        return y_d_rs, y_d_gs, fmap_rs, fmap_gs
-
-
-
-class SynthesizerTrn(nn.Module):
-  """
-  Synthesizer for Training
-  """
-
-  def __init__(self, 
-    n_vocab,
-    spec_channels,
-    segment_size,
-    inter_channels,
-    hidden_channels,
-    filter_channels,
-    n_heads,
-    n_layers,
-    kernel_size,
-    p_dropout,
-    resblock, 
-    resblock_kernel_sizes, 
-    resblock_dilation_sizes, 
-    upsample_rates, 
-    upsample_initial_channel, 
-    upsample_kernel_sizes,
-    n_speakers=0,
-    gin_channels=0,
-    use_sdp=True,
-    **kwargs):
-
-    super().__init__()
-    self.n_vocab = n_vocab
-    self.spec_channels = spec_channels
-    self.inter_channels = inter_channels
-    self.hidden_channels = hidden_channels
-    self.filter_channels = filter_channels
-    self.n_heads = n_heads
-    self.n_layers = n_layers
-    self.kernel_size = kernel_size
-    self.p_dropout = p_dropout
-    self.resblock = resblock
-    self.resblock_kernel_sizes = resblock_kernel_sizes
-    self.resblock_dilation_sizes = resblock_dilation_sizes
-    self.upsample_rates = upsample_rates
-    self.upsample_initial_channel = upsample_initial_channel
-    self.upsample_kernel_sizes = upsample_kernel_sizes
-    self.segment_size = segment_size
-    self.n_speakers = n_speakers
-    self.gin_channels = gin_channels
-
-    self.use_sdp = use_sdp
-
-    self.enc_p = TextEncoder(n_vocab,
-        inter_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout)
-    self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels)
-    self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
-    self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
-
-    self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels)
-
-    if n_speakers > 0:
-      self.emb_g = nn.Embedding(n_speakers, gin_channels)
-
-  def forward(self, x, x_lengths, sid=None, noise_scale=.667, length_scale=1, noise_scale_w=.8, max_len=None):
-    torch.onnx.export(
-        self.enc_p,
-        (x, x_lengths),
-        "ONNX_net/enc_p.onnx",
-        input_names=["x", "x_lengths"],
-        output_names=["xout", "m_p", "logs_p", "x_mask"],
-        dynamic_axes={
-          "x" : [1],
-          "xout" : [2],
-          "m_p" : [2],
-          "logs_p" : [2],
-          "x_mask" : [2]
-        },
-        verbose=True,
-    )
-    x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths)
-
-    if self.n_speakers > 0:
-      g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1]
-    else:
-      g = None
-
-    self.dp.reverse = True
-    self.dp.noise_scale = noise_scale_w
-    torch.onnx.export(
-        self.dp,
-        (x, x_mask, g),
-        "ONNX_net/dp.onnx",
-        input_names=["x", "x_mask", "g"],
-        output_names=["logw"],
-        dynamic_axes={
-          "x" : [2],
-          "x_mask" : [2],
-          "logw" : [2]
-        },
-        verbose=True,
-    )
-    logw = self.dp(x, x_mask, g=g)
-    w = torch.exp(logw) * x_mask * length_scale
-    w_ceil = torch.ceil(w)
-    y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long()
-    y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype)
-    attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1)
-    attn = commons.generate_path(w_ceil, attn_mask)
-
-    m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-    logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t']
-
-    z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale
-
-    self.flow.reverse = True
-    torch.onnx.export(
-        self.flow,
-        (z_p, y_mask, g),
-        "ONNX_net/flow.onnx",
-        input_names=["z_p", "y_mask", "g"],
-        output_names=["z"],
-        dynamic_axes={
-          "z_p" : [2],
-          "y_mask" : [2],
-          "z" : [2]
-        },
-        verbose=True,
-    )
-    z = self.flow(z_p, y_mask, g=g)
-    z_in = (z * y_mask)[:,:,:max_len]
-
-    torch.onnx.export(
-        self.dec,
-        (z_in, g),
-        "ONNX_net/dec.onnx",
-        input_names=["z_in", "g"],
-        output_names=["o"],
-        dynamic_axes={
-          "z_in" : [2],
-          "o" : [2]
-        },
-        verbose=True,
-    )
-    o = self.dec(z_in, g=g)
-    return o
diff --git a/spaces/editing-images/ledits/modified_pipeline_semantic_stable_diffusion.py b/spaces/editing-images/ledits/modified_pipeline_semantic_stable_diffusion.py
deleted file mode 100644
index 0ffde2eb430834a28a2174c0d02bd28f53e418b4..0000000000000000000000000000000000000000
--- a/spaces/editing-images/ledits/modified_pipeline_semantic_stable_diffusion.py
+++ /dev/null
@@ -1,754 +0,0 @@
-
-import inspect
-import warnings
-from itertools import repeat
-from typing import Callable, List, Optional, Union
-
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
-
-from diffusers.image_processor import VaeImageProcessor
-from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers import KarrasDiffusionSchedulers
-from diffusers.utils import logging
-from diffusers.utils.torch_utils import randn_tensor
-from diffusers.pipelines.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-# from . import SemanticStableDiffusionPipelineOutput
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-
-class SemanticStableDiffusionPipeline(DiffusionPipeline):
-    r"""
-    Pipeline for text-to-image generation with latent editing.
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
-    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
-
-    This model builds on the implementation of ['StableDiffusionPipeline']
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder. Stable Diffusion uses the text portion of
-            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
-            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
-        tokenizer (`CLIPTokenizer`):
-            Tokenizer of class
-            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
-        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-        safety_checker ([`Q16SafetyChecker`]):
-            Classification module that estimates whether generated images could be considered offensive or harmful.
-            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
-        feature_extractor ([`CLIPImageProcessor`]):
-            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
-    """
-
-    _optional_components = ["safety_checker", "feature_extractor"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: UNet2DConditionModel,
-        scheduler: KarrasDiffusionSchedulers,
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPImageProcessor,
-        requires_safety_checker: bool = True,
-    ):
-        super().__init__()
-
-        if safety_checker is None and requires_safety_checker:
-            logger.warning(
-                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
-                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
-                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
-                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
-                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
-                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
-            )
-
-        if safety_checker is not None and feature_extractor is None:
-            raise ValueError(
-                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
-                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
-            )
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            scheduler=scheduler,
-            safety_checker=safety_checker,
-            feature_extractor=feature_extractor,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
-        self.register_to_config(requires_safety_checker=requires_safety_checker)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
-    def run_safety_checker(self, image, device, dtype):
-        if self.safety_checker is None:
-            has_nsfw_concept = None
-        else:
-            if torch.is_tensor(image):
-                feature_extractor_input = self.image_processor.postprocess(image, output_type="pil")
-            else:
-                feature_extractor_input = self.image_processor.numpy_to_pil(image)
-            safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device)
-            image, has_nsfw_concept = self.safety_checker(
-                images=image, clip_input=safety_checker_input.pixel_values.to(dtype)
-            )
-        return image, has_nsfw_concept
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
-    def decode_latents(self, latents):
-        warnings.warn(
-            "The decode_latents method is deprecated and will be removed in a future version. Please"
-            " use VaeImageProcessor instead",
-            FutureWarning,
-        )
-        latents = 1 / self.vae.config.scaling_factor * latents
-        image = self.vae.decode(latents, return_dict=False)[0]
-        image = (image / 2 + 0.5).clamp(0, 1)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        image = image.cpu().permute(0, 2, 3, 1).float().numpy()
-        return image
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        callback_steps,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if (callback_steps is None) or (
-            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
-        ):
-            raise ValueError(
-                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
-                f" {type(callback_steps)}."
-            )
-
-        if prompt is not None and prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
-                " only forward one of the two."
-            )
-        elif prompt is None and prompt_embeds is None:
-            raise ValueError(
-                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
-            )
-        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
-            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
-
-        if negative_prompt is not None and negative_prompt_embeds is not None:
-            raise ValueError(
-                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
-    def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
-        shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: int = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        editing_prompt: Optional[Union[str, List[str]]] = None,
-        editing_prompt_embeddings: Optional[torch.Tensor] = None,
-        reverse_editing_direction: Optional[Union[bool, List[bool]]] = False,
-        edit_guidance_scale: Optional[Union[float, List[float]]] = 5,
-        edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
-        edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
-        edit_threshold: Optional[Union[float, List[float]]] = 0.9,
-        edit_momentum_scale: Optional[float] = 0.1,
-        edit_mom_beta: Optional[float] = 0.4,
-        edit_weights: Optional[List[float]] = None,
-        sem_guidance: Optional[List[torch.Tensor]] = None,
-
-        # DDPM additions
-        use_ddpm: bool = False,
-        wts: Optional[List[torch.Tensor]] = None,
-        zs: Optional[List[torch.Tensor]] = None
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
-                if `guidance_scale` is less than `1`).
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
-                plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            editing_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to use for Semantic guidance. Semantic guidance is disabled by setting
-                `editing_prompt = None`. Guidance direction of prompt should be specified via
-                `reverse_editing_direction`.
-            editing_prompt_embeddings (`torch.Tensor>`, *optional*):
-                Pre-computed embeddings to use for semantic guidance. Guidance direction of embedding should be
-                specified via `reverse_editing_direction`.
-            reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`):
-                Whether the corresponding prompt in `editing_prompt` should be increased or decreased.
-            edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5):
-                Guidance scale for semantic guidance. If provided as list values should correspond to `editing_prompt`.
-                `edit_guidance_scale` is defined as `s_e` of equation 6 of [SEGA
-                Paper](https://arxiv.org/pdf/2301.12247.pdf).
-            edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
-                Number of diffusion steps (for each prompt) for which semantic guidance will not be applied. Momentum
-                will still be calculated for those steps and applied once all warmup periods are over.
-                `edit_warmup_steps` is defined as `delta` (δ) of [SEGA Paper](https://arxiv.org/pdf/2301.12247.pdf).
-            edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`):
-                Number of diffusion steps (for each prompt) after which semantic guidance will no longer be applied.
-            edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9):
-                Threshold of semantic guidance.
-            edit_momentum_scale (`float`, *optional*, defaults to 0.1):
-                Scale of the momentum to be added to the semantic guidance at each diffusion step. If set to 0.0
-                momentum will be disabled. Momentum is already built up during warmup, i.e. for diffusion steps smaller
-                than `sld_warmup_steps`. Momentum will only be added to latent guidance once all warmup periods are
-                finished. `edit_momentum_scale` is defined as `s_m` of equation 7 of [SEGA
-                Paper](https://arxiv.org/pdf/2301.12247.pdf).
-            edit_mom_beta (`float`, *optional*, defaults to 0.4):
-                Defines how semantic guidance momentum builds up. `edit_mom_beta` indicates how much of the previous
-                momentum will be kept. Momentum is already built up during warmup, i.e. for diffusion steps smaller
-                than `edit_warmup_steps`. `edit_mom_beta` is defined as `beta_m` (β) of equation 8 of [SEGA
-                Paper](https://arxiv.org/pdf/2301.12247.pdf).
-            edit_weights (`List[float]`, *optional*, defaults to `None`):
-                Indicates how much each individual concept should influence the overall guidance. If no weights are
-                provided all concepts are applied equally. `edit_mom_beta` is defined as `g_i` of equation 9 of [SEGA
-                Paper](https://arxiv.org/pdf/2301.12247.pdf).
-            sem_guidance (`List[torch.Tensor]`, *optional*):
-                List of pre-generated guidance vectors to be applied at generation. Length of the list has to
-                correspond to `num_inference_steps`.
-
-        Returns:
-            [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] if `return_dict` is True,
-            otherwise a `tuple. When returning a tuple, the first element is a list with the generated images, and the
-            second element is a list of `bool`s denoting whether the corresponding generated image likely represents
-            "not-safe-for-work" (nsfw) content, according to the `safety_checker`.
-        """
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(prompt, height, width, callback_steps)
-
-        # 2. Define call parameters
-        batch_size = 1 if isinstance(prompt, str) else len(prompt)
-
-        if editing_prompt:
-            enable_edit_guidance = True
-            if isinstance(editing_prompt, str):
-                editing_prompt = [editing_prompt]
-            enabled_editing_prompts = len(editing_prompt)
-        elif editing_prompt_embeddings is not None:
-            enable_edit_guidance = True
-            enabled_editing_prompts = editing_prompt_embeddings.shape[0]
-        else:
-            enabled_editing_prompts = 0
-            enable_edit_guidance = False
-
-        # get prompt text embeddings
-        text_inputs = self.tokenizer(
-            prompt,
-            padding="max_length",
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        )
-        text_input_ids = text_inputs.input_ids
-
-        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
-            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
-            logger.warning(
-                "The following part of your input was truncated because CLIP can only handle sequences up to"
-                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-            )
-            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
-        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]
-
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        bs_embed, seq_len, _ = text_embeddings.shape
-        text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)
-        text_embeddings = text_embeddings.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        if enable_edit_guidance:
-            # get safety text embeddings
-            if editing_prompt_embeddings is None:
-                edit_concepts_input = self.tokenizer(
-                    [x for item in editing_prompt for x in repeat(item, batch_size)],
-                    padding="max_length",
-                    max_length=self.tokenizer.model_max_length,
-                    return_tensors="pt",
-                )
-
-                edit_concepts_input_ids = edit_concepts_input.input_ids
-
-                if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
-                    removed_text = self.tokenizer.batch_decode(
-                        edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
-                    )
-                    logger.warning(
-                        "The following part of your input was truncated because CLIP can only handle sequences up to"
-                        f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                    )
-                    edit_concepts_input_ids = edit_concepts_input_ids[:, : self.tokenizer.model_max_length]
-                edit_concepts = self.text_encoder(edit_concepts_input_ids.to(self.device))[0]
-            else:
-                edit_concepts = editing_prompt_embeddings.to(self.device).repeat(batch_size, 1, 1)
-
-            # duplicate text embeddings for each generation per prompt, using mps friendly method
-            bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
-            edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
-            edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_len_edit, -1)
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-        # get unconditional embeddings for classifier free guidance
-
-        if do_classifier_free_guidance:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""]
-            elif type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            max_length = text_input_ids.shape[-1]
-            uncond_input = self.tokenizer(
-                uncond_tokens,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[0]
-
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = uncond_embeddings.shape[1]
-            uncond_embeddings = uncond_embeddings.repeat(batch_size, num_images_per_prompt, 1)
-            uncond_embeddings = uncond_embeddings.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            if enable_edit_guidance:
-                text_embeddings = torch.cat([uncond_embeddings, text_embeddings, edit_concepts])
-            else:
-                text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-        # get the initial random noise unless the user supplied it
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=self.device)
-        timesteps = self.scheduler.timesteps
-        if use_ddpm:
-          t_to_idx = {int(v):k for k,v in enumerate(timesteps[-zs.shape[0]:])}
-          timesteps = timesteps[-zs.shape[0]:] 
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            text_embeddings.dtype,
-            self.device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs.
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # Initialize edit_momentum to None
-        edit_momentum = None
-
-        self.uncond_estimates = None
-        self.text_estimates = None
-        self.edit_estimates = None
-        self.sem_guidance = None
-
-        for i, t in enumerate(self.progress_bar(timesteps)):
-            # expand the latents if we are doing classifier free guidance
-            latent_model_input = (
-                torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents
-            )
-            latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-            # predict the noise residual
-            noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-            # perform guidance
-            if do_classifier_free_guidance:
-                noise_pred_out = noise_pred.chunk(2 + enabled_editing_prompts)  # [b,4, 64, 64]
-                noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1]
-                noise_pred_edit_concepts = noise_pred_out[2:]
-
-                # default text guidance
-                noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
-                # noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
-
-                if self.uncond_estimates is None:
-                    self.uncond_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_uncond.shape))
-                self.uncond_estimates[i] = noise_pred_uncond.detach().cpu()
-
-                if self.text_estimates is None:
-                    self.text_estimates = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
-                self.text_estimates[i] = noise_pred_text.detach().cpu()
-
-                if self.edit_estimates is None and enable_edit_guidance:
-                    self.edit_estimates = torch.zeros(
-                        (num_inference_steps + 1, len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape)
-                    )
-
-                if self.sem_guidance is None:
-                    self.sem_guidance = torch.zeros((num_inference_steps + 1, *noise_pred_text.shape))
-
-                if edit_momentum is None:
-                    edit_momentum = torch.zeros_like(noise_guidance)
-
-                if enable_edit_guidance:
-                    concept_weights = torch.zeros(
-                        (len(noise_pred_edit_concepts), noise_guidance.shape[0]),
-                        device=self.device,
-                        dtype=noise_guidance.dtype,
-                    )
-                    noise_guidance_edit = torch.zeros(
-                        (len(noise_pred_edit_concepts), *noise_guidance.shape),
-                        device=self.device,
-                        dtype=noise_guidance.dtype,
-                    )
-                    # noise_guidance_edit = torch.zeros_like(noise_guidance)
-                    warmup_inds = []
-                    for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts):
-                        self.edit_estimates[i, c] = noise_pred_edit_concept
-                        if isinstance(edit_guidance_scale, list):
-                            edit_guidance_scale_c = edit_guidance_scale[c]
-                        else:
-                            edit_guidance_scale_c = edit_guidance_scale
-
-                        if isinstance(edit_threshold, list):
-                            edit_threshold_c = edit_threshold[c]
-                        else:
-                            edit_threshold_c = edit_threshold
-                        if isinstance(reverse_editing_direction, list):
-                            reverse_editing_direction_c = reverse_editing_direction[c]
-                        else:
-                            reverse_editing_direction_c = reverse_editing_direction
-                        if edit_weights:
-                            edit_weight_c = edit_weights[c]
-                        else:
-                            edit_weight_c = 1.0
-                        if isinstance(edit_warmup_steps, list):
-                            edit_warmup_steps_c = edit_warmup_steps[c]
-                        else:
-                            edit_warmup_steps_c = edit_warmup_steps
-
-                        if isinstance(edit_cooldown_steps, list):
-                            edit_cooldown_steps_c = edit_cooldown_steps[c]
-                        elif edit_cooldown_steps is None:
-                            edit_cooldown_steps_c = i + 1
-                        else:
-                            edit_cooldown_steps_c = edit_cooldown_steps
-                        if i >= edit_warmup_steps_c:
-                            warmup_inds.append(c)
-                        if i >= edit_cooldown_steps_c:
-                            noise_guidance_edit[c, :, :, :, :] = torch.zeros_like(noise_pred_edit_concept)
-                            continue
-
-                        noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond
-                        # tmp_weights = (noise_pred_text - noise_pred_edit_concept).sum(dim=(1, 2, 3))
-                        tmp_weights = (noise_guidance - noise_pred_edit_concept).sum(dim=(1, 2, 3))
-
-                        tmp_weights = torch.full_like(tmp_weights, edit_weight_c)  # * (1 / enabled_editing_prompts)
-                        if reverse_editing_direction_c:
-                            noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1
-                        concept_weights[c, :] = tmp_weights
-
-                        noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c
-
-                        # torch.quantile function expects float32
-                        if noise_guidance_edit_tmp.dtype == torch.float32:
-                            tmp = torch.quantile(
-                                torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2),
-                                edit_threshold_c,
-                                dim=2,
-                                keepdim=False,
-                            )
-                        else:
-                            tmp = torch.quantile(
-                                torch.abs(noise_guidance_edit_tmp).flatten(start_dim=2).to(torch.float32),
-                                edit_threshold_c,
-                                dim=2,
-                                keepdim=False,
-                            ).to(noise_guidance_edit_tmp.dtype)
-
-                        noise_guidance_edit_tmp = torch.where(
-                            torch.abs(noise_guidance_edit_tmp) >= tmp[:, :, None, None],
-                            noise_guidance_edit_tmp,
-                            torch.zeros_like(noise_guidance_edit_tmp),
-                        )
-                        noise_guidance_edit[c, :, :, :, :] = noise_guidance_edit_tmp
-
-                        # noise_guidance_edit = noise_guidance_edit + noise_guidance_edit_tmp
-
-                    warmup_inds = torch.tensor(warmup_inds).to(self.device)
-                    if len(noise_pred_edit_concepts) > warmup_inds.shape[0] > 0:
-                        concept_weights = concept_weights.to("cpu")  # Offload to cpu
-                        noise_guidance_edit = noise_guidance_edit.to("cpu")
-
-                        concept_weights_tmp = torch.index_select(concept_weights.to(self.device), 0, warmup_inds)
-                        concept_weights_tmp = torch.where(
-                            concept_weights_tmp < 0, torch.zeros_like(concept_weights_tmp), concept_weights_tmp
-                        )
-                        concept_weights_tmp = concept_weights_tmp / concept_weights_tmp.sum(dim=0)
-                        # concept_weights_tmp = torch.nan_to_num(concept_weights_tmp)
-
-                        noise_guidance_edit_tmp = torch.index_select(
-                            noise_guidance_edit.to(self.device), 0, warmup_inds
-                        )
-                        noise_guidance_edit_tmp = torch.einsum(
-                            "cb,cbijk->bijk", concept_weights_tmp, noise_guidance_edit_tmp
-                        )
-                        noise_guidance_edit_tmp = noise_guidance_edit_tmp
-                        noise_guidance = noise_guidance + noise_guidance_edit_tmp
-
-                        self.sem_guidance[i] = noise_guidance_edit_tmp.detach().cpu()
-
-                        del noise_guidance_edit_tmp
-                        del concept_weights_tmp
-                        concept_weights = concept_weights.to(self.device)
-                        noise_guidance_edit = noise_guidance_edit.to(self.device)
-
-                    concept_weights = torch.where(
-                        concept_weights < 0, torch.zeros_like(concept_weights), concept_weights
-                    )
-
-                    concept_weights = torch.nan_to_num(concept_weights)
-
-                    noise_guidance_edit = torch.einsum("cb,cbijk->bijk", concept_weights, noise_guidance_edit)
-
-                    noise_guidance_edit = noise_guidance_edit + edit_momentum_scale * edit_momentum
-
-                    edit_momentum = edit_mom_beta * edit_momentum + (1 - edit_mom_beta) * noise_guidance_edit
-
-                    if warmup_inds.shape[0] == len(noise_pred_edit_concepts):
-                        noise_guidance = noise_guidance + noise_guidance_edit
-                        self.sem_guidance[i] = noise_guidance_edit.detach().cpu()
-
-                if sem_guidance is not None:
-                    edit_guidance = sem_guidance[i].to(self.device)
-                    noise_guidance = noise_guidance + edit_guidance
-
-                noise_pred = noise_pred_uncond + noise_guidance
-            ## ddpm ###########################################################
-            if use_ddpm:
-              
-              idx = t_to_idx[int(t)]
-              z = zs[idx] if not zs is None else None
-
-              # 1. get previous step value (=t-1)
-              prev_timestep = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
-              # 2. compute alphas, betas
-              alpha_prod_t = self.scheduler.alphas_cumprod[t]
-              alpha_prod_t_prev = self.scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.scheduler.final_alpha_cumprod
-              beta_prod_t = 1 - alpha_prod_t
-              
-              # 3. compute predicted original sample from predicted noise also called
-              # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-              pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)
-              
-              
-              # 5. compute variance: "sigma_t(η)" -> see formula (16)
-              # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)    
-              # variance = self.scheduler._get_variance(timestep, prev_timestep)
-              # variance = get_variance(model, t) #, prev_timestep)
-              beta_prod_t_prev = 1 - alpha_prod_t_prev
-              variance = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)
-              
-            
-              
-              std_dev_t = eta * variance ** (0.5)
-              # Take care of asymetric reverse process (asyrp)
-              noise_pred_direction = noise_pred
-              
-              # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-              # pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output_direction
-              pred_sample_direction = (1 - alpha_prod_t_prev - eta * variance) ** (0.5) * noise_pred_direction
-              
-              # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-              prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
-              # 8. Add noice if eta > 0
-              if eta > 0:
-                  if z is None:
-                      z = torch.randn(noise_pred.shape, device=self.device)
-                  sigma_z =  eta * variance ** (0.5) * z
-                  latents = prev_sample + sigma_z
-
-            ## ddpm ##########################################################
-                # compute the previous noisy sample x_t -> x_t-1
-            else: #if not use_ddpm:
-              latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-            # call the callback, if provided
-            if callback is not None and i % callback_steps == 0:
-                callback(i, t, latents)
-
-        
-        #  # 8. Post-processing
-        # image = self.decode_latents(latents)
-
-        # # 9. Run safety checker
-        # image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
-
-        # # 10. Convert to PIL
-        # if output_type == "pil":
-        #     image = self.numpy_to_pil(image)
-
-        # if not return_dict:
-        #     return (image, has_nsfw_concept)
-
-        # return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
-        
-        # 8. Post-processing
-        if not output_type == "latent":
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype)
-        else:
-            image = latents
-            has_nsfw_concept = None
-
-        if has_nsfw_concept is None:
-            do_denormalize = [True] * image.shape[0]
-        else:
-            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
-
-        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
-
-        if not return_dict:
-            return (image, has_nsfw_concept)
-
-        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)
\ No newline at end of file
diff --git a/spaces/ehristoforu/NLLB-Translator/langs.py b/spaces/ehristoforu/NLLB-Translator/langs.py
deleted file mode 100644
index e5e849a4f5427f5b22e1e0bcfbe00102ac0eef10..0000000000000000000000000000000000000000
--- a/spaces/ehristoforu/NLLB-Translator/langs.py
+++ /dev/null
@@ -1,204 +0,0 @@
-LANGS = [
-    "ace_Arab",
-    "ace_Latn",
-    "acm_Arab",
-    "acq_Arab",
-    "aeb_Arab",
-    "afr_Latn",
-    "ajp_Arab",
-    "aka_Latn",
-    "amh_Ethi",
-    "apc_Arab",
-    "arb_Arab",
-    "ars_Arab",
-    "ary_Arab",
-    "arz_Arab",
-    "asm_Beng",
-    "ast_Latn",
-    "awa_Deva",
-    "ayr_Latn",
-    "azb_Arab",
-    "azj_Latn",
-    "bak_Cyrl",
-    "bam_Latn",
-    "ban_Latn",
-    "bel_Cyrl",
-    "bem_Latn",
-    "ben_Beng",
-    "bho_Deva",
-    "bjn_Arab",
-    "bjn_Latn",
-    "bod_Tibt",
-    "bos_Latn",
-    "bug_Latn",
-    "bul_Cyrl",
-    "cat_Latn",
-    "ceb_Latn",
-    "ces_Latn",
-    "cjk_Latn",
-    "ckb_Arab",
-    "crh_Latn",
-    "cym_Latn",
-    "dan_Latn",
-    "deu_Latn",
-    "dik_Latn",
-    "dyu_Latn",
-    "dzo_Tibt",
-    "ell_Grek",
-    "eng_Latn",
-    "epo_Latn",
-    "est_Latn",
-    "eus_Latn",
-    "ewe_Latn",
-    "fao_Latn",
-    "pes_Arab",
-    "fij_Latn",
-    "fin_Latn",
-    "fon_Latn",
-    "fra_Latn",
-    "fur_Latn",
-    "fuv_Latn",
-    "gla_Latn",
-    "gle_Latn",
-    "glg_Latn",
-    "grn_Latn",
-    "guj_Gujr",
-    "hat_Latn",
-    "hau_Latn",
-    "heb_Hebr",
-    "hin_Deva",
-    "hne_Deva",
-    "hrv_Latn",
-    "hun_Latn",
-    "hye_Armn",
-    "ibo_Latn",
-    "ilo_Latn",
-    "ind_Latn",
-    "isl_Latn",
-    "ita_Latn",
-    "jav_Latn",
-    "jpn_Jpan",
-    "kab_Latn",
-    "kac_Latn",
-    "kam_Latn",
-    "kan_Knda",
-    "kas_Arab",
-    "kas_Deva",
-    "kat_Geor",
-    "knc_Arab",
-    "knc_Latn",
-    "kaz_Cyrl",
-    "kbp_Latn",
-    "kea_Latn",
-    "khm_Khmr",
-    "kik_Latn",
-    "kin_Latn",
-    "kir_Cyrl",
-    "kmb_Latn",
-    "kon_Latn",
-    "kor_Hang",
-    "kmr_Latn",
-    "lao_Laoo",
-    "lvs_Latn",
-    "lij_Latn",
-    "lim_Latn",
-    "lin_Latn",
-    "lit_Latn",
-    "lmo_Latn",
-    "ltg_Latn",
-    "ltz_Latn",
-    "lua_Latn",
-    "lug_Latn",
-    "luo_Latn",
-    "lus_Latn",
-    "mag_Deva",
-    "mai_Deva",
-    "mal_Mlym",
-    "mar_Deva",
-    "min_Latn",
-    "mkd_Cyrl",
-    "plt_Latn",
-    "mlt_Latn",
-    "mni_Beng",
-    "khk_Cyrl",
-    "mos_Latn",
-    "mri_Latn",
-    "zsm_Latn",
-    "mya_Mymr",
-    "nld_Latn",
-    "nno_Latn",
-    "nob_Latn",
-    "npi_Deva",
-    "nso_Latn",
-    "nus_Latn",
-    "nya_Latn",
-    "oci_Latn",
-    "gaz_Latn",
-    "ory_Orya",
-    "pag_Latn",
-    "pan_Guru",
-    "pap_Latn",
-    "pol_Latn",
-    "por_Latn",
-    "prs_Arab",
-    "pbt_Arab",
-    "quy_Latn",
-    "ron_Latn",
-    "run_Latn",
-    "rus_Cyrl",
-    "sag_Latn",
-    "san_Deva",
-    "sat_Beng",
-    "scn_Latn",
-    "shn_Mymr",
-    "sin_Sinh",
-    "slk_Latn",
-    "slv_Latn",
-    "smo_Latn",
-    "sna_Latn",
-    "snd_Arab",
-    "som_Latn",
-    "sot_Latn",
-    "spa_Latn",
-    "als_Latn",
-    "srd_Latn",
-    "srp_Cyrl",
-    "ssw_Latn",
-    "sun_Latn",
-    "swe_Latn",
-    "swh_Latn",
-    "szl_Latn",
-    "tam_Taml",
-    "tat_Cyrl",
-    "tel_Telu",
-    "tgk_Cyrl",
-    "tgl_Latn",
-    "tha_Thai",
-    "tir_Ethi",
-    "taq_Latn",
-    "taq_Tfng",
-    "tpi_Latn",
-    "tsn_Latn",
-    "tso_Latn",
-    "tuk_Latn",
-    "tum_Latn",
-    "tur_Latn",
-    "twi_Latn",
-    "tzm_Tfng",
-    "uig_Arab",
-    "ukr_Cyrl",
-    "umb_Latn",
-    "urd_Arab",
-    "uzn_Latn",
-    "vec_Latn",
-    "vie_Latn",
-    "war_Latn",
-    "wol_Latn",
-    "xho_Latn",
-    "ydd_Hebr",
-    "yor_Latn",
-    "yue_Hant",
-    "zho_Hans",
-    "zho_Hant",
-    "zul_Latn"
-]
diff --git a/spaces/exbert-project/exbert/client/src/ts/etc/_Tools.ts b/spaces/exbert-project/exbert/client/src/ts/etc/_Tools.ts
deleted file mode 100644
index e924a93cd6549ae13fb56b047cc74e21df292dbc..0000000000000000000000000000000000000000
--- a/spaces/exbert-project/exbert/client/src/ts/etc/_Tools.ts
+++ /dev/null
@@ -1,63 +0,0 @@
-import * as _ from 'lodash'
-
-function ascOrder(n1, n2) {
-    if (n1 < n2) {
-        return -1;
-    }
-    else if (n1 > n2) { 
-        return 1;
-    }
-    return 0;
-}
-
-export {findAllIndexes, insertAt_, orderedInsert_, set2SortedArray}
-
-/**
- * Find all indexes that match a particular predicate
- */
-function findAllIndexes<T>(array:Array<T>, predicate:(a:T) => boolean): number[] {
-    let fromIndex=0;
-    let results = [];
-
-    let i = _.findIndex(array, predicate, fromIndex);
-    while (i != -1) {
-        results.push(i);
-        i = _.findIndex(array, predicate, i+1)
-    }
-
-    return results;
-};
-
-function insertAt_<T>(array:Array<T>, val:T, ind:number):Array<T> {
-    array.splice(ind, 0, val);
-    return array
-}
-
-/**
- * Convert a set to an ordered array
- */
-function set2SortedArray<T>(input:Set<T>):Array<T> {
-    return Array.from(input).sort(ascOrder)
-}
-
-/**
- * Insert a value into array in sorted order IN PLACE
- * 
- * WARNING: Only handles numbers, sorted from least to greatest
- * - Assumes already sorted array
- */
-function orderedInsert_<T>(array:Array<T>, val:T, coldstart=false):Array<T> {
-    // Resort array if desired
-    if (coldstart) {
-        array.sort(ascOrder)
-    }
-
-    const ind = _.sortedIndex(array, val);
-    return insertAt_(array, val, ind)
-}
-
-export function makeRandom(len:number){
-  const a:number[] = new Array(len).fill(0)
-
-  return a.map((x) => {return _.random(-5, 5, true)})
-}
\ No newline at end of file
diff --git a/spaces/facebook/xm_transformer_600m/app.py b/spaces/facebook/xm_transformer_600m/app.py
deleted file mode 100644
index 9ff241b96ef2c6eaba9b97a927b9e6c55d4b1112..0000000000000000000000000000000000000000
--- a/spaces/facebook/xm_transformer_600m/app.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-os.system("pip install gradio==2.8.0b2")
-import gradio as gr
-import numpy as np
-
-title = "fairseq S2S"
-
-description = "Gradio Demo for fairseq S2S: speech-to-speech translation models. To use it, simply add your audio, or click one of the examples to load them. Read more at the links below."
-
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2010.05171' target='_blank'>fairseq S2T: Fast Speech-to-Text Modeling with fairseq</a> | <a href='https://github.com/pytorch/fairseq/tree/main/examples/speech_to_text' target='_blank'>Github Repo</a></p>"
-
-examples = [
-  ["common_voice_es_en.flac","xm_transformer_600m-es_en-multi_domain"],
-  ["common_voice_ru_18945535.flac","xm_transformer_600m-ru_en-multi_domain"],
-  ["common_voice_fr_19731305.mp3","xm_transformer_600m-fr_en-multi_domain"],
-  ["common_voice_en_ru.mp3","xm_transformer_600m-en_ru-multi_domain"],
-  ["common_voice_en_es.mp3","xm_transformer_600m-en_es-multi_domain"],
-  ["common_voice_en_ar.mp3","xm_transformer_600m-en_ar-multi_domain"],
-  ["common_voice_en_tr.mp3","xm_transformer_600m-en_tr-multi_domain"],
-  ["common_voice_en_fr.mp3","xm_transformer_600m-en_fr-multi_domain"],
-  ["common_voice_en_vi.mp3","xm_transformer_600m-en_vi-multi_domain"],
-]
-
-io1 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-es_en-multi_domain")
-
-io2 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-ru_en-multi_domain")
-
-io3 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_ru-multi_domain")
-
-io4 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_es-multi_domain")
-
-io5 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_zh-multi_domain")
-
-io6 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-fr_en-multi_domain")
-
-io7 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_ar-multi_domain")
-
-io8 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_tr-multi_domain")
-
-io9 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_fr-multi_domain")
-
-io10 = gr.Interface.load("huggingface/facebook/xm_transformer_600m-en_vi-multi_domain")
-
-
-
-
-
-    
-def inference(text,model):
-   if model == "xm_transformer_600m-es_en-multi_domain":
-        outtext = io1(text)
-   elif model == "xm_transformer_600m-ru_en-multi_domain":
-        outtext = io2(text)
-   elif model == "xm_transformer_600m-en_ru-multi_domain":
-        outtext = io3(text)
-   elif model == "xm_transformer_600m-en_es-multi_domain":
-        outtext = io4(text)
-   elif model == "xm_transformer_600m-en_zh-multi_domain":
-        outtext = io5(text)
-   elif model == "xm_transformer_600m-fr_en-multi_domain":
-        outtext = io6(text)
-   elif model == "xm_transformer_600m-en_ar-multi_domain":
-        outtext = io7(text)
-   elif model == "xm_transformer_600m-en_tr-multi_domain":
-        outtext = io8(text)
-   elif model == "xm_transformer_600m-en_fr-multi_domain":
-        outtext = io9(text)
-   else:
-        outtext = io10(text)
-   return outtext 
-
-
-gr.Interface(
-    inference, 
-    [gr.inputs.Audio(label="Input",type="filepath"),gr.inputs.Dropdown(choices=["xm_transformer_600m-es_en-multi_domain","xm_transformer_600m-ru_en-multi_domain","xm_transformer_600m-en_ru-multi_domain","xm_transformer_600m-en_es-multi_domain","xm_transformer_600m-fr_en-multi_domain","xm_transformer_600m-en_ar-multi_domain","xm_transformer_600m-en_tr-multi_domain","xm_transformer_600m-en_fr-multi_domain","xm_transformer_600m-en_vi-multi_domain"], type="value", default="xm_transformer_600m-es_en-multi_domain", label="model")
-],
-    gr.outputs.Audio(label="Output"),
-    article=article,
-    title=title,
-    examples=examples,
-    description=description).launch(enable_queue=True,cache_examples=False)
\ No newline at end of file
diff --git a/spaces/faisalhr1997/chat-ggml/app.py b/spaces/faisalhr1997/chat-ggml/app.py
deleted file mode 100644
index fa01e0211a1b40c2d88b763927fd99214baf60cc..0000000000000000000000000000000000000000
--- a/spaces/faisalhr1997/chat-ggml/app.py
+++ /dev/null
@@ -1,239 +0,0 @@
-from pathlib import Path
-from urllib.parse import urlparse
-
-import gradio as gr
-import psutil
-from ctransformers import AutoModelForCausalLM
-from huggingface_hub import hf_hub_download
-
-
-URL = "https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q2_K.gguf"
-repo_id = "/".join(urlparse(URL).path.strip("/").split("/")[:2])
-
-
-model_file = Path(URL).name
-
-_ = hf_hub_download(
-    repo_id=repo_id,
-    revision="main",
-    filename=model_file,
-    local_dir="models",
-    # local_dir_use_symlinks=True,
-)
-
-llm = AutoModelForCausalLM.from_pretrained(
-    _,
-    model_type="llama",
-    threads=psutil.cpu_count(logical=False),
-)
-
-TITLE = f"""<h2 align="center"> chat-ggml ({model_file})"""
-USER_NAME = "User"
-BOT_NAME = "Assistant"
-DEFAULT_INSTRUCTIONS = """The following is a conversation between a highly knowledgeable and intelligent AI assistant and a human User. In the following interactions, User and Assistant will converse and Assistant will answer User's questions.
-"""
-RETRY_COMMAND = "/retry"
-STOP_STR = f"\n{USER_NAME}:"
-STOP_SUSPECT_LIST = [":", "\n", "User"]
-
-
-def chat_accordion():
-    with gr.Accordion("Parameters", open=False):
-        temperature = gr.Slider(
-            minimum=0.1,
-            maximum=2.0,
-            value=0.8,
-            step=0.1,
-            interactive=True,
-            label="Temperature",
-        )
-        top_p = gr.Slider(
-            minimum=0.1,
-            maximum=0.99,
-            value=0.9,
-            step=0.01,
-            interactive=True,
-            label="p (nucleus sampling)",
-        )
-    return temperature, top_p
-
-
-def format_chat_prompt(message: str, chat_history, instructions: str) -> str:
-    instructions = instructions.strip(" ").strip("\n")
-    prompt = instructions
-    for turn in chat_history:
-        user_message, bot_message = turn
-        prompt = f"{prompt}\n{USER_NAME}: {user_message}\n{BOT_NAME}: {bot_message}"
-    prompt = f"{prompt}\n{USER_NAME}: {message}\n{BOT_NAME}:"
-    return prompt
-
-
-def chat():
-    with gr.Column(elem_id="chat_container"):
-        with gr.Row():
-            chatbot = gr.Chatbot(elem_id="chatbot")
-        with gr.Row():
-            inputs = gr.Textbox(
-                placeholder=f"Hello {BOT_NAME} !!",
-                label="Type an input and press Enter",
-                max_lines=3,
-            )
-
-    with gr.Row(elem_id="button_container"):
-        with gr.Column():
-            retry_button = gr.Button("♻️ Retry")
-        with gr.Column():
-            delete_turn_button = gr.Button("✨ Undo")
-        with gr.Column():
-            clear_chat_button = gr.Button("🧽 Clear")
-
-    gr.Examples(
-        [
-            ["Hey! Any recommendations for my holidays"],
-            ["What's the Everett interpretation of quantum mechanics?"],
-            [
-                "Give me a list of the top 10 dive sites you would recommend around the world."
-            ],
-            ["Can you tell me more about deep-water soloing?"],
-        ],
-        inputs=inputs,
-        label="Click on any example and press Enter in the input textbox!",
-    )
-
-    with gr.Row(elem_id="param_container"):
-        with gr.Column():
-            temperature, top_p = chat_accordion()
-        with gr.Column():
-            with gr.Accordion("Instructions", open=False):
-                instructions = gr.Textbox(
-                    placeholder="LLM instructions",
-                    value=DEFAULT_INSTRUCTIONS,
-                    lines=3,
-                    interactive=True,
-                    label="Instructions",
-                    max_lines=10,
-                    show_label=False,
-                )
-            # with gr.Accordion("Role #1", open=False):
-            #     instructions = gr.Textbox(
-            #         placeholder="Role #1 like ### Instruction",
-            #         value=USER_NAME,
-            #         lines=1,
-            #         interactive=True,
-            #         label="USER_NAME",
-            #         max_lines=1,
-            #         show_label=False,
-            #     )
-            # with gr.Accordion("Role #2", open=False):
-            #     instructions = gr.Textbox(
-            #         placeholder="Role #2 like ### Response",
-            #         value=BOT_NAME,
-            #         lines=1,
-            #         interactive=True,
-            #         label="BOT_NAME",
-            #         max_lines=1,
-            #         show_label=False,
-            #     )
-
-    def run_chat(
-        message: str, chat_history, instructions: str,  temperature: float, top_p: float
-    ):
-        if not message or (message == RETRY_COMMAND and len(chat_history) == 0):
-            yield chat_history
-            return
-
-        if message == RETRY_COMMAND and chat_history:
-            prev_turn = chat_history.pop(-1)
-            user_message, _ = prev_turn
-            message = user_message
-
-        prompt = format_chat_prompt(message, chat_history, instructions)
-        chat_history = chat_history + [[message, ""]]
-        stream = llm(
-            prompt,
-            max_new_tokens=1024,
-            stop=[STOP_STR, "<|endoftext|>"],
-            temperature=temperature,
-            top_p=top_p,
-            stream=True,
-        )
-        acc_text = ""
-        for idx, response in enumerate(stream):
-            text_token = response
-
-            if text_token in STOP_SUSPECT_LIST:
-                acc_text += text_token
-                continue
-
-            if idx == 0 and text_token.startswith(" "):
-                text_token = text_token[1:]
-
-            acc_text += text_token
-            last_turn = list(chat_history.pop(-1))
-            last_turn[-1] += acc_text
-            chat_history = chat_history + [last_turn]
-            yield chat_history
-            acc_text = ""
-
-    def delete_last_turn(chat_history):
-        if chat_history:
-            chat_history.pop(-1)
-        return {chatbot: gr.update(value=chat_history)}
-
-    def run_retry(
-        message: str, chat_history, instructions: str, temperature: float, top_p: float
-    ):
-        yield from run_chat(
-            RETRY_COMMAND, chat_history, instructions, temperature, top_p
-        )
-
-    def clear_chat():
-        return []
-
-    inputs.submit(
-        run_chat,
-        [inputs, chatbot, instructions, temperature, top_p],
-        outputs=[chatbot],
-        show_progress="minimal",
-    )
-    inputs.submit(lambda: "", inputs=None, outputs=inputs)
-    delete_turn_button.click(delete_last_turn, inputs=[chatbot], outputs=[chatbot])
-    retry_button.click(
-        run_retry,
-        [inputs, chatbot, instructions, temperature, top_p],
-        outputs=[chatbot],
-        show_progress="minimal",
-    )
-    clear_chat_button.click(clear_chat, [], chatbot)
-
-
-
-def get_demo():
-    with gr.Blocks(
-        # css=None
-        # css="""#chat_container {width: 700px; margin-left: auto; margin-right: auto;}
-        #        #button_container {width: 700px; margin-left: auto; margin-right: auto;}
-        #        #param_container {width: 700px; margin-left: auto; margin-right: auto;}"""
-        css="""#chatbot {
-    font-size: 14px;
-    min-height: 300px;
-}"""
-    ) as demo:
-        gr.HTML(TITLE)
-
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown(
-                    """**Chat, brainstorm ideas, discuss your holiday plans, and more!**
-                    """
-                )
-
-        chat()
-
-    return demo
-
-
-if __name__ == "__main__":
-    demo = get_demo()
-    demo.queue(max_size=64, concurrency_count=8)
-    demo.launch(server_name="0.0.0.0", server_port=7860)
diff --git a/spaces/falterWliame/Face_Mask_Detection/Hum Dil De Chuke Sanam Movie Hindi Dubbed Mp4 Hd Download REPACK.md b/spaces/falterWliame/Face_Mask_Detection/Hum Dil De Chuke Sanam Movie Hindi Dubbed Mp4 Hd Download REPACK.md
deleted file mode 100644
index c15a4dc9b1c8ef17567f7e7bcf347804f989b8f4..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Hum Dil De Chuke Sanam Movie Hindi Dubbed Mp4 Hd Download REPACK.md	
+++ /dev/null
@@ -1,34 +0,0 @@
-<br />
-<h1>Hum Dil De Chuke Sanam Movie Hindi Dubbed MP4 HD: A Musical Saga of Love and Sacrifice</h1>
-
-<p>Hum Dil De Chuke Sanam movie hindi dubbed mp4 hd is a 1999 Bollywood romantic drama film directed by Sanjay Leela Bhansali and featuring Salman Khan, Aishwarya Rai Bachchan, and Ajay Devgn in the lead roles. The film is based on the Gujarati novel Saraswatichandra by Govardhanram Tripathi and tells the story of Nandini, a free-spirited Indian girl who falls in love with Sameer, an Italian musician who comes to learn Indian classical music from her father. However, her father arranges her marriage with Vanraj, a successful lawyer who loves her unconditionally. The film is a musical saga of love, sacrifice, and destiny.</p>
-
-<h2>What Makes Hum Dil De Chuke Sanam Movie Hindi Dubbed MP4 HD Special?</h2>
-
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-
-<ul>
-<li>The film has a captivating story that explores the themes of love, loyalty, sacrifice, and destiny.</li>
-<li>The film has a brilliant soundtrack composed by Ismail Darbar and sung by legends like Kavita Krishnamurthy, Udit Narayan, Alka Yagnik, Kumar Sanu, and more.</li>
-<li>The film has stunning visuals and cinematography that showcase the beauty of India and Italy.</li>
-<li>The film has superb performances by the star cast, especially Aishwarya Rai Bachchan, who won several awards for her role as Nandini.</li>
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-</ul>
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-
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-<ul>
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-</ul>
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-<br />
\ No newline at end of file
diff --git a/spaces/falterWliame/Face_Mask_Detection/Janne Da Arc Discography Lossless !!EXCLUSIVE!!.md b/spaces/falterWliame/Face_Mask_Detection/Janne Da Arc Discography Lossless !!EXCLUSIVE!!.md
deleted file mode 100644
index 25ecea996e396cbcaf634f4345e45ed9f679d762..0000000000000000000000000000000000000000
--- a/spaces/falterWliame/Face_Mask_Detection/Janne Da Arc Discography Lossless !!EXCLUSIVE!!.md	
+++ /dev/null
@@ -1,113 +0,0 @@
-<br />
-<h1>Janne Da Arc Discography Lossless - The Ultimate Guide for J-Rock Fans</h1>
-
-<p>If you are a fan of J-Rock, you have probably heard of Janne Da Arc, one of the most iconic and influential bands in the genre. Formed in 1996 and disbanded in 2007, Janne Da Arc left behind a legacy of 10 studio albums, 29 singles, and countless live performances that showcase their diverse and creative musical style.</p>
-
-<p>But how can you enjoy their music in the best possible quality? The answer is to download their discography in lossless format, which preserves the original sound without any compression or degradation. In this article, we will show you how to get Janne Da Arc discography lossless, and why you should do it.</p>
-<h2>Janne Da Arc Discography Lossless</h2><br /><p><b><b>Download</b> &#128504;&#128504;&#128504; <a href="https://urlca.com/2uDcIc">https://urlca.com/2uDcIc</a></b></p><br /><br />
-
-<h2>What is lossless format?</h2>
-
-<p>Lossless format is a type of audio file that contains all the data of the original recording, without any loss of quality or information. Unlike lossy formats like MP3 or AAC, which reduce the file size by discarding some data, lossless formats keep everything intact, resulting in a more faithful and accurate reproduction of the sound.</p>
-
-<p>Some of the most common lossless formats are FLAC (Free Lossless Audio Codec), ALAC (Apple Lossless Audio Codec), WAV (Waveform Audio File Format), and APE (Monkey's Audio). These formats can be played by most media players and devices, and can also be converted to other formats if needed.</p>
-
-<h2>Why should you download Janne Da Arc discography lossless?</h2>
-
-<p>There are many reasons why you should download Janne Da Arc discography lossless, but here are some of the main ones:</p>
-
-<ul>
-<li>You will hear every detail and nuance of their music, from the powerful vocals of yasu, to the intricate guitar riffs of you, to the dynamic bass lines of ka-yu, to the atmospheric keyboards of kiyo, to the energetic drums of shuji.</li>
-<li>You will appreciate their musical evolution, from their early indie days with albums like Dearly and Chaos Mode, to their major debut with D.N.A, to their peak of popularity with Gaia and Another Story, to their experimental phase with Arcadia and Joker, to their final farewell with Singles 2.</li>
-<li>You will discover their hidden gems, such as their rare indie singles like Destination and Maria no Tsumeato, their B-sides and bonus tracks like Heaven and Red Zone, their collaborations with other artists like Gackt and Hyde, and their solo projects like Acid Black Cherry and DAMIJAW.</li>
-<li>You will support their legacy and respect their artistic vision, by listening to their music as they intended it to be heard, without any compromise or alteration.</li>
-</ul>
-
-<h2>How can you download Janne Da Arc discography lossless?</h2>
-
-<p>The easiest way to download Janne Da Arc discography lossless is to use a torrent site like Nyaa.si, which has a dedicated uploader named Janne who has shared all their albums and singles in FLAC format. You can also find some of their live DVDs and videos in ISO or VOB format.</p>
-
-<p>To download from Nyaa.si, you will need a torrent client like BitTorrent or uTorrent, which will allow you to download the files from other users who have them. You will also need a media player that can play FLAC files, such as VLC or Foobar2000.</p>
-
-<p>Here are the steps to download Janne Da Arc discography lossless from Nyaa.si:</p>
-
-<ol>
-<li>Go to <a href="https://nyaa.si/?f=0&c=0_0&q=Janne+Da+Arc&s=seeders&o=desc">https://nyaa.si/?f=0&c=0_0&q=Janne+Da+Arc&s=seeders&o=desc</a>, which will show you all the results uploaded by Janne.</li>
-<li>Select the files that you want to download by clicking on the checkboxes next to them. You can also click on the name of each file to see more details and comments.</li>
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-<li>Once your download is complete, you can open the files with your media player and enjoy Janne Da Arc discography lossless!</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Janne Da Arc discography lossless is a must-have for any J-Rock fan who wants to experience their music in its full glory. By downloading their discography in lossless format, you will hear every detail and nuance of their music, appreciate their musical evolution, discover their hidden gems, and support their legacy.</p>
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-<p>We hope this article has helped you understand what is lossless format, why you should download Janne Da Arc discography lossless, and how you can do it. If you have any questions or comments, feel free to leave them below. And if you liked this article, please share it with your friends who might be interested in Janne Da Arc discography lossless!</p>
-<p></p>
-<h2>What are the benefits of listening to Janne Da Arc discography lossless?</h2>
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-<ul>
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-</ul>
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-<p>Downloading Janne Da Arc discography lossless is easy and convenient, but there are some tips that you should follow to make sure you get the best experience possible. Here are some of them:</p>
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-<ul>
-<li>Make sure you have enough storage space on your device or hard drive, as lossless files are larger than lossy files. For example, a FLAC file can be 10 times bigger than an MP3 file of the same song.</li>
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-<h2>Conclusion</h2>
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-<ul>
-<li>You may have difficulty finding some of their rare or obscure releases, such as their indie demos, their fan club CDs, their tribute albums, or their unreleased songs.</li>
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-</ul>
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-<h2>How can you overcome these challenges?</h2>
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-<ul>
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-<p>Thank you for reading and happy listening!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git "a/spaces/fatiXbelha/sd/BSQL Hacker Download Automated SQL Injection Tool /FREE\\\\.md" "b/spaces/fatiXbelha/sd/BSQL Hacker Download Automated SQL Injection Tool /FREE\\\\.md"
deleted file mode 100644
index a5b8526505d7889dceb1c3eb833f821d3b8a4b2e..0000000000000000000000000000000000000000
--- "a/spaces/fatiXbelha/sd/BSQL Hacker Download Automated SQL Injection Tool /FREE\\\\.md"	
+++ /dev/null
@@ -1,88 +0,0 @@
-## BSQL Hacker Download Automated SQL Injection Tool
-
-
-
- 
-
-  
-
- 
-
-**Download »»» [https://www.google.com/url?q=https%3A%2F%2Furlgoal.com%2F2tBKHK&sa=D&sntz=1&usg=AOvVaw2VtlMVK2tzq9AdaQc76zZ5](https://www.google.com/url?q=https%3A%2F%2Furlgoal.com%2F2tBKHK&sa=D&sntz=1&usg=AOvVaw2VtlMVK2tzq9AdaQc76zZ5)**
-
-
-
- 
-
- 
-
- 
-
- 
-
- 
-
-# BSQL Hacker: A Powerful Tool for Automated SQL Injection
-
- 
-
-SQL injection is a common web application attack that exploits the vulnerability of poorly coded SQL queries. By injecting malicious syntax into the queries, an attacker can gain unauthorized access to the database, steal sensitive data, execute commands, or cause damage. SQL injection can affect virtually any database that uses SQL as the language for interaction.
-
- 
-
-However, not all SQL injection attacks are easy to perform or detect. Some databases have mechanisms to prevent or obscure the error messages that indicate a successful injection. In such cases, an attacker may need to use a technique called blind SQL injection, which relies on subtle changes in the response time or content to infer information from the database. Blind SQL injection can be very time-consuming and tedious to perform manually.
-
- 
-
-That's where BSQL Hacker comes in handy. BSQL Hacker is an automated SQL injection tool that can exploit SQL injection vulnerabilities in virtually any database. It supports four different types of SQL injection: blind, time-based blind, deep blind (based on advanced time delays), and error-based. It can also automate most of the new SQL injection methods that rely on blind SQL injection.
-
- 
-
-BSQL Hacker has an easy mode SQL injection wizard that guides the user through the steps of finding and exploiting a SQL injection vulnerability. It also has an automated attack mode that can dump the whole database schema and data for the following DBMS: MS-SQL Server, ORACLE, and MySQL (experimental). BSQL Hacker can also load and save templates and attack files, allowing users to share and update SQL injection exploits via an exploit repository.
-
- 
-
-BSQL Hacker has both a console and a GUI interface, and supports various connection features such as proxy, SSL, NTLM, basic auth, custom headers, cookies, etc. It also has advanced configuration options and supports RegEx signatures for identifying true and false responses.
-
- 
-
-BSQL Hacker is an open source tool that can be downloaded from GitHub[^1^] or Darknet[^2^]. It is designed for experienced users as well as beginners who want to automate SQL injections (especially blind SQL injections). However, it should be used only for ethical purposes and with permission from the target website.
-
-  
-
-## How to Use BSQL Hacker
-
- 
-
-To use BSQL Hacker, you need to download and install it from the official sources. You can choose between a console or a GUI interface, depending on your preference. The GUI interface has an easy mode SQL injection wizard that can guide you through the steps of finding and exploiting a SQL injection vulnerability. The console interface has more options and flexibility for advanced users.
-
- 
-
-The basic steps of using BSQL Hacker are as follows:
-
- 
-
-1. Enter the target URL and select the injection point (query string, post, cookie, or header).
-
-2. Test the injection by sending a request and analyzing the response. BSQL Hacker will try to detect the type of SQL injection and the database server.
-
-3. Select the attack mode (blind, time-based blind, deep blind, or error-based) and configure the settings such as delay, timeout, threads, etc.
-
-4. Start the attack and wait for the results. BSQL Hacker will try to extract information from the database such as version, user, tables, columns, data, etc.
-
-5. Save the results or export them to XML format.
-
-
-
-You can also load and save templates and attack files that contain predefined settings and exploits for specific websites or databases. You can also update and share these files via an exploit repository that works like Metasploit.
-
- 
-
-BSQL Hacker is a powerful tool for automated SQL injection that can save you a lot of time and effort. However, it is not a magic bullet that can hack any website with a single click. You still need to have some knowledge of SQL injection techniques and how to bypass security mechanisms such as firewalls, WAFs, CSRF tokens, etc. You also need to be careful not to cause any damage or harm to the target website or database.
-
- 145887f19f
-
- 
-
- 
-
diff --git a/spaces/fatiXbelha/sd/Explore the World of Pokmon GO on PC and Mac with BlueStacks - The Most Trusted Android Gaming Platform.md b/spaces/fatiXbelha/sd/Explore the World of Pokmon GO on PC and Mac with BlueStacks - The Most Trusted Android Gaming Platform.md
deleted file mode 100644
index c7d0423821757170ad0ebe8dfa6bc0e19c70c0a2..0000000000000000000000000000000000000000
--- a/spaces/fatiXbelha/sd/Explore the World of Pokmon GO on PC and Mac with BlueStacks - The Most Trusted Android Gaming Platform.md	
+++ /dev/null
@@ -1,167 +0,0 @@
-<br />
-<h1>How to Download Pokemon Go for PC Windows 7</h1>
-<p>Pokemon Go is one of the most popular mobile games in the world, but did you know that you can also play it on your PC? In this article, we will show you how to download Pokemon Go for PC Windows 7 using an Android emulator, a fake GPS app, and a few simple steps. Whether you want to catch more Pokemon, explore new locations, or just enjoy the game on a bigger screen, this guide will help you achieve your goals.</p>
- <h2>What is Pokemon Go and why play it on PC?</h2>
-<p>Pokemon Go is an augmented reality game that lets you discover and catch Pokemon in the real world using your smartphone's camera, GPS, and internet connection. You can also battle other players, join teams, complete quests, collect items, hatch eggs, and more. The game is free to play, but offers in-app purchases for extra features and items.</p>
-<h2>download pokemon go for pc windows 7</h2><br /><p><b><b>Download</b> ---> <a href="https://urllie.com/2uNCyc">https://urllie.com/2uNCyc</a></b></p><br /><br />
-<p>Playing Pokemon Go on PC has several advantages over playing it on your phone. For example, you can:</p>
-<ul>
-<li>Enjoy better graphics and performance</li>
-<li>Save battery life and data usage</li>
-<li>Use keyboard and mouse controls</li>
-<li>Access more locations without moving</li>
-<li>Avoid potential risks of walking around with your phone</li>
-</ul>
-<p>However, playing Pokemon Go on PC also has some drawbacks. For instance, you may:</p>
-<ul>
-<li>Lose some of the fun and immersion of the game</li>
-<li>Violate the terms of service of the game</li>
-<li>Risk getting banned for cheating</li>
-<li>Encounter technical issues or glitches</li>
-</ul>
-<p>Therefore, before you decide to play Pokemon Go on PC, you should weigh the pros and cons carefully and proceed at your own risk.</p>
- <h2>What do you need to play Pokemon Go on PC?</h2>
-<p>To play Pokemon Go on PC Windows 7, you need three things:</p>
-<ol>
-<li>An Android emulator</li>
-<li>A Google account</li>
-<li>A fake GPS app</li>
-</ol>
-<p>We will explain each of these components in more detail below.</p>
- <h3>An Android emulator</h3>
-<p>An Android emulator is a software that allows you to run Android apps on your PC. There are many Android emulators available online, but we recommend using BlueStacks. BlueStacks is one of the most popular and reliable emulators that can run Pokemon Go smoothly. It also has features like multi-instance, keyboard mapping, game mode, etc. that can enhance your gaming experience.</p>
- <h3>A Google account</h3>
-<p>A Google account is required to access the Google Play Store and download Pokemon Go. If you already have a Google account, you can use it to sign in to BlueStacks. If you don't have one, you can create one for free.</p>
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- <h3>A fake GPS app</h3>
-<p>A fake GPS app is a tool that allows you to change your location on your device. This is necessary because Pokemon Go relies on your real-world location to find Pokemon. If you don't spoof your location, you will not be able to play the game on your PC. However, spoofing your location is also considered cheating by the game developers, so you should be careful not to jump around too fast or too far. Otherwise, you may get detected and banned from the game. Therefore, use a fake GPS app at your own discretion and responsibility.</p>
- <h2>How to install and run Pokemon Go on PC?</h2>
-<p>Now that you have everything you need, let's get started with the installation and setup process. Follow these steps to download Pokemon Go for PC Windows 7:</p>
- <h3>Download and install BlueStacks</h3>
-<p>First, you need to download and install BlueStacks on your PC. Here's how:</p>
-<ol>
-<li>Go to the official website of BlueStacks and click on the "Download BlueStacks" button.</li>
-<li>Once the download is complete, run the installer and follow the instructions on the screen.</li>
-<li>After the installation is done, launch BlueStacks and sign in with your Google account.</li>
-</ol>
-<p>You can also watch this video tutorial for more details:</p>
-<iframe width="560" height="315" src="https://www.youtube.com/embed/0M7UxXQIjw0" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
- <h3>Download and install Pokemon Go</h3>
-<p>Next, you need to download and install Pokemon Go on BlueStacks. Here's how:</p>
-<ol>
-<li>In BlueStacks, go to the Google Play Store and search for "Pokemon Go".</li>
-<li>Select the game from the results and click on the "Install" button.</li>
-<li>Wait for the game to download and install on your emulator.</li>
-</ol>
-<p>You can also use this link to download Pokemon Go directly from the Google Play Store:</p>
-<a href="">Pokemon GO - Apps on Google Play</a>
- <h3>Download and install a fake GPS app</h3>
-<p>Finally, you need to download and install a fake GPS app on BlueStacks. Here's how:</p>
-<ol>
-<li>In BlueStacks, go to the Google Play Store and search for "Fake GPS".</li>
-<li>Select a fake GPS app from the results and click on the "Install" button. We recommend using Fake GPS Location by Lexa.</li>
-<li>Wait for the app to download and install on your emulator.</li>
-</ol>
-<p>You can also use this link to download Fake GPS Location by Lexa directly from the Google Play Store:</p>
-<a href="">Fake GPS location - Apps on Google Play</a>
- <h3>Configure your location settings</h3>
-<p>Before you launch Pokemon Go, you need to configure your location settings on BlueStacks. Here's how:</p>
-<ol>
-<li>In BlueStacks, go to the Settings menu and select Location.</li>
-<li>Make sure that the Mode is set to High accuracy.</li>
-<li>Go back to the Settings menu and select Apps.</li>
-<li>Select Google Play Services and clear its cache and data.</li>
-<li>Select Pokemon Go and clear its cache and data.</li>
-<li>Go back to the Home screen and launch the fake GPS app.</li>
-<li>Select a location that you want to spoof and click on the "Play" button.</li>
-</ol>
- <h3>Launch Pokemon Go and enjoy</h3>
-<p>You are now ready to launch Pokemon Go and play it on your PC. Here's how:</p>
-<ol>
-<li>In BlueStacks, go to the Home screen and launch Pokemon Go.</li>
-<li>Wait for the game to load and sign in with your Google account or Pokemon Trainer Club account.</li>
-<li>Accept the terms of service and privacy policy of the game.</li>
-<li>You should see a map of your spoofed location with nearby Pokemon, PokeStops, Gyms, etc.</li>
-<li>You can use your mouse or keyboard to move around, interact with objects, catch Pokemon, etc.</li>
-</ol>
- <p>Congratulations! You have successfully downloaded Pokemon Go for PC Windows 7. Now you can enjoy the game on a bigger screen with better graphics and performance. However, remember to be careful not to jump around too fast or too far, as this may trigger a soft ban or a permanent ban from the game. Also, be respectful of other players and do not use any cheats or hacks that may ruin their experience. Have fun!</p>
- <h2>Conclusion</h2>
-<p>Pokemon Go is an amazing game that lets you explore the world of Pokemon in real life. However, if you want to play it on your PC Windows 7, you need an Android emulator, a Google account, and a fake GPS app. In this article, we showed you how to download Pokemon Go for PC Windows 7 using BlueStacks, one of the best Android em ulators available online. We also explained the requirements and precautions of playing Pokemon Go on PC, and gave you a step-by-step guide on how to install and run the game. We hope you found this article helpful and informative. If you have any questions or feedback, please leave a comment below. Thank you for reading and happy gaming!</p>
- <h2>FAQs</h2>
-<p>Here are some of the most frequently asked questions about downloading Pokemon Go for PC Windows 7:</p>
- <h3>Is it legal to play Pokemon Go on PC?</h3>
-<p>There is no clear answer to this question, as different countries may have different laws and regulations regarding the use of emulators and spoofing apps. However, playing Pokemon Go on PC is definitely against the terms of service of the game, which state that you must not use any unauthorized software or devices that modify or interfere with the game. Therefore, if you choose to play Pokemon Go on PC, you are doing so at your own risk and responsibility.</p>
- <h3>Is it safe to play Pokemon Go on PC?</h3>
-<p>Playing Pokemon Go on PC may not be safe for several reasons. First, you may expose your PC to malware or viruses by downloading and installing untrusted software. Second, you may compromise your Google account or Pokemon Trainer Club account by signing in to a third-party app. Third, you may get banned from the game for cheating or violating the terms of service. Therefore, if you decide to play Pokemon Go on PC, you should take precautions such as using antivirus software, creating a backup account, and spoofing your location carefully.</p>
- <h3>How can I avoid getting banned from Pokemon Go?</h3>
-<p>There is no guarantee that you will not get banned from Pokemon Go if you play it on PC, as the game developers are constantly updating their detection and enforcement methods. However, there are some tips that may help you reduce the chances of getting banned. For example, you can:</p>
-<ul>
-<li>Avoid jumping around too fast or too far between locations</li>
-<li>Avoid using multiple accounts or devices</li>
-<li>Avoid using any other cheats or hacks that may give you an unfair advantage</li>
-<li>Avoid playing during major events or updates</li>
-<li>Avoid reporting or interacting with other players who may report you</li>
-</ul>
-<p>However, these tips are not foolproof and may not work for everyone. Therefore, you should always be prepared for the possibility of getting banned and losing your progress.</p>
- <h3>Can I play Pokemon Go on PC with other emulators?</h3>
-<p>Yes, you can play Pokemon Go on PC with other emulators besides BlueStacks. However, not all emulators are compatible or reliable for running the game. Some of the other emulators that may work for playing Pokemon Go on PC are:</p>
-<ul>
-<li>NoxPlayer</li>
-<li>MEmu</li>
-<li>LDPlayer</li>
-<li>Andy</li>
-<li>KOPlayer</li>
-</ul>
-<p>You can try these emulators at your own risk and preference, but we recommend using BlueStacks as it has proven to be one of the best emulators for playing Pokemon Go on PC.</p>
- <h3>Can I play Pokemon Go on PC without an emulator?</h3>
-<p>No, you cannot play Pokemon Go on PC without an emulator. The game is designed for mobile devices and requires an Android or iOS operating system to run. Therefore, you need an emulator to simulate an Android environment on your PC and run the game. There is no official or unofficial way to play Pokemon Go on PC without an emulator.</p> 401be4b1e0<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/perf_hooks.d.ts b/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/perf_hooks.d.ts
deleted file mode 100644
index 5c0b228e7d2a75d3d2726f4c4e02681dba341cac..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/@types/node/ts4.8/perf_hooks.d.ts
+++ /dev/null
@@ -1,625 +0,0 @@
-/**
- * This module provides an implementation of a subset of the W3C [Web Performance APIs](https://w3c.github.io/perf-timing-primer/) as well as additional APIs for
- * Node.js-specific performance measurements.
- *
- * Node.js supports the following [Web Performance APIs](https://w3c.github.io/perf-timing-primer/):
- *
- * * [High Resolution Time](https://www.w3.org/TR/hr-time-2)
- * * [Performance Timeline](https://w3c.github.io/performance-timeline/)
- * * [User Timing](https://www.w3.org/TR/user-timing/)
- *
- * ```js
- * const { PerformanceObserver, performance } = require('perf_hooks');
- *
- * const obs = new PerformanceObserver((items) => {
- *   console.log(items.getEntries()[0].duration);
- *   performance.clearMarks();
- * });
- * obs.observe({ type: 'measure' });
- * performance.measure('Start to Now');
- *
- * performance.mark('A');
- * doSomeLongRunningProcess(() => {
- *   performance.measure('A to Now', 'A');
- *
- *   performance.mark('B');
- *   performance.measure('A to B', 'A', 'B');
- * });
- * ```
- * @see [source](https://github.com/nodejs/node/blob/v18.0.0/lib/perf_hooks.js)
- */
-declare module 'perf_hooks' {
-    import { AsyncResource } from 'node:async_hooks';
-    type EntryType = 'node' | 'mark' | 'measure' | 'gc' | 'function' | 'http2' | 'http';
-    interface NodeGCPerformanceDetail {
-        /**
-         * When `performanceEntry.entryType` is equal to 'gc', `the performance.kind` property identifies
-         * the type of garbage collection operation that occurred.
-         * See perf_hooks.constants for valid values.
-         */
-        readonly kind?: number | undefined;
-        /**
-         * When `performanceEntry.entryType` is equal to 'gc', the `performance.flags`
-         * property contains additional information about garbage collection operation.
-         * See perf_hooks.constants for valid values.
-         */
-        readonly flags?: number | undefined;
-    }
-    /**
-     * @since v8.5.0
-     */
-    class PerformanceEntry {
-        protected constructor();
-        /**
-         * The total number of milliseconds elapsed for this entry. This value will not
-         * be meaningful for all Performance Entry types.
-         * @since v8.5.0
-         */
-        readonly duration: number;
-        /**
-         * The name of the performance entry.
-         * @since v8.5.0
-         */
-        readonly name: string;
-        /**
-         * The high resolution millisecond timestamp marking the starting time of the
-         * Performance Entry.
-         * @since v8.5.0
-         */
-        readonly startTime: number;
-        /**
-         * The type of the performance entry. It may be one of:
-         *
-         * * `'node'` (Node.js only)
-         * * `'mark'` (available on the Web)
-         * * `'measure'` (available on the Web)
-         * * `'gc'` (Node.js only)
-         * * `'function'` (Node.js only)
-         * * `'http2'` (Node.js only)
-         * * `'http'` (Node.js only)
-         * @since v8.5.0
-         */
-        readonly entryType: EntryType;
-        /**
-         * Additional detail specific to the `entryType`.
-         * @since v16.0.0
-         */
-        readonly detail?: NodeGCPerformanceDetail | unknown | undefined; // TODO: Narrow this based on entry type.
-        toJSON(): any;
-    }
-    class PerformanceMark extends PerformanceEntry {
-        readonly duration: 0;
-        readonly entryType: 'mark';
-    }
-    class PerformanceMeasure extends PerformanceEntry {
-        readonly entryType: 'measure';
-    }
-    /**
-     * _This property is an extension by Node.js. It is not available in Web browsers._
-     *
-     * Provides timing details for Node.js itself. The constructor of this class
-     * is not exposed to users.
-     * @since v8.5.0
-     */
-    class PerformanceNodeTiming extends PerformanceEntry {
-        /**
-         * The high resolution millisecond timestamp at which the Node.js process
-         * completed bootstrapping. If bootstrapping has not yet finished, the property
-         * has the value of -1.
-         * @since v8.5.0
-         */
-        readonly bootstrapComplete: number;
-        /**
-         * The high resolution millisecond timestamp at which the Node.js environment was
-         * initialized.
-         * @since v8.5.0
-         */
-        readonly environment: number;
-        /**
-         * The high resolution millisecond timestamp of the amount of time the event loop
-         * has been idle within the event loop's event provider (e.g. `epoll_wait`). This
-         * does not take CPU usage into consideration. If the event loop has not yet
-         * started (e.g., in the first tick of the main script), the property has the
-         * value of 0.
-         * @since v14.10.0, v12.19.0
-         */
-        readonly idleTime: number;
-        /**
-         * The high resolution millisecond timestamp at which the Node.js event loop
-         * exited. If the event loop has not yet exited, the property has the value of -1\.
-         * It can only have a value of not -1 in a handler of the `'exit'` event.
-         * @since v8.5.0
-         */
-        readonly loopExit: number;
-        /**
-         * The high resolution millisecond timestamp at which the Node.js event loop
-         * started. If the event loop has not yet started (e.g., in the first tick of the
-         * main script), the property has the value of -1.
-         * @since v8.5.0
-         */
-        readonly loopStart: number;
-        /**
-         * The high resolution millisecond timestamp at which the V8 platform was
-         * initialized.
-         * @since v8.5.0
-         */
-        readonly v8Start: number;
-    }
-    interface EventLoopUtilization {
-        idle: number;
-        active: number;
-        utilization: number;
-    }
-    /**
-     * @param util1 The result of a previous call to eventLoopUtilization()
-     * @param util2 The result of a previous call to eventLoopUtilization() prior to util1
-     */
-    type EventLoopUtilityFunction = (util1?: EventLoopUtilization, util2?: EventLoopUtilization) => EventLoopUtilization;
-    interface MarkOptions {
-        /**
-         * Additional optional detail to include with the mark.
-         */
-        detail?: unknown | undefined;
-        /**
-         * An optional timestamp to be used as the mark time.
-         * @default `performance.now()`.
-         */
-        startTime?: number | undefined;
-    }
-    interface MeasureOptions {
-        /**
-         * Additional optional detail to include with the mark.
-         */
-        detail?: unknown | undefined;
-        /**
-         * Duration between start and end times.
-         */
-        duration?: number | undefined;
-        /**
-         * Timestamp to be used as the end time, or a string identifying a previously recorded mark.
-         */
-        end?: number | string | undefined;
-        /**
-         * Timestamp to be used as the start time, or a string identifying a previously recorded mark.
-         */
-        start?: number | string | undefined;
-    }
-    interface TimerifyOptions {
-        /**
-         * A histogram object created using
-         * `perf_hooks.createHistogram()` that will record runtime durations in
-         * nanoseconds.
-         */
-        histogram?: RecordableHistogram | undefined;
-    }
-    interface Performance {
-        /**
-         * If name is not provided, removes all PerformanceMark objects from the Performance Timeline.
-         * If name is provided, removes only the named mark.
-         * @param name
-         */
-        clearMarks(name?: string): void;
-        /**
-         * If name is not provided, removes all PerformanceMeasure objects from the Performance Timeline.
-         * If name is provided, removes only the named measure.
-         * @param name
-         * @since v16.7.0
-         */
-        clearMeasures(name?: string): void;
-        /**
-         * Returns a list of `PerformanceEntry` objects in chronological order with respect to `performanceEntry.startTime`.
-         * If you are only interested in performance entries of certain types or that have certain names, see
-         * `performance.getEntriesByType()` and `performance.getEntriesByName()`.
-         * @since v16.7.0
-         */
-        getEntries(): PerformanceEntry[];
-        /**
-         * Returns a list of `PerformanceEntry` objects in chronological order with respect to `performanceEntry.startTime`
-         * whose `performanceEntry.name` is equal to `name`, and optionally, whose `performanceEntry.entryType` is equal to `type`.
-         * @param name
-         * @param type
-         * @since v16.7.0
-         */
-        getEntriesByName(name: string, type?: EntryType): PerformanceEntry[];
-        /**
-         * Returns a list of `PerformanceEntry` objects in chronological order with respect to `performanceEntry.startTime`
-         * whose `performanceEntry.entryType` is equal to `type`.
-         * @param type
-         * @since v16.7.0
-         */
-        getEntriesByType(type: EntryType): PerformanceEntry[];
-        /**
-         * Creates a new PerformanceMark entry in the Performance Timeline.
-         * A PerformanceMark is a subclass of PerformanceEntry whose performanceEntry.entryType is always 'mark',
-         * and whose performanceEntry.duration is always 0.
-         * Performance marks are used to mark specific significant moments in the Performance Timeline.
-         * @param name
-         * @return The PerformanceMark entry that was created
-         */
-        mark(name?: string, options?: MarkOptions): PerformanceMark;
-        /**
-         * Creates a new PerformanceMeasure entry in the Performance Timeline.
-         * A PerformanceMeasure is a subclass of PerformanceEntry whose performanceEntry.entryType is always 'measure',
-         * and whose performanceEntry.duration measures the number of milliseconds elapsed since startMark and endMark.
-         *
-         * The startMark argument may identify any existing PerformanceMark in the the Performance Timeline, or may identify
-         * any of the timestamp properties provided by the PerformanceNodeTiming class. If the named startMark does not exist,
-         * then startMark is set to timeOrigin by default.
-         *
-         * The endMark argument must identify any existing PerformanceMark in the the Performance Timeline or any of the timestamp
-         * properties provided by the PerformanceNodeTiming class. If the named endMark does not exist, an error will be thrown.
-         * @param name
-         * @param startMark
-         * @param endMark
-         * @return The PerformanceMeasure entry that was created
-         */
-        measure(name: string, startMark?: string, endMark?: string): PerformanceMeasure;
-        measure(name: string, options: MeasureOptions): PerformanceMeasure;
-        /**
-         * An instance of the PerformanceNodeTiming class that provides performance metrics for specific Node.js operational milestones.
-         */
-        readonly nodeTiming: PerformanceNodeTiming;
-        /**
-         * @return the current high resolution millisecond timestamp
-         */
-        now(): number;
-        /**
-         * The timeOrigin specifies the high resolution millisecond timestamp from which all performance metric durations are measured.
-         */
-        readonly timeOrigin: number;
-        /**
-         * Wraps a function within a new function that measures the running time of the wrapped function.
-         * A PerformanceObserver must be subscribed to the 'function' event type in order for the timing details to be accessed.
-         * @param fn
-         */
-        timerify<T extends (...params: any[]) => any>(fn: T, options?: TimerifyOptions): T;
-        /**
-         * eventLoopUtilization is similar to CPU utilization except that it is calculated using high precision wall-clock time.
-         * It represents the percentage of time the event loop has spent outside the event loop's event provider (e.g. epoll_wait).
-         * No other CPU idle time is taken into consideration.
-         */
-        eventLoopUtilization: EventLoopUtilityFunction;
-    }
-    interface PerformanceObserverEntryList {
-        /**
-         * Returns a list of `PerformanceEntry` objects in chronological order
-         * with respect to `performanceEntry.startTime`.
-         *
-         * ```js
-         * const {
-         *   performance,
-         *   PerformanceObserver
-         * } = require('perf_hooks');
-         *
-         * const obs = new PerformanceObserver((perfObserverList, observer) => {
-         *   console.log(perfObserverList.getEntries());
-         *
-         *    * [
-         *    *   PerformanceEntry {
-         *    *     name: 'test',
-         *    *     entryType: 'mark',
-         *    *     startTime: 81.465639,
-         *    *     duration: 0
-         *    *   },
-         *    *   PerformanceEntry {
-         *    *     name: 'meow',
-         *    *     entryType: 'mark',
-         *    *     startTime: 81.860064,
-         *    *     duration: 0
-         *    *   }
-         *    * ]
-         *
-         *
-         *   performance.clearMarks();
-         *   performance.clearMeasures();
-         *   observer.disconnect();
-         * });
-         * obs.observe({ type: 'mark' });
-         *
-         * performance.mark('test');
-         * performance.mark('meow');
-         * ```
-         * @since v8.5.0
-         */
-        getEntries(): PerformanceEntry[];
-        /**
-         * Returns a list of `PerformanceEntry` objects in chronological order
-         * with respect to `performanceEntry.startTime` whose `performanceEntry.name` is
-         * equal to `name`, and optionally, whose `performanceEntry.entryType` is equal to`type`.
-         *
-         * ```js
-         * const {
-         *   performance,
-         *   PerformanceObserver
-         * } = require('perf_hooks');
-         *
-         * const obs = new PerformanceObserver((perfObserverList, observer) => {
-         *   console.log(perfObserverList.getEntriesByName('meow'));
-         *
-         *    * [
-         *    *   PerformanceEntry {
-         *    *     name: 'meow',
-         *    *     entryType: 'mark',
-         *    *     startTime: 98.545991,
-         *    *     duration: 0
-         *    *   }
-         *    * ]
-         *
-         *   console.log(perfObserverList.getEntriesByName('nope')); // []
-         *
-         *   console.log(perfObserverList.getEntriesByName('test', 'mark'));
-         *
-         *    * [
-         *    *   PerformanceEntry {
-         *    *     name: 'test',
-         *    *     entryType: 'mark',
-         *    *     startTime: 63.518931,
-         *    *     duration: 0
-         *    *   }
-         *    * ]
-         *
-         *   console.log(perfObserverList.getEntriesByName('test', 'measure')); // []
-         *
-         *   performance.clearMarks();
-         *   performance.clearMeasures();
-         *   observer.disconnect();
-         * });
-         * obs.observe({ entryTypes: ['mark', 'measure'] });
-         *
-         * performance.mark('test');
-         * performance.mark('meow');
-         * ```
-         * @since v8.5.0
-         */
-        getEntriesByName(name: string, type?: EntryType): PerformanceEntry[];
-        /**
-         * Returns a list of `PerformanceEntry` objects in chronological order
-         * with respect to `performanceEntry.startTime` whose `performanceEntry.entryType`is equal to `type`.
-         *
-         * ```js
-         * const {
-         *   performance,
-         *   PerformanceObserver
-         * } = require('perf_hooks');
-         *
-         * const obs = new PerformanceObserver((perfObserverList, observer) => {
-         *   console.log(perfObserverList.getEntriesByType('mark'));
-         *
-         *    * [
-         *    *   PerformanceEntry {
-         *    *     name: 'test',
-         *    *     entryType: 'mark',
-         *    *     startTime: 55.897834,
-         *    *     duration: 0
-         *    *   },
-         *    *   PerformanceEntry {
-         *    *     name: 'meow',
-         *    *     entryType: 'mark',
-         *    *     startTime: 56.350146,
-         *    *     duration: 0
-         *    *   }
-         *    * ]
-         *
-         *   performance.clearMarks();
-         *   performance.clearMeasures();
-         *   observer.disconnect();
-         * });
-         * obs.observe({ type: 'mark' });
-         *
-         * performance.mark('test');
-         * performance.mark('meow');
-         * ```
-         * @since v8.5.0
-         */
-        getEntriesByType(type: EntryType): PerformanceEntry[];
-    }
-    type PerformanceObserverCallback = (list: PerformanceObserverEntryList, observer: PerformanceObserver) => void;
-    class PerformanceObserver extends AsyncResource {
-        constructor(callback: PerformanceObserverCallback);
-        /**
-         * Disconnects the `PerformanceObserver` instance from all notifications.
-         * @since v8.5.0
-         */
-        disconnect(): void;
-        /**
-         * Subscribes the `PerformanceObserver` instance to notifications of new `PerformanceEntry` instances identified either by `options.entryTypes`or `options.type`:
-         *
-         * ```js
-         * const {
-         *   performance,
-         *   PerformanceObserver
-         * } = require('perf_hooks');
-         *
-         * const obs = new PerformanceObserver((list, observer) => {
-         *   // Called once asynchronously. `list` contains three items.
-         * });
-         * obs.observe({ type: 'mark' });
-         *
-         * for (let n = 0; n < 3; n++)
-         *   performance.mark(`test${n}`);
-         * ```
-         * @since v8.5.0
-         */
-        observe(
-            options:
-                | {
-                      entryTypes: ReadonlyArray<EntryType>;
-                      buffered?: boolean | undefined;
-                  }
-                | {
-                      type: EntryType;
-                      buffered?: boolean | undefined;
-                  }
-        ): void;
-    }
-    namespace constants {
-        const NODE_PERFORMANCE_GC_MAJOR: number;
-        const NODE_PERFORMANCE_GC_MINOR: number;
-        const NODE_PERFORMANCE_GC_INCREMENTAL: number;
-        const NODE_PERFORMANCE_GC_WEAKCB: number;
-        const NODE_PERFORMANCE_GC_FLAGS_NO: number;
-        const NODE_PERFORMANCE_GC_FLAGS_CONSTRUCT_RETAINED: number;
-        const NODE_PERFORMANCE_GC_FLAGS_FORCED: number;
-        const NODE_PERFORMANCE_GC_FLAGS_SYNCHRONOUS_PHANTOM_PROCESSING: number;
-        const NODE_PERFORMANCE_GC_FLAGS_ALL_AVAILABLE_GARBAGE: number;
-        const NODE_PERFORMANCE_GC_FLAGS_ALL_EXTERNAL_MEMORY: number;
-        const NODE_PERFORMANCE_GC_FLAGS_SCHEDULE_IDLE: number;
-    }
-    const performance: Performance;
-    interface EventLoopMonitorOptions {
-        /**
-         * The sampling rate in milliseconds.
-         * Must be greater than zero.
-         * @default 10
-         */
-        resolution?: number | undefined;
-    }
-    interface Histogram {
-        /**
-         * Returns a `Map` object detailing the accumulated percentile distribution.
-         * @since v11.10.0
-         */
-        readonly percentiles: Map<number, number>;
-        /**
-         * The number of times the event loop delay exceeded the maximum 1 hour event
-         * loop delay threshold.
-         * @since v11.10.0
-         */
-        readonly exceeds: number;
-        /**
-         * The minimum recorded event loop delay.
-         * @since v11.10.0
-         */
-        readonly min: number;
-        /**
-         * The maximum recorded event loop delay.
-         * @since v11.10.0
-         */
-        readonly max: number;
-        /**
-         * The mean of the recorded event loop delays.
-         * @since v11.10.0
-         */
-        readonly mean: number;
-        /**
-         * The standard deviation of the recorded event loop delays.
-         * @since v11.10.0
-         */
-        readonly stddev: number;
-        /**
-         * Resets the collected histogram data.
-         * @since v11.10.0
-         */
-        reset(): void;
-        /**
-         * Returns the value at the given percentile.
-         * @since v11.10.0
-         * @param percentile A percentile value in the range (0, 100].
-         */
-        percentile(percentile: number): number;
-    }
-    interface IntervalHistogram extends Histogram {
-        /**
-         * Enables the update interval timer. Returns `true` if the timer was
-         * started, `false` if it was already started.
-         * @since v11.10.0
-         */
-        enable(): boolean;
-        /**
-         * Disables the update interval timer. Returns `true` if the timer was
-         * stopped, `false` if it was already stopped.
-         * @since v11.10.0
-         */
-        disable(): boolean;
-    }
-    interface RecordableHistogram extends Histogram {
-        /**
-         * @since v15.9.0, v14.18.0
-         * @param val The amount to record in the histogram.
-         */
-        record(val: number | bigint): void;
-        /**
-         * Calculates the amount of time (in nanoseconds) that has passed since the
-         * previous call to `recordDelta()` and records that amount in the histogram.
-         *
-         * ## Examples
-         * @since v15.9.0, v14.18.0
-         */
-        recordDelta(): void;
-        /**
-         * Adds the values from other to this histogram.
-         * @since v17.4.0, v16.14.0
-         * @param other Recordable Histogram to combine with
-         */
-         add(other: RecordableHistogram): void;
-    }
-    /**
-     * _This property is an extension by Node.js. It is not available in Web browsers._
-     *
-     * Creates an `IntervalHistogram` object that samples and reports the event loop
-     * delay over time. The delays will be reported in nanoseconds.
-     *
-     * Using a timer to detect approximate event loop delay works because the
-     * execution of timers is tied specifically to the lifecycle of the libuv
-     * event loop. That is, a delay in the loop will cause a delay in the execution
-     * of the timer, and those delays are specifically what this API is intended to
-     * detect.
-     *
-     * ```js
-     * const { monitorEventLoopDelay } = require('perf_hooks');
-     * const h = monitorEventLoopDelay({ resolution: 20 });
-     * h.enable();
-     * // Do something.
-     * h.disable();
-     * console.log(h.min);
-     * console.log(h.max);
-     * console.log(h.mean);
-     * console.log(h.stddev);
-     * console.log(h.percentiles);
-     * console.log(h.percentile(50));
-     * console.log(h.percentile(99));
-     * ```
-     * @since v11.10.0
-     */
-    function monitorEventLoopDelay(options?: EventLoopMonitorOptions): IntervalHistogram;
-    interface CreateHistogramOptions {
-        /**
-         * The minimum recordable value. Must be an integer value greater than 0.
-         * @default 1
-         */
-        min?: number | bigint | undefined;
-        /**
-         * The maximum recordable value. Must be an integer value greater than min.
-         * @default Number.MAX_SAFE_INTEGER
-         */
-        max?: number | bigint | undefined;
-        /**
-         * The number of accuracy digits. Must be a number between 1 and 5.
-         * @default 3
-         */
-        figures?: number | undefined;
-    }
-    /**
-     * Returns a `RecordableHistogram`.
-     * @since v15.9.0, v14.18.0
-     */
-    function createHistogram(options?: CreateHistogramOptions): RecordableHistogram;
-
-    import { performance as _performance } from 'perf_hooks';
-    global {
-        /**
-         * `performance` is a global reference for `require('perf_hooks').performance`
-         * https://nodejs.org/api/globals.html#performance
-         * @since v16.0.0
-         */
-        var performance: typeof globalThis extends {
-            onmessage: any;
-            performance: infer T;
-        }
-            ? T
-            : typeof _performance;
-    }
-}
-declare module 'node:perf_hooks' {
-    export * from 'perf_hooks';
-}
diff --git a/spaces/fffiloni/lama-video-watermark-remover/bin/predict_inner_features.py b/spaces/fffiloni/lama-video-watermark-remover/bin/predict_inner_features.py
deleted file mode 100644
index 4f9f7a11a6c4757a4eaa05cf1ac648d372f7e02f..0000000000000000000000000000000000000000
--- a/spaces/fffiloni/lama-video-watermark-remover/bin/predict_inner_features.py
+++ /dev/null
@@ -1,119 +0,0 @@
-#!/usr/bin/env python3
-
-# Example command:
-# ./bin/predict.py \
-#       model.path=<path to checkpoint, prepared by make_checkpoint.py> \
-#       indir=<path to input data> \
-#       outdir=<where to store predicts>
-
-import logging
-import os
-import sys
-import traceback
-
-from saicinpainting.evaluation.utils import move_to_device
-
-os.environ['OMP_NUM_THREADS'] = '1'
-os.environ['OPENBLAS_NUM_THREADS'] = '1'
-os.environ['MKL_NUM_THREADS'] = '1'
-os.environ['VECLIB_MAXIMUM_THREADS'] = '1'
-os.environ['NUMEXPR_NUM_THREADS'] = '1'
-
-import cv2
-import hydra
-import numpy as np
-import torch
-import tqdm
-import yaml
-from omegaconf import OmegaConf
-from torch.utils.data._utils.collate import default_collate
-
-from saicinpainting.training.data.datasets import make_default_val_dataset
-from saicinpainting.training.trainers import load_checkpoint, DefaultInpaintingTrainingModule
-from saicinpainting.utils import register_debug_signal_handlers, get_shape
-
-LOGGER = logging.getLogger(__name__)
-
-
-@hydra.main(config_path='../configs/prediction', config_name='default_inner_features.yaml')
-def main(predict_config: OmegaConf):
-    try:
-        register_debug_signal_handlers()  # kill -10 <pid> will result in traceback dumped into log
-
-        device = torch.device(predict_config.device)
-
-        train_config_path = os.path.join(predict_config.model.path, 'config.yaml')
-        with open(train_config_path, 'r') as f:
-            train_config = OmegaConf.create(yaml.safe_load(f))
-
-        checkpoint_path = os.path.join(predict_config.model.path, 'models', predict_config.model.checkpoint)
-        model = load_checkpoint(train_config, checkpoint_path, strict=False)
-        model.freeze()
-        model.to(device)
-
-        assert isinstance(model, DefaultInpaintingTrainingModule), 'Only DefaultInpaintingTrainingModule is supported'
-        assert isinstance(getattr(model.generator, 'model', None), torch.nn.Sequential)
-
-        if not predict_config.indir.endswith('/'):
-            predict_config.indir += '/'
-
-        dataset = make_default_val_dataset(predict_config.indir, **predict_config.dataset)
-
-        max_level = max(predict_config.levels)
-
-        with torch.no_grad():
-            for img_i in tqdm.trange(len(dataset)):
-                mask_fname = dataset.mask_filenames[img_i]
-                cur_out_fname = os.path.join(predict_config.outdir, os.path.splitext(mask_fname[len(predict_config.indir):])[0])
-                os.makedirs(os.path.dirname(cur_out_fname), exist_ok=True)
-
-                batch = move_to_device(default_collate([dataset[img_i]]), device)
-
-                img = batch['image']
-                mask = batch['mask']
-                mask[:] = 0
-                mask_h, mask_w = mask.shape[-2:]
-                mask[:, :,
-                    mask_h // 2 - predict_config.hole_radius : mask_h // 2 + predict_config.hole_radius,
-                    mask_w // 2 - predict_config.hole_radius : mask_w // 2 + predict_config.hole_radius] = 1
-
-                masked_img = torch.cat([img * (1 - mask), mask], dim=1)
-
-                feats = masked_img
-                for level_i, level in enumerate(model.generator.model):
-                    feats = level(feats)
-                    if level_i in predict_config.levels:
-                        cur_feats = torch.cat([f for f in feats if torch.is_tensor(f)], dim=1) \
-                            if isinstance(feats, tuple) else feats
-
-                        if predict_config.slice_channels:
-                            cur_feats = cur_feats[:, slice(*predict_config.slice_channels)]
-
-                        cur_feat = cur_feats.pow(2).mean(1).pow(0.5).clone()
-                        cur_feat -= cur_feat.min()
-                        cur_feat /= cur_feat.std()
-                        cur_feat = cur_feat.clamp(0, 1) / 1
-                        cur_feat = cur_feat.cpu().numpy()[0]
-                        cur_feat *= 255
-                        cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
-                        cv2.imwrite(cur_out_fname + f'_lev{level_i:02d}_norm.png', cur_feat)
-
-                        # for channel_i in predict_config.channels:
-                        #
-                        #     cur_feat = cur_feats[0, channel_i].clone().detach().cpu().numpy()
-                        #     cur_feat -= cur_feat.min()
-                        #     cur_feat /= cur_feat.max()
-                        #     cur_feat *= 255
-                        #     cur_feat = np.clip(cur_feat, 0, 255).astype('uint8')
-                        #     cv2.imwrite(cur_out_fname + f'_lev{level_i}_ch{channel_i}.png', cur_feat)
-                    elif level_i >= max_level:
-                        break
-    except KeyboardInterrupt:
-        LOGGER.warning('Interrupted by user')
-    except Exception as ex:
-        LOGGER.critical(f'Prediction failed due to {ex}:\n{traceback.format_exc()}')
-        sys.exit(1)
-
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/fgpzen/remove-photo-object/app.py b/spaces/fgpzen/remove-photo-object/app.py
deleted file mode 100644
index 1958f8cb7dec781e0f66c3cc6bf29ad1fed11866..0000000000000000000000000000000000000000
--- a/spaces/fgpzen/remove-photo-object/app.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# App code based on: https://github.com/petergro-hub/ComicInpainting
-# Model based on: https://github.com/saic-mdal/lama
-
-import numpy as np
-import pandas as pd
-import streamlit as st
-import os
-from datetime import datetime
-from PIL import Image
-from streamlit_drawable_canvas import st_canvas
-from io import BytesIO
-from copy import deepcopy
-
-from src.core import process_inpaint
-
-
-def image_download_button(pil_image, filename: str, fmt: str, label="Download"):
-    if fmt not in ["jpg", "png"]:
-        raise Exception(f"Unknown image format (Available: {fmt} - case sensitive)")
-    
-    pil_format = "JPEG" if fmt == "jpg" else "PNG"
-    file_format = "jpg" if fmt == "jpg" else "png"
-    mime = "image/jpeg" if fmt == "jpg" else "image/png"
-    
-    buf = BytesIO()
-    pil_image.save(buf, format=pil_format)
-    
-    return st.download_button(
-        label=label,
-        data=buf.getvalue(),
-        file_name=f'{filename}.{file_format}',
-        mime=mime,
-    )
-
-
-
-if "button_id" not in st.session_state:
-    st.session_state["button_id"] = ""
-if "color_to_label" not in st.session_state:
-    st.session_state["color_to_label"] = {}
-
-if 'reuse_image' not in st.session_state:
-    st.session_state.reuse_image = None
-def set_image(img):
-    st.session_state.reuse_image = img
-
-uploaded_file = st.file_uploader("Choose image", accept_multiple_files=False, type=["png", "jpg", "jpeg"])
-
-if uploaded_file is not None:
-    
-    if st.session_state.reuse_image is not None:
-        img_input = Image.fromarray(st.session_state.reuse_image)
-    else:
-        bytes_data = uploaded_file.getvalue()
-        img_input = Image.open(BytesIO(bytes_data)).convert("RGBA")
-
-    # Resize the image while maintaining aspect ratio
-    max_size = 2000
-    img_width, img_height = img_input.size
-    if img_width > max_size or img_height > max_size:
-        if img_width > img_height:
-            new_width = max_size
-            new_height = int((max_size / img_width) * img_height)
-        else:
-            new_height = max_size
-            new_width = int((max_size / img_height) * img_width)
-        img_input = img_input.resize((new_width, new_height))
-    
-    stroke_width = st.slider("Brush size", 1, 100, 50)
-
-    st.write("**Now draw (brush) the part of image that you want to remove.**")
-    
-    # Canvas size logic
-    canvas_bg = deepcopy(img_input)
-    aspect_ratio = canvas_bg.width / canvas_bg.height
-    streamlit_width = 720
-    
-    # Max width is 720. Resize the height to maintain its aspectratio.
-    if canvas_bg.width > streamlit_width:
-        canvas_bg = canvas_bg.resize((streamlit_width, int(streamlit_width / aspect_ratio)))
-    
-    canvas_result = st_canvas(
-        stroke_color="rgba(255, 0, 255, 1)",
-        stroke_width=stroke_width,
-        background_image=canvas_bg,
-        width=canvas_bg.width,
-        height=canvas_bg.height,
-        drawing_mode="freedraw",
-        key="compute_arc_length", 
-    )
-    
-    if canvas_result.image_data is not None:
-        im = np.array(Image.fromarray(canvas_result.image_data.astype(np.uint8)).resize(img_input.size))
-        background = np.where(
-            (im[:, :, 0] == 0) & 
-            (im[:, :, 1] == 0) & 
-            (im[:, :, 2] == 0)
-        )
-        drawing = np.where(
-            (im[:, :, 0] == 255) & 
-            (im[:, :, 1] == 0) & 
-            (im[:, :, 2] == 255)
-        )
-        im[background]=[0,0,0,255]
-        im[drawing]=[0,0,0,0] # RGBA
-        
-        reuse = False
-        
-        if st.button('Submit'):
-            
-            with st.spinner("AI is doing the magic!"):
-                output = process_inpaint(np.array(img_input), np.array(im)) #TODO Put button here
-                img_output = Image.fromarray(output).convert("RGB")
-            
-            st.write("AI has finished the job!")
-            st.image(img_output)
-            # reuse = st.button('Edit again (Re-use this image)', on_click=set_image, args=(inpainted_img, ))
-            
-            uploaded_name = os.path.splitext(uploaded_file.name)[0]
-            image_download_button(
-                pil_image=img_output,
-                filename=uploaded_name,
-                fmt="jpg",
-                label="Download Image"
-            )
-            
-            st.info("**TIP**: If the result is not perfect, you can download it then "
-                    "upload then remove the artifacts.")
diff --git a/spaces/freeCS-dot-org/phi-1_5/README.md b/spaces/freeCS-dot-org/phi-1_5/README.md
deleted file mode 100644
index 3090f1d6d0a6330b2c87a4ef28efc97e67b8ed24..0000000000000000000000000000000000000000
--- a/spaces/freeCS-dot-org/phi-1_5/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Phi-1 5
-emoji: 🏆
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.44.0
-app_file: app.py
-pinned: false
-license: other
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/g4f/freegpt-webui/client/css/buttons.css b/spaces/g4f/freegpt-webui/client/css/buttons.css
deleted file mode 100644
index e13f52d9a0414daaa80518bd205913a645a29563..0000000000000000000000000000000000000000
--- a/spaces/g4f/freegpt-webui/client/css/buttons.css
+++ /dev/null
@@ -1,4 +0,0 @@
-.buttons {
-	display: flex;
-	justify-content: left;
-}
diff --git a/spaces/gaviego/removebg/app.py b/spaces/gaviego/removebg/app.py
deleted file mode 100644
index 0760397d88fb26f250a8c4e151753c2b291ddf12..0000000000000000000000000000000000000000
--- a/spaces/gaviego/removebg/app.py
+++ /dev/null
@@ -1,199 +0,0 @@
-import gradio as gr
-import cv2
-import gradio as gr
-import os
-from pathlib import Path
-from PIL import Image
-import numpy as np
-import torch
-from torch.autograd import Variable
-from torchvision import transforms
-import torch.nn.functional as F
-import matplotlib.pyplot as plt
-import warnings
-import tempfile
-from zipfile import ZipFile
-
-warnings.filterwarnings("ignore")
-
-
-# project imports
-from data_loader_cache import normalize, im_reader, im_preprocess 
-from models import *
-
-#Helpers
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-    
-class GOSNormalize(object):
-    '''
-    Normalize the Image using torch.transforms
-    '''
-    def __init__(self, mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]):
-        self.mean = mean
-        self.std = std
-
-    def __call__(self,image):
-        image = normalize(image,self.mean,self.std)
-        return image
-
-
-transform =  transforms.Compose([GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0])])
-
-def load_image(im_path, hypar):
-    im = im_reader(im_path)
-    im, im_shp = im_preprocess(im, hypar["cache_size"])
-    im = torch.divide(im,255.0)
-    shape = torch.from_numpy(np.array(im_shp))
-    return transform(im).unsqueeze(0), shape.unsqueeze(0) # make a batch of image, shape
-
-
-def build_model(hypar,device):
-    net = hypar["model"]#GOSNETINC(3,1)
-
-    # convert to half precision
-    if(hypar["model_digit"]=="half"):
-        net.half()
-        for layer in net.modules():
-            if isinstance(layer, nn.BatchNorm2d):
-                layer.float()
-
-    net.to(device)
-
-    if(hypar["restore_model"]!=""):
-        net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"], map_location=device))
-        net.to(device)
-    net.eval()  
-    return net
-
-    
-def predict(net,  inputs_val, shapes_val, hypar, device):
-    '''
-    Given an Image, predict the mask
-    '''
-    net.eval()
-
-    if(hypar["model_digit"]=="full"):
-        inputs_val = inputs_val.type(torch.FloatTensor)
-    else:
-        inputs_val = inputs_val.type(torch.HalfTensor)
-
-  
-    inputs_val_v = Variable(inputs_val, requires_grad=False).to(device) # wrap inputs in Variable
-   
-    ds_val = net(inputs_val_v)[0] # list of 6 results
-
-    pred_val = ds_val[0][0,:,:,:] # B x 1 x H x W    # we want the first one which is the most accurate prediction
-
-    ## recover the prediction spatial size to the orignal image size
-    pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[0][0],shapes_val[0][1]),mode='bilinear'))
-
-    ma = torch.max(pred_val)
-    mi = torch.min(pred_val)
-    pred_val = (pred_val-mi)/(ma-mi) # max = 1
-
-    if device == 'cuda': torch.cuda.empty_cache()
-    return (pred_val.detach().cpu().numpy()*255).astype(np.uint8) # it is the mask we need
-    
-# Set Parameters
-hypar = {} # paramters for inferencing
-
-
-hypar["model_path"] ="./saved_models" ## load trained weights from this path
-hypar["restore_model"] = "isnet.pth" ## name of the to-be-loaded weights
-hypar["interm_sup"] = False ## indicate if activate intermediate feature supervision
-
-##  choose floating point accuracy --
-hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
-hypar["seed"] = 0
-
-hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
-
-## data augmentation parameters ---
-hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
-hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
-
-hypar["model"] = ISNetDIS()
-
- # Build Model
-net = build_model(hypar, device)
-
-
-def inference(image_path):
-  
-  image_tensor, orig_size = load_image(image_path, hypar) 
-  mask = predict(net, image_tensor, orig_size, hypar, device)
-  
-  pil_mask = Image.fromarray(mask).convert('L')
-  im_rgb = Image.open(image_path).convert("RGB")
-  
-  im_rgba = im_rgb.copy()
-  im_rgba.putalpha(pil_mask)
-  file_name = Path(image_path).stem+"_nobg.png"
-  file_path = Path(Path(image_path).parent,file_name)
-  im_rgba.save(file_path)
-  return str(file_path.resolve())
-
-def bw(image_files):
-    print(image_files)
-    output = []
-    for idx, file in enumerate(image_files):
-        print(file.name)
-        img = Image.open(file.name)
-        img = img.convert("L")
-        output.append(img)
-    print(output)
-    return output
-
-def bw_single(image_file):
-    img = Image.open(image_file)
-    img = img.convert("L")
-    return img
-
-def batch(image_files):
-    output = []
-    for idx, file in enumerate(image_files):
-        file = inference(file.name)
-        output.append(file)
-    
-    with ZipFile("tmp.zip", "w") as zipObj:
-        for idx, file in enumerate(output):
-            zipObj.write(file, file.split("/")[-1])
-    return output,"tmp.zip"
-
-with gr.Blocks() as iface:
-    gr.Markdown("# Remove Background")
-    gr.HTML("Uses <a href='https://github.com/xuebinqin/DIS'>DIS</a> to remove background")
-    with gr.Tab("Single Image"):
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image(type='filepath')
-            with gr.Column():
-                image_output = gr.Image(interactive=False)
-        with gr.Row():
-            with gr.Column():
-                single_removebg = gr.Button("Remove Bg")
-            with gr.Column():
-                single_clear = gr.Button("Clear")
-
-    
-    with gr.Tab("Batch"):
-        with gr.Row():
-            with gr.Column():
-                images = gr.File(file_count="multiple", file_types=["image"])
-            with gr.Column():
-                gallery = gr.Gallery()
-                file_list = gr.Files(interactive=False)
-
-        with gr.Row():
-            with gr.Column():
-                batch_removebg = gr.Button("Batch Process")
-            with gr.Column():
-                batch_clear = gr.Button("Clear")
-    #Events
-    single_removebg.click(inference, inputs=image, outputs=image_output)
-    batch_removebg.click(batch, inputs=images, outputs=[gallery,file_list])
-    single_clear.click(lambda: None, None, image, queue=False)
-    batch_clear.click(lambda: None, None, images, queue=False)
-
-iface.launch()
\ No newline at end of file
diff --git a/spaces/genevera/AudioToken/README.md b/spaces/genevera/AudioToken/README.md
deleted file mode 100644
index 62c6d781b29ee92b1c380611952c8e0a1d5d3288..0000000000000000000000000000000000000000
--- a/spaces/genevera/AudioToken/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: AudioToken
-emoji: 🏆
-colorFrom: indigo
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/ext_loader.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/ext_loader.py
deleted file mode 100644
index 08132d2c1b9a1c28880e4bab4d4fa1ba39d9d083..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/ext_loader.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import importlib
-import os
-import pkgutil
-import warnings
-from collections import namedtuple
-
-import torch
-
-if torch.__version__ != 'parrots':
-
-    def load_ext(name, funcs):
-        ext = importlib.import_module('mmcv.' + name)
-        for fun in funcs:
-            assert hasattr(ext, fun), f'{fun} miss in module {name}'
-        return ext
-else:
-    from parrots import extension
-    from parrots.base import ParrotsException
-
-    has_return_value_ops = [
-        'nms',
-        'softnms',
-        'nms_match',
-        'nms_rotated',
-        'top_pool_forward',
-        'top_pool_backward',
-        'bottom_pool_forward',
-        'bottom_pool_backward',
-        'left_pool_forward',
-        'left_pool_backward',
-        'right_pool_forward',
-        'right_pool_backward',
-        'fused_bias_leakyrelu',
-        'upfirdn2d',
-        'ms_deform_attn_forward',
-        'pixel_group',
-        'contour_expand',
-    ]
-
-    def get_fake_func(name, e):
-
-        def fake_func(*args, **kwargs):
-            warnings.warn(f'{name} is not supported in parrots now')
-            raise e
-
-        return fake_func
-
-    def load_ext(name, funcs):
-        ExtModule = namedtuple('ExtModule', funcs)
-        ext_list = []
-        lib_root = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))
-        for fun in funcs:
-            try:
-                ext_fun = extension.load(fun, name, lib_dir=lib_root)
-            except ParrotsException as e:
-                if 'No element registered' not in e.message:
-                    warnings.warn(e.message)
-                ext_fun = get_fake_func(fun, e)
-                ext_list.append(ext_fun)
-            else:
-                if fun in has_return_value_ops:
-                    ext_list.append(ext_fun.op)
-                else:
-                    ext_list.append(ext_fun.op_)
-        return ExtModule(*ext_list)
-
-
-def check_ops_exist():
-    ext_loader = pkgutil.find_loader('mmcv._ext')
-    return ext_loader is not None
diff --git a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/misc.py b/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/misc.py
deleted file mode 100644
index 2c58d0d7fee9fe3d4519270ad8c1e998d0d8a18c..0000000000000000000000000000000000000000
--- a/spaces/georgefen/Face-Landmark-ControlNet/annotator/uniformer/mmcv/utils/misc.py
+++ /dev/null
@@ -1,377 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import collections.abc
-import functools
-import itertools
-import subprocess
-import warnings
-from collections import abc
-from importlib import import_module
-from inspect import getfullargspec
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable):
-            return x
-        return tuple(repeat(x, n))
-
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def is_str(x):
-    """Whether the input is an string instance.
-
-    Note: This method is deprecated since python 2 is no longer supported.
-    """
-    return isinstance(x, str)
-
-
-def import_modules_from_strings(imports, allow_failed_imports=False):
-    """Import modules from the given list of strings.
-
-    Args:
-        imports (list | str | None): The given module names to be imported.
-        allow_failed_imports (bool): If True, the failed imports will return
-            None. Otherwise, an ImportError is raise. Default: False.
-
-    Returns:
-        list[module] | module | None: The imported modules.
-
-    Examples:
-        >>> osp, sys = import_modules_from_strings(
-        ...     ['os.path', 'sys'])
-        >>> import os.path as osp_
-        >>> import sys as sys_
-        >>> assert osp == osp_
-        >>> assert sys == sys_
-    """
-    if not imports:
-        return
-    single_import = False
-    if isinstance(imports, str):
-        single_import = True
-        imports = [imports]
-    if not isinstance(imports, list):
-        raise TypeError(
-            f'custom_imports must be a list but got type {type(imports)}')
-    imported = []
-    for imp in imports:
-        if not isinstance(imp, str):
-            raise TypeError(
-                f'{imp} is of type {type(imp)} and cannot be imported.')
-        try:
-            imported_tmp = import_module(imp)
-        except ImportError:
-            if allow_failed_imports:
-                warnings.warn(f'{imp} failed to import and is ignored.',
-                              UserWarning)
-                imported_tmp = None
-            else:
-                raise ImportError
-        imported.append(imported_tmp)
-    if single_import:
-        imported = imported[0]
-    return imported
-
-
-def iter_cast(inputs, dst_type, return_type=None):
-    """Cast elements of an iterable object into some type.
-
-    Args:
-        inputs (Iterable): The input object.
-        dst_type (type): Destination type.
-        return_type (type, optional): If specified, the output object will be
-            converted to this type, otherwise an iterator.
-
-    Returns:
-        iterator or specified type: The converted object.
-    """
-    if not isinstance(inputs, abc.Iterable):
-        raise TypeError('inputs must be an iterable object')
-    if not isinstance(dst_type, type):
-        raise TypeError('"dst_type" must be a valid type')
-
-    out_iterable = map(dst_type, inputs)
-
-    if return_type is None:
-        return out_iterable
-    else:
-        return return_type(out_iterable)
-
-
-def list_cast(inputs, dst_type):
-    """Cast elements of an iterable object into a list of some type.
-
-    A partial method of :func:`iter_cast`.
-    """
-    return iter_cast(inputs, dst_type, return_type=list)
-
-
-def tuple_cast(inputs, dst_type):
-    """Cast elements of an iterable object into a tuple of some type.
-
-    A partial method of :func:`iter_cast`.
-    """
-    return iter_cast(inputs, dst_type, return_type=tuple)
-
-
-def is_seq_of(seq, expected_type, seq_type=None):
-    """Check whether it is a sequence of some type.
-
-    Args:
-        seq (Sequence): The sequence to be checked.
-        expected_type (type): Expected type of sequence items.
-        seq_type (type, optional): Expected sequence type.
-
-    Returns:
-        bool: Whether the sequence is valid.
-    """
-    if seq_type is None:
-        exp_seq_type = abc.Sequence
-    else:
-        assert isinstance(seq_type, type)
-        exp_seq_type = seq_type
-    if not isinstance(seq, exp_seq_type):
-        return False
-    for item in seq:
-        if not isinstance(item, expected_type):
-            return False
-    return True
-
-
-def is_list_of(seq, expected_type):
-    """Check whether it is a list of some type.
-
-    A partial method of :func:`is_seq_of`.
-    """
-    return is_seq_of(seq, expected_type, seq_type=list)
-
-
-def is_tuple_of(seq, expected_type):
-    """Check whether it is a tuple of some type.
-
-    A partial method of :func:`is_seq_of`.
-    """
-    return is_seq_of(seq, expected_type, seq_type=tuple)
-
-
-def slice_list(in_list, lens):
-    """Slice a list into several sub lists by a list of given length.
-
-    Args:
-        in_list (list): The list to be sliced.
-        lens(int or list): The expected length of each out list.
-
-    Returns:
-        list: A list of sliced list.
-    """
-    if isinstance(lens, int):
-        assert len(in_list) % lens == 0
-        lens = [lens] * int(len(in_list) / lens)
-    if not isinstance(lens, list):
-        raise TypeError('"indices" must be an integer or a list of integers')
-    elif sum(lens) != len(in_list):
-        raise ValueError('sum of lens and list length does not '
-                         f'match: {sum(lens)} != {len(in_list)}')
-    out_list = []
-    idx = 0
-    for i in range(len(lens)):
-        out_list.append(in_list[idx:idx + lens[i]])
-        idx += lens[i]
-    return out_list
-
-
-def concat_list(in_list):
-    """Concatenate a list of list into a single list.
-
-    Args:
-        in_list (list): The list of list to be merged.
-
-    Returns:
-        list: The concatenated flat list.
-    """
-    return list(itertools.chain(*in_list))
-
-
-def check_prerequisites(
-        prerequisites,
-        checker,
-        msg_tmpl='Prerequisites "{}" are required in method "{}" but not '
-        'found, please install them first.'):  # yapf: disable
-    """A decorator factory to check if prerequisites are satisfied.
-
-    Args:
-        prerequisites (str of list[str]): Prerequisites to be checked.
-        checker (callable): The checker method that returns True if a
-            prerequisite is meet, False otherwise.
-        msg_tmpl (str): The message template with two variables.
-
-    Returns:
-        decorator: A specific decorator.
-    """
-
-    def wrap(func):
-
-        @functools.wraps(func)
-        def wrapped_func(*args, **kwargs):
-            requirements = [prerequisites] if isinstance(
-                prerequisites, str) else prerequisites
-            missing = []
-            for item in requirements:
-                if not checker(item):
-                    missing.append(item)
-            if missing:
-                print(msg_tmpl.format(', '.join(missing), func.__name__))
-                raise RuntimeError('Prerequisites not meet.')
-            else:
-                return func(*args, **kwargs)
-
-        return wrapped_func
-
-    return wrap
-
-
-def _check_py_package(package):
-    try:
-        import_module(package)
-    except ImportError:
-        return False
-    else:
-        return True
-
-
-def _check_executable(cmd):
-    if subprocess.call(f'which {cmd}', shell=True) != 0:
-        return False
-    else:
-        return True
-
-
-def requires_package(prerequisites):
-    """A decorator to check if some python packages are installed.
-
-    Example:
-        >>> @requires_package('numpy')
-        >>> func(arg1, args):
-        >>>     return numpy.zeros(1)
-        array([0.])
-        >>> @requires_package(['numpy', 'non_package'])
-        >>> func(arg1, args):
-        >>>     return numpy.zeros(1)
-        ImportError
-    """
-    return check_prerequisites(prerequisites, checker=_check_py_package)
-
-
-def requires_executable(prerequisites):
-    """A decorator to check if some executable files are installed.
-
-    Example:
-        >>> @requires_executable('ffmpeg')
-        >>> func(arg1, args):
-        >>>     print(1)
-        1
-    """
-    return check_prerequisites(prerequisites, checker=_check_executable)
-
-
-def deprecated_api_warning(name_dict, cls_name=None):
-    """A decorator to check if some arguments are deprecate and try to replace
-    deprecate src_arg_name to dst_arg_name.
-
-    Args:
-        name_dict(dict):
-            key (str): Deprecate argument names.
-            val (str): Expected argument names.
-
-    Returns:
-        func: New function.
-    """
-
-    def api_warning_wrapper(old_func):
-
-        @functools.wraps(old_func)
-        def new_func(*args, **kwargs):
-            # get the arg spec of the decorated method
-            args_info = getfullargspec(old_func)
-            # get name of the function
-            func_name = old_func.__name__
-            if cls_name is not None:
-                func_name = f'{cls_name}.{func_name}'
-            if args:
-                arg_names = args_info.args[:len(args)]
-                for src_arg_name, dst_arg_name in name_dict.items():
-                    if src_arg_name in arg_names:
-                        warnings.warn(
-                            f'"{src_arg_name}" is deprecated in '
-                            f'`{func_name}`, please use "{dst_arg_name}" '
-                            'instead')
-                        arg_names[arg_names.index(src_arg_name)] = dst_arg_name
-            if kwargs:
-                for src_arg_name, dst_arg_name in name_dict.items():
-                    if src_arg_name in kwargs:
-
-                        assert dst_arg_name not in kwargs, (
-                            f'The expected behavior is to replace '
-                            f'the deprecated key `{src_arg_name}` to '
-                            f'new key `{dst_arg_name}`, but got them '
-                            f'in the arguments at the same time, which '
-                            f'is confusing. `{src_arg_name} will be '
-                            f'deprecated in the future, please '
-                            f'use `{dst_arg_name}` instead.')
-
-                        warnings.warn(
-                            f'"{src_arg_name}" is deprecated in '
-                            f'`{func_name}`, please use "{dst_arg_name}" '
-                            'instead')
-                        kwargs[dst_arg_name] = kwargs.pop(src_arg_name)
-
-            # apply converted arguments to the decorated method
-            output = old_func(*args, **kwargs)
-            return output
-
-        return new_func
-
-    return api_warning_wrapper
-
-
-def is_method_overridden(method, base_class, derived_class):
-    """Check if a method of base class is overridden in derived class.
-
-    Args:
-        method (str): the method name to check.
-        base_class (type): the class of the base class.
-        derived_class (type | Any): the class or instance of the derived class.
-    """
-    assert isinstance(base_class, type), \
-        "base_class doesn't accept instance, Please pass class instead."
-
-    if not isinstance(derived_class, type):
-        derived_class = derived_class.__class__
-
-    base_method = getattr(base_class, method)
-    derived_method = getattr(derived_class, method)
-    return derived_method != base_method
-
-
-def has_method(obj: object, method: str) -> bool:
-    """Check whether the object has a method.
-
-    Args:
-        method (str): The method name to check.
-        obj (object): The object to check.
-
-    Returns:
-        bool: True if the object has the method else False.
-    """
-    return hasattr(obj, method) and callable(getattr(obj, method))
diff --git a/spaces/giseldo/story_point_estimator_metrics/database/tawos/README.md b/spaces/giseldo/story_point_estimator_metrics/database/tawos/README.md
deleted file mode 100644
index 55c427206c5b31e98592845aa12717c51d46d8fc..0000000000000000000000000000000000000000
--- a/spaces/giseldo/story_point_estimator_metrics/database/tawos/README.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Tawosi Dataset
-
-This directory consists 46 files, two files per each of the 26-3\*=23 projects:
-- 23 files with "\_deep-se" suffix are prepared to be used by Deep-SE.
-- 23 files with "\_tfidf-se" suffix are prepared to be used by TF/IDF-SE.
-
-\* <sub>One of the repositories including three projects has been removed from the public domain during the time that the manuscript for this study [1] was under revision. Therefore, although the paper reports the results for all 26 projects, the replication package includes 23 projects as we refrain from publishing the data for the three remaining projects in accordance with The General Data Protection Regulation.</sub> 
-
-These 23 files are collected from 12 open source repositories by Tawosi et al. up until August, 2020.
-The files named after their project key as "[project key]\_[approach].csv" e.g. MESOS_deep-se.csv, which is the set of issues collected from Appache repository Mesos project, and contains the features that Deep-SE needs for prediction. The following table shows the list of projects and the repositories where the project was collected from.   
-
-## Project list
-
-| Repository   | Project                           | Key        | File for Deep-SE          | File for TF/IDF-SE        |
-|--------------|-----------------------------------|------------|---------------------------|---------------------------|
-| Apache       | Mesos                             | MESOS      | MESOS_deeep-se.csv        | MESOS_tfidf-se.csv        |
-| Apache       | Alloy                             | ALOY       | ALOY_deeep-se.csv         | ALOY_tfidf-se.csv         |
-| Appcelerator | Appcelerator studio               | TISTUD     | TISTUD_deeep-se.csv       | TISTUD_tfidf-se.csv       |
-| Appcelerator | Aptana studio                     | APSTUD     | APSTUD_deeep-se.csv       | APSTUD_tfidf-se.csv       |
-| Appcelerator | Command-Line Interface            | CLI        | CLI_deeep-se.csv          | CLI_tfidf-se.csv          |
-| Appcelerator | Daemon                            | DAEMON     | DAEMON_deeep-se.csv       | DAEMON_tfidf-se.csv       |
-| Appcelerator | Documentation                     | TIDOC      | TIDOC_deeep-se.csv        | TIDOC_tfidf-se.csv        |
-| Appcelerator | Titanium                          | TIMOB      | TIMOB_deeep-se.csv        | TIMOB_tfidf-se.csv        |
-| Atlassian    | Clover                            | CLOV       | CLOV_deeep-se.csv         | CLOV_tfidf-se.csv         |
-| Atlassian    | Confluence Cloud                  | CONFCLOUD  | CONFCLOUD_deeep-se.csv    | CONFCLOUD_tfidf-se.csv    |
-| Atlassian    | Confluence Server and Data Center | CONFSERVER | CONFSERVER_deeep-se.csv   | CONFSERVER_tfidf-se.csv   |
-| DNNSoftware  | DNN                               | DNN        | DNN_deeep-se.csv          | DNN_tfidf-se.csv          |
-| Duraspace    | Duracloud                         | DURACLOUD  | DURACLOUD_deeep-se.csv    | DURACLOUD_tfidf-se.csv    |
-| Hyperledger  | Fabric                            | FAB        | FAB_deeep-se.csv          | FAB_tfidf-se.csv          |
-| Hyperledger  | Sawtooth                          | STL        | STL_deeep-se.csv          | STL_tfidf-se.csv          |
-| Lsstcorp     | Data management                   | DM         | DM_deeep-se.csv           | DM_tfidf-se.csv           |
-| MongoDB      | Compass                           | COMPASS    | COMPASS_deeep-se.csv      | COMPASS_tfidf-se.csv      |
-| MongoDB      | Core Server                       | SERVER     | SERVER_deeep-se.csv       | SERVER_tfidf-se.csv       |
-| MongoDB      | Evergreen                         | EVG        | EVG_deeep-se.csv          | EVG_tfidf-se.csv          |
-| Moodle       | Moodle                            | MDL        | MDL_deeep-se.csv          | MDL_tfidf-se.csv          |
-| Mulesoft     | Mule                              | MULE       | MULE_deeep-se.csv         | MULE_tfidf-se.csv         |
-| Sonatype     | Sonatype’s Nexus                  | NEXUS      | NEXUS_deeep-se.csv        | NEXUS_tfidf-se.csv        |
-| Spring       | Spring XD                         | XD         | XD_deeep-se.csv           | XD_tfidf-se.csv           |
-
-## Content of the files 
-
-- Each csv file for Deep-SE approach contains 4 columns: *issuekey*, *created*, *title*, *description*, and *storypoint*. 
-
-- Each csv file for TF/IDF-SE approach contains more than 4 columns: starting with *issuekey*, *created*, *storypoint*, *context*, *codesnippet*, and a set of one-hot columns for issue type (header starting with t\_) followed by component(s) (header starting with c\_). 
-
-- The issues are sorted based on issue's creation time (i.e. the former issues was created before the latter issues).
-
-[1] Vali Tawosi, Rebecca Moussa, and Federica Sarro. "Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study." IEEE Transactions on Software Engineering, no. TBA (2022): pp. TBA.
diff --git a/spaces/gotiQspiryo/whisper-ui/examples/3d Max 2012 Product Key WORK.md b/spaces/gotiQspiryo/whisper-ui/examples/3d Max 2012 Product Key WORK.md
deleted file mode 100644
index 70c91e2bfaddc664dd8a168387f248feb2111b66..0000000000000000000000000000000000000000
--- a/spaces/gotiQspiryo/whisper-ui/examples/3d Max 2012 Product Key WORK.md	
+++ /dev/null
@@ -1,125 +0,0 @@
-
-<h1>3d Max 2012 Product Key: What You Need to Know</h1>
-
-<p>3d Max 2012 is a powerful software for 3D modeling, animation, rendering, and visual effects. It is widely used by professionals in the fields of architecture, design, engineering, entertainment, and media. If you want to use this software, you need to have a valid product key that matches your product and license type.</p>
-
-<p>A product key is a unique code that identifies the relationship between the software and the product. It is required for installation and activation of Autodesk products. It also helps to differentiate products that are sold independently or as part of a suite or collection.</p>
-<h2>3d max 2012 product key</h2><br /><p><b><b>Download</b> &#10001; &#10001; &#10001; <a href="https://urlgoal.com/2uyM1K">https://urlgoal.com/2uyM1K</a></b></p><br /><br />
-
-<p>In this article, we will explain how to find and use your 3d Max 2012 product key, as well as some tips and tricks for getting the most out of your software.</p>
-
-<h2>How to Find Your 3d Max 2012 Product Key</h2>
-
-<p>There are different ways to find your 3d Max 2012 product key, depending on how you purchased your software. Here are some common methods:</p>
-
-<ul>
-<li>If you bought your software from an authorized Autodesk reseller or online store, you can find your product key on the software box, or in the confirmation email that you received after purchase.</li>
-<li>If you downloaded your software from Autodesk Account or Autodesk Education Community, you can find your product key in the "Product Information" section of your account page.</li>
-<li>If you are a subscriber or a maintenance plan customer, you can find your product key in the "Product & Services" section of your account page.</li>
-<li>If you are using a trial version of the software, you can find your product key in the "About" dialog box of the software.</li>
-</ul>
-
-<p>The product key for 3d Max 2012 is 128D1. However, if you are using a different version of the software, or if you are using a suite or collection that includes 3d Max 2012, you may have a different product key. You can check the full list of product keys for Autodesk 2010-2013 products <a href="https://microsolresources.com/tech-resources/article/product-keys-for-autodesk-products-2010-2013/">here</a>.</p>
-
-<h2>How to Use Your 3d Max 2012 Product Key</h2>
-
-<p>Once you have your product key, you can use it to install and activate your 3d Max 2012 software. Here are the steps to follow:</p>
-
-<ol>
-<li>Download the installer file for your software from Autodesk Account or Autodesk Education Community.</li>
-<li>Run the installer file and follow the instructions on the screen.</li>
-<li>When prompted, enter your serial number and product key. The serial number is a unique code that identifies your license. You can find it in the same place as your product key.</li>
-<li>Complete the installation process and launch your software.</li>
-<li>When prompted, sign in with your Autodesk ID and password. This will activate your software and link it to your account.</li>
-</ol>
-
-<p>If you have any issues with installation or activation, you can contact Autodesk Support or visit their <a href="https://knowledge.autodesk.com/support/3ds-max">knowledge base</a> for more information.</p>
-
-<h2>Tips and Tricks for Getting the Most Out of Your 3d Max 2012 Product Key</h2>
-
-<p>Now that you have installed and activated your 3d Max 2012 software, you can start creating amazing 3D projects. Here are some tips and tricks to help you get the most out of your product key:</p>
-<p></p>
-
-<ul>
-<li>Keep your product key safe and secure. Do not share it with anyone or post it online. If you lose it or forget it, you can always find it in your Autodesk Account or contact Autodesk Support.</li>
-<li>Make sure you have the latest updates and service packs for your software. This will ensure that your software runs smoothly and has the latest features and fixes. You can check for updates in the "Updates & Add-ons" section of your account page.</li>
-<li>Explore the various tools and features that 3d Max 2012 has to offer. You can learn more about them by watching tutorials, reading documentation, or joining forums and communities. You can also access online learning resources from within the software by clicking on the "Help" menu.</li>
-<li>Take advantage of the benefits that come with your subscription or maintenance plan. You can access cloud services, technical support, flexible licensing options, and more. You can also upgrade to newer versions of the software as they become available.</li>
-</ul>
-
-<p>We hope this article has helped you understand how to find and use your 3d Max 2012 product key. If you have any questions or feedback, feel free to leave a comment below.</p>
-<h2>What are the Benefits of Using 3d Max 2012 Product Key</h2>
-
-<p>Using 3d Max 2012 product key has many benefits for your 3D projects. Here are some of them:</p>
-
-<ul>
-<li>You can access the full features and functions of 3d Max 2012 software, such as modeling, animation, rendering, dynamics, scripting, and more.</li>
-<li>You can create stunning 3D visuals for various purposes, such as games, films, TV shows, commercials, architectural designs, engineering simulations, and more.</li>
-<li>You can work with a wide range of file formats and interoperability options, such as DWG, FBX, OBJ, DAE, and more.</li>
-<li>You can customize and extend your software with plugins, scripts, and SDKs.</li>
-<li>You can collaborate with other artists and professionals using online services and cloud computing.</li>
-</ul>
-
-<p>With 3d Max 2012 product key, you can unleash your creativity and imagination in 3D.</p>
-
-<h2>How to Troubleshoot Your 3d Max 2012 Product Key</h2>
-
-<p>Sometimes, you may encounter some issues or errors with your 3d Max 2012 product key. Here are some common problems and solutions:</p>
-
-<ul>
-<li>If your product key is invalid or incorrect, make sure you entered it correctly and that it matches your product and license type. You can also try to find your product key again using the methods mentioned above.</li>
-<li>If your product key is expired or revoked, contact Autodesk Support or your reseller for assistance. You may need to renew your subscription or maintenance plan, or request a new product key.</li>
-<li>If your product key is already in use or exceeded the limit of activations, contact Autodesk Support or your reseller for assistance. You may need to deactivate or transfer your license to another device.</li>
-<li>If you have any other issues or errors with your product key, visit the Autodesk <a href="https://knowledge.autodesk.com/support/3ds-max">knowledge base</a> or <a href="https://forums.autodesk.com/t5/3ds-max-forum/bd-p/area-c1">forums</a> for more information and solutions.</li>
-</ul>
-
-<p>If you need further help or support with your product key, you can always contact Autodesk Support or your reseller for assistance.</p>
-<h2>What are the Features of 3d Max 2012 Product Key</h2>
-
-<p>3d Max 2012 product key gives you access to a variety of features that can enhance your 3D projects. Here are some of them:</p>
-
-<ul>
-<li>You can create realistic and detailed 3D models using a range of modeling tools, such as polygon, spline, NURBS, and subdivision surface modeling.</li>
-<li>You can animate your 3D models using a range of animation tools, such as keyframe, procedural, character studio, motion capture, and motion mixer animation.</li>
-<li>You can render your 3D models using a range of rendering tools, such as scanline, mental ray, iray, Quicksilver, and Nitrous viewport rendering.</li>
-<li>You can add dynamic effects to your 3D models using a range of simulation tools, such as cloth, hair and fur, particles, fluids, rigid and soft bodies, and reactor physics.</li>
-<li>You can script and customize your software using a range of programming tools, such as MAXScript, Python, .NET, C++, and SDKs.</li>
-</ul>
-
-<p>With 3d Max 2012 product key, you can create amazing 3D visuals for any purpose.</p>
-
-<h2>How to Upgrade Your 3d Max 2012 Product Key</h2>
-
-<p>If you want to upgrade your 3d Max 2012 product key to a newer version of the software, you have several options. Here are some of them:</p>
-
-<ul>
-<li>If you are a subscriber or a maintenance plan customer, you can upgrade to the latest version of the software as soon as it becomes available. You can download the installer file from your Autodesk Account or Autodesk Education Community.</li>
-<li>If you are not a subscriber or a maintenance plan customer, you can purchase a new subscription or maintenance plan for the latest version of the software. You can contact your reseller or visit the Autodesk online store for more information.</li>
-<li>If you want to upgrade to a different product or suite that includes 3d Max 2012 or a newer version of it, you can trade in your old product key for a new one. You can contact your reseller or visit the Autodesk online store for more information.</li>
-</ul>
-
-<p>If you need further help or support with upgrading your product key, you can always contact Autodesk Support or your reseller for assistance.</p>
-<h2>What are the Alternatives to 3d Max 2012 Product Key</h2>
-
-<p>If you are looking for alternatives to 3d Max 2012 product key, you have several options. Here are some of them:</p>
-
-<ul>
-<li>You can use other Autodesk products that are similar to 3d Max 2012, such as Maya, MotionBuilder, Mudbox, or Softimage. These products have different features and workflows, but they can also create high-quality 3D visuals. You can find more information about these products on the Autodesk website.</li>
-<li>You can use other 3D software that are not from Autodesk, such as Blender, Cinema 4D, Houdini, or ZBrush. These software have different advantages and disadvantages, but they can also create amazing 3D visuals. You can find more information about these software on their respective websites.</li>
-<li>You can use online 3D tools that do not require installation or product keys, such as Sketchfab, Tinkercad, or Vectary. These tools are easy to use and accessible from any device, but they may have limited features and capabilities. You can find more information about these tools on their respective websites.</li>
-</ul>
-
-<p>With these alternatives, you can still create stunning 3D projects without using 3d Max 2012 product key.</p>
-
-<h2>Conclusion</h2>
-
-<p>In this article, we have explained how to find and use your 3d Max 2012 product key, as well as some tips and tricks for getting the most out of your software. We have also discussed some of the benefits, features, and alternatives of using 3d Max 2012 product key. We hope this article has helped you understand how to create amazing 3D visuals with 3d Max 2012 product key.</p>
-
-<p>If you have any questions or feedback, feel free to leave a comment below.</p>
-<h2>Conclusion</h2>
-
-<p>In this article, we have explained how to find and use your 3d Max 2012 product key, as well as some tips and tricks for getting the most out of your software. We have also discussed some of the benefits, features, and alternatives of using 3d Max 2012 product key. We hope this article has helped you understand how to create amazing 3D visuals with 3d Max 2012 product key.</p>
-
-<p>If you have any questions or feedback, feel free to leave a comment below.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/gradio-discord-bots/gpt-35-turbo/app.py b/spaces/gradio-discord-bots/gpt-35-turbo/app.py
deleted file mode 100644
index e7aab82019451af5a754e138ea09f143f3e87583..0000000000000000000000000000000000000000
--- a/spaces/gradio-discord-bots/gpt-35-turbo/app.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import gradio as gr
-import os
-import openai
-import gradio as gr
-from gradio import ChatInterface
-import time
-
-# Get the value of the openai_api_key from environment variable
-openai.api_key = os.getenv("OPENAI_API_KEY")
-
-# Import things that are needed generically from langchain
-from langchain import LLMMathChain, SerpAPIWrapper
-from langchain.agents import AgentType, initialize_agent, load_tools
-from langchain.chat_models import ChatOpenAI
-from langchain.tools import BaseTool, StructuredTool, Tool, tool
-from langchain.tools import MoveFileTool, format_tool_to_openai_function
-from langchain.schema import (
-    AIMessage,
-    HumanMessage,
-    SystemMessage
-)
-from langchain.utilities import WikipediaAPIWrapper
-from langchain.tools import AIPluginTool
-
-def predict(inputs, chatbot):
-
-    messages = []
-    for conv in chatbot:
-        user = conv[0]
-        messages.append({"role": "user", "content":user })
-        assistant = conv[1]
-        messages.append({"role": "assistant", "content":assistant})
-    messages.append({"role": "user", "content": inputs})
-
-    # a ChatCompletion request
-    response = openai.ChatCompletion.create(
-        model='gpt-3.5-turbo',
-        messages= messages, # example :  [{'role': 'user', 'content': "What is life? Answer in three words."}],
-        temperature=1.0,
-        stream=True  # for streaming the output to chatbot
-    )
-
-    partial_message = ""
-    for chunk in response:
-        if len(chunk['choices'][0]['delta']) != 0:
-          print(chunk['choices'][0]['delta']['content'])
-          partial_message = partial_message + chunk['choices'][0]['delta']['content']
-          yield partial_message 
-
-
-interface = gr.ChatInterface(predict)
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    # GPT 3.5 Discord Bot powered by gradio!
-
-    To use this space as a discord bot, first install the gradio_client
-    
-    ```bash
-    pip install gradio_client
-    ```
-    
-    Then run the following command
-    
-    ```python
-    client = grc.Client.duplicate("gradio-discord-bots/gpt-35-turbo", private=False, secrets={"OPENAI_API_KEY": "<your-key-here>"}, sleep_timeout=2880)
-    client.deploy_discord(api_names=["chat"])
-    """)
-    with gr.Row(visible=False):
-        interface.render()
-    
-demo.queue(concurrency_count=100).launch()
\ No newline at end of file
diff --git a/spaces/gradio/xgboost-income-prediction-with-explainability/DESCRIPTION.md b/spaces/gradio/xgboost-income-prediction-with-explainability/DESCRIPTION.md
deleted file mode 100644
index cd69ef8e1e3e3ab360b84e76c9cc222a74d4246f..0000000000000000000000000000000000000000
--- a/spaces/gradio/xgboost-income-prediction-with-explainability/DESCRIPTION.md
+++ /dev/null
@@ -1 +0,0 @@
-This demo takes in 12 inputs from the user in dropdowns and sliders and predicts income. It also has a separate button for explaining the prediction.
\ No newline at end of file
diff --git a/spaces/gyugnsu/DragGan-Inversion/stylegan_human/__init__.py b/spaces/gyugnsu/DragGan-Inversion/stylegan_human/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/hackathon-somos-nlp-2023/discriminacion_gitana/Dockerfile b/spaces/hackathon-somos-nlp-2023/discriminacion_gitana/Dockerfile
deleted file mode 100644
index f082970621660a3a398d4266140ceb3a4baa4895..0000000000000000000000000000000000000000
--- a/spaces/hackathon-somos-nlp-2023/discriminacion_gitana/Dockerfile
+++ /dev/null
@@ -1,6 +0,0 @@
-FROM argilla/argilla-quickstart:latest
-
-# Define datasets to preload: full=all datasets, single=one dataset, and none=no datasets.
-ENV LOAD_DATASETS=single
-
-CMD whoami && /start_quickstart_argilla.sh
\ No newline at end of file
diff --git "a/spaces/hands012/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py" "b/spaces/hands012/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
deleted file mode 100644
index 505086455af8d2676055ab084cf97058b954c7d5..0000000000000000000000000000000000000000
--- "a/spaces/hands012/gpt-academic/crazy_functions/\347\220\206\350\247\243PDF\346\226\207\346\241\243\345\206\205\345\256\271.py"
+++ /dev/null
@@ -1,112 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
-
-    ############################## <第 0 步，切割PDF> ##################################
-    # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
-    # 的长度必须小于 2500 个 Token
-    file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
-
-    TOKEN_LIMIT_PER_FRAGMENT = 2500
-
-    from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
-    from request_llm.bridge_all import model_info
-    enc = model_info["gpt-3.5-turbo"]['tokenizer']
-    def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
-    paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=file_content,  get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
-    page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
-        txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
-    # 为了更好的效果，我们剥离Introduction之后的部分（如果有）
-    paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
-    
-    ############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
-    final_results = []
-    final_results.append(paper_meta)
-
-    ############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
-    i_say_show_user = f'首先你在英文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。"           # 用户提示
-    chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[])    # 更新UI
-
-    iteration_results = []
-    last_iteration_result = paper_meta  # 初始值是摘要
-    MAX_WORD_TOTAL = 4096
-    n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
-    for i in range(n_fragment):
-        NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
-        i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
-        i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i][:200]}"
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user,  # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
-                                                                           llm_kwargs, chatbot, 
-                                                                           history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
-                                                                           sys_prompt="Extract the main idea of this section."  # 提示
-                                                                        ) 
-        iteration_results.append(gpt_say)
-        last_iteration_result = gpt_say
-
-    ############################## <第 3 步，整理history> ##################################
-    final_results.extend(iteration_results)
-    final_results.append(f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。')
-    # 接下来两句话只显示在界面上，不起实际作用
-    i_say_show_user = f'接下来，你是一名专业的学术教授，利用以上信息，使用中文回答我的问题。'; gpt_say = "[Local Message] 收到。"
-    chatbot.append([i_say_show_user, gpt_say])
-
-    ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
-    _, final_results = input_clipping("", final_results, max_token_limit=3200)
-    yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
-
-
-@CatchException
-def 理解PDF文档内容标准文件输入(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    import glob, os
-
-    # 基本信息：功能、贡献者
-    chatbot.append([
-        "函数插件功能？",
-        "理解PDF论文内容，并且将结合上下文内容，进行学术解答。函数插件贡献者: Hanzoe, binary-husky"])
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    # 尝试导入依赖，如果缺少依赖，则给出安装建议
-    try:
-        import fitz
-    except:
-        report_execption(chatbot, history, 
-            a = f"解析项目: {txt}", 
-            b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 清空历史，以免输入溢出
-    history = []
-
-    # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
-    # 如果没找到任何文件
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到任何.tex或.pdf文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    txt = file_manifest[0]
-    # 开始正式执行任务
-    yield from 解析PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/harshitv804/LawGPT/README.md b/spaces/harshitv804/LawGPT/README.md
deleted file mode 100644
index 28d1733a21282a63569a193977e9d728dffc05ef..0000000000000000000000000000000000000000
--- a/spaces/harshitv804/LawGPT/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: LawGPT
-emoji: 🏃
-colorFrom: yellow
-colorTo: pink
-sdk: gradio
-sdk_version: 3.39.0
-app_file: app.py
-pinned: false
-license: gpl
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/harshvardhansb/ObjectDetection/README.md b/spaces/harshvardhansb/ObjectDetection/README.md
deleted file mode 100644
index 9c40dcdc3d8c852db2b42a2da2d8f867029378b4..0000000000000000000000000000000000000000
--- a/spaces/harshvardhansb/ObjectDetection/README.md
+++ /dev/null
@@ -1,68 +0,0 @@
-This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
-
-## Available Scripts
-
-In the project directory, you can run:
-
-### `yarn start`
-
-Runs the app in the development mode.<br />
-Open [http://localhost:3000](http://localhost:3000) to view it in the browser.
-
-The page will reload if you make edits.<br />
-You will also see any lint errors in the console.
-
-### `yarn test`
-
-Launches the test runner in the interactive watch mode.<br />
-See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
-
-### `yarn build`
-
-Builds the app for production to the `build` folder.<br />
-It correctly bundles React in production mode and optimizes the build for the best performance.
-
-The build is minified and the filenames include the hashes.<br />
-Your app is ready to be deployed!
-
-See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
-
-### `yarn eject`
-
-**Note: this is a one-way operation. Once you `eject`, you can’t go back!**
-
-If you aren’t satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
-
-Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you’re on your own.
-
-You don’t have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn’t feel obligated to use this feature. However we understand that this tool wouldn’t be useful if you couldn’t customize it when you are ready for it.
-
-## Learn More
-
-You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
-
-To learn React, check out the [React documentation](https://reactjs.org/).
-
-### Code Splitting
-
-This section has moved here: https://facebook.github.io/create-react-app/docs/code-splitting
-
-### Analyzing the Bundle Size
-
-This section has moved here: https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size
-
-### Making a Progressive Web App
-
-This section has moved here: https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app
-
-### Advanced Configuration
-
-This section has moved here: https://facebook.github.io/create-react-app/docs/advanced-configuration
-
-### Deployment
-
-This section has moved here: https://facebook.github.io/create-react-app/docs/deployment
-
-### `yarn build` fails to minify
-
-This section has moved here: https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify
diff --git a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/dataloaders.py b/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/dataloaders.py
deleted file mode 100644
index 26201c3c78fcf6ee030be9ffdc2f9b7128f2b2b5..0000000000000000000000000000000000000000
--- a/spaces/hca97/Mosquito-Detection/my_models/torch_hub_cache/yolov5/utils/dataloaders.py
+++ /dev/null
@@ -1,1222 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
-"""
-Dataloaders and dataset utils
-"""
-
-import contextlib
-import glob
-import hashlib
-import json
-import math
-import os
-import random
-import shutil
-import time
-from itertools import repeat
-from multiprocessing.pool import Pool, ThreadPool
-from pathlib import Path
-from threading import Thread
-from urllib.parse import urlparse
-
-import numpy as np
-import psutil
-import torch
-import torch.nn.functional as F
-import torchvision
-import yaml
-from PIL import ExifTags, Image, ImageOps
-from torch.utils.data import DataLoader, Dataset, dataloader, distributed
-from tqdm import tqdm
-
-from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
-                                 letterbox, mixup, random_perspective)
-from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
-                           check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
-                           xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
-from utils.torch_utils import torch_distributed_zero_first
-
-# Parameters
-HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data'
-IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm'  # include image suffixes
-VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv'  # include video suffixes
-LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
-RANK = int(os.getenv('RANK', -1))
-PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true'  # global pin_memory for dataloaders
-
-# Get orientation exif tag
-for orientation in ExifTags.TAGS.keys():
-    if ExifTags.TAGS[orientation] == 'Orientation':
-        break
-
-
-def get_hash(paths):
-    # Returns a single hash value of a list of paths (files or dirs)
-    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
-    h = hashlib.sha256(str(size).encode())  # hash sizes
-    h.update(''.join(paths).encode())  # hash paths
-    return h.hexdigest()  # return hash
-
-
-def exif_size(img):
-    # Returns exif-corrected PIL size
-    s = img.size  # (width, height)
-    with contextlib.suppress(Exception):
-        rotation = dict(img._getexif().items())[orientation]
-        if rotation in [6, 8]:  # rotation 270 or 90
-            s = (s[1], s[0])
-    return s
-
-
-def exif_transpose(image):
-    """
-    Transpose a PIL image accordingly if it has an EXIF Orientation tag.
-    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
-
-    :param image: The image to transpose.
-    :return: An image.
-    """
-    exif = image.getexif()
-    orientation = exif.get(0x0112, 1)  # default 1
-    if orientation > 1:
-        method = {
-            2: Image.FLIP_LEFT_RIGHT,
-            3: Image.ROTATE_180,
-            4: Image.FLIP_TOP_BOTTOM,
-            5: Image.TRANSPOSE,
-            6: Image.ROTATE_270,
-            7: Image.TRANSVERSE,
-            8: Image.ROTATE_90}.get(orientation)
-        if method is not None:
-            image = image.transpose(method)
-            del exif[0x0112]
-            image.info['exif'] = exif.tobytes()
-    return image
-
-
-def seed_worker(worker_id):
-    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
-    worker_seed = torch.initial_seed() % 2 ** 32
-    np.random.seed(worker_seed)
-    random.seed(worker_seed)
-
-
-def create_dataloader(path,
-                      imgsz,
-                      batch_size,
-                      stride,
-                      single_cls=False,
-                      hyp=None,
-                      augment=False,
-                      cache=False,
-                      pad=0.0,
-                      rect=False,
-                      rank=-1,
-                      workers=8,
-                      image_weights=False,
-                      quad=False,
-                      prefix='',
-                      shuffle=False,
-                      seed=0):
-    if rect and shuffle:
-        LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
-        shuffle = False
-    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
-        dataset = LoadImagesAndLabels(
-            path,
-            imgsz,
-            batch_size,
-            augment=augment,  # augmentation
-            hyp=hyp,  # hyperparameters
-            rect=rect,  # rectangular batches
-            cache_images=cache,
-            single_cls=single_cls,
-            stride=int(stride),
-            pad=pad,
-            image_weights=image_weights,
-            prefix=prefix)
-
-    batch_size = min(batch_size, len(dataset))
-    nd = torch.cuda.device_count()  # number of CUDA devices
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])  # number of workers
-    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    loader = DataLoader if image_weights else InfiniteDataLoader  # only DataLoader allows for attribute updates
-    generator = torch.Generator()
-    generator.manual_seed(6148914691236517205 + seed + RANK)
-    return loader(dataset,
-                  batch_size=batch_size,
-                  shuffle=shuffle and sampler is None,
-                  num_workers=nw,
-                  sampler=sampler,
-                  pin_memory=PIN_MEMORY,
-                  collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
-                  worker_init_fn=seed_worker,
-                  generator=generator), dataset
-
-
-class InfiniteDataLoader(dataloader.DataLoader):
-    """ Dataloader that reuses workers
-
-    Uses same syntax as vanilla DataLoader
-    """
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
-        self.iterator = super().__iter__()
-
-    def __len__(self):
-        return len(self.batch_sampler.sampler)
-
-    def __iter__(self):
-        for _ in range(len(self)):
-            yield next(self.iterator)
-
-
-class _RepeatSampler:
-    """ Sampler that repeats forever
-
-    Args:
-        sampler (Sampler)
-    """
-
-    def __init__(self, sampler):
-        self.sampler = sampler
-
-    def __iter__(self):
-        while True:
-            yield from iter(self.sampler)
-
-
-class LoadScreenshots:
-    # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
-    def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
-        # source = [screen_number left top width height] (pixels)
-        check_requirements('mss')
-        import mss
-
-        source, *params = source.split()
-        self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0
-        if len(params) == 1:
-            self.screen = int(params[0])
-        elif len(params) == 4:
-            left, top, width, height = (int(x) for x in params)
-        elif len(params) == 5:
-            self.screen, left, top, width, height = (int(x) for x in params)
-        self.img_size = img_size
-        self.stride = stride
-        self.transforms = transforms
-        self.auto = auto
-        self.mode = 'stream'
-        self.frame = 0
-        self.sct = mss.mss()
-
-        # Parse monitor shape
-        monitor = self.sct.monitors[self.screen]
-        self.top = monitor['top'] if top is None else (monitor['top'] + top)
-        self.left = monitor['left'] if left is None else (monitor['left'] + left)
-        self.width = width or monitor['width']
-        self.height = height or monitor['height']
-        self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height}
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        # mss screen capture: get raw pixels from the screen as np array
-        im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
-        s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
-
-        if self.transforms:
-            im = self.transforms(im0)  # transforms
-        else:
-            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
-        self.frame += 1
-        return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s
-
-
-class LoadImages:
-    # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
-    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
-        if isinstance(path, str) and Path(path).suffix == '.txt':  # *.txt file with img/vid/dir on each line
-            path = Path(path).read_text().rsplit()
-        files = []
-        for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
-            p = str(Path(p).resolve())
-            if '*' in p:
-                files.extend(sorted(glob.glob(p, recursive=True)))  # glob
-            elif os.path.isdir(p):
-                files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))  # dir
-            elif os.path.isfile(p):
-                files.append(p)  # files
-            else:
-                raise FileNotFoundError(f'{p} does not exist')
-
-        images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
-        videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
-        ni, nv = len(images), len(videos)
-
-        self.img_size = img_size
-        self.stride = stride
-        self.files = images + videos
-        self.nf = ni + nv  # number of files
-        self.video_flag = [False] * ni + [True] * nv
-        self.mode = 'image'
-        self.auto = auto
-        self.transforms = transforms  # optional
-        self.vid_stride = vid_stride  # video frame-rate stride
-        if any(videos):
-            self._new_video(videos[0])  # new video
-        else:
-            self.cap = None
-        assert self.nf > 0, f'No images or videos found in {p}. ' \
-                            f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
-
-    def __iter__(self):
-        self.count = 0
-        return self
-
-    def __next__(self):
-        if self.count == self.nf:
-            raise StopIteration
-        path = self.files[self.count]
-
-        if self.video_flag[self.count]:
-            # Read video
-            self.mode = 'video'
-            for _ in range(self.vid_stride):
-                self.cap.grab()
-            ret_val, im0 = self.cap.retrieve()
-            while not ret_val:
-                self.count += 1
-                self.cap.release()
-                if self.count == self.nf:  # last video
-                    raise StopIteration
-                path = self.files[self.count]
-                self._new_video(path)
-                ret_val, im0 = self.cap.read()
-
-            self.frame += 1
-            # im0 = self._cv2_rotate(im0)  # for use if cv2 autorotation is False
-            s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '
-
-        else:
-            # Read image
-            self.count += 1
-            im0 = cv2.imread(path)  # BGR
-            assert im0 is not None, f'Image Not Found {path}'
-            s = f'image {self.count}/{self.nf} {path}: '
-
-        if self.transforms:
-            im = self.transforms(im0)  # transforms
-        else:
-            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
-            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-            im = np.ascontiguousarray(im)  # contiguous
-
-        return path, im, im0, self.cap, s
-
-    def _new_video(self, path):
-        # Create a new video capture object
-        self.frame = 0
-        self.cap = cv2.VideoCapture(path)
-        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
-        self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META))  # rotation degrees
-        # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493
-
-    def _cv2_rotate(self, im):
-        # Rotate a cv2 video manually
-        if self.orientation == 0:
-            return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
-        elif self.orientation == 180:
-            return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
-        elif self.orientation == 90:
-            return cv2.rotate(im, cv2.ROTATE_180)
-        return im
-
-    def __len__(self):
-        return self.nf  # number of files
-
-
-class LoadStreams:
-    # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP streams`
-    def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
-        torch.backends.cudnn.benchmark = True  # faster for fixed-size inference
-        self.mode = 'stream'
-        self.img_size = img_size
-        self.stride = stride
-        self.vid_stride = vid_stride  # video frame-rate stride
-        sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
-        n = len(sources)
-        self.sources = [clean_str(x) for x in sources]  # clean source names for later
-        self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
-        for i, s in enumerate(sources):  # index, source
-            # Start thread to read frames from video stream
-            st = f'{i + 1}/{n}: {s}... '
-            if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'):  # if source is YouTube video
-                # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
-                check_requirements(('pafy', 'youtube_dl==2020.12.2'))
-                import pafy
-                s = pafy.new(s).getbest(preftype='mp4').url  # YouTube URL
-            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
-            if s == 0:
-                assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
-                assert not is_kaggle(), '--source 0 webcam unsupported on Kaggle. Rerun command in a local environment.'
-            cap = cv2.VideoCapture(s)
-            assert cap.isOpened(), f'{st}Failed to open {s}'
-            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-            fps = cap.get(cv2.CAP_PROP_FPS)  # warning: may return 0 or nan
-            self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf')  # infinite stream fallback
-            self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback
-
-            _, self.imgs[i] = cap.read()  # guarantee first frame
-            self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
-            LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)')
-            self.threads[i].start()
-        LOGGER.info('')  # newline
-
-        # check for common shapes
-        s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])
-        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
-        self.auto = auto and self.rect
-        self.transforms = transforms  # optional
-        if not self.rect:
-            LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
-
-    def update(self, i, cap, stream):
-        # Read stream `i` frames in daemon thread
-        n, f = 0, self.frames[i]  # frame number, frame array
-        while cap.isOpened() and n < f:
-            n += 1
-            cap.grab()  # .read() = .grab() followed by .retrieve()
-            if n % self.vid_stride == 0:
-                success, im = cap.retrieve()
-                if success:
-                    self.imgs[i] = im
-                else:
-                    LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
-                    self.imgs[i] = np.zeros_like(self.imgs[i])
-                    cap.open(stream)  # re-open stream if signal was lost
-            time.sleep(0.0)  # wait time
-
-    def __iter__(self):
-        self.count = -1
-        return self
-
-    def __next__(self):
-        self.count += 1
-        if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'):  # q to quit
-            cv2.destroyAllWindows()
-            raise StopIteration
-
-        im0 = self.imgs.copy()
-        if self.transforms:
-            im = np.stack([self.transforms(x) for x in im0])  # transforms
-        else:
-            im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0])  # resize
-            im = im[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
-            im = np.ascontiguousarray(im)  # contiguous
-
-        return self.sources, im, im0, None, ''
-
-    def __len__(self):
-        return len(self.sources)  # 1E12 frames = 32 streams at 30 FPS for 30 years
-
-
-def img2label_paths(img_paths):
-    # Define label paths as a function of image paths
-    sa, sb = f'{os.sep}images{os.sep}', f'{os.sep}labels{os.sep}'  # /images/, /labels/ substrings
-    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]
-
-
-class LoadImagesAndLabels(Dataset):
-    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
-    cache_version = 0.6  # dataset labels *.cache version
-    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
-
-    def __init__(self,
-                 path,
-                 img_size=640,
-                 batch_size=16,
-                 augment=False,
-                 hyp=None,
-                 rect=False,
-                 image_weights=False,
-                 cache_images=False,
-                 single_cls=False,
-                 stride=32,
-                 pad=0.0,
-                 min_items=0,
-                 prefix=''):
-        self.img_size = img_size
-        self.augment = augment
-        self.hyp = hyp
-        self.image_weights = image_weights
-        self.rect = False if image_weights else rect
-        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
-        self.mosaic_border = [-img_size // 2, -img_size // 2]
-        self.stride = stride
-        self.path = path
-        self.albumentations = Albumentations(size=img_size) if augment else None
-
-        try:
-            f = []  # image files
-            for p in path if isinstance(path, list) else [path]:
-                p = Path(p)  # os-agnostic
-                if p.is_dir():  # dir
-                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
-                    # f = list(p.rglob('*.*'))  # pathlib
-                elif p.is_file():  # file
-                    with open(p) as t:
-                        t = t.read().strip().splitlines()
-                        parent = str(p.parent) + os.sep
-                        f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t]  # to global path
-                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # to global path (pathlib)
-                else:
-                    raise FileNotFoundError(f'{prefix}{p} does not exist')
-            self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
-            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
-            assert self.im_files, f'{prefix}No images found'
-        except Exception as e:
-            raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e
-
-        # Check cache
-        self.label_files = img2label_paths(self.im_files)  # labels
-        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
-        try:
-            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
-            assert cache['version'] == self.cache_version  # matches current version
-            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash
-        except Exception:
-            cache, exists = self.cache_labels(cache_path, prefix), False  # run cache ops
-
-        # Display cache
-        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
-        if exists and LOCAL_RANK in {-1, 0}:
-            d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt'
-            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)  # display cache results
-            if cache['msgs']:
-                LOGGER.info('\n'.join(cache['msgs']))  # display warnings
-        assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'
-
-        # Read cache
-        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
-        labels, shapes, self.segments = zip(*cache.values())
-        nl = len(np.concatenate(labels, 0))  # number of labels
-        assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'
-        self.labels = list(labels)
-        self.shapes = np.array(shapes)
-        self.im_files = list(cache.keys())  # update
-        self.label_files = img2label_paths(cache.keys())  # update
-
-        # Filter images
-        if min_items:
-            include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)
-            LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset')
-            self.im_files = [self.im_files[i] for i in include]
-            self.label_files = [self.label_files[i] for i in include]
-            self.labels = [self.labels[i] for i in include]
-            self.segments = [self.segments[i] for i in include]
-            self.shapes = self.shapes[include]  # wh
-
-        # Create indices
-        n = len(self.shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
-        nb = bi[-1] + 1  # number of batches
-        self.batch = bi  # batch index of image
-        self.n = n
-        self.indices = range(n)
-
-        # Update labels
-        include_class = []  # filter labels to include only these classes (optional)
-        self.segments = list(self.segments)
-        include_class_array = np.array(include_class).reshape(1, -1)
-        for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
-            if include_class:
-                j = (label[:, 0:1] == include_class_array).any(1)
-                self.labels[i] = label[j]
-                if segment:
-                    self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem]
-            if single_cls:  # single-class training, merge all classes into 0
-                self.labels[i][:, 0] = 0
-
-        # Rectangular Training
-        if self.rect:
-            # Sort by aspect ratio
-            s = self.shapes  # wh
-            ar = s[:, 1] / s[:, 0]  # aspect ratio
-            irect = ar.argsort()
-            self.im_files = [self.im_files[i] for i in irect]
-            self.label_files = [self.label_files[i] for i in irect]
-            self.labels = [self.labels[i] for i in irect]
-            self.segments = [self.segments[i] for i in irect]
-            self.shapes = s[irect]  # wh
-            ar = ar[irect]
-
-            # Set training image shapes
-            shapes = [[1, 1]] * nb
-            for i in range(nb):
-                ari = ar[bi == i]
-                mini, maxi = ari.min(), ari.max()
-                if maxi < 1:
-                    shapes[i] = [maxi, 1]
-                elif mini > 1:
-                    shapes[i] = [1, 1 / mini]
-
-            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
-
-        # Cache images into RAM/disk for faster training
-        if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix):
-            cache_images = False
-        self.ims = [None] * n
-        self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
-        if cache_images:
-            b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
-            self.im_hw0, self.im_hw = [None] * n, [None] * n
-            fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
-            results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
-            pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
-            for i, x in pbar:
-                if cache_images == 'disk':
-                    b += self.npy_files[i].stat().st_size
-                else:  # 'ram'
-                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
-                    b += self.ims[i].nbytes
-                pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})'
-            pbar.close()
-
-    def check_cache_ram(self, safety_margin=0.1, prefix=''):
-        # Check image caching requirements vs available memory
-        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
-        n = min(self.n, 30)  # extrapolate from 30 random images
-        for _ in range(n):
-            im = cv2.imread(random.choice(self.im_files))  # sample image
-            ratio = self.img_size / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
-            b += im.nbytes * ratio ** 2
-        mem_required = b * self.n / n  # GB required to cache dataset into RAM
-        mem = psutil.virtual_memory()
-        cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
-        if not cache:
-            LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, '
-                        f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
-                        f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
-        return cache
-
-    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
-        # Cache dataset labels, check images and read shapes
-        x = {}  # dict
-        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
-        desc = f'{prefix}Scanning {path.parent / path.stem}...'
-        with Pool(NUM_THREADS) as pool:
-            pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
-                        desc=desc,
-                        total=len(self.im_files),
-                        bar_format=TQDM_BAR_FORMAT)
-            for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
-                nm += nm_f
-                nf += nf_f
-                ne += ne_f
-                nc += nc_f
-                if im_file:
-                    x[im_file] = [lb, shape, segments]
-                if msg:
-                    msgs.append(msg)
-                pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt'
-
-        pbar.close()
-        if msgs:
-            LOGGER.info('\n'.join(msgs))
-        if nf == 0:
-            LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')
-        x['hash'] = get_hash(self.label_files + self.im_files)
-        x['results'] = nf, nm, ne, nc, len(self.im_files)
-        x['msgs'] = msgs  # warnings
-        x['version'] = self.cache_version  # cache version
-        try:
-            np.save(path, x)  # save cache for next time
-            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
-            LOGGER.info(f'{prefix}New cache created: {path}')
-        except Exception as e:
-            LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}')  # not writeable
-        return x
-
-    def __len__(self):
-        return len(self.im_files)
-
-    # def __iter__(self):
-    #     self.count = -1
-    #     print('ran dataset iter')
-    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
-    #     return self
-
-    def __getitem__(self, index):
-        index = self.indices[index]  # linear, shuffled, or image_weights
-
-        hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp['mosaic']
-        if mosaic:
-            # Load mosaic
-            img, labels = self.load_mosaic(index)
-            shapes = None
-
-            # MixUp augmentation
-            if random.random() < hyp['mixup']:
-                img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))
-
-        else:
-            # Load image
-            img, (h0, w0), (h, w) = self.load_image(index)
-
-            # Letterbox
-            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
-            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
-            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling
-
-            labels = self.labels[index].copy()
-            if labels.size:  # normalized xywh to pixel xyxy format
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
-
-            if self.augment:
-                img, labels = random_perspective(img,
-                                                 labels,
-                                                 degrees=hyp['degrees'],
-                                                 translate=hyp['translate'],
-                                                 scale=hyp['scale'],
-                                                 shear=hyp['shear'],
-                                                 perspective=hyp['perspective'])
-
-        nl = len(labels)  # number of labels
-        if nl:
-            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)
-
-        if self.augment:
-            # Albumentations
-            img, labels = self.albumentations(img, labels)
-            nl = len(labels)  # update after albumentations
-
-            # HSV color-space
-            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
-            # Flip up-down
-            if random.random() < hyp['flipud']:
-                img = np.flipud(img)
-                if nl:
-                    labels[:, 2] = 1 - labels[:, 2]
-
-            # Flip left-right
-            if random.random() < hyp['fliplr']:
-                img = np.fliplr(img)
-                if nl:
-                    labels[:, 1] = 1 - labels[:, 1]
-
-            # Cutouts
-            # labels = cutout(img, labels, p=0.5)
-            # nl = len(labels)  # update after cutout
-
-        labels_out = torch.zeros((nl, 6))
-        if nl:
-            labels_out[:, 1:] = torch.from_numpy(labels)
-
-        # Convert
-        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
-        img = np.ascontiguousarray(img)
-
-        return torch.from_numpy(img), labels_out, self.im_files[index], shapes
-
-    def load_image(self, i):
-        # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
-        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
-        if im is None:  # not cached in RAM
-            if fn.exists():  # load npy
-                im = np.load(fn)
-            else:  # read image
-                im = cv2.imread(f)  # BGR
-                assert im is not None, f'Image Not Found {f}'
-            h0, w0 = im.shape[:2]  # orig hw
-            r = self.img_size / max(h0, w0)  # ratio
-            if r != 1:  # if sizes are not equal
-                interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
-                im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)
-            return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
-        return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized
-
-    def cache_images_to_disk(self, i):
-        # Saves an image as an *.npy file for faster loading
-        f = self.npy_files[i]
-        if not f.exists():
-            np.save(f.as_posix(), cv2.imread(self.im_files[i]))
-
-    def load_mosaic(self, index):
-        # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
-        labels4, segments4 = [], []
-        s = self.img_size
-        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y
-        indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
-        random.shuffle(indices)
-        for i, index in enumerate(indices):
-            # Load image
-            img, _, (h, w) = self.load_image(index)
-
-            # place img in img4
-            if i == 0:  # top left
-                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
-                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
-            elif i == 1:  # top right
-                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
-                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
-            elif i == 2:  # bottom left
-                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
-                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
-            elif i == 3:  # bottom right
-                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
-                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
-
-            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-            padw = x1a - x1b
-            padh = y1a - y1b
-
-            # Labels
-            labels, segments = self.labels[index].copy(), self.segments[index].copy()
-            if labels.size:
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
-                segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
-            labels4.append(labels)
-            segments4.extend(segments)
-
-        # Concat/clip labels
-        labels4 = np.concatenate(labels4, 0)
-        for x in (labels4[:, 1:], *segments4):
-            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-        # img4, labels4 = replicate(img4, labels4)  # replicate
-
-        # Augment
-        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
-        img4, labels4 = random_perspective(img4,
-                                           labels4,
-                                           segments4,
-                                           degrees=self.hyp['degrees'],
-                                           translate=self.hyp['translate'],
-                                           scale=self.hyp['scale'],
-                                           shear=self.hyp['shear'],
-                                           perspective=self.hyp['perspective'],
-                                           border=self.mosaic_border)  # border to remove
-
-        return img4, labels4
-
-    def load_mosaic9(self, index):
-        # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
-        labels9, segments9 = [], []
-        s = self.img_size
-        indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
-        random.shuffle(indices)
-        hp, wp = -1, -1  # height, width previous
-        for i, index in enumerate(indices):
-            # Load image
-            img, _, (h, w) = self.load_image(index)
-
-            # place img in img9
-            if i == 0:  # center
-                img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-                h0, w0 = h, w
-                c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
-            elif i == 1:  # top
-                c = s, s - h, s + w, s
-            elif i == 2:  # top right
-                c = s + wp, s - h, s + wp + w, s
-            elif i == 3:  # right
-                c = s + w0, s, s + w0 + w, s + h
-            elif i == 4:  # bottom right
-                c = s + w0, s + hp, s + w0 + w, s + hp + h
-            elif i == 5:  # bottom
-                c = s + w0 - w, s + h0, s + w0, s + h0 + h
-            elif i == 6:  # bottom left
-                c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
-            elif i == 7:  # left
-                c = s - w, s + h0 - h, s, s + h0
-            elif i == 8:  # top left
-                c = s - w, s + h0 - hp - h, s, s + h0 - hp
-
-            padx, pady = c[:2]
-            x1, y1, x2, y2 = (max(x, 0) for x in c)  # allocate coords
-
-            # Labels
-            labels, segments = self.labels[index].copy(), self.segments[index].copy()
-            if labels.size:
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
-                segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
-            labels9.append(labels)
-            segments9.extend(segments)
-
-            # Image
-            img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]
-            hp, wp = h, w  # height, width previous
-
-        # Offset
-        yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border)  # mosaic center x, y
-        img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
-
-        # Concat/clip labels
-        labels9 = np.concatenate(labels9, 0)
-        labels9[:, [1, 3]] -= xc
-        labels9[:, [2, 4]] -= yc
-        c = np.array([xc, yc])  # centers
-        segments9 = [x - c for x in segments9]
-
-        for x in (labels9[:, 1:], *segments9):
-            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-        # img9, labels9 = replicate(img9, labels9)  # replicate
-
-        # Augment
-        img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste'])
-        img9, labels9 = random_perspective(img9,
-                                           labels9,
-                                           segments9,
-                                           degrees=self.hyp['degrees'],
-                                           translate=self.hyp['translate'],
-                                           scale=self.hyp['scale'],
-                                           shear=self.hyp['shear'],
-                                           perspective=self.hyp['perspective'],
-                                           border=self.mosaic_border)  # border to remove
-
-        return img9, labels9
-
-    @staticmethod
-    def collate_fn(batch):
-        im, label, path, shapes = zip(*batch)  # transposed
-        for i, lb in enumerate(label):
-            lb[:, 0] = i  # add target image index for build_targets()
-        return torch.stack(im, 0), torch.cat(label, 0), path, shapes
-
-    @staticmethod
-    def collate_fn4(batch):
-        im, label, path, shapes = zip(*batch)  # transposed
-        n = len(shapes) // 4
-        im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
-
-        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
-        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
-        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # scale
-        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
-            i *= 4
-            if random.random() < 0.5:
-                im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear',
-                                    align_corners=False)[0].type(im[i].type())
-                lb = label[i]
-            else:
-                im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2)
-                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
-            im4.append(im1)
-            label4.append(lb)
-
-        for i, lb in enumerate(label4):
-            lb[:, 0] = i  # add target image index for build_targets()
-
-        return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4
-
-
-# Ancillary functions --------------------------------------------------------------------------------------------------
-def flatten_recursive(path=DATASETS_DIR / 'coco128'):
-    # Flatten a recursive directory by bringing all files to top level
-    new_path = Path(f'{str(path)}_flat')
-    if os.path.exists(new_path):
-        shutil.rmtree(new_path)  # delete output folder
-    os.makedirs(new_path)  # make new output folder
-    for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)):
-        shutil.copyfile(file, new_path / Path(file).name)
-
-
-def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders import *; extract_boxes()
-    # Convert detection dataset into classification dataset, with one directory per class
-    path = Path(path)  # images dir
-    shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None  # remove existing
-    files = list(path.rglob('*.*'))
-    n = len(files)  # number of files
-    for im_file in tqdm(files, total=n):
-        if im_file.suffix[1:] in IMG_FORMATS:
-            # image
-            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
-            h, w = im.shape[:2]
-
-            # labels
-            lb_file = Path(img2label_paths([str(im_file)])[0])
-            if Path(lb_file).exists():
-                with open(lb_file) as f:
-                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels
-
-                for j, x in enumerate(lb):
-                    c = int(x[0])  # class
-                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename
-                    if not f.parent.is_dir():
-                        f.parent.mkdir(parents=True)
-
-                    b = x[1:] * [w, h, w, h]  # box
-                    # b[2:] = b[2:].max()  # rectangle to square
-                    b[2:] = b[2:] * 1.2 + 3  # pad
-                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
-
-                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
-                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
-                    assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
-
-
-def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
-    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
-    Usage: from utils.dataloaders import *; autosplit()
-    Arguments
-        path:            Path to images directory
-        weights:         Train, val, test weights (list, tuple)
-        annotated_only:  Only use images with an annotated txt file
-    """
-    path = Path(path)  # images dir
-    files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
-    n = len(files)  # number of files
-    random.seed(0)  # for reproducibility
-    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
-
-    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
-    for x in txt:
-        if (path.parent / x).exists():
-            (path.parent / x).unlink()  # remove existing
-
-    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
-    for i, img in tqdm(zip(indices, files), total=n):
-        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
-            with open(path.parent / txt[i], 'a') as f:
-                f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n')  # add image to txt file
-
-
-def verify_image_label(args):
-    # Verify one image-label pair
-    im_file, lb_file, prefix = args
-    nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', []  # number (missing, found, empty, corrupt), message, segments
-    try:
-        # verify images
-        im = Image.open(im_file)
-        im.verify()  # PIL verify
-        shape = exif_size(im)  # image size
-        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
-        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
-        if im.format.lower() in ('jpg', 'jpeg'):
-            with open(im_file, 'rb') as f:
-                f.seek(-2, 2)
-                if f.read() != b'\xff\xd9':  # corrupt JPEG
-                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
-                    msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
-
-        # verify labels
-        if os.path.isfile(lb_file):
-            nf = 1  # label found
-            with open(lb_file) as f:
-                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
-                if any(len(x) > 6 for x in lb):  # is segment
-                    classes = np.array([x[0] for x in lb], dtype=np.float32)
-                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
-                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
-                lb = np.array(lb, dtype=np.float32)
-            nl = len(lb)
-            if nl:
-                assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
-                assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
-                assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
-                _, i = np.unique(lb, axis=0, return_index=True)
-                if len(i) < nl:  # duplicate row check
-                    lb = lb[i]  # remove duplicates
-                    if segments:
-                        segments = [segments[x] for x in i]
-                    msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
-            else:
-                ne = 1  # label empty
-                lb = np.zeros((0, 5), dtype=np.float32)
-        else:
-            nm = 1  # label missing
-            lb = np.zeros((0, 5), dtype=np.float32)
-        return im_file, lb, shape, segments, nm, nf, ne, nc, msg
-    except Exception as e:
-        nc = 1
-        msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
-        return [None, None, None, None, nm, nf, ne, nc, msg]
-
-
-class HUBDatasetStats():
-    """ Class for generating HUB dataset JSON and `-hub` dataset directory
-
-    Arguments
-        path:           Path to data.yaml or data.zip (with data.yaml inside data.zip)
-        autodownload:   Attempt to download dataset if not found locally
-
-    Usage
-        from utils.dataloaders import HUBDatasetStats
-        stats = HUBDatasetStats('coco128.yaml', autodownload=True)  # usage 1
-        stats = HUBDatasetStats('path/to/coco128.zip')  # usage 2
-        stats.get_json(save=False)
-        stats.process_images()
-    """
-
-    def __init__(self, path='coco128.yaml', autodownload=False):
-        # Initialize class
-        zipped, data_dir, yaml_path = self._unzip(Path(path))
-        try:
-            with open(check_yaml(yaml_path), errors='ignore') as f:
-                data = yaml.safe_load(f)  # data dict
-                if zipped:
-                    data['path'] = data_dir
-        except Exception as e:
-            raise Exception('error/HUB/dataset_stats/yaml_load') from e
-
-        check_dataset(data, autodownload)  # download dataset if missing
-        self.hub_dir = Path(data['path'] + '-hub')
-        self.im_dir = self.hub_dir / 'images'
-        self.im_dir.mkdir(parents=True, exist_ok=True)  # makes /images
-        self.stats = {'nc': data['nc'], 'names': list(data['names'].values())}  # statistics dictionary
-        self.data = data
-
-    @staticmethod
-    def _find_yaml(dir):
-        # Return data.yaml file
-        files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml'))  # try root level first and then recursive
-        assert files, f'No *.yaml file found in {dir}'
-        if len(files) > 1:
-            files = [f for f in files if f.stem == dir.stem]  # prefer *.yaml files that match dir name
-            assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed'
-        assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}'
-        return files[0]
-
-    def _unzip(self, path):
-        # Unzip data.zip
-        if not str(path).endswith('.zip'):  # path is data.yaml
-            return False, None, path
-        assert Path(path).is_file(), f'Error unzipping {path}, file not found'
-        unzip_file(path, path=path.parent)
-        dir = path.with_suffix('')  # dataset directory == zip name
-        assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
-        return True, str(dir), self._find_yaml(dir)  # zipped, data_dir, yaml_path
-
-    def _hub_ops(self, f, max_dim=1920):
-        # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing
-        f_new = self.im_dir / Path(f).name  # dataset-hub image filename
-        try:  # use PIL
-            im = Image.open(f)
-            r = max_dim / max(im.height, im.width)  # ratio
-            if r < 1.0:  # image too large
-                im = im.resize((int(im.width * r), int(im.height * r)))
-            im.save(f_new, 'JPEG', quality=50, optimize=True)  # save
-        except Exception as e:  # use OpenCV
-            LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
-            im = cv2.imread(f)
-            im_height, im_width = im.shape[:2]
-            r = max_dim / max(im_height, im_width)  # ratio
-            if r < 1.0:  # image too large
-                im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
-            cv2.imwrite(str(f_new), im)
-
-    def get_json(self, save=False, verbose=False):
-        # Return dataset JSON for Ultralytics HUB
-        def _round(labels):
-            # Update labels to integer class and 6 decimal place floats
-            return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]
-
-        for split in 'train', 'val', 'test':
-            if self.data.get(split) is None:
-                self.stats[split] = None  # i.e. no test set
-                continue
-            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
-            x = np.array([
-                np.bincount(label[:, 0].astype(int), minlength=self.data['nc'])
-                for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics')])  # shape(128x80)
-            self.stats[split] = {
-                'instance_stats': {
-                    'total': int(x.sum()),
-                    'per_class': x.sum(0).tolist()},
-                'image_stats': {
-                    'total': dataset.n,
-                    'unlabelled': int(np.all(x == 0, 1).sum()),
-                    'per_class': (x > 0).sum(0).tolist()},
-                'labels': [{
-                    str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)]}
-
-        # Save, print and return
-        if save:
-            stats_path = self.hub_dir / 'stats.json'
-            print(f'Saving {stats_path.resolve()}...')
-            with open(stats_path, 'w') as f:
-                json.dump(self.stats, f)  # save stats.json
-        if verbose:
-            print(json.dumps(self.stats, indent=2, sort_keys=False))
-        return self.stats
-
-    def process_images(self):
-        # Compress images for Ultralytics HUB
-        for split in 'train', 'val', 'test':
-            if self.data.get(split) is None:
-                continue
-            dataset = LoadImagesAndLabels(self.data[split])  # load dataset
-            desc = f'{split} images'
-            for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc):
-                pass
-        print(f'Done. All images saved to {self.im_dir}')
-        return self.im_dir
-
-
-# Classification dataloaders -------------------------------------------------------------------------------------------
-class ClassificationDataset(torchvision.datasets.ImageFolder):
-    """
-    YOLOv5 Classification Dataset.
-    Arguments
-        root:  Dataset path
-        transform:  torchvision transforms, used by default
-        album_transform: Albumentations transforms, used if installed
-    """
-
-    def __init__(self, root, augment, imgsz, cache=False):
-        super().__init__(root=root)
-        self.torch_transforms = classify_transforms(imgsz)
-        self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
-        self.cache_ram = cache is True or cache == 'ram'
-        self.cache_disk = cache == 'disk'
-        self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples]  # file, index, npy, im
-
-    def __getitem__(self, i):
-        f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
-        if self.cache_ram and im is None:
-            im = self.samples[i][3] = cv2.imread(f)
-        elif self.cache_disk:
-            if not fn.exists():  # load npy
-                np.save(fn.as_posix(), cv2.imread(f))
-            im = np.load(fn)
-        else:  # read image
-            im = cv2.imread(f)  # BGR
-        if self.album_transforms:
-            sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']
-        else:
-            sample = self.torch_transforms(im)
-        return sample, j
-
-
-def create_classification_dataloader(path,
-                                     imgsz=224,
-                                     batch_size=16,
-                                     augment=True,
-                                     cache=False,
-                                     rank=-1,
-                                     workers=8,
-                                     shuffle=True):
-    # Returns Dataloader object to be used with YOLOv5 Classifier
-    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
-        dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
-    batch_size = min(batch_size, len(dataset))
-    nd = torch.cuda.device_count()
-    nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
-    sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
-    generator = torch.Generator()
-    generator.manual_seed(6148914691236517205 + RANK)
-    return InfiniteDataLoader(dataset,
-                              batch_size=batch_size,
-                              shuffle=shuffle and sampler is None,
-                              num_workers=nw,
-                              sampler=sampler,
-                              pin_memory=PIN_MEMORY,
-                              worker_init_fn=seed_worker,
-                              generator=generator)  # or DataLoader(persistent_workers=True)
diff --git a/spaces/helidem/Projet-L3-Image/README.md b/spaces/helidem/Projet-L3-Image/README.md
deleted file mode 100644
index 83f93781da3288f99b5ddf4e3cb75f97120526a5..0000000000000000000000000000000000000000
--- a/spaces/helidem/Projet-L3-Image/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Projet L3 Image
-emoji: 📉
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 3.29.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/hezhaoqia/vits-simple-api/static/js/bootstrap.bundle.min.js b/spaces/hezhaoqia/vits-simple-api/static/js/bootstrap.bundle.min.js
deleted file mode 100644
index d7524d10561650af9b8044b4991bc327507846c8..0000000000000000000000000000000000000000
--- a/spaces/hezhaoqia/vits-simple-api/static/js/bootstrap.bundle.min.js
+++ /dev/null
@@ -1,7 +0,0 @@
-/*!
-  * Bootstrap v4.6.2 (https://getbootstrap.com/)
-  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
-  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
-  */
-!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?e(exports,require("jquery")):"function"==typeof define&&define.amd?define(["exports","jquery"],e):e((t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap={},t.jQuery)}(this,(function(t,e){"use strict";function n(t){return t&&"object"==typeof t&&"default"in t?t:{default:t}}var i=n(e);function o(t,e){for(var n=0;n<e.length;n++){var i=e[n];i.enumerable=i.enumerable||!1,i.configurable=!0,"value"in i&&(i.writable=!0),Object.defineProperty(t,i.key,i)}}function r(t,e,n){return e&&o(t.prototype,e),n&&o(t,n),Object.defineProperty(t,"prototype",{writable:!1}),t}function a(){return a=Object.assign?Object.assign.bind():function(t){for(var e=1;e<arguments.length;e++){var n=arguments[e];for(var i in n)Object.prototype.hasOwnProperty.call(n,i)&&(t[i]=n[i])}return t},a.apply(this,arguments)}function s(t,e){return s=Object.setPrototypeOf?Object.setPrototypeOf.bind():function(t,e){return t.__proto__=e,t},s(t,e)}var 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e.toggle=function(){if(!this._element.disabled&&!i.default(this._element).hasClass($t)){var e=i.default(this._menu).hasClass(Jt);t._clearMenus(),e||this.show(!0)}},e.show=function(e){if(void 0===e&&(e=!1),!(this._element.disabled||i.default(this._element).hasClass($t)||i.default(this._menu).hasClass(Jt))){var n={relatedTarget:this._element},o=i.default.Event("show.bs.dropdown",n),r=t._getParentFromElement(this._element);if(i.default(r).trigger(o),!o.isDefaultPrevented()){if(!this._inNavbar&&e){if("undefined"==typeof Yt)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");var a=this._element;"parent"===this._config.reference?a=r:u.isElement(this._config.reference)&&(a=this._config.reference,"undefined"!=typeof this._config.reference.jquery&&(a=this._config.reference[0])),"scrollParent"!==this._config.boundary&&i.default(r).addClass("position-static"),this._popper=new Yt(a,this._menu,this._getPopperConfig())}"ontouchstart"in document.documentElement&&0===i.default(r).closest(".navbar-nav").length&&i.default(document.body).children().on("mouseover",null,i.default.noop),this._element.focus(),this._element.setAttribute("aria-expanded",!0),i.default(this._menu).toggleClass(Jt),i.default(r).toggleClass(Jt).trigger(i.default.Event("shown.bs.dropdown",n))}}},e.hide=function(){if(!this._element.disabled&&!i.default(this._element).hasClass($t)&&i.default(this._menu).hasClass(Jt)){var e={relatedTarget:this._element},n=i.default.Event(te,e),o=t._getParentFromElement(this._element);i.default(o).trigger(n),n.isDefaultPrevented()||(this._popper&&this._popper.destroy(),i.default(this._menu).toggleClass(Jt),i.default(o).toggleClass(Jt).trigger(i.default.Event(ee,e)))}},e.dispose=function(){i.default.removeData(this._element,Kt),i.default(this._element).off(".bs.dropdown"),this._element=null,this._menu=null,null!==this._popper&&(this._popper.destroy(),this._popper=null)},e.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},e._addEventListeners=function(){var t=this;i.default(this._element).on("click.bs.dropdown",(function(e){e.preventDefault(),e.stopPropagation(),t.toggle()}))},e._getConfig=function(t){return t=a({},this.constructor.Default,i.default(this._element).data(),t),u.typeCheckConfig(zt,t,this.constructor.DefaultType),t},e._getMenuElement=function(){if(!this._menu){var e=t._getParentFromElement(this._element);e&&(this._menu=e.querySelector(re))}return this._menu},e._getPlacement=function(){var t=i.default(this._element.parentNode),e="bottom-start";return t.hasClass("dropup")?e=i.default(this._menu).hasClass(Zt)?"top-end":"top-start":t.hasClass("dropright")?e="right-start":t.hasClass("dropleft")?e="left-start":i.default(this._menu).hasClass(Zt)&&(e="bottom-end"),e},e._detectNavbar=function(){return i.default(this._element).closest(".navbar").length>0},e._getOffset=function(){var t=this,e={};return"function"==typeof this._config.offset?e.fn=function(e){return e.offsets=a({},e.offsets,t._config.offset(e.offsets,t._element)),e}:e.offset=this._config.offset,e},e._getPopperConfig=function(){var t={placement:this._getPlacement(),modifiers:{offset:this._getOffset(),flip:{enabled:this._config.flip},preventOverflow:{boundariesElement:this._config.boundary}}};return"static"===this._config.display&&(t.modifiers.applyStyle={enabled:!1}),a({},t,this._config.popperConfig)},t._jQueryInterface=function(e){return this.each((function(){var n=i.default(this).data(Kt);if(n||(n=new t(this,"object"==typeof e?e:null),i.default(this).data(Kt,n)),"string"==typeof e){if("undefined"==typeof n[e])throw new TypeError('No method named "'+e+'"');n[e]()}}))},t._clearMenus=function(e){if(!e||3!==e.which&&("keyup"!==e.type||9===e.which))for(var n=[].slice.call(document.querySelectorAll(oe)),o=0,r=n.length;o<r;o++){var a=t._getParentFromElement(n[o]),s=i.default(n[o]).data(Kt),l={relatedTarget:n[o]};if(e&&"click"===e.type&&(l.clickEvent=e),s){var u=s._menu;if(i.default(a).hasClass(Jt)&&!(e&&("click"===e.type&&/input|textarea/i.test(e.target.tagName)||"keyup"===e.type&&9===e.which)&&i.default.contains(a,e.target))){var f=i.default.Event(te,l);i.default(a).trigger(f),f.isDefaultPrevented()||("ontouchstart"in document.documentElement&&i.default(document.body).children().off("mouseover",null,i.default.noop),n[o].setAttribute("aria-expanded","false"),s._popper&&s._popper.destroy(),i.default(u).removeClass(Jt),i.default(a).removeClass(Jt).trigger(i.default.Event(ee,l)))}}}},t._getParentFromElement=function(t){var e,n=u.getSelectorFromElement(t);return n&&(e=document.querySelector(n)),e||t.parentNode},t._dataApiKeydownHandler=function(e){if(!(/input|textarea/i.test(e.target.tagName)?32===e.which||27!==e.which&&(40!==e.which&&38!==e.which||i.default(e.target).closest(re).length):!Gt.test(e.which))&&!this.disabled&&!i.default(this).hasClass($t)){var n=t._getParentFromElement(this),o=i.default(n).hasClass(Jt);if(o||27!==e.which){if(e.preventDefault(),e.stopPropagation(),!o||27===e.which||32===e.which)return 27===e.which&&i.default(n.querySelector(oe)).trigger("focus"),void i.default(this).trigger("click");var r=[].slice.call(n.querySelectorAll(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)")).filter((function(t){return i.default(t).is(":visible")}));if(0!==r.length){var a=r.indexOf(e.target);38===e.which&&a>0&&a--,40===e.which&&a<r.length-1&&a++,a<0&&(a=0),r[a].focus()}}}},r(t,null,[{key:"VERSION",get:function(){return"4.6.2"}},{key:"Default",get:function(){return ae}},{key:"DefaultType",get:function(){return se}}]),t}();i.default(document).on(ie,oe,le._dataApiKeydownHandler).on(ie,re,le._dataApiKeydownHandler).on(ne+" keyup.bs.dropdown.data-api",le._clearMenus).on(ne,oe,(function(t){t.preventDefault(),t.stopPropagation(),le._jQueryInterface.call(i.default(this),"toggle")})).on(ne,".dropdown 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this._isShown?this.hide():this.show(t)},e.show=function(t){var e=this;if(!this._isShown&&!this._isTransitioning){var n=i.default.Event(ge,{relatedTarget:t});i.default(this._element).trigger(n),n.isDefaultPrevented()||(this._isShown=!0,i.default(this._element).hasClass(ce)&&(this._isTransitioning=!0),this._checkScrollbar(),this._setScrollbar(),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),i.default(this._element).on(be,'[data-dismiss="modal"]',(function(t){return e.hide(t)})),i.default(this._dialog).on(Ee,(function(){i.default(e._element).one("mouseup.dismiss.bs.modal",(function(t){i.default(t.target).is(e._element)&&(e._ignoreBackdropClick=!0)}))})),this._showBackdrop((function(){return e._showElement(t)})))}},e.hide=function(t){var e=this;if(t&&t.preventDefault(),this._isShown&&!this._isTransitioning){var n=i.default.Event("hide.bs.modal");if(i.default(this._element).trigger(n),this._isShown&&!n.isDefaultPrevented()){this._isShown=!1;var o=i.default(this._element).hasClass(ce);if(o&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),i.default(document).off(_e),i.default(this._element).removeClass(he),i.default(this._element).off(be),i.default(this._dialog).off(Ee),o){var r=u.getTransitionDurationFromElement(this._element);i.default(this._element).one(u.TRANSITION_END,(function(t){return e._hideModal(t)})).emulateTransitionEnd(r)}else this._hideModal()}}},e.dispose=function(){[window,this._element,this._dialog].forEach((function(t){return i.default(t).off(".bs.modal")})),i.default(document).off(_e),i.default.removeData(this._element,ue),this._config=null,this._element=null,this._dialog=null,this._backdrop=null,this._isShown=null,this._isBodyOverflowing=null,this._ignoreBackdropClick=null,this._isTransitioning=null,this._scrollbarWidth=null},e.handleUpdate=function(){this._adjustDialog()},e._getConfig=function(t){return t=a({},Te,t),u.typeCheckConfig("modal",t,Ce),t},e._triggerBackdropTransition=function(){var t=this,e=i.default.Event("hidePrevented.bs.modal");if(i.default(this._element).trigger(e),!e.isDefaultPrevented()){var n=this._element.scrollHeight>document.documentElement.clientHeight;n||(this._element.style.overflowY="hidden"),this._element.classList.add(pe);var o=u.getTransitionDurationFromElement(this._dialog);i.default(this._element).off(u.TRANSITION_END),i.default(this._element).one(u.TRANSITION_END,(function(){t._element.classList.remove(pe),n||i.default(t._element).one(u.TRANSITION_END,(function(){t._element.style.overflowY=""})).emulateTransitionEnd(t._element,o)})).emulateTransitionEnd(o),this._element.focus()}},e._showElement=function(t){var e=this,n=i.default(this._element).hasClass(ce),o=this._dialog?this._dialog.querySelector(".modal-body"):null;this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.appendChild(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),i.default(this._dialog).hasClass("modal-dialog-scrollable")&&o?o.scrollTop=0:this._element.scrollTop=0,n&&u.reflow(this._element),i.default(this._element).addClass(he),this._config.focus&&this._enforceFocus();var r=i.default.Event("shown.bs.modal",{relatedTarget:t}),a=function(){e._config.focus&&e._element.focus(),e._isTransitioning=!1,i.default(e._element).trigger(r)};if(n){var s=u.getTransitionDurationFromElement(this._dialog);i.default(this._dialog).one(u.TRANSITION_END,a).emulateTransitionEnd(s)}else a()},e._enforceFocus=function(){var t=this;i.default(document).off(_e).on(_e,(function(e){document!==e.target&&t._element!==e.target&&0===i.default(t._element).has(e.target).length&&t._element.focus()}))},e._setEscapeEvent=function(){var t=this;this._isShown?i.default(this._element).on(ye,(function(e){t._config.keyboard&&27===e.which?(e.preventDefault(),t.hide()):t._config.keyboard||27!==e.which||t._triggerBackdropTransition()})):this._isShown||i.default(this._element).off(ye)},e._setResizeEvent=function(){var t=this;this._isShown?i.default(window).on(ve,(function(e){return t.handleUpdate(e)})):i.default(window).off(ve)},e._hideModal=function(){var t=this;this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._showBackdrop((function(){i.default(document.body).removeClass(de),t._resetAdjustments(),t._resetScrollbar(),i.default(t._element).trigger(me)}))},e._removeBackdrop=function(){this._backdrop&&(i.default(this._backdrop).remove(),this._backdrop=null)},e._showBackdrop=function(t){var 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diff --git a/spaces/hf4all/web-ui/_next/static/chunks/539bbe41.64229b30659f90aa.js b/spaces/hf4all/web-ui/_next/static/chunks/539bbe41.64229b30659f90aa.js
deleted file mode 100644
index 5874c95b74caeb368f908d327f9975426dbbaae5..0000000000000000000000000000000000000000
--- a/spaces/hf4all/web-ui/_next/static/chunks/539bbe41.64229b30659f90aa.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/hjianganthony/fetch_ner/src/utils.py b/spaces/hjianganthony/fetch_ner/src/utils.py
deleted file mode 100644
index 10acc1de87e55a5d01c744e5ba64b1791451a419..0000000000000000000000000000000000000000
--- a/spaces/hjianganthony/fetch_ner/src/utils.py
+++ /dev/null
@@ -1,319 +0,0 @@
-# helper functions
-from typing import List, Dict, Tuple
-import re
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import pandas as pd
-import numpy as np
-import pickle, json
-# from IPython.display import clear_output
-
-import spacy
-from spacy.tokens import DocBin
-from spacy.training import offsets_to_biluo_tags
-import en_fetch_ner_spacy_tsf
-nlp = en_fetch_ner_spacy_tsf.load()
-# clear_output()
-
-import nltk
-nltk.download('stopwords')
-
-from nltk.corpus import stopwords
-stop_words = set(stopwords.words('english'))
-additional_stop_words = {'pack'}
-stop_words.update(additional_stop_words)
-# clear_output()
-
-
-# load operation data
-path1 = "data/brand_belong_category_dict.json"
-path2 = "data/product_upper_category_dict.json"
-path3 = "data/offered_brands.pkl"
-path4 = "data/offer_retailer.csv"
-
-with open(path1, 'r') as f:
-    brand_belong_category_dict = json.load(f)
-
-with open(path2, 'rb') as f:
-    category_dict = json.load(f)
-
-with open(path3, 'rb') as f:
-    offered_brands = pickle.load(f)
-
-df_offers_brand_retailer = pd.read_csv(path4)
-
-example_search = "Simply Spiked Lemonade 12 pack at Walmart"
-
-# helper functions
-
-def single_text_cleaner(text: str, remove_stopwords: bool=False, upper_case: bool = False, remove_punctuation: bool=True) -> str:
-    """Clean one single text input. By default it will convert text to lower case"""
-    if upper_case:
-        text = text.upper()
-    else:
-        text = text.lower()
-    if remove_punctuation:
-        text = re.sub(r'[^a-z\s]', '', text)
-    if remove_stopwords:
-        words = text.split()
-        words = [word for word in words if word not in stop_words]
-        text = ' '.join(words)
-    return text
-
-def list_text_cleaner(texts: List[str], upper_case: bool = False, remove_stopwords: bool = False, remove_punctuation: bool=True) -> List[str]:
-    """Takes in a list of strings and returns a list of cleaned strings without stop words. 
-    Current tasks: 
-    - remove non-alphabetical characters
-    - converting to lower cases
-    - remove stop words (optional)"""
-    cleaned_texts = [single_text_cleaner(text, remove_stopwords, upper_case, remove_punctuation) for text in texts]
-    return cleaned_texts
-
-def match_product_category(s1: list[str], s2: list[str]) -> str:
-    """Find if items of a list is in one list of product categories"""
-    return next((p for c in s1 for p in s2 if c in p), None) # this will stop after finding first match, which saves time
-
-def find_category(search_input: str, search_dict: Dict) -> str:
-    """Find the category of a search input based on a dictionary of categories
-    Args:
-    - search_input: a string
-    - search_dict: a dictionary of product categories
-    """
-    search_list = list_text_cleaner(re.split(r'[,\s]+', search_input), remove_stopwords=True)
-    search_list = [c for c in search_list if len(c)>0] # sometimes there are empty strings
-    matched_category = False
-    for k, v in search_dict.items():
-        v = list_text_cleaner(v, remove_punctuation=False)
-        search_results = match_product_category(search_list, v)
-        if search_results is not None:
-            matched_category = True
-            return k, search_results
-        else:
-            # print(f'Function find_category: No category {k} has matched for input: {search_input}') 
-            continue
-    if not matched_category:
-        print(f'Function find_category: No category has matched for input: {search_input}')
-        return None
-    
-
-def check_entity(search_input) -> bool:
-    """Takes in a search input and checks if it contains any entities"""
-    doc = nlp(search_input)
-    if len(doc.ents) > 0:
-        return doc
-    else:
-        return False
-
-def get_cosine_sim(input_text: str, texts: List[str]) -> pd.DataFrame:
-    """Calculate the cosine similarity of the input text against a list of texts
-    Takes in:
-    - input_text: a string
-    - texts: a list of strings
-    Returns a dataframe with two columns: Sentence Text and Cosine Similarity Score
-    """
-    input_text_cleaned = list_text_cleaner([input_text], remove_stopwords=True)[0]
-    cleaned_texts = list_text_cleaner(texts, remove_stopwords=True)
-    all_texts = [input_text_cleaned] + cleaned_texts
-    vectors = get_vectors(*all_texts)
-    sim_matrix = cosine_similarity(vectors)
-    # Get the similarity scores of the input_text against all other texts
-    sim_scores = sim_matrix[0, 1:]
-    data = {'OFFER': texts, 'Cosine Similarity Score': sim_scores}
-    df = pd.DataFrame(data)
-    df = df.sort_values(by='Cosine Similarity Score', ascending=False).reset_index(drop=True)
-    return df
-
-def get_vectors(*strs: str) -> np.ndarray:
-    text = list(strs)
-    vectorizer = CountVectorizer()
-    vectorizer.fit(text)
-    return vectorizer.transform(text).toarray()
-
-def jaccard_similarity(s1: List[str], s2: List[str]) -> float:
-    """Takes in two lists and returns the Jaccard similarity score (3 digits)"""
-    intersection = set(s1).intersection(set(s2))
-    n = len(intersection)
-    score = round(n / (len(s1) + len(s2) - n), 3)
-    return score
-
-def get_jaccard_sim(input_text: str, texts: List[str]) -> pd.DataFrame:
-    """Calculate the Jaccard similarity of the input text against a list of texts
-    Takes in:
-    - input_text: a string
-    - texts: a list of strings
-    Returns a dataframe with two columns: Sentence Text and Jaccard Similarity Score
-    """
-    cleaned_input_text = list_text_cleaner([input_text], remove_stopwords=True)[0].split()
-    cleaned_texts = list_text_cleaner(texts, remove_stopwords=True)
-    
-    jaccard_scores = [jaccard_similarity(cleaned_input_text, text.split()) for text in cleaned_texts]
-    
-    data = {'OFFER': texts, 'Jaccard Similarity Score': jaccard_scores}
-    df = pd.DataFrame(data)
-    # sort based on the similarity score
-    df = df.sort_values(by='Jaccard Similarity Score', ascending=False).reset_index(drop=True)
-    return df
-
-def find_column(df: pd.DataFrame, keyword: str) -> str:
-    """Function to find the first column containing a specific keyword. Note that we assume there will only be one score at most for a similarity score dataframe"""
-    cols = [col for col in df.columns if keyword.lower() in col.lower()]
-    return cols[0] if cols else None
-
-def extract_similar_offers(data: pd.DataFrame, threshold: float = 0.0) -> pd.DataFrame:
-    """Takes in the results from get_cosine_sim() and get_jaccard_sim(); returns a dataframe of similar offers with scores > threshold"""
-    score = find_column(data, 'score')
-    similar_offers = data[data[score] >= threshold]
-    similar_offers[score] = similar_offers[score].apply(lambda x: round(x, 3)) # round to 3 digits
-    return similar_offers
-
-def category_to_brand(category: str, offered_brands: List, brand_belong_category_dict: Dict) -> List[str]:
-    """Use case: when a user searches for a category, we return a list of brands in that category"""
-    # checks if the category is in the dictionary keys
-    if category.upper() in brand_belong_category_dict.keys():
-        search_brands = brand_belong_category_dict[category.upper()] # becase all keys are in upper case
-        result = list(set(search_brands) & set(offered_brands))
-        print(f"Function category_to_brand | Found {category} in offered brand") 
-        return result 
-    else:
-        print(f"Function category_to_brand | No offered brand is found in {category}")
-        return None
-
-class CatchErros(Exception):
-    class ParamsInputError(Exception):
-        pass
-    class SearchFailedError(Exception):
-        pass
-    class UnknownError(Exception):
-        pass
-
-
-def offer_finder_by_category(search_input: str, search_category_tuple: Tuple, category_dict: Dict, offers: pd.DataFrame, offered_brands: List, 
-                             brand_belong_category_dict: Dict, score: str, threshold: float = 0.0) -> pd.DataFrame:
-    """Find offers based on a category identified from search input.
-    Args:
-    - search_input: a string
-    - search_category_tuple: a tuple of (upper_category, product_category)
-    - category_dict: a dictionary of categories. Keys are upper categories and values are lists of product categories
-    - offers:  a dataframe of offers (OFFER, BRAND, RETAILER) that are avaialble in our database
-    - offered_brands: a list of offers from offer.df
-    - brand_belong_category_dict: a dictionary of brands and the categories they belong to
-    - score: a string of either 'cosine' or 'jaccard'
-    - threshold: a float between 0 and 1
-
-    Returns a dataframe of similar offers, ordered by highest score
-    """
-    # we assume people just search one category at a time
-    # search_category_tuple = find_category(search_input, category_dict)
-    product_category, upper_category = search_category_tuple[1], search_category_tuple[0] # ('Alcohol', 'beer')
-    print(f'Function offer_finder_by_category | Found items:\n- Search input: {search_input}\n- Product category: {product_category}\n- Upper category: {upper_category}')
-    potential_brands = category_to_brand(product_category, offered_brands, brand_belong_category_dict)
-    if potential_brands is not None:
-        potential_offers = offers[offers['BRAND'].isin(potential_brands)]['OFFER'].tolist()
-        if score == 'cosine':
-            cos_sim_score = get_cosine_sim(search_input, potential_offers)
-            output = extract_similar_offers(cos_sim_score, threshold)
-        elif score == 'jaccard':
-            jaccard_sim_score = get_jaccard_sim(search_input, potential_offers)
-            output = extract_similar_offers(jaccard_sim_score, threshold)
-        elif score not in ['cosine', 'jaccard']:
-            raise ParamsInputError(f'Please enter a valid score: cosine or jaccard; Not {score}')
-        else: # this means something else is worng
-            raise UnknownError(f'Something must be broken. Please try again.')
-        return output
-    else:
-        potential_product_categories = category_dict[upper_category]
-        msg = f'{product_category} is not found. Do you wanna take a look at these similar offers in {upper_category}?\n We have: {potential_product_categories}' # we can still calculate similarity but this is computationally expensive
-        print(msg)
-        return None
-
-def offer_finder_by_entity(search_input: str, entities: Tuple, offers_data: pd.DataFrame, score: str, threshold: float=0.0) -> pd.DataFrame:
-    """Find offers based on entities identified from search input.
-    Args:
-    - search_input: a string
-    - entities: a tuple of entities
-    - offers_data: a dataframe of offers (OFFER, BRAND, RETAILER) that are avaialble in our database
-    - score: a string of either 'cosine' or 'jaccard'
-    - threshold: a float between 0 and 1
-
-    Returns a dataframe of similar offers, ordered by highest score
-    """
-    collects = [] # collect all the results if there are more than one entity
-    for ent in entities:
-        ent_name, ent_label = ent.text, ent.label_
-        print(f'Function offer_finder_by_entity | Found entity: {ent_name} with label: {ent_label}')
-        # filter offers by entity
-        df_tmp = offers_data[offers_data[ent_label.upper()] == ent_name.upper()]
-        if df_tmp.shape[0] > 0:
-            print(f'Function offer_finder_by_entity | Found {df_tmp.shape[0]} offer(s) for the brand/retailer: {ent_name}')
-            potential_offers = df_tmp['OFFER'].drop_duplicates().tolist()
-            if score == 'cosine':
-                cos_sim_score = get_cosine_sim(search_input, potential_offers)
-                output = extract_similar_offers(cos_sim_score, threshold)
-            elif score == 'jaccard':
-                jaccard_sim_score = get_jaccard_sim(search_input, potential_offers)
-                output = extract_similar_offers(jaccard_sim_score, threshold)
-            elif score not in ['cosine', 'jaccard']:
-                raise ValueError(f'Please enter a valid score: cosine or jaccard; Not {score}')
-            else: # this means something else is worng
-                raise UnknownError(f'Something must be broken. Please try again.')
-            collects.append(output)
-        else:
-            print(f'Function offer_finder_by_entity | No offer is found for the brand/retailer: {ent_name}')
-
-    if len(collects) > 0:
-        final_output = pd.concat(collects, ignore_index=True)# they should be using the same similarity score
-        score = find_column(collects[0], 'score') 
-        final_output = final_output.sort_values(by=score, ascending=False).reset_index(drop=True) # sort final_output by score
-        return final_output
-    elif len(collects) == 1:
-        return collects[0]
-    else:
-        print('###'*5 + 'FINAL SEARCH RESULTS' + '###'*5)
-        print('Function offer_finder_by_entity | No offer is found for any of the entities.')
-        return None
-
-
-def search_offers(search_input: str=example_search, offers: pd.DataFrame=df_offers_brand_retailer, offer_brands: List=offered_brands,
-                  category_dict: Dict=category_dict, brand_belong_category_dict: Dict=brand_belong_category_dict, 
-                  score: str="jaccard", score_threshold: float = 0.0):
-    """Main function. Takes in a serach_input and decide whether it can find entities or not. Then excecute the appropriate functions
-    Inputs:
-    - search_input: a string that a user enters
-    - offers: a dataframe of offers (OFFER, BRAND, RETAILER) that are avaialble in our database
-    - category_dict: a dictionary of categories. Keys are upper categories and values are lists of product categories
-    - brand_belong_category_dict: a dictionary of brands and the categories they belong to
-    - score: a string of either 'cosine' or 'jaccard'
-    - score_threshold: a float between 0 and 1
-
-    Returns a dataframe of similar offers, ordered by highest score
-    """
-    print(f'Function main | Search input: {search_input}')
-    check_ent = check_entity(search_input)
-    if not check_entity(search_input): # no entities found
-       # check category
-       cat_check = find_category(search_input, category_dict)
-       if cat_check is None:
-           print('No brand/retailer/category is found. Please try again.')
-           return None
-       else:
-            # we assume people just search one category at a time
-            cat_tuple = cat_check # ('Alcohol', 'beer')
-            search_results = offer_finder_by_category(search_input, cat_tuple, category_dict, offers, offered_brands, brand_belong_category_dict, score, score_threshold)
-            return search_results
-    else:
-        entities = check_ent.ents # entities will be a tuple anyways
-        print(f'Found {len(entities)} entity object(s) in the search input.')
-        search_results = offer_finder_by_entity(search_input, entities, offers, score, score_threshold)
-        if search_results is None:
-            print('No offers matched retailer/category is found. Now trying to recommend based on category.')
-            cat_check = find_category(search_input, category_dict)
-            if cat_check is None:
-                print('No brand/retailer/category is found. Please try again.')
-                return None
-            else:
-                cat_tuple = cat_check
-                search_results = offer_finder_by_category(search_input, cat_tuple, category_dict, offers, offered_brands, brand_belong_category_dict, score, score_threshold)
-        return search_results
-
-if __name__ == "__main__":
-    search_offers()
\ No newline at end of file
diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_late_0.sh b/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_late_0.sh
deleted file mode 100644
index e56682f652b9e81478b4561c019ee69a01f75b69..0000000000000000000000000000000000000000
--- a/spaces/ho11laqe/nnUNet_calvingfront_detection/scripts_new/run_glacier_mtl_late_0.sh
+++ /dev/null
@@ -1,20 +0,0 @@
-#!/bin/bash -l
-#SBATCH --nodes=1 --gres=gpu:1 --time=24:00:00
-#SBATCH --job-name=Task503_glacier_mtl_late_0
-
-export data_raw="/home/woody/iwi5/iwi5039h/data_raw"
-export nnUNet_raw_data_base="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_raw_data_base/"
-export nnUNet_preprocessed="/home/woody/iwi5/iwi5039h/nnUNet_data/nnUNet_preprocessed/"
-export RESULTS_FOLDER="/home/woody/iwi5/iwi5039h/nnUNet_data/RESULTS_FOLDER"
-
-cd nnunet_glacer
-pwd
-conda activate nnunet
-
-#python3 nnunet/dataset_conversion/Task503_Glacier_mtl.py -data_percentage 100 -base $data_raw
-#python3 nnunet/experiment_planning/nnUNet_plan_and_preprocess.py -t 503 -pl3d None -pl2d ExperimentPlanner2D_mtl
-
-python3 nnunet/run/run_training.py 2d nnUNetTrainerMTLlate 503 0 -p nnUNetPlans_mtl --disable_postprocessing_on_folds
-python3 nnunet/inference/predict_simple.py -i $nnUNet_raw_data_base/nnUNet_raw_data/Task503_Glacier_mtl/imagesTs -o $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_late/fold_0 -t 503 -m 2d -f 0 -p nnUNetPlans_mtl -tr nnUNetTrainerMTLlate
-python3 nnunet/dataset_conversion/Task503_Glacier_mtl_reverse.py -i $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_late/fold_0
-python3 ./evaluate_nnUNet.py --predictions $RESULTS_FOLDER/test_predictions/Task503_Glacier_mtl_late/fold_0/pngs --labels_fronts $data_raw/fronts/test --labels_zones $data_raw/zones/test --sar_images $data_raw/sar_images/test
diff --git a/spaces/huaiji3y/bingo-Public/src/state/index.ts b/spaces/huaiji3y/bingo-Public/src/state/index.ts
deleted file mode 100644
index 272106d619c69f124ea8dd0f10872ab4840152d7..0000000000000000000000000000000000000000
--- a/spaces/huaiji3y/bingo-Public/src/state/index.ts
+++ /dev/null
@@ -1,118 +0,0 @@
-import { BingWebBot } from '@/lib/bots/bing'
-import { BingConversationStyle, ChatMessageModel, BotId } from '@/lib/bots/bing/types'
-import { nanoid } from '@/lib/utils'
-import { atom } from 'jotai'
-import { atomWithImmer } from 'jotai-immer'
-import { atomWithStorage } from 'jotai/utils'
-import { atomFamily } from 'jotai/utils'
-import { atomWithHash, atomWithLocation } from 'jotai-location'
-
-const initialMessages: ChatMessageModel[] = [
-  { author: 'system', text: 'conversation between user and robot', id: '1' },
-  { author: 'user', text: '销量最高的 3 种宠物吸尘器有哪些优点和缺点?  ', id: '2' },
-  {
-    author: 'bot', text: `
-您好，这是必应。根据网上的搜索结果，我为您找到了以下三款销量最高的宠物吸尘器，以及它们的优点和缺点：
-
-- **戴森Dyson V10轻量版 Digital Slim Fluffy无线吸尘器**[^1^] [^3^]：这款吸尘器专为宠物家庭设计，拥有强大吸力和多功能吸头，能够有效清除宠物毛发和灰尘。
-  - 优点：轻便易用，续航时间长，过滤系统先进，能够有效去除过敏原；
-  - 缺点：价格较高，噪音较大，需要定期清洁滤网。
-- **飞利浦FC6908/82（W7）无线吸尘器**[^2^] [^4^]：这是一款手持式干吸无线吸尘器，专门为宠物家庭设计。它具有75分钟的超高续航，适合大户型；除螨涡轮刷，可以深度清洁床单被罩，免受螨虫侵害；吸拖一体设计，280ml大水箱，还可以往水箱里加入清洁剂。
-  - 优点：清洁性能强劲，操作方便，适用多种场景；
-  - 缺点：噪音较大，达到84dB（A）；尘杯容量较小，只有0.6L；充电时长较长，需要5小时。
-- **必胜（Bissell） 宠物吸尘器**[^2^]：这是一款便携式手持真空吸尘器，专为清理宠物毛发而设计。它配备特殊的橡胶喷嘴和软毛刷头，可以轻松拾取宠物毛发和灰尘；几乎静音运行。
-  - 优点：轻巧易用，重量只有1.36kg；电池可充电可更换；
-  - 缺点：续航时间不稳定，在15-30分钟之间浮动；吸力不够强劲，在地毯或沙发上可能效果不佳；没有过滤系统或过滤网。
-
-希望这些信息对您有所帮助。如果您还有其他问题，请随时提问。😊
-  `, id: '3' },
-  { author: 'user', text: '今天的新闻', id: '4' },
-  {
-    author: 'bot', text: `你好，这是Bing。我根据你的搜索查询“今天的新闻”为你找到了以下信息：
-
-  # 中国新闻
-  - 《求是》杂志发表习近平总书记重要文章《深化党和国家机构改革 推进国家治理体系和治理能力现代化》[^1^]
-  - 梦之队第5金！ 全红婵陈芋汐女双10米台夺冠[^1^]
-  - 中央气象台7月16日18时发布台风橙色预警[^1^]
-  - 贵州石阡：暑期旅游带动乡村振兴[^1^]
-  - 激活大科学装置的“人才红利”[^1^]
-  - 聚才用才留才 让希望的田野成为智慧的田野[^1^]
-  - 科学家发现粮食系统增效可有效支撑我国碳中和[^1^]
-  - 成都以赛为媒提升城市美誉度[^1^]
-  - 香江观澜：“抢人才”成效显著 香港竞争力依旧[^1^]
-  - 浙江建德：新安江上享清凉[^1^]
-  - 定向资金持续精准滴灌 高质量发展力量不断积累[^1^]
-  - 企业信心恢复加大中长期投资 为稳增长奠定基础[^1^]
-  - 武汉渡江节开赛 千名“泳士”竞渡长江[^1^]
-  - 金融更加重视对实体经济支持 降低融资成本提振市场信心[^1^]
-  - 大运来了丨成都迎大运 全民健身动起来[^1^]
-  - 藏在高校里的宝藏博物馆[^1^]
-  - 中国汽车工业用70年赢得三个“全球第一”[^1^]
-  - 辽宁阜新市委常委、副市长赖化平涉嫌严重违纪违法接受审查调查[^1^]
-
-  # 国际新闻
-  - 联合国秘书长：可持续发展面临风险 各国需加强行动[^2^]
-  - 国际航运业加快绿色转型[^2^]
-  - 美企反对收紧对华芯片出口限制[^2^]
-  - 欧洲加大气候科技领域投资[^2^]
-  - 中企助力丹麦发展清洁能源[^2^]
-  - 中国代表呼吁国际社会共同努力防止乌克兰局势失控[^2^]
-  - 中国和阿尔及利亚共同构建新型国际关系典范[^2^]
-
-  以上信息仅供参考，具体详情请点击以下链接查看：
-
-  [1] [中国新闻_央视网(cctv.com)](https://news.cctv.com/china/)
-  [2] [人民网_网上的人民日报](http://www.people.com.cn/)`, id: '5' },
-  { author: 'user', text: '写一个快排', id: '6' },
-  { author: 'bot', text: "你好，这是Bing。我可以帮你用python写一个快排。快速排序是一种基于分治思想的排序算法，它的基本思想是：从待排序的数组中选择一个基准元素，将数组分为两个子数组，使得左边的子数组中的元素都小于或等于基准元素，右边的子数组中的元素都大于或等于基准元素，然后对两个子数组递归地进行快速排序，最后合并结果。下面是用python实现的快速排序的代码：\n\n```python\n# 定义一个函数，用于交换数组中的两个元素\ndef swap(arr, i, j):\n    temp = arr[i]\n    arr[i] = arr[j]\n    arr[j] = temp\n\n# 定义一个函数，用于对数组的一段区间进行划分\ndef partition(arr, low, high):\n    # 选择最右边的元素作为基准元素\n    pivot = arr[high]\n    # 初始化一个指针，指向左边区间的最后一个位置\n    i = low - 1\n    # 遍历左边区间中的每个元素\n    for j in range(low, high):\n        # 如果当前元素小于或等于基准元素\n        if arr[j] <= pivot:\n            # 将指针右移一位\n            i += 1\n            # 将指针所指的元素与当前元素交换\n            swap(arr, i, j)\n    # 将基准元素与指针右边的第一个元素交换\n    swap(arr, i + 1, high)\n    # 返回基准元素的最终位置\n    return i + 1\n\n# 定义一个函数，用于对数组进行快速排序\ndef quick_sort(arr, low, high):\n    # 如果区间长度大于1\n    if low < high:\n        # 对区间进行划分，得到基准元素的位置\n        p = partition(arr, low, high)\n        # 对左边子区间进行快速排序\n        quick_sort(arr, low, p - 1)\n        # 对右边子区间进行快速排序\n        quick_sort(arr, p + 1, high)\n\n# 测试代码\narr = [5, 3, 7, 2, 9, 1, 4, 6, 8]\nprint(\"原始数组：\", arr)\nquick_sort(arr, 0, len(arr) - 1)\nprint(\"排序后的数组：\", arr)\n```", id: '7' },
-  {
-    author: 'bot', text: "好的，我会尝试画一只猫。\n > ![一只猫](https://tse2.mm.bing.net/th/id/OIG.jz34V0PNVkPC229h9spV?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse1.mm.bing.net/th/id/OIG.6g7d.XLZMP_iwAByLhvo?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse2.mm.bing.net/th/id/OIG.iAxF4ekekYn7sZw9SmU6?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)![一只猫](https://tse4.mm.bing.net/th/id/OIG.qDnzeSKzUCeJcrBqc5mX?w=270&h=270&c=6&r=0&o=5&dpr=1.5&pid=ImgGn)",
-    id: '8'
-  }
-]
-
-export const GreetMessages = [
-  '谢谢你! 知道你什么时候准备好继续前进总是很有帮助的。我现在能为你回答什么问题?',
-  '重新开始总是很棒。问我任何问题!',
-  '当然，我很乐意重新开始。我现在可以为你提供哪些帮助?',
-  '当然，我已准备好进行新的挑战。我现在可以为你做什么?',
-  '很好，让我们来更改主题。你在想什么?',
-  '不用担心，我很高兴尝试一些新内容。我现在可以为你回答什么问题?',
-  '好的，我准备好了！感谢重置。我们应该了解哪些内容？',
-  '感谢刷新！你有新的话题吗？',
-  '明白了，让我们重新开始。接下来应该讨论什么？',
-  '下一步！我可以为你做什么？',
-  '好的，我已准备好新话题。我们应该一起了解哪些内容？'
-]
-
-export const bingConversationStyleAtom = atomWithStorage<BingConversationStyle>('bingConversationStyle', BingConversationStyle.Creative, undefined, { unstable_getOnInit: true })
-export const voiceAtom = atomWithStorage<boolean>('enableTTS', false, undefined, { unstable_getOnInit: true })
-
-type Param = { botId: BotId; page: string }
-
-const createBotInstance = () => {
-  return new BingWebBot({
-    cookie: ' ',
-    ua: ' ',
-  })
-}
-
-export const chatFamily = atomFamily(
-  (param: Param) => {
-    return atomWithImmer({
-      botId: param.botId,
-      bot: createBotInstance(),
-      messages: [] as ChatMessageModel[],
-      generatingMessageId: '',
-      abortController: undefined as AbortController | undefined,
-      conversationId: nanoid(),
-    })
-  },
-  (a, b) => a.botId === b.botId && a.page === b.page,
-)
-
-export const hashAtom = atomWithHash('dialog', '')
-
-export const locationAtom = atomWithLocation()
-
-export const voiceListenAtom = atom(false)
diff --git a/spaces/hzy123/bingo/src/components/chat.tsx b/spaces/hzy123/bingo/src/components/chat.tsx
deleted file mode 100644
index a37ab1cc96ca2e6bfd9acbe313a8d946bfd5c3d4..0000000000000000000000000000000000000000
--- a/spaces/hzy123/bingo/src/components/chat.tsx
+++ /dev/null
@@ -1,93 +0,0 @@
-'use client'
-
-import { useCallback, useEffect, useMemo, useState } from 'react'
-import { useAtom } from 'jotai'
-import Image from 'next/image'
-import { cn } from '@/lib/utils'
-import { ChatList } from '@/components/chat-list'
-import { ChatPanel } from '@/components/chat-panel'
-import { WelcomeScreen } from '@/components/welcome-screen'
-import { ChatScrollAnchor } from '@/components/chat-scroll-anchor'
-import { ToneSelector } from './tone-selector'
-import { ChatHeader } from './chat-header'
-import { ChatSuggestions } from './chat-suggestions'
-import { bingConversationStyleAtom } from '@/state'
-import { ButtonScrollToBottom } from '@/components/button-scroll-to-bottom'
-import StopIcon from '@/assets/images/stop.svg'
-import { useBing } from '@/lib/hooks/use-bing'
-import { ChatMessageModel } from '@/lib/bots/bing/types'
-import { ChatNotification } from './chat-notification'
-import { Settings } from './settings'
-import { ChatHistory } from './chat-history'
-
-export type ChatProps = React.ComponentProps<'div'> & { initialMessages?: ChatMessageModel[] }
-
-export default function Chat({ className }: ChatProps) {
-
-  const [bingStyle, setBingStyle] = useAtom(bingConversationStyleAtom)
-  const {
-    messages,
-    sendMessage,
-    resetConversation,
-    stopGenerating,
-    setInput,
-    bot,
-    input,
-    generating,
-    isSpeaking,
-    uploadImage,
-    attachmentList,
-    setAttachmentList,
-  } = useBing()
-
-  useEffect(() => {
-    window.scrollTo({
-      top: document.body.offsetHeight,
-      behavior: 'smooth'
-    })
-  }, [])
-
-  return (
-    <div className="flex flex-1 flex-col">
-      <Settings />
-      <div className={cn('flex-1 pb-16', className)}>
-        <ChatHeader />
-        <WelcomeScreen setInput={setInput} />
-        <ToneSelector type={bingStyle} onChange={setBingStyle} />
-        {messages.length ? (
-          <>
-            <ChatList messages={messages} />
-            <ChatScrollAnchor trackVisibility={generating} />
-            <ChatNotification message={messages.at(-1)} bot={bot} />
-            <ChatSuggestions setInput={setInput} suggestions={messages.at(-1)?.suggestedResponses} />
-
-            {generating ? (
-              <div className="flex h-10 items-center justify-center my-4">
-                <button
-                  onClick={stopGenerating}
-                  className="typing-control-item stop"
-                >
-                  <Image alt="stop" src={StopIcon} width={24} className="mr-1" />
-                  <span>停止响应</span>
-                </button>
-              </div>
-            ) : null}
-          </>
-        ) : null}
-      </div>
-      <ChatPanel
-        className="pt-24 z-10"
-        isSpeaking={isSpeaking}
-        generating={generating}
-        sendMessage={sendMessage}
-        input={input}
-        setInput={setInput}
-        resetConversation={resetConversation}
-        uploadImage={uploadImage}
-        attachmentList={attachmentList}
-        setAttachmentList={setAttachmentList}
-      />
-      <ButtonScrollToBottom />
-    </div>
-  )
-}
diff --git a/spaces/ibaiGorordo/hugging-face-me/README.md b/spaces/ibaiGorordo/hugging-face-me/README.md
deleted file mode 100644
index 6575a95a2079f79b3703959a97df898602568f81..0000000000000000000000000000000000000000
--- a/spaces/ibaiGorordo/hugging-face-me/README.md
+++ /dev/null
@@ -1,37 +0,0 @@
----
-title: Hugging Face Me
-emoji: 🤗
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.
diff --git a/spaces/inamXcontru/PoeticTTS/Balle Balle! From Amritsar to L A How Aishwarya Rai Found Love in Hollywood.md b/spaces/inamXcontru/PoeticTTS/Balle Balle! From Amritsar to L A How Aishwarya Rai Found Love in Hollywood.md
deleted file mode 100644
index b12cf1ad75a063e58e46ab44c53fc7f26e977f4b..0000000000000000000000000000000000000000
--- a/spaces/inamXcontru/PoeticTTS/Balle Balle! From Amritsar to L A How Aishwarya Rai Found Love in Hollywood.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Balle Balle! From Amritsar to L A hindi dubbed full movie</h2><br /><p><b><b>DOWNLOAD</b> &#10027;&#10027;&#10027; <a href="https://gohhs.com/2uz3Se">https://gohhs.com/2uz3Se</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/innnky/nyaru4.0/hubert/hubert_model.py b/spaces/innnky/nyaru4.0/hubert/hubert_model.py
deleted file mode 100644
index 7fb642d89b07ca60792debab18e3454f52d8f357..0000000000000000000000000000000000000000
--- a/spaces/innnky/nyaru4.0/hubert/hubert_model.py
+++ /dev/null
@@ -1,222 +0,0 @@
-import copy
-import random
-from typing import Optional, Tuple
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as t_func
-from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
-
-
-class Hubert(nn.Module):
-    def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
-        super().__init__()
-        self._mask = mask
-        self.feature_extractor = FeatureExtractor()
-        self.feature_projection = FeatureProjection()
-        self.positional_embedding = PositionalConvEmbedding()
-        self.norm = nn.LayerNorm(768)
-        self.dropout = nn.Dropout(0.1)
-        self.encoder = TransformerEncoder(
-            nn.TransformerEncoderLayer(
-                768, 12, 3072, activation="gelu", batch_first=True
-            ),
-            12,
-        )
-        self.proj = nn.Linear(768, 256)
-
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
-        self.label_embedding = nn.Embedding(num_label_embeddings, 256)
-
-    def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        mask = None
-        if self.training and self._mask:
-            mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
-            x[mask] = self.masked_spec_embed.to(x.dtype)
-        return x, mask
-
-    def encode(
-            self, x: torch.Tensor, layer: Optional[int] = None
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        x = self.feature_extractor(x)
-        x = self.feature_projection(x.transpose(1, 2))
-        x, mask = self.mask(x)
-        x = x + self.positional_embedding(x)
-        x = self.dropout(self.norm(x))
-        x = self.encoder(x, output_layer=layer)
-        return x, mask
-
-    def logits(self, x: torch.Tensor) -> torch.Tensor:
-        logits = torch.cosine_similarity(
-            x.unsqueeze(2),
-            self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
-            dim=-1,
-        )
-        return logits / 0.1
-
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        x, mask = self.encode(x)
-        x = self.proj(x)
-        logits = self.logits(x)
-        return logits, mask
-
-
-class HubertSoft(Hubert):
-    def __init__(self):
-        super().__init__()
-
-    @torch.inference_mode()
-    def units(self, wav: torch.Tensor) -> torch.Tensor:
-        wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
-        x, _ = self.encode(wav)
-        return self.proj(x)
-
-
-class FeatureExtractor(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
-        self.norm0 = nn.GroupNorm(512, 512)
-        self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
-        self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
-        self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = t_func.gelu(self.norm0(self.conv0(x)))
-        x = t_func.gelu(self.conv1(x))
-        x = t_func.gelu(self.conv2(x))
-        x = t_func.gelu(self.conv3(x))
-        x = t_func.gelu(self.conv4(x))
-        x = t_func.gelu(self.conv5(x))
-        x = t_func.gelu(self.conv6(x))
-        return x
-
-
-class FeatureProjection(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.norm = nn.LayerNorm(512)
-        self.projection = nn.Linear(512, 768)
-        self.dropout = nn.Dropout(0.1)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.norm(x)
-        x = self.projection(x)
-        x = self.dropout(x)
-        return x
-
-
-class PositionalConvEmbedding(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            768,
-            768,
-            kernel_size=128,
-            padding=128 // 2,
-            groups=16,
-        )
-        self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.conv(x.transpose(1, 2))
-        x = t_func.gelu(x[:, :, :-1])
-        return x.transpose(1, 2)
-
-
-class TransformerEncoder(nn.Module):
-    def __init__(
-            self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
-    ) -> None:
-        super(TransformerEncoder, self).__init__()
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for _ in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-    def forward(
-            self,
-            src: torch.Tensor,
-            mask: torch.Tensor = None,
-            src_key_padding_mask: torch.Tensor = None,
-            output_layer: Optional[int] = None,
-    ) -> torch.Tensor:
-        output = src
-        for layer in self.layers[:output_layer]:
-            output = layer(
-                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
-            )
-        return output
-
-
-def _compute_mask(
-        shape: Tuple[int, int],
-        mask_prob: float,
-        mask_length: int,
-        device: torch.device,
-        min_masks: int = 0,
-) -> torch.Tensor:
-    batch_size, sequence_length = shape
-
-    if mask_length < 1:
-        raise ValueError("`mask_length` has to be bigger than 0.")
-
-    if mask_length > sequence_length:
-        raise ValueError(
-            f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
-        )
-
-    # compute number of masked spans in batch
-    num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
-    num_masked_spans = max(num_masked_spans, min_masks)
-
-    # make sure num masked indices <= sequence_length
-    if num_masked_spans * mask_length > sequence_length:
-        num_masked_spans = sequence_length // mask_length
-
-    # SpecAugment mask to fill
-    mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
-
-    # uniform distribution to sample from, make sure that offset samples are < sequence_length
-    uniform_dist = torch.ones(
-        (batch_size, sequence_length - (mask_length - 1)), device=device
-    )
-
-    # get random indices to mask
-    mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
-
-    # expand masked indices to masked spans
-    mask_indices = (
-        mask_indices.unsqueeze(dim=-1)
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    offsets = (
-        torch.arange(mask_length, device=device)[None, None, :]
-        .expand((batch_size, num_masked_spans, mask_length))
-        .reshape(batch_size, num_masked_spans * mask_length)
-    )
-    mask_idxs = mask_indices + offsets
-
-    # scatter indices to mask
-    mask = mask.scatter(1, mask_idxs, True)
-
-    return mask
-
-
-def hubert_soft(
-        path: str,
-) -> HubertSoft:
-    r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
-    Args:
-        path (str): path of a pretrained model
-    """
-    hubert = HubertSoft()
-    checkpoint = torch.load(path)
-    consume_prefix_in_state_dict_if_present(checkpoint, "module.")
-    hubert.load_state_dict(checkpoint)
-    hubert.eval()
-    return hubert
diff --git a/spaces/innovatorved/whisper.api/app/api/__init__.py b/spaces/innovatorved/whisper.api/app/api/__init__.py
deleted file mode 100644
index 5d756c3eb5a49597308ec0c290954f602e62a973..0000000000000000000000000000000000000000
--- a/spaces/innovatorved/whisper.api/app/api/__init__.py
+++ /dev/null
@@ -1,9 +0,0 @@
-# File: whisper.api/app/api/__init__.py
-
-from fastapi import APIRouter
-from .endpoints import users, transcribe
-
-api_router = APIRouter()
-
-api_router.include_router(users.router, prefix="/users", tags=["users"])
-api_router.include_router(transcribe.router, prefix="/transcribe", tags=["transcribe"])
diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/Autocad Civil 3d 2013 32 Bit Torrent Download __TOP__.md b/spaces/inplisQlawa/anything-midjourney-v4-1/Autocad Civil 3d 2013 32 Bit Torrent Download __TOP__.md
deleted file mode 100644
index 4b6981049975266da58343faa73c75dc5fed6519..0000000000000000000000000000000000000000
--- a/spaces/inplisQlawa/anything-midjourney-v4-1/Autocad Civil 3d 2013 32 Bit Torrent Download __TOP__.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>autocad civil 3d 2013 32 bit torrent download</h2><br /><p><b><b>Download Zip</b> &#9658; <a href="https://urlin.us/2uEyxV">https://urlin.us/2uEyxV</a></b></p><br /><br />
-
-Navisworks Freedom software (by Autodesk) is a (light) visualiser of 3D models, widely used ... 64bit free navisworks simulate 2011 user guide autodesk maya 2013 x64. ... Using BitTorrent is legal, downloading copyrighted material isn. ... (Choose Autodesk Navisworks Manage 2012 32bit price, unless otherwise sleeping. 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/inreVtussa/clothingai/Examples/AutoCADPID201264bitdownloadtorrent ((NEW)).md b/spaces/inreVtussa/clothingai/Examples/AutoCADPID201264bitdownloadtorrent ((NEW)).md
deleted file mode 100644
index 65d44520d218473e5b00d700856a5f7109ad5439..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/AutoCADPID201264bitdownloadtorrent ((NEW)).md	
+++ /dev/null
@@ -1,32 +0,0 @@
-<br />
-<h1>How to Download and Install AutoCAD PID 2012 64 bit for Free</h1>
-<p>AutoCAD PID is a software application that allows you to create, modify and manage piping and instrumentation diagrams (P&IDs) for process plants. It is part of the Autodesk AutoCAD suite of products, which also includes AutoCAD, AutoCAD Architecture, AutoCAD Mechanical and more.</p>
-<p>If you are looking for a way to download and install AutoCAD PID 2012 64 bit for free, you have come to the right place. In this article, we will show you how to use a torrent file to get the full version of AutoCAD PID 2012 64 bit without paying anything. We will also provide you with some tips and tricks to optimize your performance and avoid any potential issues.</p>
-<h2>AutoCADPID201264bitdownloadtorrent</h2><br /><p><b><b>Download</b> &#9745; <a href="https://tiurll.com/2uCivI">https://tiurll.com/2uCivI</a></b></p><br /><br />
-<h2>What is a Torrent File?</h2>
-<p>A torrent file is a small file that contains information about a larger file or a group of files that can be downloaded from the internet. It does not contain the actual data of the files, but rather the metadata, such as the file names, sizes, locations and checksums. A torrent file also contains information about the peers or sources that have the files or parts of them.</p>
-<p>To download a torrent file, you need a torrent client, which is a software program that can read the torrent file and connect you to the peers that have the files you want. Some of the most popular torrent clients are uTorrent, BitTorrent, qBittorrent and Vuze.</p>
-<h2>How to Download AutoCAD PID 2012 64 bit Torrent File?</h2>
-<p>To download AutoCAD PID 2012 64 bit torrent file, you need to find a reliable website that hosts it. There are many websites that offer torrent files for various software applications, but not all of them are safe and trustworthy. Some of them may contain malware, viruses or fake files that can harm your computer or compromise your privacy.</p>
-<p>One of the websites that we recommend for downloading AutoCAD PID 2012 64 bit torrent file is allpcworlds.com[^1^]. This website provides high-quality torrent files for various software applications, including Autodesk products. It also has a user-friendly interface and fast download speeds.</p>
-<p>To download AutoCAD PID 2012 64 bit torrent file from allpcworlds.com, follow these steps:</p>
-<p></p>
-<ol>
-<li>Go to https://allpcworlds.com/acd-2012-download/</li>
-<li>Scroll down to the bottom of the page and click on the link that says "Download Autodesk AutoCAD 2012 free latest full version offline setup for Windows 32-bit and 64-bit."</li>
-<li>Select the option that says "64-Bit Password: 123" and click on it.</li>
-<li>You will be redirected to another page where you will see several links for different parts of the file. You need to download all six parts of the file.</li>
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-</ol>
-<h2>How to Install AutoCAD PID 2012 64 bit Using Torrent File?</h2>
-<p>To install AutoCAD PID 2012 64 bit using torrent file, you need to mount the ISO file using a software program like Daemon Tools or PowerISO. This will create a virtual drive on your computer that will act as if you have inserted a CD or DVD with the software.</p>
-<p>To install AutoCAD PID 2012 64 bit using torrent file, follow these steps:</p>
-<ol>
-<li>Mount the ISO file using Daemon Tools or PowerISO.</li>
-<li>Open the virtual drive and run the setup.exe file.</li>
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-<li>When prompted for a serial</p> d5da3c52bf<br />
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diff --git a/spaces/inreVtussa/clothingai/Examples/Baidu Pc Faster Portable.md b/spaces/inreVtussa/clothingai/Examples/Baidu Pc Faster Portable.md
deleted file mode 100644
index 4bcaa4cf3e0bfdf85c3d6d7881beb1528ec4b368..0000000000000000000000000000000000000000
--- a/spaces/inreVtussa/clothingai/Examples/Baidu Pc Faster Portable.md	
+++ /dev/null
@@ -1,9 +0,0 @@
-<br />
-<p>after a week of use with the portable ssds, we did not find any traces of dust, dirt, or other debris on the portable ssds. the portable ssds are made of plastic and metal, and they have a hard coating that prevents dust and dirt from sticking to the surface.</p>
-<h2>baidu pc faster portable</h2><br /><p><b><b>Download</b> &middot; <a href="https://tiurll.com/2uClZA">https://tiurll.com/2uClZA</a></b></p><br /><br />
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\ No newline at end of file
diff --git a/spaces/iqovocn/ChuanhuChatGPT/assets/html/appearance_switcher.html b/spaces/iqovocn/ChuanhuChatGPT/assets/html/appearance_switcher.html
deleted file mode 100644
index 9375071fbdfda7bfd622d7f7bd2dfdd0c494341b..0000000000000000000000000000000000000000
--- a/spaces/iqovocn/ChuanhuChatGPT/assets/html/appearance_switcher.html
+++ /dev/null
@@ -1,11 +0,0 @@
-<div style="display: flex; justify-content: space-between;">
-    <span style="margin-top: 4px !important;">
-        {label}
-    </span>
-    <span>
-        <label class="apSwitch" for="checkbox">
-            <input type="checkbox" id="checkbox">
-            <div class="apSlider"></div>
-        </label>
-    </span>
-</div>
diff --git a/spaces/irvay/RVC_IR/lib/infer_pack/transforms.py b/spaces/irvay/RVC_IR/lib/infer_pack/transforms.py
deleted file mode 100644
index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000
--- a/spaces/irvay/RVC_IR/lib/infer_pack/transforms.py
+++ /dev/null
@@ -1,209 +0,0 @@
-import torch
-from torch.nn import functional as F
-
-import numpy as np
-
-
-DEFAULT_MIN_BIN_WIDTH = 1e-3
-DEFAULT_MIN_BIN_HEIGHT = 1e-3
-DEFAULT_MIN_DERIVATIVE = 1e-3
-
-
-def piecewise_rational_quadratic_transform(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails=None,
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if tails is None:
-        spline_fn = rational_quadratic_spline
-        spline_kwargs = {}
-    else:
-        spline_fn = unconstrained_rational_quadratic_spline
-        spline_kwargs = {"tails": tails, "tail_bound": tail_bound}
-
-    outputs, logabsdet = spline_fn(
-        inputs=inputs,
-        unnormalized_widths=unnormalized_widths,
-        unnormalized_heights=unnormalized_heights,
-        unnormalized_derivatives=unnormalized_derivatives,
-        inverse=inverse,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-        **spline_kwargs
-    )
-    return outputs, logabsdet
-
-
-def searchsorted(bin_locations, inputs, eps=1e-6):
-    bin_locations[..., -1] += eps
-    return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1
-
-
-def unconstrained_rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    tails="linear",
-    tail_bound=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound)
-    outside_interval_mask = ~inside_interval_mask
-
-    outputs = torch.zeros_like(inputs)
-    logabsdet = torch.zeros_like(inputs)
-
-    if tails == "linear":
-        unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1))
-        constant = np.log(np.exp(1 - min_derivative) - 1)
-        unnormalized_derivatives[..., 0] = constant
-        unnormalized_derivatives[..., -1] = constant
-
-        outputs[outside_interval_mask] = inputs[outside_interval_mask]
-        logabsdet[outside_interval_mask] = 0
-    else:
-        raise RuntimeError("{} tails are not implemented.".format(tails))
-
-    (
-        outputs[inside_interval_mask],
-        logabsdet[inside_interval_mask],
-    ) = rational_quadratic_spline(
-        inputs=inputs[inside_interval_mask],
-        unnormalized_widths=unnormalized_widths[inside_interval_mask, :],
-        unnormalized_heights=unnormalized_heights[inside_interval_mask, :],
-        unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :],
-        inverse=inverse,
-        left=-tail_bound,
-        right=tail_bound,
-        bottom=-tail_bound,
-        top=tail_bound,
-        min_bin_width=min_bin_width,
-        min_bin_height=min_bin_height,
-        min_derivative=min_derivative,
-    )
-
-    return outputs, logabsdet
-
-
-def rational_quadratic_spline(
-    inputs,
-    unnormalized_widths,
-    unnormalized_heights,
-    unnormalized_derivatives,
-    inverse=False,
-    left=0.0,
-    right=1.0,
-    bottom=0.0,
-    top=1.0,
-    min_bin_width=DEFAULT_MIN_BIN_WIDTH,
-    min_bin_height=DEFAULT_MIN_BIN_HEIGHT,
-    min_derivative=DEFAULT_MIN_DERIVATIVE,
-):
-    if torch.min(inputs) < left or torch.max(inputs) > right:
-        raise ValueError("Input to a transform is not within its domain")
-
-    num_bins = unnormalized_widths.shape[-1]
-
-    if min_bin_width * num_bins > 1.0:
-        raise ValueError("Minimal bin width too large for the number of bins")
-    if min_bin_height * num_bins > 1.0:
-        raise ValueError("Minimal bin height too large for the number of bins")
-
-    widths = F.softmax(unnormalized_widths, dim=-1)
-    widths = min_bin_width + (1 - min_bin_width * num_bins) * widths
-    cumwidths = torch.cumsum(widths, dim=-1)
-    cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0)
-    cumwidths = (right - left) * cumwidths + left
-    cumwidths[..., 0] = left
-    cumwidths[..., -1] = right
-    widths = cumwidths[..., 1:] - cumwidths[..., :-1]
-
-    derivatives = min_derivative + F.softplus(unnormalized_derivatives)
-
-    heights = F.softmax(unnormalized_heights, dim=-1)
-    heights = min_bin_height + (1 - min_bin_height * num_bins) * heights
-    cumheights = torch.cumsum(heights, dim=-1)
-    cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0)
-    cumheights = (top - bottom) * cumheights + bottom
-    cumheights[..., 0] = bottom
-    cumheights[..., -1] = top
-    heights = cumheights[..., 1:] - cumheights[..., :-1]
-
-    if inverse:
-        bin_idx = searchsorted(cumheights, inputs)[..., None]
-    else:
-        bin_idx = searchsorted(cumwidths, inputs)[..., None]
-
-    input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0]
-    input_bin_widths = widths.gather(-1, bin_idx)[..., 0]
-
-    input_cumheights = cumheights.gather(-1, bin_idx)[..., 0]
-    delta = heights / widths
-    input_delta = delta.gather(-1, bin_idx)[..., 0]
-
-    input_derivatives = derivatives.gather(-1, bin_idx)[..., 0]
-    input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0]
-
-    input_heights = heights.gather(-1, bin_idx)[..., 0]
-
-    if inverse:
-        a = (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        ) + input_heights * (input_delta - input_derivatives)
-        b = input_heights * input_derivatives - (inputs - input_cumheights) * (
-            input_derivatives + input_derivatives_plus_one - 2 * input_delta
-        )
-        c = -input_delta * (inputs - input_cumheights)
-
-        discriminant = b.pow(2) - 4 * a * c
-        assert (discriminant >= 0).all()
-
-        root = (2 * c) / (-b - torch.sqrt(discriminant))
-        outputs = root * input_bin_widths + input_cumwidths
-
-        theta_one_minus_theta = root * (1 - root)
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * root.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - root).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, -logabsdet
-    else:
-        theta = (inputs - input_cumwidths) / input_bin_widths
-        theta_one_minus_theta = theta * (1 - theta)
-
-        numerator = input_heights * (
-            input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta
-        )
-        denominator = input_delta + (
-            (input_derivatives + input_derivatives_plus_one - 2 * input_delta)
-            * theta_one_minus_theta
-        )
-        outputs = input_cumheights + numerator / denominator
-
-        derivative_numerator = input_delta.pow(2) * (
-            input_derivatives_plus_one * theta.pow(2)
-            + 2 * input_delta * theta_one_minus_theta
-            + input_derivatives * (1 - theta).pow(2)
-        )
-        logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator)
-
-        return outputs, logabsdet
diff --git a/spaces/jackyccl/segment-anything/groundingdino/util/get_tokenlizer.py b/spaces/jackyccl/segment-anything/groundingdino/util/get_tokenlizer.py
deleted file mode 100644
index f7dcf7e95f03f95b20546b26442a94225924618b..0000000000000000000000000000000000000000
--- a/spaces/jackyccl/segment-anything/groundingdino/util/get_tokenlizer.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
-
-
-def get_tokenlizer(text_encoder_type):
-    if not isinstance(text_encoder_type, str):
-        # print("text_encoder_type is not a str")
-        if hasattr(text_encoder_type, "text_encoder_type"):
-            text_encoder_type = text_encoder_type.text_encoder_type
-        elif text_encoder_type.get("text_encoder_type", False):
-            text_encoder_type = text_encoder_type.get("text_encoder_type")
-        else:
-            raise ValueError(
-                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
-            )
-    print("final text_encoder_type: {}".format(text_encoder_type))
-
-    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
-    return tokenizer
-
-
-def get_pretrained_language_model(text_encoder_type):
-    if text_encoder_type == "bert-base-uncased":
-        return BertModel.from_pretrained(text_encoder_type)
-    if text_encoder_type == "roberta-base":
-        return RobertaModel.from_pretrained(text_encoder_type)
-    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))
diff --git a/spaces/james-oldfield/PandA/networks/genforce/models/perceptual_model.py b/spaces/james-oldfield/PandA/networks/genforce/models/perceptual_model.py
deleted file mode 100644
index 1f21496f7b52dbf102a197fda2a12b8e3770a87e..0000000000000000000000000000000000000000
--- a/spaces/james-oldfield/PandA/networks/genforce/models/perceptual_model.py
+++ /dev/null
@@ -1,148 +0,0 @@
-# python 3.7
-"""Contains the VGG16 model for perceptual feature extraction.
-
-This file is particularly used for computing perceptual loss and hence is highly
-recommended to use with pre-trained weights.
-
-The PyTorch weights can be downloaded from
-
-https://drive.google.com/file/d/1qQ-r7MYZ8ZcjQQFe17eQfJbOAuE3eS0y/view?usp=sharing
-
-which is converted from the Keras model
-
-https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5
-
-The variable mapping is shown below
-
-pth_to_tf_var_mapping = {
-    'layer0.weight':  'block1_conv1_W_1:0',  # [64, 3, 3, 3]
-    'layer0.bias':    'block1_conv1_b_1:0',  # [64]
-    'layer2.weight':  'block1_conv2_W_1:0',  # [64, 64, 3, 3]
-    'layer2.bias':    'block1_conv2_b_1:0',  # [64]
-    'layer5.weight':  'block2_conv1_W_1:0',  # [128, 64, 3, 3]
-    'layer5.bias':    'block2_conv1_b_1:0',  # [128]
-    'layer7.weight':  'block2_conv2_W_1:0',  # [128, 128, 3, 3]
-    'layer7.bias':    'block2_conv2_b_1:0',  # [128]
-    'layer10.weight': 'block3_conv1_W_1:0',  # [256, 128, 3, 3]
-    'layer10.bias':   'block3_conv1_b_1:0',  # [256]
-    'layer12.weight': 'block3_conv2_W_1:0',  # [256, 256, 3, 3]
-    'layer12.bias':   'block3_conv2_b_1:0',  # [256]
-    'layer14.weight': 'block3_conv3_W_1:0',  # [256, 256, 3, 3]
-    'layer14.bias':   'block3_conv3_b_1:0',  # [256]
-    'layer17.weight': 'block4_conv1_W_1:0',  # [512, 256, 3, 3]
-    'layer17.bias':   'block4_conv1_b_1:0',  # [512]
-    'layer19.weight': 'block4_conv2_W_1:0',  # [512, 512, 3, 3]
-    'layer19.bias':   'block4_conv2_b_1:0',  # [512]
-    'layer21.weight': 'block4_conv3_W_1:0',  # [512, 512, 3, 3]
-    'layer21.bias':   'block4_conv3_b_1:0',  # [512]
-    'layer24.weight': 'block5_conv1_W_1:0',  # [512, 512, 3, 3]
-    'layer24.bias':   'block5_conv1_b_1:0',  # [512]
-    'layer26.weight': 'block5_conv2_W_1:0',  # [512, 512, 3, 3]
-    'layer26.bias':   'block5_conv2_b_1:0',  # [512]
-    'layer28.weight': 'block5_conv3_W_1:0',  # [512, 512, 3, 3]
-    'layer28.bias':   'block5_conv3_b_1:0',  # [512]
-}
-"""
-
-import os
-import warnings
-from collections import OrderedDict
-import numpy as np
-
-import torch
-import torch.nn as nn
-
-__all__ = ['PerceptualModel']
-
-_MEAN_STATS = (103.939, 116.779, 123.68)
-
-
-class PerceptualModel(nn.Module):
-    """Defines the VGG16 structure as the perceptual network.
-
-    This model takes `RGB` images with data format `NCHW` as the raw inputs, and
-    outputs the perceptual feature. This following operations will be performed
-    to preprocess the inputs to match the preprocessing during the model
-    training:
-    (1) Shift pixel range to [0, 255].
-    (2) Change channel order to `BGR`.
-    (3) Subtract the statistical mean.
-
-    NOTE: The three fully connected layers on top of the model are dropped.
-    """
-
-    def __init__(self,
-                 output_layer_idx=23,
-                 min_val=-1.0,
-                 max_val=1.0,
-                 pretrained_weight_path=None):
-        """Defines the network structure.
-
-        Args:
-            output_layer_idx: Index of layer whose output will be used as the
-                perceptual feature. (default: 23, which is the `block4_conv3`
-                layer activated by `ReLU` function)
-            min_val: Minimum value of the raw input. (default: -1.0)
-            max_val: Maximum value of the raw input. (default: 1.0)
-            pretrained_weight_path: Path to the pretrained weights.
-                (default: None)
-        """
-        super().__init__()
-        self.vgg16 = nn.Sequential(OrderedDict({
-            'layer0': nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1),
-            'layer1': nn.ReLU(inplace=True),
-            'layer2': nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
-            'layer3': nn.ReLU(inplace=True),
-            'layer4': nn.MaxPool2d(kernel_size=2, stride=2),
-            'layer5': nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
-            'layer6': nn.ReLU(inplace=True),
-            'layer7': nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
-            'layer8': nn.ReLU(inplace=True),
-            'layer9': nn.MaxPool2d(kernel_size=2, stride=2),
-            'layer10': nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
-            'layer11': nn.ReLU(inplace=True),
-            'layer12': nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
-            'layer13': nn.ReLU(inplace=True),
-            'layer14': nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
-            'layer15': nn.ReLU(inplace=True),
-            'layer16': nn.MaxPool2d(kernel_size=2, stride=2),
-            'layer17': nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
-            'layer18': nn.ReLU(inplace=True),
-            'layer19': nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
-            'layer20': nn.ReLU(inplace=True),
-            'layer21': nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
-            'layer22': nn.ReLU(inplace=True),
-            'layer23': nn.MaxPool2d(kernel_size=2, stride=2),
-            'layer24': nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
-            'layer25': nn.ReLU(inplace=True),
-            'layer26': nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
-            'layer27': nn.ReLU(inplace=True),
-            'layer28': nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
-            'layer29': nn.ReLU(inplace=True),
-            'layer30': nn.MaxPool2d(kernel_size=2, stride=2),
-        }))
-        self.output_layer_idx = output_layer_idx
-        self.min_val = min_val
-        self.max_val = max_val
-        self.mean = torch.from_numpy(np.array(_MEAN_STATS)).view(1, 3, 1, 1)
-        self.mean = self.mean.type(torch.FloatTensor)
-
-        self.pretrained_weight_path = pretrained_weight_path
-        if os.path.isfile(self.pretrained_weight_path):
-            self.vgg16.load_state_dict(
-                torch.load(self.pretrained_weight_path, map_location='cpu'))
-        else:
-            warnings.warn('No pre-trained weights found for perceptual model!')
-
-    def forward(self, x):
-        x = (x - self.min_val) * 255.0 / (self.max_val - self.min_val)
-        x = x.flip(1)  # RGB to BGR
-        x = x - self.mean.to(x)
-        # TODO: Resize image?
-        for idx, layer in enumerate(self.vgg16.children()):
-            if idx == self.output_layer_idx:
-                break
-            x = layer(x)
-        # x = x.permute(0, 2, 3, 1)
-        x = x.flatten(start_dim=1)
-        return x
diff --git a/spaces/jcenaa/Segment-Any-RGBD/CONTRIBUTING.md b/spaces/jcenaa/Segment-Any-RGBD/CONTRIBUTING.md
deleted file mode 100644
index 7141f8d55f5d491525cf73b4958ff560f65e7a1a..0000000000000000000000000000000000000000
--- a/spaces/jcenaa/Segment-Any-RGBD/CONTRIBUTING.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# Contributing to OVSeg
-We want to make contributing to this project as easy and transparent as
-possible.
-
-## Pull Requests
-We actively welcome your pull requests.
-
-1. Fork the repo and create your branch from `main`.
-2. If you've added code that should be tested, add tests.
-3. If you've changed APIs, update the documentation.
-4. Ensure the test suite passes.
-5. Make sure your code lints.
-6. If you haven't already, complete the Contributor License Agreement ("CLA").
-
-## Contributor License Agreement ("CLA")
-In order to accept your pull request, we need you to submit a CLA. You only need
-to do this once to work on any of Meta's open source projects.
-
-Complete your CLA here: <https://code.facebook.com/cla>
-
-## Issues
-We use GitHub issues to track public bugs. Please ensure your description is
-clear and has sufficient instructions to be able to reproduce the issue.
-
-Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
-disclosure of security bugs. In those cases, please go through the process
-outlined on that page and do not file a public issue.
-
-
-## License
-By contributing to OVSeg, you agree that your contributions will be licensed
-under the LICENSE file in the root directory of this source tree.
diff --git a/spaces/jerichosy/DIGIMAP-Colorization-Web-App/colorizers/base_color.py b/spaces/jerichosy/DIGIMAP-Colorization-Web-App/colorizers/base_color.py
deleted file mode 100644
index 00beb39e9f6f73b06ebea0314fc23a0bc75f23b7..0000000000000000000000000000000000000000
--- a/spaces/jerichosy/DIGIMAP-Colorization-Web-App/colorizers/base_color.py
+++ /dev/null
@@ -1,24 +0,0 @@
-
-import torch
-from torch import nn
-
-class BaseColor(nn.Module):
-	def __init__(self):
-		super(BaseColor, self).__init__()
-
-		self.l_cent = 50.
-		self.l_norm = 100.
-		self.ab_norm = 110.
-
-	def normalize_l(self, in_l):
-		return (in_l-self.l_cent)/self.l_norm
-
-	def unnormalize_l(self, in_l):
-		return in_l*self.l_norm + self.l_cent
-
-	def normalize_ab(self, in_ab):
-		return in_ab/self.ab_norm
-
-	def unnormalize_ab(self, in_ab):
-		return in_ab*self.ab_norm
-
diff --git a/spaces/jinmao/2/modules/utils.py b/spaces/jinmao/2/modules/utils.py
deleted file mode 100644
index 859363f58c47600b8c8af81bd4aa2e3b23346c03..0000000000000000000000000000000000000000
--- a/spaces/jinmao/2/modules/utils.py
+++ /dev/null
@@ -1,436 +0,0 @@
-# -*- coding:utf-8 -*-
-from __future__ import annotations
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Tuple, Type
-import logging
-import json
-import os
-import datetime
-import hashlib
-import csv
-import requests
-import re
-import html
-
-import gradio as gr
-from pypinyin import lazy_pinyin
-import tiktoken
-import mdtex2html
-from markdown import markdown
-from pygments import highlight
-from pygments.lexers import get_lexer_by_name
-from pygments.formatters import HtmlFormatter
-
-from modules.presets import *
-import modules.shared as shared
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s",
-)
-
-if TYPE_CHECKING:
-    from typing import TypedDict
-
-    class DataframeData(TypedDict):
-        headers: List[str]
-        data: List[List[str | int | bool]]
-
-
-def count_token(message):
-    encoding = tiktoken.get_encoding("cl100k_base")
-    input_str = f"role: {message['role']}, content: {message['content']}"
-    length = len(encoding.encode(input_str))
-    return length
-
-
-def markdown_to_html_with_syntax_highlight(md_str):
-    def replacer(match):
-        lang = match.group(1) or "text"
-        code = match.group(2)
-
-        try:
-            lexer = get_lexer_by_name(lang, stripall=True)
-        except ValueError:
-            lexer = get_lexer_by_name("text", stripall=True)
-
-        formatter = HtmlFormatter()
-        highlighted_code = highlight(code, lexer, formatter)
-
-        return f'<pre><code class="{lang}">{highlighted_code}</code></pre>'
-
-    code_block_pattern = r"```(\w+)?\n([\s\S]+?)\n```"
-    md_str = re.sub(code_block_pattern, replacer, md_str, flags=re.MULTILINE)
-
-    html_str = markdown(md_str)
-    return html_str
-
-
-def normalize_markdown(md_text: str) -> str:
-    lines = md_text.split("\n")
-    normalized_lines = []
-    inside_list = False
-
-    for i, line in enumerate(lines):
-        if re.match(r"^(\d+\.|-|\*|\+)\s", line.strip()):
-            if not inside_list and i > 0 and lines[i - 1].strip() != "":
-                normalized_lines.append("")
-            inside_list = True
-            normalized_lines.append(line)
-        elif inside_list and line.strip() == "":
-            if i < len(lines) - 1 and not re.match(
-                r"^(\d+\.|-|\*|\+)\s", lines[i + 1].strip()
-            ):
-                normalized_lines.append(line)
-            continue
-        else:
-            inside_list = False
-            normalized_lines.append(line)
-
-    return "\n".join(normalized_lines)
-
-
-def convert_mdtext(md_text):
-    code_block_pattern = re.compile(r"```(.*?)(?:```|$)", re.DOTALL)
-    inline_code_pattern = re.compile(r"`(.*?)`", re.DOTALL)
-    code_blocks = code_block_pattern.findall(md_text)
-    non_code_parts = code_block_pattern.split(md_text)[::2]
-
-    result = []
-    for non_code, code in zip(non_code_parts, code_blocks + [""]):
-        if non_code.strip():
-            non_code = normalize_markdown(non_code)
-            if inline_code_pattern.search(non_code):
-                result.append(markdown(non_code, extensions=["tables"]))
-            else:
-                result.append(mdtex2html.convert(non_code, extensions=["tables"]))
-        if code.strip():
-            # _, code = detect_language(code)  # 暂时去除代码高亮功能，因为在大段代码的情况下会出现问题
-            # code = code.replace("\n\n", "\n") # 暂时去除代码中的空行，因为在大段代码的情况下会出现问题
-            code = f"\n```{code}\n\n```"
-            code = markdown_to_html_with_syntax_highlight(code)
-            result.append(code)
-    result = "".join(result)
-    result += ALREADY_CONVERTED_MARK
-    return result
-
-
-def convert_asis(userinput):
-    return f"<p style=\"white-space:pre-wrap;\">{html.escape(userinput)}</p>"+ALREADY_CONVERTED_MARK
-
-def detect_converted_mark(userinput):
-    if userinput.endswith(ALREADY_CONVERTED_MARK):
-        return True
-    else:
-        return False
-
-
-def detect_language(code):
-    if code.startswith("\n"):
-        first_line = ""
-    else:
-        first_line = code.strip().split("\n", 1)[0]
-    language = first_line.lower() if first_line else ""
-    code_without_language = code[len(first_line) :].lstrip() if first_line else code
-    return language, code_without_language
-
-
-def construct_text(role, text):
-    return {"role": role, "content": text}
-
-
-def construct_user(text):
-    return construct_text("user", text)
-
-
-def construct_system(text):
-    return construct_text("system", text)
-
-
-def construct_assistant(text):
-    return construct_text("assistant", text)
-
-
-def construct_token_message(token, stream=False):
-    return f"Token 计数: {token}"
-
-def delete_first_conversation(history, previous_token_count):
-    if history:
-        del history[:2]
-        del previous_token_count[0]
-    return (
-        history,
-        previous_token_count,
-        construct_token_message(sum(previous_token_count)),
-    )
-
-
-def delete_last_conversation(chatbot, history, previous_token_count):
-    if len(chatbot) > 0 and standard_error_msg in chatbot[-1][1]:
-        logging.info("由于包含报错信息，只删除chatbot记录")
-        chatbot.pop()
-        return chatbot, history
-    if len(history) > 0:
-        logging.info("删除了一组对话历史")
-        history.pop()
-        history.pop()
-    if len(chatbot) > 0:
-        logging.info("删除了一组chatbot对话")
-        chatbot.pop()
-    if len(previous_token_count) > 0:
-        logging.info("删除了一组对话的token计数记录")
-        previous_token_count.pop()
-    return (
-        chatbot,
-        history,
-        previous_token_count,
-        construct_token_message(sum(previous_token_count)),
-    )
-
-
-def save_file(filename, system, history, chatbot):
-    logging.info("保存对话历史中……")
-    os.makedirs(HISTORY_DIR, exist_ok=True)
-    if filename.endswith(".json"):
-        json_s = {"system": system, "history": history, "chatbot": chatbot}
-        print(json_s)
-        with open(os.path.join(HISTORY_DIR, filename), "w") as f:
-            json.dump(json_s, f)
-    elif filename.endswith(".md"):
-        md_s = f"system: \n- {system} \n"
-        for data in history:
-            md_s += f"\n{data['role']}: \n- {data['content']} \n"
-        with open(os.path.join(HISTORY_DIR, filename), "w", encoding="utf8") as f:
-            f.write(md_s)
-    logging.info("保存对话历史完毕")
-    return os.path.join(HISTORY_DIR, filename)
-
-
-def save_chat_history(filename, system, history, chatbot):
-    if filename == "":
-        return
-    if not filename.endswith(".json"):
-        filename += ".json"
-    return save_file(filename, system, history, chatbot)
-
-
-def export_markdown(filename, system, history, chatbot):
-    if filename == "":
-        return
-    if not filename.endswith(".md"):
-        filename += ".md"
-    return save_file(filename, system, history, chatbot)
-
-
-def load_chat_history(filename, system, history, chatbot):
-    logging.info("加载对话历史中……")
-    if type(filename) != str:
-        filename = filename.name
-    try:
-        with open(os.path.join(HISTORY_DIR, filename), "r") as f:
-            json_s = json.load(f)
-        try:
-            if type(json_s["history"][0]) == str:
-                logging.info("历史记录格式为旧版，正在转换……")
-                new_history = []
-                for index, item in enumerate(json_s["history"]):
-                    if index % 2 == 0:
-                        new_history.append(construct_user(item))
-                    else:
-                        new_history.append(construct_assistant(item))
-                json_s["history"] = new_history
-                logging.info(new_history)
-        except:
-            # 没有对话历史
-            pass
-        logging.info("加载对话历史完毕")
-        return filename, json_s["system"], json_s["history"], json_s["chatbot"]
-    except FileNotFoundError:
-        logging.info("没有找到对话历史文件，不执行任何操作")
-        return filename, system, history, chatbot
-
-
-def sorted_by_pinyin(list):
-    return sorted(list, key=lambda char: lazy_pinyin(char)[0][0])
-
-
-def get_file_names(dir, plain=False, filetypes=[".json"]):
-    logging.info(f"获取文件名列表，目录为{dir}，文件类型为{filetypes}，是否为纯文本列表{plain}")
-    files = []
-    try:
-        for type in filetypes:
-            files += [f for f in os.listdir(dir) if f.endswith(type)]
-    except FileNotFoundError:
-        files = []
-    files = sorted_by_pinyin(files)
-    if files == []:
-        files = [""]
-    if plain:
-        return files
-    else:
-        return gr.Dropdown.update(choices=files)
-
-
-def get_history_names(plain=False):
-    logging.info("获取历史记录文件名列表")
-    return get_file_names(HISTORY_DIR, plain)
-
-
-def load_template(filename, mode=0):
-    logging.info(f"加载模板文件{filename}，模式为{mode}（0为返回字典和下拉菜单，1为返回下拉菜单，2为返回字典）")
-    lines = []
-    logging.info("Loading template...")
-    if filename.endswith(".json"):
-        with open(os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8") as f:
-            lines = json.load(f)
-        lines = [[i["act"], i["prompt"]] for i in lines]
-    else:
-        with open(
-            os.path.join(TEMPLATES_DIR, filename), "r", encoding="utf8"
-        ) as csvfile:
-            reader = csv.reader(csvfile)
-            lines = list(reader)
-        lines = lines[1:]
-    if mode == 1:
-        return sorted_by_pinyin([row[0] for row in lines])
-    elif mode == 2:
-        return {row[0]: row[1] for row in lines}
-    else:
-        choices = sorted_by_pinyin([row[0] for row in lines])
-        return {row[0]: row[1] for row in lines}, gr.Dropdown.update(
-            choices=choices, value=choices[0]
-        )
-
-
-def get_template_names(plain=False):
-    logging.info("获取模板文件名列表")
-    return get_file_names(TEMPLATES_DIR, plain, filetypes=[".csv", "json"])
-
-
-def get_template_content(templates, selection, original_system_prompt):
-    logging.info(f"应用模板中，选择为{selection}，原始系统提示为{original_system_prompt}")
-    try:
-        return templates[selection]
-    except:
-        return original_system_prompt
-
-
-def reset_state():
-    logging.info("重置状态")
-    return [], [], [], construct_token_message(0)
-
-
-def reset_textbox():
-    logging.debug("重置文本框")
-    return gr.update(value="")
-
-
-def reset_default():
-    newurl = shared.state.reset_api_url()
-    os.environ.pop("HTTPS_PROXY", None)
-    os.environ.pop("https_proxy", None)
-    return gr.update(value=newurl), gr.update(value=""), "API URL 和代理已重置"
-
-
-def change_api_url(url):
-    shared.state.set_api_url(url)
-    msg = f"API地址更改为了{url}"
-    logging.info(msg)
-    return msg
-
-
-def change_proxy(proxy):
-    os.environ["HTTPS_PROXY"] = proxy
-    msg = f"代理更改为了{proxy}"
-    logging.info(msg)
-    return msg
-
-
-def hide_middle_chars(s):
-    if s is None:
-        return ""
-    if len(s) <= 8:
-        return s
-    else:
-        head = s[:4]
-        tail = s[-4:]
-        hidden = "*" * (len(s) - 8)
-        return head + hidden + tail
-
-
-def submit_key(key):
-    key = key.strip()
-    msg = f"API密钥更改为了{hide_middle_chars(key)}"
-    logging.info(msg)
-    return key, msg
-
-
-def sha1sum(filename):
-    sha1 = hashlib.sha1()
-    sha1.update(filename.encode("utf-8"))
-    return sha1.hexdigest()
-
-
-def replace_today(prompt):
-    today = datetime.datetime.today().strftime("%Y-%m-%d")
-    return prompt.replace("{current_date}", today)
-
-
-def get_geoip():
-    response = requests.get("https://ipapi.co/json/", timeout=5)
-    try:
-        data = response.json()
-    except:
-        data = {"error": True, "reason": "连接ipapi失败"}
-    if "error" in data.keys():
-        logging.warning(f"无法获取IP地址信息。\n{data}")
-        if data["reason"] == "RateLimited":
-            return (
-                f"获取IP地理位置失败，因为达到了检测IP的速率限制。聊天功能可能仍然可用，但请注意，如果您的IP地址在不受支持的地区，您可能会遇到问题。"
-            )
-        else:
-            return f"获取IP地理位置失败。原因：{data['reason']}。你仍然可以使用聊天功能。"
-    else:
-        country = data["country_name"]
-        if country == "China":
-            text = "**您的IP区域：中国。请立即检查代理设置，在不受支持的地区使用API可能导致账号被封禁。**"
-        else:
-            text = f"您的IP区域：{country}。"
-        logging.info(text)
-        return text
-
-
-def find_n(lst, max_num):
-    n = len(lst)
-    total = sum(lst)
-
-    if total < max_num:
-        return n
-
-    for i in range(len(lst)):
-        if total - lst[i] < max_num:
-            return n - i - 1
-        total = total - lst[i]
-    return 1
-
-
-def start_outputing():
-    logging.debug("显示取消按钮，隐藏发送按钮")
-    return gr.Button.update(visible=False), gr.Button.update(visible=True)
-
-
-def end_outputing():
-    return (
-        gr.Button.update(visible=True),
-        gr.Button.update(visible=False),
-    )
-
-
-def cancel_outputing():
-    logging.info("中止输出……")
-    shared.state.interrupt()
-
-def transfer_input(inputs):
-    # 一次性返回，降低延迟
-    textbox = reset_textbox()
-    outputing = start_outputing()
-    return inputs, gr.update(value="")
diff --git a/spaces/jlmarrugom/voice_fixer_app/voicefixer/tools/modules/fDomainHelper.py b/spaces/jlmarrugom/voice_fixer_app/voicefixer/tools/modules/fDomainHelper.py
deleted file mode 100644
index 285433ccbeea0cb30851fddc74fcdd057982b110..0000000000000000000000000000000000000000
--- a/spaces/jlmarrugom/voice_fixer_app/voicefixer/tools/modules/fDomainHelper.py
+++ /dev/null
@@ -1,234 +0,0 @@
-from torchlibrosa.stft import STFT, ISTFT, magphase
-import torch
-import torch.nn as nn
-import numpy as np
-from voicefixer.tools.modules.pqmf import PQMF
-
-class FDomainHelper(nn.Module):
-    def __init__(
-        self,
-        window_size=2048,
-        hop_size=441,
-        center=True,
-        pad_mode="reflect",
-        window="hann",
-        freeze_parameters=True,
-        subband=None,
-        root="/Users/admin/Documents/projects/",
-    ):
-        super(FDomainHelper, self).__init__()
-        self.subband = subband
-        # assert torchlibrosa.__version__ == "0.0.7", "Error: Found torchlibrosa version %s. Please install 0.0.7 version of torchlibrosa by: pip install torchlibrosa==0.0.7." % torchlibrosa.__version__
-        if self.subband is None:
-            self.stft = STFT(
-                n_fft=window_size,
-                hop_length=hop_size,
-                win_length=window_size,
-                window=window,
-                center=center,
-                pad_mode=pad_mode,
-                freeze_parameters=freeze_parameters,
-            )
-
-            self.istft = ISTFT(
-                n_fft=window_size,
-                hop_length=hop_size,
-                win_length=window_size,
-                window=window,
-                center=center,
-                pad_mode=pad_mode,
-                freeze_parameters=freeze_parameters,
-            )
-        else:
-            self.stft = STFT(
-                n_fft=window_size // self.subband,
-                hop_length=hop_size // self.subband,
-                win_length=window_size // self.subband,
-                window=window,
-                center=center,
-                pad_mode=pad_mode,
-                freeze_parameters=freeze_parameters,
-            )
-
-            self.istft = ISTFT(
-                n_fft=window_size // self.subband,
-                hop_length=hop_size // self.subband,
-                win_length=window_size // self.subband,
-                window=window,
-                center=center,
-                pad_mode=pad_mode,
-                freeze_parameters=freeze_parameters,
-            )
-
-        if subband is not None and root is not None:
-            self.qmf = PQMF(subband, 64, root)
-
-    def complex_spectrogram(self, input, eps=0.0):
-        # [batchsize, samples]
-        # return [batchsize, 2, t-steps, f-bins]
-        real, imag = self.stft(input)
-        return torch.cat([real, imag], dim=1)
-
-    def reverse_complex_spectrogram(self, input, eps=0.0, length=None):
-        # [batchsize, 2[real,imag], t-steps, f-bins]
-        wav = self.istft(input[:, 0:1, ...], input[:, 1:2, ...], length=length)
-        return wav
-
-    def spectrogram(self, input, eps=0.0):
-        (real, imag) = self.stft(input.float())
-        return torch.clamp(real**2 + imag**2, eps, np.inf) ** 0.5
-
-    def spectrogram_phase(self, input, eps=0.0):
-        (real, imag) = self.stft(input.float())
-        mag = torch.clamp(real**2 + imag**2, eps, np.inf) ** 0.5
-        cos = real / mag
-        sin = imag / mag
-        return mag, cos, sin
-
-    def wav_to_spectrogram_phase(self, input, eps=1e-8):
-        """Waveform to spectrogram.
-
-        Args:
-          input: (batch_size, channels_num, segment_samples)
-
-        Outputs:
-          output: (batch_size, channels_num, time_steps, freq_bins)
-        """
-        sp_list = []
-        cos_list = []
-        sin_list = []
-        channels_num = input.shape[1]
-        for channel in range(channels_num):
-            mag, cos, sin = self.spectrogram_phase(input[:, channel, :], eps=eps)
-            sp_list.append(mag)
-            cos_list.append(cos)
-            sin_list.append(sin)
-
-        sps = torch.cat(sp_list, dim=1)
-        coss = torch.cat(cos_list, dim=1)
-        sins = torch.cat(sin_list, dim=1)
-        return sps, coss, sins
-
-    def spectrogram_phase_to_wav(self, sps, coss, sins, length):
-        channels_num = sps.size()[1]
-        res = []
-        for i in range(channels_num):
-            res.append(
-                self.istft(
-                    sps[:, i : i + 1, ...] * coss[:, i : i + 1, ...],
-                    sps[:, i : i + 1, ...] * sins[:, i : i + 1, ...],
-                    length,
-                )
-            )
-            res[-1] = res[-1].unsqueeze(1)
-        return torch.cat(res, dim=1)
-
-    def wav_to_spectrogram(self, input, eps=1e-8):
-        """Waveform to spectrogram.
-
-        Args:
-          input: (batch_size,channels_num, segment_samples)
-
-        Outputs:
-          output: (batch_size, channels_num, time_steps, freq_bins)
-        """
-        sp_list = []
-        channels_num = input.shape[1]
-        for channel in range(channels_num):
-            sp_list.append(self.spectrogram(input[:, channel, :], eps=eps))
-        output = torch.cat(sp_list, dim=1)
-        return output
-
-    def spectrogram_to_wav(self, input, spectrogram, length=None):
-        """Spectrogram to waveform.
-        Args:
-          input: (batch_size, segment_samples, channels_num)
-          spectrogram: (batch_size, channels_num, time_steps, freq_bins)
-
-        Outputs:
-          output: (batch_size, segment_samples, channels_num)
-        """
-        channels_num = input.shape[1]
-        wav_list = []
-        for channel in range(channels_num):
-            (real, imag) = self.stft(input[:, channel, :])
-            (_, cos, sin) = magphase(real, imag)
-            wav_list.append(
-                self.istft(
-                    spectrogram[:, channel : channel + 1, :, :] * cos,
-                    spectrogram[:, channel : channel + 1, :, :] * sin,
-                    length,
-                )
-            )
-
-        output = torch.stack(wav_list, dim=1)
-        return output
-
-    # todo the following code is not bug free!
-    def wav_to_complex_spectrogram(self, input, eps=0.0):
-        # [batchsize , channels, samples]
-        # [batchsize, 2[real,imag]*channels, t-steps, f-bins]
-        res = []
-        channels_num = input.shape[1]
-        for channel in range(channels_num):
-            res.append(self.complex_spectrogram(input[:, channel, :], eps=eps))
-        return torch.cat(res, dim=1)
-
-    def complex_spectrogram_to_wav(self, input, eps=0.0, length=None):
-        # [batchsize, 2[real,imag]*channels, t-steps, f-bins]
-        # return  [batchsize, channels, samples]
-        channels = input.size()[1] // 2
-        wavs = []
-        for i in range(channels):
-            wavs.append(
-                self.reverse_complex_spectrogram(
-                    input[:, 2 * i : 2 * i + 2, ...], eps=eps, length=length
-                )
-            )
-            wavs[-1] = wavs[-1].unsqueeze(1)
-        return torch.cat(wavs, dim=1)
-
-    def wav_to_complex_subband_spectrogram(self, input, eps=0.0):
-        # [batchsize, channels, samples]
-        # [batchsize, 2[real,imag]*subband*channels, t-steps, f-bins]
-        subwav = self.qmf.analysis(input)  # [batchsize, subband*channels, samples]
-        subspec = self.wav_to_complex_spectrogram(subwav)
-        return subspec
-
-    def complex_subband_spectrogram_to_wav(self, input, eps=0.0):
-        # [batchsize, 2[real,imag]*subband*channels, t-steps, f-bins]
-        # [batchsize, channels, samples]
-        subwav = self.complex_spectrogram_to_wav(input)
-        data = self.qmf.synthesis(subwav)
-        return data
-
-    def wav_to_mag_phase_subband_spectrogram(self, input, eps=1e-8):
-        """
-        :param input:
-        :param eps:
-        :return:
-            loss = torch.nn.L1Loss()
-            models = FDomainHelper(subband=4)
-            data = torch.randn((3,1, 44100*3))
-
-            sps, coss, sins = models.wav_to_mag_phase_subband_spectrogram(data)
-            wav = models.mag_phase_subband_spectrogram_to_wav(sps,coss,sins,44100*3//4)
-
-            print(loss(data,wav))
-            print(torch.max(torch.abs(data-wav)))
-
-        """
-        # [batchsize, channels, samples]
-        # [batchsize, 2[real,imag]*subband*channels, t-steps, f-bins]
-        subwav = self.qmf.analysis(input)  # [batchsize, subband*channels, samples]
-        sps, coss, sins = self.wav_to_spectrogram_phase(subwav, eps=eps)
-        return sps, coss, sins
-
-    def mag_phase_subband_spectrogram_to_wav(self, sps, coss, sins, length, eps=0.0):
-        # [batchsize, 2[real,imag]*subband*channels, t-steps, f-bins]
-        # [batchsize, channels, samples]
-        subwav = self.spectrogram_phase_to_wav(
-            sps, coss, sins, length + self.qmf.pad_samples // self.qmf.N
-        )
-        data = self.qmf.synthesis(subwav)
-        return data
diff --git a/spaces/jmartinezot/find_plane_pointcloud/app.py b/spaces/jmartinezot/find_plane_pointcloud/app.py
deleted file mode 100644
index 9adfcef67f3cf72bdde5eeef8434af7827c9708c..0000000000000000000000000000000000000000
--- a/spaces/jmartinezot/find_plane_pointcloud/app.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import gradio as gr
-import open3d as o3d
-import numpy as np
-import pandas as pd
-from PIL import Image
-from gradio.components import File, Slider, Image, Dataframe, Model3D
-import tempfile
-
-def point_cloud_to_image(point_cloud):
-    # Create Open3D visualization object
-    vis = o3d.visualization.Visualizer()
-    vis.create_window()
-    vis.add_geometry(point_cloud)
-
-    # Render the visualization and get the image
-    vis.poll_events()
-    vis.update_renderer()
-    img = vis.capture_screen_float_buffer()
-
-    # Convert image to PIL Image format
-    pil_img = Image.fromarray(np.uint8(np.asarray(img)*255))
-
-    return pil_img
-
-def create_file_mesh_from_pcd(pcd, alpha): 
-    
-    temp_file = tempfile.NamedTemporaryFile(suffix=".obj", delete=False)
-    file_path = temp_file.name
-    temp_file.close()
-    
-    # print(f"alpha={alpha:.3f}")
-    mesh = o3d.geometry.TriangleMesh.create_from_point_cloud_alpha_shape(pcd, alpha)
-    mesh.compute_vertex_normals()
-    o3d.io.write_triangle_mesh(file_path, mesh, write_triangle_uvs=True)
-    return file_path
-
-def plane_detection(pointcloud_path, voxel_size=0.05, distance_threshold=0.01, num_iterations=1000, alpha=2):
-    # Load point cloud from file
-    pcd = o3d.io.read_point_cloud(pointcloud_path.name)
-
-    # Downsample the point cloud to reduce computational load
-    pcd = pcd.voxel_down_sample(voxel_size=voxel_size)
-
-    # Find plane using RANSAC algorithm
-    plane_model, inliers = pcd.segment_plane(distance_threshold=distance_threshold,
-                                             ransac_n=3,
-                                             num_iterations=num_iterations)
-
-    # Get inlier and outlier point cloud
-    inlier_cloud = pcd.select_by_index(inliers)
-    outlier_cloud = pcd.select_by_index(inliers, invert=True)
-    # extract the coefficients of the plane
-    a, b, c, d = plane_model
-    plane_model = np.array([[a], [b], [c], [d]])
-    df = pd.DataFrame(plane_model.reshape(1, 4), columns=["a", "b", "c", "d"])
-    input_path = create_file_mesh_from_pcd(pcd, alpha)
-    inlier_path = create_file_mesh_from_pcd(inlier_cloud, alpha)
-    outlier_path = create_file_mesh_from_pcd(outlier_cloud, alpha)
-    # Return inlier point cloud, outlier point cloud, and plane model
-    # return point_cloud_to_image(inlier_cloud), point_cloud_to_image(outlier_cloud), df
-    return input_path, inlier_path, outlier_path, df
-
-outputs = [
-    # show pcd inlier point cloud
-    Model3D(label="Input Cloud", clear_color=[1.0, 1.0, 1.0, 1.0]),
-    # show pcd inlier point cloud
-    Model3D(label="Inlier Cloud", clear_color=[1.0, 1.0, 1.0, 1.0]),
-    # show pcd outlier point cloud
-    Model3D(label="Outlier Cloud", clear_color=[1.0, 1.0, 1.0, 1.0]),
-    # show the centroids and counts, which is a numpy array
-    Dataframe(label="Coefficients of the plane model", type="pandas")
-]
-
-# Create Gradio interface
-iface = gr.Interface(plane_detection, 
-                     inputs=[
-                         File(label="Point cloud file (.ply or .pcd format)"),
-                         Slider(label="Voxel size", minimum=0.001, maximum=50, step=1, value=2),
-                         Slider(label="Distance threshold", minimum=0.001, maximum=50, step=0.01, value=5),
-                         Slider(label="Number of iterations", minimum=1, maximum=10000, step=1, value=100),
-                         Slider(label="Alpha for surface reconstruction", minimum=0.02, maximum=100, step=0.01, value=2),
-                     ],
-                     outputs=outputs,
-                     title="Plane Detection using RANSAC",
-                     description="This app takes as input a point cloud file (.ply or .pcd format), voxel size, distance threshold, and number of iterations, finds a plane using RANSAC algorithm, displays the inlier and outlier point clouds, and returns the inlier point cloud, outlier point cloud, and the plane model.",
-                     allow_flagging="never",
-                     examples=[
-                        # ["Pointclouds/p1.ply", 2, 5, 100, 2]
-                        ["Pointclouds/cloud_bin_0.ply", 0.001, 0.03, 100, 0.05],
-                        ["Pointclouds/cloud_bin_20.ply", 0.001, 0.03, 100, 0.05],
-                        ["Pointclouds/cloud_bin_30.ply", 0.001, 0.03, 100, 0.05],
-                        ["Pointclouds/cloud_bin_40.ply", 0.001, 0.03, 100, 0.05],
-                        ["Pointclouds/cloud_bin_50.ply", 0.001, 0.03, 100, 0.05],
-                        ],
-                     )
-
-
-# Launch the interface
-iface.launch()
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/web_middlewares.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/web_middlewares.py
deleted file mode 100644
index fabcc449a2107211fd99cd59f576a2d855d0e042..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/aiohttp/web_middlewares.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import re
-from typing import TYPE_CHECKING, Awaitable, Callable, Tuple, Type, TypeVar
-
-from .typedefs import Handler
-from .web_exceptions import HTTPPermanentRedirect, _HTTPMove
-from .web_request import Request
-from .web_response import StreamResponse
-from .web_urldispatcher import SystemRoute
-
-__all__ = (
-    "middleware",
-    "normalize_path_middleware",
-)
-
-if TYPE_CHECKING:  # pragma: no cover
-    from .web_app import Application
-
-_Func = TypeVar("_Func")
-
-
-async def _check_request_resolves(request: Request, path: str) -> Tuple[bool, Request]:
-    alt_request = request.clone(rel_url=path)
-
-    match_info = await request.app.router.resolve(alt_request)
-    alt_request._match_info = match_info
-
-    if match_info.http_exception is None:
-        return True, alt_request
-
-    return False, request
-
-
-def middleware(f: _Func) -> _Func:
-    f.__middleware_version__ = 1  # type: ignore[attr-defined]
-    return f
-
-
-_Middleware = Callable[[Request, Handler], Awaitable[StreamResponse]]
-
-
-def normalize_path_middleware(
-    *,
-    append_slash: bool = True,
-    remove_slash: bool = False,
-    merge_slashes: bool = True,
-    redirect_class: Type[_HTTPMove] = HTTPPermanentRedirect,
-) -> _Middleware:
-    """Factory for producing a middleware that normalizes the path of a request.
-
-    Normalizing means:
-        - Add or remove a trailing slash to the path.
-        - Double slashes are replaced by one.
-
-    The middleware returns as soon as it finds a path that resolves
-    correctly. The order if both merge and append/remove are enabled is
-        1) merge slashes
-        2) append/remove slash
-        3) both merge slashes and append/remove slash.
-    If the path resolves with at least one of those conditions, it will
-    redirect to the new path.
-
-    Only one of `append_slash` and `remove_slash` can be enabled. If both
-    are `True` the factory will raise an assertion error
-
-    If `append_slash` is `True` the middleware will append a slash when
-    needed. If a resource is defined with trailing slash and the request
-    comes without it, it will append it automatically.
-
-    If `remove_slash` is `True`, `append_slash` must be `False`. When enabled
-    the middleware will remove trailing slashes and redirect if the resource
-    is defined
-
-    If merge_slashes is True, merge multiple consecutive slashes in the
-    path into one.
-    """
-    correct_configuration = not (append_slash and remove_slash)
-    assert correct_configuration, "Cannot both remove and append slash"
-
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        if isinstance(request.match_info.route, SystemRoute):
-            paths_to_check = []
-            if "?" in request.raw_path:
-                path, query = request.raw_path.split("?", 1)
-                query = "?" + query
-            else:
-                query = ""
-                path = request.raw_path
-
-            if merge_slashes:
-                paths_to_check.append(re.sub("//+", "/", path))
-            if append_slash and not request.path.endswith("/"):
-                paths_to_check.append(path + "/")
-            if remove_slash and request.path.endswith("/"):
-                paths_to_check.append(path[:-1])
-            if merge_slashes and append_slash:
-                paths_to_check.append(re.sub("//+", "/", path + "/"))
-            if merge_slashes and remove_slash:
-                merged_slashes = re.sub("//+", "/", path)
-                paths_to_check.append(merged_slashes[:-1])
-
-            for path in paths_to_check:
-                path = re.sub("^//+", "/", path)  # SECURITY: GHSA-v6wp-4m6f-gcjg
-                resolves, request = await _check_request_resolves(request, path)
-                if resolves:
-                    raise redirect_class(request.raw_path + query)
-
-        return await handler(request)
-
-    return impl
-
-
-def _fix_request_current_app(app: "Application") -> _Middleware:
-    @middleware
-    async def impl(request: Request, handler: Handler) -> StreamResponse:
-        with request.match_info.set_current_app(app):
-            return await handler(request)
-
-    return impl
diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/google_readers/gdocs.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/google_readers/gdocs.py
deleted file mode 100644
index e7134877a0cf0d998609532b9819218d50eaec5e..0000000000000000000000000000000000000000
--- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/gpt_index/readers/google_readers/gdocs.py
+++ /dev/null
@@ -1,157 +0,0 @@
-"""Google docs reader."""
-
-import logging
-import os
-from typing import Any, List
-
-from gpt_index.readers.base import BaseReader
-from gpt_index.readers.schema.base import Document
-
-SCOPES = ["https://www.googleapis.com/auth/documents.readonly"]
-
-
-# Copyright 2019 Google LLC
-#
-# 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.
-
-
-class GoogleDocsReader(BaseReader):
-    """Google Docs reader.
-
-    Reads a page from Google Docs
-
-    """
-
-    def __init__(self) -> None:
-        """Initialize with parameters."""
-        try:
-            import google  # noqa: F401
-            import google_auth_oauthlib  # noqa: F401
-            import googleapiclient  # noqa: F401
-        except ImportError:
-            raise ImportError(
-                "`google_auth_oauthlib`, `googleapiclient` and `google` "
-                "must be installed to use the GoogleDocsReader.\n"
-                "Please run `pip install --upgrade google-api-python-client "
-                "google-auth-httplib2 google-auth-oauthlib`."
-            )
-
-    def load_data(self, document_ids: List[str]) -> List[Document]:
-        """Load data from the input directory.
-
-        Args:
-            document_ids (List[str]): a list of document ids.
-        """
-        if document_ids is None:
-            raise ValueError('Must specify a "document_ids" in `load_kwargs`.')
-
-        results = []
-        for document_id in document_ids:
-            doc = self._load_doc(document_id)
-            results.append(Document(doc, extra_info={"document_id": document_id}))
-        return results
-
-    def _load_doc(self, document_id: str) -> str:
-        """Load a document from Google Docs.
-
-        Args:
-            document_id: the document id.
-
-        Returns:
-            The document text.
-        """
-        import googleapiclient.discovery as discovery
-
-        credentials = self._get_credentials()
-        docs_service = discovery.build("docs", "v1", credentials=credentials)
-        doc = docs_service.documents().get(documentId=document_id).execute()
-        doc_content = doc.get("body").get("content")
-        return self._read_structural_elements(doc_content)
-
-    def _get_credentials(self) -> Any:
-        """Get valid user credentials from storage.
-
-        The file token.json stores the user's access and refresh tokens, and is
-        created automatically when the authorization flow completes for the first
-        time.
-
-        Returns:
-            Credentials, the obtained credential.
-        """
-        from google.auth.transport.requests import Request
-        from google.oauth2.credentials import Credentials
-        from google_auth_oauthlib.flow import InstalledAppFlow
-
-        creds = None
-        if os.path.exists("token.json"):
-            creds = Credentials.from_authorized_user_file("token.json", SCOPES)
-        # If there are no (valid) credentials available, let the user log in.
-        if not creds or not creds.valid:
-            if creds and creds.expired and creds.refresh_token:
-                creds.refresh(Request())
-            else:
-                flow = InstalledAppFlow.from_client_secrets_file(
-                    "credentials.json", SCOPES
-                )
-                creds = flow.run_local_server(port=0)
-            # Save the credentials for the next run
-            with open("token.json", "w") as token:
-                token.write(creds.to_json())
-
-        return creds
-
-    def _read_paragraph_element(self, element: Any) -> Any:
-        """Return the text in the given ParagraphElement.
-
-        Args:
-            element: a ParagraphElement from a Google Doc.
-        """
-        text_run = element.get("textRun")
-        if not text_run:
-            return ""
-        return text_run.get("content")
-
-    def _read_structural_elements(self, elements: List[Any]) -> Any:
-        """Recurse through a list of Structural Elements.
-
-        Read a document's text where text may be in nested elements.
-
-        Args:
-            elements: a list of Structural Elements.
-        """
-        text = ""
-        for value in elements:
-            if "paragraph" in value:
-                elements = value.get("paragraph").get("elements")
-                for elem in elements:
-                    text += self._read_paragraph_element(elem)
-            elif "table" in value:
-                # The text in table cells are in nested Structural Elements
-                # and tables may be nested.
-                table = value.get("table")
-                for row in table.get("tableRows"):
-                    cells = row.get("tableCells")
-                    for cell in cells:
-                        text += self._read_structural_elements(cell.get("content"))
-            elif "tableOfContents" in value:
-                # The text in the TOC is also in a Structural Element.
-                toc = value.get("tableOfContents")
-                text += self._read_structural_elements(toc.get("content"))
-        return text
-
-
-if __name__ == "__main__":
-    reader = GoogleDocsReader()
-    logging.info(
-        reader.load_data(document_ids=["11ctUj_tEf5S8vs_dk8_BNi-Zk8wW5YFhXkKqtmU_4B8"])
-    )
diff --git a/spaces/johnyang/ChatPaper111/utils.py b/spaces/johnyang/ChatPaper111/utils.py
deleted file mode 100644
index 4e9778be8d941eb34d6cd63d964b9b4798a71722..0000000000000000000000000000000000000000
--- a/spaces/johnyang/ChatPaper111/utils.py
+++ /dev/null
@@ -1,24 +0,0 @@
-
-from typing import Set
-
-
-def get_filtered_keys_from_object(obj: object, *keys: str) -> Set[str]:
-    """
-    Get filtered list of object variable names.
-    :param keys: List of keys to include. If the first key is "not", the remaining keys will be removed from the class keys.
-    :return: List of class keys.
-    """
-    class_keys = obj.__dict__.keys()
-    if not keys:
-        return class_keys
-
-    # Remove the passed keys from the class keys.
-    if keys[0] == "not":
-        return {key for key in class_keys if key not in keys[1:]}
-    # Check if all passed keys are valid
-    if invalid_keys := set(keys) - class_keys:
-        raise ValueError(
-            f"Invalid keys: {invalid_keys}",
-        )
-    # Only return specified keys that are in class_keys
-    return {key for key in keys if key in class_keys}
diff --git a/spaces/jonathang/dob_breed/app.py b/spaces/jonathang/dob_breed/app.py
deleted file mode 100644
index 65238f09ac7532a98829cef1ce6f3d13d5a5d875..0000000000000000000000000000000000000000
--- a/spaces/jonathang/dob_breed/app.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-import skimage
-
-learn = load_learner('dog_breed.pkl')
-
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-title = "Doge Breed Classifier"
-description = "A dog breed classifier trained on duckduckgo images with fastai."
-interpretation='default'
-enable_queue=True
-
-gr.Interface(
-    fn=predict,
-    inputs=gr.inputs.Image(shape=(512, 512)),
-    outputs=gr.outputs.Label(num_top_classes=3), 
-    title=title,
-    description=description,
-    interpretation=interpretation,
-    enable_queue=enable_queue
-).launch()
\ No newline at end of file
diff --git a/spaces/jordonpeter01/MusicGen2/audiocraft/modules/conditioners.py b/spaces/jordonpeter01/MusicGen2/audiocraft/modules/conditioners.py
deleted file mode 100644
index 82792316024b88d4c5c38b0a28f443627771d509..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/MusicGen2/audiocraft/modules/conditioners.py
+++ /dev/null
@@ -1,990 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from collections import defaultdict
-from copy import deepcopy
-from dataclasses import dataclass, field
-from itertools import chain
-import logging
-import math
-import random
-import re
-import typing as tp
-import warnings
-
-from einops import rearrange
-from num2words import num2words
-import spacy
-from transformers import T5EncoderModel, T5Tokenizer  # type: ignore
-import torchaudio
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-from torch.nn.utils.rnn import pad_sequence
-
-from .streaming import StreamingModule
-from .transformer import create_sin_embedding
-from ..data.audio_dataset import SegmentInfo
-from ..utils.autocast import TorchAutocast
-from ..utils.utils import hash_trick, length_to_mask, collate
-
-
-logger = logging.getLogger(__name__)
-TextCondition = tp.Optional[str]  # a text condition can be a string or None (if doesn't exist)
-ConditionType = tp.Tuple[Tensor, Tensor]  # condition, mask
-
-
-class WavCondition(tp.NamedTuple):
-    wav: Tensor
-    length: Tensor
-    path: tp.List[tp.Optional[str]] = []
-
-
-def nullify_condition(condition: ConditionType, dim: int = 1):
-    """This function transforms an input condition to a null condition.
-    The way it is done by converting it to a single zero vector similarly
-    to how it is done inside WhiteSpaceTokenizer and NoopTokenizer.
-
-    Args:
-        condition (ConditionType): a tuple of condition and mask (tp.Tuple[Tensor, Tensor])
-        dim (int): the dimension that will be truncated (should be the time dimension)
-        WARNING!: dim should not be the batch dimension!
-    Returns:
-        ConditionType: a tuple of null condition and mask
-    """
-    assert dim != 0, "dim cannot be the batch dimension!"
-    assert type(condition) == tuple and \
-        type(condition[0]) == Tensor and \
-        type(condition[1]) == Tensor, "'nullify_condition' got an unexpected input type!"
-    cond, mask = condition
-    B = cond.shape[0]
-    last_dim = cond.dim() - 1
-    out = cond.transpose(dim, last_dim)
-    out = 0. * out[..., :1]
-    out = out.transpose(dim, last_dim)
-    mask = torch.zeros((B, 1), device=out.device).int()
-    assert cond.dim() == out.dim()
-    return out, mask
-
-
-def nullify_wav(wav: Tensor) -> WavCondition:
-    """Create a nullified WavCondition from a wav tensor with appropriate shape.
-
-    Args:
-        wav (Tensor): tensor of shape [B, T]
-    Returns:
-        WavCondition: wav condition with nullified wav.
-    """
-    null_wav, _ = nullify_condition((wav, torch.zeros_like(wav)), dim=wav.dim() - 1)
-    return WavCondition(
-        wav=null_wav,
-        length=torch.tensor([0] * wav.shape[0], device=wav.device),
-        path=['null_wav'] * wav.shape[0]
-    )
-
-
-@dataclass
-class ConditioningAttributes:
-    text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict)
-    wav: tp.Dict[str, WavCondition] = field(default_factory=dict)
-
-    def __getitem__(self, item):
-        return getattr(self, item)
-
-    @property
-    def text_attributes(self):
-        return self.text.keys()
-
-    @property
-    def wav_attributes(self):
-        return self.wav.keys()
-
-    @property
-    def attributes(self):
-        return {"text": self.text_attributes, "wav": self.wav_attributes}
-
-    def to_flat_dict(self):
-        return {
-            **{f"text.{k}": v for k, v in self.text.items()},
-            **{f"wav.{k}": v for k, v in self.wav.items()},
-        }
-
-    @classmethod
-    def from_flat_dict(cls, x):
-        out = cls()
-        for k, v in x.items():
-            kind, att = k.split(".")
-            out[kind][att] = v
-        return out
-
-
-class SegmentWithAttributes(SegmentInfo):
-    """Base class for all dataclasses that are used for conditioning.
-    All child classes should implement `to_condition_attributes` that converts
-    the existing attributes to a dataclass of type ConditioningAttributes.
-    """
-    def to_condition_attributes(self) -> ConditioningAttributes:
-        raise NotImplementedError()
-
-
-class Tokenizer:
-    """Base class for all tokenizers
-    (in case we want to introduce more advances tokenizers in the future).
-    """
-    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
-        raise NotImplementedError()
-
-
-class WhiteSpaceTokenizer(Tokenizer):
-    """This tokenizer should be used for natural language descriptions.
-    For example:
-    ["he didn't, know he's going home.", 'shorter sentence'] =>
-    [[78, 62, 31,  4, 78, 25, 19, 34],
-    [59, 77,  0,  0,  0,  0,  0,  0]]
-    """
-    PUNCTUATIONS = "?:!.,;"
-
-    def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm",
-                 lemma: bool = True, stopwords: bool = True) -> None:
-        self.n_bins = n_bins
-        self.pad_idx = pad_idx
-        self.lemma = lemma
-        self.stopwords = stopwords
-        try:
-            self.nlp = spacy.load(language)
-        except IOError:
-            spacy.cli.download(language)  # type: ignore
-            self.nlp = spacy.load(language)
-
-    @tp.no_type_check
-    def __call__(
-        self,
-        texts: tp.List[tp.Optional[str]],
-        return_text: bool = False
-    ) -> tp.Tuple[Tensor, Tensor]:
-        """Take a list of strings and convert them to a tensor of indices.
-
-        Args:
-            texts (tp.List[str]): List of strings.
-            return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False.
-        Returns:
-            tp.Tuple[Tensor, Tensor]:
-                - Indices of words in the LUT.
-                - And a mask indicating where the padding tokens are
-        """
-        output, lengths = [], []
-        texts = deepcopy(texts)
-        for i, text in enumerate(texts):
-            # if current sample doesn't have a certain attribute, replace with pad token
-            if text is None:
-                output.append(Tensor([self.pad_idx]))
-                lengths.append(0)
-                continue
-
-            # convert numbers to words
-            text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text)  # type: ignore
-            # normalize text
-            text = self.nlp(text)  # type: ignore
-            # remove stopwords
-            if self.stopwords:
-                text = [w for w in text if not w.is_stop]  # type: ignore
-            # remove punctuations
-            text = [w for w in text if w.text not in self.PUNCTUATIONS]  # type: ignore
-            # lemmatize if needed
-            text = [getattr(t, "lemma_" if self.lemma else "text") for t in text]  # type: ignore
-
-            texts[i] = " ".join(text)
-            lengths.append(len(text))
-            # convert to tensor
-            tokens = Tensor([hash_trick(w, self.n_bins) for w in text])
-            output.append(tokens)
-
-        mask = length_to_mask(torch.IntTensor(lengths)).int()
-        padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t()
-        if return_text:
-            return padded_output, mask, texts  # type: ignore
-        return padded_output, mask
-
-
-class NoopTokenizer(Tokenizer):
-    """This tokenizer should be used for global conditioners such as: artist, genre, key, etc.
-    The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split
-    strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will
-    split it to ["Jeff", "Buckley"] and return an index per word.
-
-    For example:
-    ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101]
-    ["Metal", "Rock", "Classical"] => [0, 223, 51]
-    """
-    def __init__(self, n_bins: int, pad_idx: int = 0):
-        self.n_bins = n_bins
-        self.pad_idx = pad_idx
-
-    def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[Tensor, Tensor]:
-        output, lengths = [], []
-        for text in texts:
-            # if current sample doesn't have a certain attribute, replace with pad token
-            if text is None:
-                output.append(self.pad_idx)
-                lengths.append(0)
-            else:
-                output.append(hash_trick(text, self.n_bins))
-                lengths.append(1)
-
-        tokens = torch.LongTensor(output).unsqueeze(1)
-        mask = length_to_mask(torch.IntTensor(lengths)).int()
-        return tokens, mask
-
-
-class BaseConditioner(nn.Module):
-    """Base model for all conditioner modules. We allow the output dim to be different
-    than the hidden dim for two reasons: 1) keep our LUTs small when the vocab is large;
-    2) make all condition dims consistent.
-
-    Args:
-        dim (int): Hidden dim of the model (text-encoder/LUT).
-        output_dim (int): Output dim of the conditioner.
-    """
-    def __init__(self, dim, output_dim):
-        super().__init__()
-        self.dim = dim
-        self.output_dim = output_dim
-        self.output_proj = nn.Linear(dim, output_dim)
-
-    def tokenize(self, *args, **kwargs) -> tp.Any:
-        """Should be any part of the processing that will lead to a synchronization
-        point, e.g. BPE tokenization with transfer to the GPU.
-
-        The returned value will be saved and return later when calling forward().
-        """
-        raise NotImplementedError()
-
-    def forward(self, inputs: tp.Any) -> ConditionType:
-        """Gets input that should be used as conditioning (e.g, genre, description or a waveform).
-        Outputs a ConditionType, after the input data was embedded as a dense vector.
-
-        Returns:
-            ConditionType:
-                - A tensor of size [B, T, D] where B is the batch size, T is the length of the
-                  output embedding and D is the dimension of the embedding.
-                - And a mask indicating where the padding tokens.
-        """
-        raise NotImplementedError()
-
-
-class TextConditioner(BaseConditioner):
-    ...
-
-
-class LUTConditioner(TextConditioner):
-    """Lookup table TextConditioner.
-
-    Args:
-        n_bins (int): Number of bins.
-        dim (int): Hidden dim of the model (text-encoder/LUT).
-        output_dim (int): Output dim of the conditioner.
-        tokenizer (str): Name of the tokenizer.
-        pad_idx (int, optional): Index for padding token. Defaults to 0.
-    """
-    def __init__(self, n_bins: int, dim: int, output_dim: int, tokenizer: str, pad_idx: int = 0):
-        super().__init__(dim, output_dim)
-        self.embed = nn.Embedding(n_bins, dim)
-        self.tokenizer: Tokenizer
-        if tokenizer == "whitespace":
-            self.tokenizer = WhiteSpaceTokenizer(n_bins, pad_idx=pad_idx)
-        elif tokenizer == "noop":
-            self.tokenizer = NoopTokenizer(n_bins, pad_idx=pad_idx)
-        else:
-            raise ValueError(f"unrecognized tokenizer `{tokenizer}`.")
-
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]:
-        device = self.embed.weight.device
-        tokens, mask = self.tokenizer(x)
-        tokens, mask = tokens.to(device), mask.to(device)
-        return tokens, mask
-
-    def forward(self, inputs: tp.Tuple[torch.Tensor, torch.Tensor]) -> ConditionType:
-        tokens, mask = inputs
-        embeds = self.embed(tokens)
-        embeds = self.output_proj(embeds)
-        embeds = (embeds * mask.unsqueeze(-1))
-        return embeds, mask
-
-
-class T5Conditioner(TextConditioner):
-    """T5-based TextConditioner.
-
-    Args:
-        name (str): Name of the T5 model.
-        output_dim (int): Output dim of the conditioner.
-        finetune (bool): Whether to fine-tune T5 at train time.
-        device (str): Device for T5 Conditioner.
-        autocast_dtype (tp.Optional[str], optional): Autocast dtype.
-        word_dropout (float, optional): Word dropout probability.
-        normalize_text (bool, optional): Whether to apply text normalization.
-    """
-    MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b",
-              "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large",
-              "google/flan-t5-xl", "google/flan-t5-xxl"]
-    MODELS_DIMS = {
-        "t5-small": 512,
-        "t5-base": 768,
-        "t5-large": 1024,
-        "t5-3b": 1024,
-        "t5-11b": 1024,
-        "google/flan-t5-small": 512,
-        "google/flan-t5-base": 768,
-        "google/flan-t5-large": 1024,
-        "google/flan-t5-3b": 1024,
-        "google/flan-t5-11b": 1024,
-    }
-
-    def __init__(self, name: str, output_dim: int, finetune: bool, device: str,
-                 autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0.,
-                 normalize_text: bool = False):
-        assert name in self.MODELS, f"unrecognized t5 model name (should in {self.MODELS})"
-        super().__init__(self.MODELS_DIMS[name], output_dim)
-        self.device = device
-        self.name = name
-        self.finetune = finetune
-        self.word_dropout = word_dropout
-
-        if autocast_dtype is None or self.device == 'cpu':
-            self.autocast = TorchAutocast(enabled=False)
-            if self.device != 'cpu':
-                logger.warning("T5 has no autocast, this might lead to NaN")
-        else:
-            dtype = getattr(torch, autocast_dtype)
-            assert isinstance(dtype, torch.dtype)
-            logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}")
-            self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype)
-        # Let's disable logging temporarily because T5 will vomit some errors otherwise.
-        # thanks https://gist.github.com/simon-weber/7853144
-        previous_level = logging.root.manager.disable
-        logging.disable(logging.ERROR)
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            try:
-                self.t5_tokenizer = T5Tokenizer.from_pretrained(name)
-                t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune)
-            finally:
-                logging.disable(previous_level)
-        if finetune:
-            self.t5 = t5
-        else:
-            # this makes sure that the t5 models is not part
-            # of the saved checkpoint
-            self.__dict__["t5"] = t5.to(device)
-
-        self.normalize_text = normalize_text
-        if normalize_text:
-            self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True)
-
-    def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]:
-        # if current sample doesn't have a certain attribute, replace with empty string
-        entries: tp.List[str] = [xi if xi is not None else "" for xi in x]
-        if self.normalize_text:
-            _, _, entries = self.text_normalizer(entries, return_text=True)
-        if self.word_dropout > 0. and self.training:
-            new_entries = []
-            for entry in entries:
-                words = [word for word in entry.split(" ") if random.random() >= self.word_dropout]
-                new_entries.append(" ".join(words))
-            entries = new_entries
-
-        empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""])
-
-        inputs = self.t5_tokenizer(entries, return_tensors="pt", padding=True).to(self.device)
-        mask = inputs["attention_mask"]
-        mask[empty_idx, :] = 0  # zero-out index where the input is non-existant
-        return inputs
-
-    def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType:
-        mask = inputs["attention_mask"]
-        with torch.set_grad_enabled(self.finetune), self.autocast:
-            embeds = self.t5(**inputs).last_hidden_state
-        embeds = self.output_proj(embeds.to(self.output_proj.weight))
-        embeds = (embeds * mask.unsqueeze(-1))
-        return embeds, mask
-
-
-class WaveformConditioner(BaseConditioner):
-    """Base class for all conditioners that take a waveform as input.
-    Classes that inherit must implement `_get_wav_embedding` that outputs
-    a continuous tensor, and `_downsampling_factor` that returns the down-sampling
-    factor of the embedding model.
-
-    Args:
-        dim (int): The internal representation dimension.
-        output_dim (int): Output dimension.
-        device (tp.Union[torch.device, str]): Device.
-    """
-    def __init__(self, dim: int, output_dim: int, device: tp.Union[torch.device, str]):
-        super().__init__(dim, output_dim)
-        self.device = device
-
-    def tokenize(self, wav_length: WavCondition) -> WavCondition:
-        wav, length, path = wav_length
-        assert length is not None
-        return WavCondition(wav.to(self.device), length.to(self.device), path)
-
-    def _get_wav_embedding(self, wav: Tensor) -> Tensor:
-        """Gets as input a wav and returns a dense vector of conditions."""
-        raise NotImplementedError()
-
-    def _downsampling_factor(self):
-        """Returns the downsampling factor of the embedding model."""
-        raise NotImplementedError()
-
-    def forward(self, inputs: WavCondition) -> ConditionType:
-        """
-        Args:
-            input (WavCondition): Tuple of (waveform, lengths).
-        Returns:
-            ConditionType: Dense vector representing the conditioning along with its' mask.
-        """
-        wav, lengths, path = inputs
-        with torch.no_grad():
-            embeds = self._get_wav_embedding(wav)
-        embeds = embeds.to(self.output_proj.weight)
-        embeds = self.output_proj(embeds)
-
-        if lengths is not None:
-            lengths = lengths / self._downsampling_factor()
-            mask = length_to_mask(lengths, max_len=embeds.shape[1]).int()  # type: ignore
-        else:
-            mask = torch.ones_like(embeds)
-        embeds = (embeds * mask.unsqueeze(2).to(self.device))
-
-        return embeds, mask
-
-
-class ChromaStemConditioner(WaveformConditioner):
-    """Chroma conditioner that uses DEMUCS to first filter out drums and bass. The is followed by
-    the insight the drums and bass often dominate the chroma, leading to the chroma not containing the
-    information about melody.
-
-    Args:
-        output_dim (int): Output dimension for the conditioner.
-        sample_rate (int): Sample rate for the chroma extractor.
-        n_chroma (int): Number of chroma for the chroma extractor.
-        radix2_exp (int): Radix2 exponent for the chroma extractor.
-        duration (float): Duration used during training. This is later used for correct padding
-            in case we are using chroma as prefix.
-        match_len_on_eval (bool, optional): If True then all chromas are padded to the training
-            duration. Defaults to False.
-        eval_wavs (str, optional): Path to a json egg with waveform, this waveforms are used as
-            conditions during eval (for cases where we don't want to leak test conditions like MusicCaps).
-            Defaults to None.
-        n_eval_wavs (int, optional): Limits the number of waveforms used for conditioning. Defaults to 0.
-        device (tp.Union[torch.device, str], optional): Device for the conditioner.
-        **kwargs: Additional parameters for the chroma extractor.
-    """
-    def __init__(self, output_dim: int, sample_rate: int, n_chroma: int, radix2_exp: int,
-                 duration: float, match_len_on_eval: bool = True, eval_wavs: tp.Optional[str] = None,
-                 n_eval_wavs: int = 0, device: tp.Union[torch.device, str] = "cpu", **kwargs):
-        from demucs import pretrained
-        super().__init__(dim=n_chroma, output_dim=output_dim, device=device)
-        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
-        self.sample_rate = sample_rate
-        self.match_len_on_eval = match_len_on_eval
-        self.duration = duration
-        self.__dict__["demucs"] = pretrained.get_model('htdemucs').to(device)
-        self.stem2idx = {'drums': 0, 'bass': 1, 'other': 2, 'vocal': 3}
-        self.stem_idx = torch.LongTensor([self.stem2idx['vocal'], self.stem2idx['other']]).to(device)
-        self.chroma = ChromaExtractor(sample_rate=sample_rate, n_chroma=n_chroma, radix2_exp=radix2_exp,
-                                      device=device, **kwargs)
-        self.chroma_len = self._get_chroma_len()
-
-    def _downsampling_factor(self):
-        return self.chroma.winhop
-
-    def _get_chroma_len(self):
-        """Get length of chroma during training"""
-        dummy_wav = torch.zeros((1, self.sample_rate * self.duration), device=self.device)
-        dummy_chr = self.chroma(dummy_wav)
-        return dummy_chr.shape[1]
-
-    @torch.no_grad()
-    def _get_filtered_wav(self, wav):
-        from demucs.apply import apply_model
-        from demucs.audio import convert_audio
-        with self.autocast:
-            wav = convert_audio(wav, self.sample_rate, self.demucs.samplerate, self.demucs.audio_channels)
-            stems = apply_model(self.demucs, wav, device=self.device)
-            stems = stems[:, self.stem_idx]  # extract stem
-            stems = stems.sum(1)  # merge extracted stems
-            stems = stems.mean(1, keepdim=True)  # mono
-            stems = convert_audio(stems, self.demucs.samplerate, self.sample_rate, 1)
-            return stems
-
-    @torch.no_grad()
-    def _get_wav_embedding(self, wav):
-        # avoid 0-size tensors when we are working with null conds
-        if wav.shape[-1] == 1:
-            return self.chroma(wav)
-        stems = self._get_filtered_wav(wav)
-        chroma = self.chroma(stems)
-
-        if self.match_len_on_eval:
-            b, t, c = chroma.shape
-            if t > self.chroma_len:
-                chroma = chroma[:, :self.chroma_len]
-                logger.debug(f'chroma was truncated! ({t} -> {chroma.shape[1]})')
-            elif t < self.chroma_len:
-                # chroma = F.pad(chroma, (0, 0, 0, self.chroma_len - t))
-                n_repeat = int(math.ceil(self.chroma_len / t))
-                chroma = chroma.repeat(1, n_repeat, 1)
-                chroma = chroma[:, :self.chroma_len]
-                logger.debug(f'chroma was zero-padded! ({t} -> {chroma.shape[1]})')
-        return chroma
-
-
-class ChromaExtractor(nn.Module):
-    """Chroma extraction class, handles chroma extraction and quantization.
-
-    Args:
-        sample_rate (int): Sample rate.
-        n_chroma (int): Number of chroma to consider.
-        radix2_exp (int): Radix2 exponent.
-        nfft (tp.Optional[int], optional): Number of FFT.
-        winlen (tp.Optional[int], optional): Window length.
-        winhop (tp.Optional[int], optional): Window hop size.
-        argmax (bool, optional): Whether to use argmax. Defaults to False.
-        norm (float, optional): Norm for chroma normalization. Defaults to inf.
-        device (tp.Union[torch.device, str], optional): Device to use. Defaults to cpu.
-    """
-    def __init__(self, sample_rate: int, n_chroma: int = 12, radix2_exp: int = 12,
-                 nfft: tp.Optional[int] = None, winlen: tp.Optional[int] = None, winhop: tp.Optional[int] = None,
-                 argmax: bool = False, norm: float = torch.inf, device: tp.Union[torch.device, str] = "cpu"):
-        super().__init__()
-        from librosa import filters
-        self.device = device
-        self.autocast = TorchAutocast(enabled=device != "cpu", device_type=self.device, dtype=torch.float32)
-        self.winlen = winlen or 2 ** radix2_exp
-        self.nfft = nfft or self.winlen
-        self.winhop = winhop or (self.winlen // 4)
-        self.sr = sample_rate
-        self.n_chroma = n_chroma
-        self.norm = norm
-        self.argmax = argmax
-        self.window = torch.hann_window(self.winlen).to(device)
-        self.fbanks = torch.from_numpy(filters.chroma(sr=sample_rate, n_fft=self.nfft, tuning=0,
-                                                      n_chroma=self.n_chroma)).to(device)
-        self.spec = torchaudio.transforms.Spectrogram(n_fft=self.nfft, win_length=self.winlen,
-                                                      hop_length=self.winhop, power=2, center=True,
-                                                      pad=0, normalized=True).to(device)
-
-    def forward(self, wav):
-        with self.autocast:
-            T = wav.shape[-1]
-            # in case we are getting a wav that was dropped out (nullified)
-            # make sure wav length is no less that nfft
-            if T < self.nfft:
-                pad = self.nfft - T
-                r = 0 if pad % 2 == 0 else 1
-                wav = F.pad(wav, (pad // 2, pad // 2 + r), 'constant', 0)
-                assert wav.shape[-1] == self.nfft, f'expected len {self.nfft} but got {wav.shape[-1]}'
-            spec = self.spec(wav).squeeze(1)
-            raw_chroma = torch.einsum("cf,...ft->...ct", self.fbanks, spec)
-            norm_chroma = torch.nn.functional.normalize(raw_chroma, p=self.norm, dim=-2, eps=1e-6)
-            norm_chroma = rearrange(norm_chroma, "b d t -> b t d")
-
-            if self.argmax:
-                idx = norm_chroma.argmax(-1, keepdims=True)
-                norm_chroma[:] = 0
-                norm_chroma.scatter_(dim=-1, index=idx, value=1)
-
-            return norm_chroma
-
-
-def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str):
-    """Utility function for nullifying an attribute inside an ConditioningAttributes object.
-    If the condition is of type "wav", then nullify it using "nullify_condition".
-    If the condition is of any other type, set its' value to None.
-    Works in-place.
-    """
-    if condition_type not in ["text", "wav"]:
-        raise ValueError(
-            "dropout_condition got an unexpected condition type!"
-            f" expected 'wav' or 'text' but got '{condition_type}'"
-        )
-
-    if condition not in getattr(sample, condition_type):
-        raise ValueError(
-            "dropout_condition received an unexpected condition!"
-            f" expected wav={sample.wav.keys()} and text={sample.text.keys()}"
-            f"but got '{condition}' of type '{condition_type}'!"
-        )
-
-    if condition_type == "wav":
-        wav, length, path = sample.wav[condition]
-        sample.wav[condition] = nullify_wav(wav)
-    else:
-        sample.text[condition] = None
-
-    return sample
-
-
-class DropoutModule(nn.Module):
-    """Base class for all dropout modules."""
-    def __init__(self, seed: int = 1234):
-        super().__init__()
-        self.rng = torch.Generator()
-        self.rng.manual_seed(seed)
-
-
-class AttributeDropout(DropoutModule):
-    """Applies dropout with a given probability per attribute. This is different from the behavior of
-    ClassifierFreeGuidanceDropout as this allows for attributes to be dropped out separately. For example,
-    "artist" can be dropped while "genre" remains. This is in contrast to ClassifierFreeGuidanceDropout
-    where if "artist" is dropped "genre" must also be dropped.
-
-    Args:
-        p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example:
-            ...
-            "genre": 0.1,
-            "artist": 0.5,
-            "wav": 0.25,
-            ...
-        active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False.
-        seed (int, optional): Random seed.
-    """
-    def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.active_on_eval = active_on_eval
-        # construct dict that return the values from p otherwise 0
-        self.p = {}
-        for condition_type, probs in p.items():
-            self.p[condition_type] = defaultdict(lambda: 0, probs)
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of conditions.
-        Returns:
-            tp.List[ConditioningAttributes]: List of conditions after certain attributes were set to None.
-        """
-        if not self.training and not self.active_on_eval:
-            return samples
-
-        samples = deepcopy(samples)
-
-        for condition_type, ps in self.p.items():  # for condition types [text, wav]
-            for condition, p in ps.items():  # for attributes of each type (e.g., [artist, genre])
-                if torch.rand(1, generator=self.rng).item() < p:
-                    for sample in samples:
-                        dropout_condition(sample, condition_type, condition)
-
-        return samples
-
-    def __repr__(self):
-        return f"AttributeDropout({dict(self.p)})"
-
-
-class ClassifierFreeGuidanceDropout(DropoutModule):
-    """Applies Classifier Free Guidance dropout, meaning all attributes
-    are dropped with the same probability.
-
-    Args:
-        p (float): Probability to apply condition dropout during training.
-        seed (int): Random seed.
-    """
-    def __init__(self, p: float, seed: int = 1234):
-        super().__init__(seed=seed)
-        self.p = p
-
-    def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]:
-        """
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of conditions.
-        Returns:
-            tp.List[ConditioningAttributes]: List of conditions after all attributes were set to None.
-        """
-        if not self.training:
-            return samples
-
-        # decide on which attributes to drop in a batched fashion
-        drop = torch.rand(1, generator=self.rng).item() < self.p
-        if not drop:
-            return samples
-
-        # nullify conditions of all attributes
-        samples = deepcopy(samples)
-
-        for condition_type in ["wav", "text"]:
-            for sample in samples:
-                for condition in sample.attributes[condition_type]:
-                    dropout_condition(sample, condition_type, condition)
-
-        return samples
-
-    def __repr__(self):
-        return f"ClassifierFreeGuidanceDropout(p={self.p})"
-
-
-class ConditioningProvider(nn.Module):
-    """Main class to provide conditions given all the supported conditioners.
-
-    Args:
-        conditioners (dict): Dictionary of conditioners.
-        merge_text_conditions_p (float, optional): Probability to merge all text sources
-            into a single text condition. Defaults to 0.
-        drop_desc_p (float, optional): Probability to drop the original description
-            when merging all text sources into a single text condition. Defaults to 0.
-        device (tp.Union[torch.device, str], optional): Device for conditioners and output condition types.
-    """
-    def __init__(
-        self,
-        conditioners: tp.Dict[str, BaseConditioner],
-        merge_text_conditions_p: float = 0,
-        drop_desc_p: float = 0,
-        device: tp.Union[torch.device, str] = "cpu",
-    ):
-        super().__init__()
-        self.device = device
-        self.merge_text_conditions_p = merge_text_conditions_p
-        self.drop_desc_p = drop_desc_p
-        self.conditioners = nn.ModuleDict(conditioners)
-
-    @property
-    def text_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)]
-
-    @property
-    def wav_conditions(self):
-        return [k for k, v in self.conditioners.items() if isinstance(v, WaveformConditioner)]
-
-    @property
-    def has_wav_condition(self):
-        return len(self.wav_conditions) > 0
-
-    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
-        """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly.
-        This should be called before starting any real GPU work to avoid synchronization points.
-        This will return a dict matching conditioner names to their arbitrary tokenized representations.
-
-        Args:
-            inputs (list[ConditioningAttribres]): List of ConditioningAttributes objects containing
-                text and wav conditions.
-        """
-        assert all([type(x) == ConditioningAttributes for x in inputs]), \
-            "got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]" \
-            f" but types were {set([type(x) for x in inputs])}"
-
-        output = {}
-        text = self._collate_text(inputs)
-        wavs = self._collate_wavs(inputs)
-
-        assert set(text.keys() | wavs.keys()).issubset(set(self.conditioners.keys())), \
-            f"got an unexpected attribute! Expected {self.conditioners.keys()}, got {text.keys(), wavs.keys()}"
-
-        for attribute, batch in chain(text.items(), wavs.items()):
-            output[attribute] = self.conditioners[attribute].tokenize(batch)
-        return output
-
-    def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]:
-        """Compute pairs of `(embedding, mask)` using the configured conditioners
-        and the tokenized representations. The output is for example:
-
-            {
-                "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])),
-                "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])),
-                ...
-            }
-
-        Args:
-            tokenized (dict): Dict of tokenized representations as returned by `tokenize()`.
-        """
-        output = {}
-        for attribute, inputs in tokenized.items():
-            condition, mask = self.conditioners[attribute](inputs)
-            output[attribute] = (condition, mask)
-        return output
-
-    def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]:
-        """Given a list of ConditioningAttributes objects, compile a dictionary where the keys
-        are the attributes and the values are the aggregated input per attribute.
-        For example:
-        Input:
-        [
-            ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...),
-            ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...),
-        ]
-        Output:
-        {
-            "genre": ["Rock", "Hip-hop"],
-            "description": ["A rock song with a guitar solo", "A hip-hop verse"]
-        }
-        """
-        batch_per_attribute: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list)
-
-        def _merge_conds(cond, merge_text_conditions_p=0, drop_desc_p=0):
-            def is_valid(k, v):
-                k_valid = k in ['key', 'bpm', 'genre', 'moods', 'instrument']
-                v_valid = v is not None and isinstance(v, (int, float, str, list))
-                return k_valid and v_valid
-
-            def process_value(v):
-                if isinstance(v, (int, float, str)):
-                    return v
-                if isinstance(v, list):
-                    return ", ".join(v)
-                else:
-                    RuntimeError(f"unknown type for text value! ({type(v), v})")
-
-            desc = cond.text['description']
-            meta_data = ""
-            if random.uniform(0, 1) < merge_text_conditions_p:
-                meta_pairs = [f'{k}: {process_value(v)}' for k, v in cond.text.items() if is_valid(k, v)]
-                random.shuffle(meta_pairs)
-                meta_data = ". ".join(meta_pairs)
-                desc = desc if not random.uniform(0, 1) < drop_desc_p else None
-
-            if desc is None:
-                desc = meta_data if len(meta_data) > 1 else None
-            else:
-                desc = desc.rstrip('.') + ". " + meta_data
-            cond.text['description'] = desc.strip() if desc else None
-
-        if self.training and self.merge_text_conditions_p:
-            for sample in samples:
-                _merge_conds(sample, self.merge_text_conditions_p, self.drop_desc_p)
-
-        texts = [x.text for x in samples]
-        for text in texts:
-            for condition in self.text_conditions:
-                batch_per_attribute[condition].append(text[condition])
-
-        return batch_per_attribute
-
-    def _collate_wavs(self, samples: tp.List[ConditioningAttributes]):
-        """Generate a dict where the keys are attributes by which we fetch similar wavs,
-        and the values are Tensors of wavs according to said attribtues.
-
-        *Note*: by the time the samples reach this function, each sample should have some waveform
-        inside the "wav" attribute. It should be either:
-        1. A real waveform
-        2. A null waveform due to the sample having no similar waveforms (nullified by the dataset)
-        3. A null waveform due to it being dropped in a dropout module (nullified by dropout)
-
-        Args:
-            samples (tp.List[ConditioningAttributes]): List of ConditioningAttributes samples.
-        Returns:
-            dict: A dicionary mapping an attribute name to wavs.
-        """
-        wavs = defaultdict(list)
-        lens = defaultdict(list)
-        paths = defaultdict(list)
-        out = {}
-
-        for sample in samples:
-            for attribute in self.wav_conditions:
-                wav, length, path = sample.wav[attribute]
-                wavs[attribute].append(wav.flatten())
-                lens[attribute].append(length)
-                paths[attribute].append(path)
-
-        # stack all wavs to a single tensor
-        for attribute in self.wav_conditions:
-            stacked_wav, _ = collate(wavs[attribute], dim=0)
-            out[attribute] = WavCondition(stacked_wav.unsqueeze(1),
-                                          torch.cat(lens['self_wav']), paths[attribute])  # type: ignore
-
-        return out
-
-
-class ConditionFuser(StreamingModule):
-    """Condition fuser handles the logic to combine the different conditions
-    to the actual model input.
-
-    Args:
-        fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse
-            each condition. For example:
-            {
-                "prepend": ["description"],
-                "sum": ["genre", "bpm"],
-                "cross": ["description"],
-            }
-        cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention.
-        cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used.
-    """
-    FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"]
-
-    def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False,
-                 cross_attention_pos_emb_scale: float = 1.0):
-        super().__init__()
-        assert all(
-            [k in self.FUSING_METHODS for k in fuse2cond.keys()]
-        ), f"got invalid fuse method, allowed methods: {self.FUSING_MEHTODS}"
-        self.cross_attention_pos_emb = cross_attention_pos_emb
-        self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale
-        self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond
-        self.cond2fuse: tp.Dict[str, str] = {}
-        for fuse_method, conditions in fuse2cond.items():
-            for condition in conditions:
-                self.cond2fuse[condition] = fuse_method
-
-    def forward(
-        self,
-        input: Tensor,
-        conditions: tp.Dict[str, ConditionType]
-    ) -> tp.Tuple[Tensor, tp.Optional[Tensor]]:
-        """Fuse the conditions to the provided model input.
-
-        Args:
-            input (Tensor): Transformer input.
-            conditions (tp.Dict[str, ConditionType]): Dict of conditions.
-        Returns:
-            tp.Tuple[Tensor, Tensor]: The first tensor is the transformer input
-                after the conditions have been fused. The second output tensor is the tensor
-                used for cross-attention or None if no cross attention inputs exist.
-        """
-        B, T, _ = input.shape
-
-        if 'offsets' in self._streaming_state:
-            first_step = False
-            offsets = self._streaming_state['offsets']
-        else:
-            first_step = True
-            offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device)
-
-        assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \
-            f"given conditions contain unknown attributes for fuser, " \
-            f"expected {self.cond2fuse.keys()}, got {conditions.keys()}"
-        cross_attention_output = None
-        for cond_type, (cond, cond_mask) in conditions.items():
-            op = self.cond2fuse[cond_type]
-            if op == "sum":
-                input += cond
-            elif op == "input_interpolate":
-                cond = rearrange(cond, "b t d -> b d t")
-                cond = F.interpolate(cond, size=input.shape[1])
-                input += rearrange(cond, "b d t -> b t d")
-            elif op == "prepend":
-                if first_step:
-                    input = torch.cat([cond, input], dim=1)
-            elif op == "cross":
-                if cross_attention_output is not None:
-                    cross_attention_output = torch.cat([cross_attention_output, cond], dim=1)
-                else:
-                    cross_attention_output = cond
-            else:
-                raise ValueError(f"unknown op ({op})")
-
-        if self.cross_attention_pos_emb and cross_attention_output is not None:
-            positions = torch.arange(
-                cross_attention_output.shape[1],
-                device=cross_attention_output.device
-            ).view(1, -1, 1)
-            pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1])
-            cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb
-
-        if self._is_streaming:
-            self._streaming_state['offsets'] = offsets + T
-
-        return input, cross_attention_output
diff --git a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/collapsible.tsx b/spaces/jordonpeter01/ai-comic-factory/src/components/ui/collapsible.tsx
deleted file mode 100644
index 9fa48946afd1eb56bd932377fd888e3986304676..0000000000000000000000000000000000000000
--- a/spaces/jordonpeter01/ai-comic-factory/src/components/ui/collapsible.tsx
+++ /dev/null
@@ -1,11 +0,0 @@
-"use client"
-
-import * as CollapsiblePrimitive from "@radix-ui/react-collapsible"
-
-const Collapsible = CollapsiblePrimitive.Root
-
-const CollapsibleTrigger = CollapsiblePrimitive.CollapsibleTrigger
-
-const CollapsibleContent = CollapsiblePrimitive.CollapsibleContent
-
-export { Collapsible, CollapsibleTrigger, CollapsibleContent }
diff --git a/spaces/josedolot/HybridNet_Demo2/utils/sync_batchnorm/comm.py b/spaces/josedolot/HybridNet_Demo2/utils/sync_batchnorm/comm.py
deleted file mode 100644
index 922f8c4a3adaa9b32fdcaef09583be03b0d7eb2b..0000000000000000000000000000000000000000
--- a/spaces/josedolot/HybridNet_Demo2/utils/sync_batchnorm/comm.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# -*- coding: utf-8 -*-
-# File   : comm.py
-# Author : Jiayuan Mao
-# Email  : maojiayuan@gmail.com
-# Date   : 27/01/2018
-# 
-# This file is part of Synchronized-BatchNorm-PyTorch.
-# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
-# Distributed under MIT License.
-
-import queue
-import collections
-import threading
-
-__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster']
-
-
-class FutureResult(object):
-    """A thread-safe future implementation. Used only as one-to-one pipe."""
-
-    def __init__(self):
-        self._result = None
-        self._lock = threading.Lock()
-        self._cond = threading.Condition(self._lock)
-
-    def put(self, result):
-        with self._lock:
-            assert self._result is None, 'Previous result has\'t been fetched.'
-            self._result = result
-            self._cond.notify()
-
-    def get(self):
-        with self._lock:
-            if self._result is None:
-                self._cond.wait()
-
-            res = self._result
-            self._result = None
-            return res
-
-
-_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
-_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])
-
-
-class SlavePipe(_SlavePipeBase):
-    """Pipe for master-slave communication."""
-
-    def run_slave(self, msg):
-        self.queue.put((self.identifier, msg))
-        ret = self.result.get()
-        self.queue.put(True)
-        return ret
-
-
-class SyncMaster(object):
-    """An abstract `SyncMaster` object.
-
-    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
-    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
-    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
-    and passed to a registered callback.
-    - After receiving the messages, the master device should gather the information and determine to message passed
-    back to each slave devices.
-    """
-
-    def __init__(self, master_callback):
-        """
-
-        Args:
-            master_callback: a callback to be invoked after having collected messages from slave devices.
-        """
-        self._master_callback = master_callback
-        self._queue = queue.Queue()
-        self._registry = collections.OrderedDict()
-        self._activated = False
-
-    def __getstate__(self):
-        return {'master_callback': self._master_callback}
-
-    def __setstate__(self, state):
-        self.__init__(state['master_callback'])
-
-    def register_slave(self, identifier):
-        """
-        Register an slave device.
-
-        Args:
-            identifier: an identifier, usually is the device id.
-
-        Returns: a `SlavePipe` object which can be used to communicate with the master device.
-
-        """
-        if self._activated:
-            assert self._queue.empty(), 'Queue is not clean before next initialization.'
-            self._activated = False
-            self._registry.clear()
-        future = FutureResult()
-        self._registry[identifier] = _MasterRegistry(future)
-        return SlavePipe(identifier, self._queue, future)
-
-    def run_master(self, master_msg):
-        """
-        Main entry for the master device in each forward pass.
-        The messages were first collected from each devices (including the master device), and then
-        an callback will be invoked to compute the message to be sent back to each devices
-        (including the master device).
-
-        Args:
-            master_msg: the message that the master want to send to itself. This will be placed as the first
-            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.
-
-        Returns: the message to be sent back to the master device.
-
-        """
-        self._activated = True
-
-        intermediates = [(0, master_msg)]
-        for i in range(self.nr_slaves):
-            intermediates.append(self._queue.get())
-
-        results = self._master_callback(intermediates)
-        assert results[0][0] == 0, 'The first result should belongs to the master.'
-
-        for i, res in results:
-            if i == 0:
-                continue
-            self._registry[i].result.put(res)
-
-        for i in range(self.nr_slaves):
-            assert self._queue.get() is True
-
-        return results[0][1]
-
-    @property
-    def nr_slaves(self):
-        return len(self._registry)
diff --git a/spaces/julien-c/push-model-from-web/mobilenet/README.md b/spaces/julien-c/push-model-from-web/mobilenet/README.md
deleted file mode 100644
index 2a1230fc7e939cd880641fe54489a89f482db381..0000000000000000000000000000000000000000
--- a/spaces/julien-c/push-model-from-web/mobilenet/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-license: mit
-tags:
-  - image-classification
-  - tfjs
----
-
-## TensorFlow.js version of Mobilenet
-
-Pushed from Web
-
-![](coffee.jpg)
diff --git a/spaces/kazumak/webui/app.py b/spaces/kazumak/webui/app.py
deleted file mode 100644
index 9aaf59d293dce427101bb90b54274afd589cedfd..0000000000000000000000000000000000000000
--- a/spaces/kazumak/webui/app.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-from subprocess import getoutput
-
-gpu_info = getoutput('nvidia-smi')
-if("A10G" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+4c06c79.d20221205-cp38-cp38-linux_x86_64.whl")
-elif("T4" in gpu_info):
-    os.system(f"pip install -q https://github.com/camenduru/stable-diffusion-webui-colab/releases/download/0.0.15/xformers-0.0.15.dev0+1515f77.d20221130-cp38-cp38-linux_x86_64.whl")
-
-os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui /home/user/app/stable-diffusion-webui")
-os.chdir("/home/user/app/stable-diffusion-webui")
-
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/env_patch.py -O /home/user/app/env_patch.py")
-os.system(f"sed -i -e '/import image_from_url_text/r /home/user/app/env_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(modelmerger_interface, \"Checkpoint Merger\", \"modelmerger\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/(train_interface, \"Train\", \"ti\"),/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/extensions_interface, \"Extensions\", \"extensions\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e '/settings_interface, \"Settings\", \"settings\"/d' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f'''sed -i -e "s/document.getElementsByTagName('gradio-app')\[0\].shadowRoot/!!document.getElementsByTagName('gradio-app')[0].shadowRoot ? document.getElementsByTagName('gradio-app')[0].shadowRoot : document/g" /home/user/app/stable-diffusion-webui/script.js''')
-os.system(f"sed -i -e 's/                show_progress=False,/                show_progress=True,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/shared.demo.launch/shared.demo.queue().launch/g' /home/user/app/stable-diffusion-webui/webui.py")
-#os.system(f"sed -i -e 's/inputs=\[component\],/&\\n                queue=False,/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-#os.system(f"sed -i -e 's/outputs=\[token_counter\]/outputs=[token_counter], queue=False/g' /home/user/app/stable-diffusion-webui/modules/ui.py")
-os.system(f"sed -i -e 's/outputs=\[/queue=False, &/g' home/user/app/stable-diffusion-webui/modules/ui.py")
-
-# ----------------------------Please duplicate this space and delete this block if you don't want to see the extra header----------------------------
-os.system(f"wget -q https://github.com/camenduru/webui/raw/main/header_patch.py -O /home/user/app/header_patch.py")
-os.system(f"sed -i -e '/demo:/r /home/user/app/header_patch.py' /home/user/app/stable-diffusion-webui/modules/ui.py")
-# ---------------------------------------------------------------------------------------------------------------------------------------------------
-
-#os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.ckpt")
-#os.system(f"wget -q https://huggingface.co/Linaqruf/anything-v3.0/resolve/main/Anything-V3.0.vae.pt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Anything-V3.0-pruned.vae.pt")
-os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-
-if "IS_SHARED_UI" in os.environ:
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-config.json -O /home/user/app/shared-config.json")
-    os.system(f"wget -q https://github.com/camenduru/webui/raw/main/shared-ui-config.json -O /home/user/app/shared-ui-config.json")
-    os.system(f"python launch.py --force-enable-xformers --disable-console-progressbars --enable-console-prompts --ui-config-file /home/user/app/shared-ui-config.json --ui-settings-file /home/user/app/shared-config.json --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding")
-else:
-    # Please duplicate this space and delete # character in front of the custom script you want to use or add here more custom scripts with same structure os.system(f"wget -q https://CUSTOM_SCRIPT_URL -O /home/user/app/stable-diffusion-webui/scripts/CUSTOM_SCRIPT_NAME.py")
-    os.system(f"wget -q https://gist.github.com/camenduru/9ec5f8141db9902e375967e93250860f/raw/d0bcf01786f20107c329c03f8968584ee67be12a/run_n_times.py -O /home/user/app/stable-diffusion-webui/scripts/run_n_times.py")
-    
-    # Please duplicate this space and delete # character in front of the extension you want to use or add here more extensions with same structure os.system(f"git clone https://EXTENSION_GIT_URL /home/user/app/stable-diffusion-webui/extensions/EXTENSION_NAME")
-    #os.system(f"git clone https://github.com/camenduru/stable-diffusion-webui-artists-to-study /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-artists-to-study")
-    os.system(f"git clone https://github.com/yfszzx/stable-diffusion-webui-images-browser /home/user/app/stable-diffusion-webui/extensions/stable-diffusion-webui-images-browser")
-    os.system(f"git clone https://github.com/deforum-art/deforum-for-automatic1111-webui /home/user/app/stable-diffusion-webui/extensions/deforum-for-automatic1111-webui")
-    
-    # Please duplicate this space and delete # character in front of the model you want to use or add here more ckpts with same structure os.system(f"wget -q https://CKPT_URL -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/CKPT_NAME.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/Arcane-Diffusion/resolve/main/arcane-diffusion-v3.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/arcane-diffusion-v3.ckpt")
-    #os.system(f"wget -q https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion/resolve/main/Cyberpunk-Anime-Diffusion.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/Cyberpunk-Anime-Diffusion.ckpt")
-    #os.system(f"wget -q https://huggingface.co/prompthero/midjourney-v4-diffusion/resolve/main/mdjrny-v4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/mdjrny-v4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/nitrosocke/mo-di-diffusion/resolve/main/moDi-v1-pruned.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/moDi-v1-pruned.ckpt")
-    #os.system(f"wget -q https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/resolve/main/PaperCut_v1.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/PaperCut_v1.ckpt")
-    #os.system(f"wget -q https://huggingface.co/lilpotat/sa/resolve/main/samdoesarts_style.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/samdoesarts_style.ckpt")
-    #os.system(f"wget -q https://huggingface.co/hakurei/waifu-diffusion-v1-3/resolve/main/wd-v1-3-float32.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/wd-v1-3-float32.ckpt")
-    #os.system(f"wget -q https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-4.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.ckpt")
-    #os.system(f"wget -q https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/sd-v1-5-inpainting.ckpt")
-    #os.system(f"wget -q https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.ckpt")
-    #os.system(f"wget -q https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml -O /home/user/app/stable-diffusion-webui/models/Stable-diffusion/768-v-ema.yaml")
-    os.system(f"python launch.py --force-enable-xformers --ui-config-file /home/user/app/ui-config.json --ui-settings-file /home/user/app/config.json --disable-console-progressbars --enable-console-prompts --cors-allow-origins huggingface.co,hf.space --no-progressbar-hiding --api --skip-torch-cuda-test --disable-safe-unpickle")
diff --git a/spaces/kdrkdrkdr/HinaTTS/export_model.py b/spaces/kdrkdrkdr/HinaTTS/export_model.py
deleted file mode 100644
index 98a49835df5a7a2486e76ddf94fbbb4444b52203..0000000000000000000000000000000000000000
--- a/spaces/kdrkdrkdr/HinaTTS/export_model.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import torch
-
-if __name__ == '__main__':
-    model_path = "saved_model/11/model.pth"
-    output_path = "saved_model/11/model1.pth"
-    checkpoint_dict = torch.load(model_path, map_location='cpu')
-    checkpoint_dict_new = {}
-    for k, v in checkpoint_dict.items():
-        if k == "optimizer":
-            print("remove optimizer")
-            continue
-        checkpoint_dict_new[k] = v
-    torch.save(checkpoint_dict_new, output_path)
diff --git a/spaces/kerls/is-this-food-photo-instagram-worthy/app.py b/spaces/kerls/is-this-food-photo-instagram-worthy/app.py
deleted file mode 100644
index b3c097b46baefb77b8ea88ef8b2fb793b901f7b9..0000000000000000000000000000000000000000
--- a/spaces/kerls/is-this-food-photo-instagram-worthy/app.py
+++ /dev/null
@@ -1,20 +0,0 @@
-from fastai.vision.all import *
-import gradio as gr
-
-# def greet(name):
-#     return "Hello " + name + "!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
-
-learn = load_learner('export.pkl')
-
-labels = learn.dls.vocab
-def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
-
-examples = ['aesthetic-pancakes.jpg', 'messy-oatmeal.png', 'messy-plating.png']
-
-gr.Interface(fn=predict, inputs=gr.Image(shape=(512, 512)), outputs=gr.Label(num_top_classes=3), examples=examples).launch(share=False)
diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker/src/gradio_demo.py b/spaces/kevinwang676/ChatGLM2-SadTalker/src/gradio_demo.py
deleted file mode 100644
index 1e70005831b9f29dc3c7f39642364bc325a4c8a4..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-SadTalker/src/gradio_demo.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import torch, uuid
-import os, sys, shutil
-from src.utils.preprocess import CropAndExtract
-from src.test_audio2coeff import Audio2Coeff  
-from src.facerender.animate import AnimateFromCoeff
-from src.generate_batch import get_data
-from src.generate_facerender_batch import get_facerender_data
-
-from src.utils.init_path import init_path
-
-from pydub import AudioSegment
-
-
-def mp3_to_wav(mp3_filename,wav_filename,frame_rate):
-    mp3_file = AudioSegment.from_file(file=mp3_filename)
-    mp3_file.set_frame_rate(frame_rate).export(wav_filename,format="wav")
-
-
-class SadTalker():
-
-    def __init__(self, checkpoint_path='checkpoints', config_path='src/config', lazy_load=False):
-
-        if torch.cuda.is_available() :
-            device = "cuda"
-        else:
-            device = "cpu"
-        
-        self.device = device
-
-        os.environ['TORCH_HOME']= checkpoint_path
-
-        self.checkpoint_path = checkpoint_path
-        self.config_path = config_path
-      
-
-    def test(self, source_image, driven_audio, preprocess='crop', 
-        still_mode=False,  use_enhancer=False, batch_size=1, size=256, 
-        pose_style = 0, exp_scale=1.0, 
-        use_ref_video = False,
-        ref_video = None,
-        ref_info = None,
-        use_idle_mode = False,
-        length_of_audio = 0, use_blink=True,
-        result_dir='./results/'):
-
-        self.sadtalker_paths = init_path(self.checkpoint_path, self.config_path, size, False, preprocess)
-        print(self.sadtalker_paths)
-            
-        self.audio_to_coeff = Audio2Coeff(self.sadtalker_paths, self.device)
-        self.preprocess_model = CropAndExtract(self.sadtalker_paths, self.device)
-        self.animate_from_coeff = AnimateFromCoeff(self.sadtalker_paths, self.device)
-
-        time_tag = str(uuid.uuid4())
-        save_dir = os.path.join(result_dir, time_tag)
-        os.makedirs(save_dir, exist_ok=True)
-
-        input_dir = os.path.join(save_dir, 'input')
-        os.makedirs(input_dir, exist_ok=True)
-
-        print(source_image)
-        pic_path = os.path.join(input_dir, os.path.basename(source_image)) 
-        shutil.move(source_image, input_dir)
-
-        if driven_audio is not None and os.path.isfile(driven_audio):
-            audio_path = os.path.join(input_dir, os.path.basename(driven_audio))  
-
-            #### mp3 to wav
-            if '.mp3' in audio_path:
-                mp3_to_wav(driven_audio, audio_path.replace('.mp3', '.wav'), 16000)
-                audio_path = audio_path.replace('.mp3', '.wav')
-            else:
-                shutil.move(driven_audio, input_dir)
-
-        elif use_idle_mode:
-            audio_path = os.path.join(input_dir, 'idlemode_'+str(length_of_audio)+'.wav') ## generate audio from this new audio_path
-            from pydub import AudioSegment
-            one_sec_segment = AudioSegment.silent(duration=1000*length_of_audio)  #duration in milliseconds
-            one_sec_segment.export(audio_path, format="wav")
-        else:
-            print(use_ref_video, ref_info)
-            assert use_ref_video == True and ref_info == 'all'
-
-        if use_ref_video and ref_info == 'all': # full ref mode
-            ref_video_videoname = os.path.basename(ref_video)
-            audio_path = os.path.join(save_dir, ref_video_videoname+'.wav')
-            print('new audiopath:',audio_path)
-            # if ref_video contains audio, set the audio from ref_video.
-            cmd = r"ffmpeg -y -hide_banner -loglevel error -i %s %s"%(ref_video, audio_path)
-            os.system(cmd)        
-
-        os.makedirs(save_dir, exist_ok=True)
-        
-        #crop image and extract 3dmm from image
-        first_frame_dir = os.path.join(save_dir, 'first_frame_dir')
-        os.makedirs(first_frame_dir, exist_ok=True)
-        first_coeff_path, crop_pic_path, crop_info = self.preprocess_model.generate(pic_path, first_frame_dir, preprocess, True, size)
-        
-        if first_coeff_path is None:
-            raise AttributeError("No face is detected")
-
-        if use_ref_video:
-            print('using ref video for genreation')
-            ref_video_videoname = os.path.splitext(os.path.split(ref_video)[-1])[0]
-            ref_video_frame_dir = os.path.join(save_dir, ref_video_videoname)
-            os.makedirs(ref_video_frame_dir, exist_ok=True)
-            print('3DMM Extraction for the reference video providing pose')
-            ref_video_coeff_path, _, _ =  self.preprocess_model.generate(ref_video, ref_video_frame_dir, preprocess, source_image_flag=False)
-        else:
-            ref_video_coeff_path = None
-
-        if use_ref_video:
-            if ref_info == 'pose':
-                ref_pose_coeff_path = ref_video_coeff_path
-                ref_eyeblink_coeff_path = None
-            elif ref_info == 'blink':
-                ref_pose_coeff_path = None
-                ref_eyeblink_coeff_path = ref_video_coeff_path
-            elif ref_info == 'pose+blink':
-                ref_pose_coeff_path = ref_video_coeff_path
-                ref_eyeblink_coeff_path = ref_video_coeff_path
-            elif ref_info == 'all':            
-                ref_pose_coeff_path = None
-                ref_eyeblink_coeff_path = None
-            else:
-                raise('error in refinfo')
-        else:
-            ref_pose_coeff_path = None
-            ref_eyeblink_coeff_path = None
-
-        #audio2ceoff
-        if use_ref_video and ref_info == 'all':
-            coeff_path = ref_video_coeff_path # self.audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
-        else:
-            batch = get_data(first_coeff_path, audio_path, self.device, ref_eyeblink_coeff_path=ref_eyeblink_coeff_path, still=still_mode, idlemode=use_idle_mode, length_of_audio=length_of_audio, use_blink=use_blink) # longer audio?
-            coeff_path = self.audio_to_coeff.generate(batch, save_dir, pose_style, ref_pose_coeff_path)
-
-        #coeff2video
-        data = get_facerender_data(coeff_path, crop_pic_path, first_coeff_path, audio_path, batch_size, still_mode=still_mode, preprocess=preprocess, size=size, expression_scale = exp_scale)
-        return_path = self.animate_from_coeff.generate(data, save_dir,  pic_path, crop_info, enhancer='gfpgan' if use_enhancer else None, preprocess=preprocess, img_size=size)
-        video_name = data['video_name']
-        print(f'The generated video is named {video_name} in {save_dir}')
-
-        del self.preprocess_model
-        del self.audio_to_coeff
-        del self.animate_from_coeff
-
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-            torch.cuda.synchronize()
-            
-        import gc; gc.collect()
-        
-        return return_path
-
-    
\ No newline at end of file
diff --git a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/util/generate_list.py b/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/util/generate_list.py
deleted file mode 100644
index 943d906781063c3584a7e5b5c784f8aac0694985..0000000000000000000000000000000000000000
--- a/spaces/kevinwang676/ChatGLM2-VC-SadTalker/src/face3d/util/generate_list.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""This script is to generate training list files for Deep3DFaceRecon_pytorch
-"""
-
-import os
-
-# save path to training data
-def write_list(lms_list, imgs_list, msks_list, mode='train',save_folder='datalist', save_name=''):
-    save_path = os.path.join(save_folder, mode)
-    if not os.path.isdir(save_path):
-        os.makedirs(save_path)
-    with open(os.path.join(save_path, save_name + 'landmarks.txt'), 'w') as fd:
-        fd.writelines([i + '\n' for i in lms_list])
-
-    with open(os.path.join(save_path, save_name + 'images.txt'), 'w') as fd:
-        fd.writelines([i + '\n' for i in imgs_list])
-
-    with open(os.path.join(save_path, save_name + 'masks.txt'), 'w') as fd:
-        fd.writelines([i + '\n' for i in msks_list])   
-
-# check if the path is valid
-def check_list(rlms_list, rimgs_list, rmsks_list):
-    lms_list, imgs_list, msks_list = [], [], []
-    for i in range(len(rlms_list)):
-        flag = 'false'
-        lm_path = rlms_list[i]
-        im_path = rimgs_list[i]
-        msk_path = rmsks_list[i]
-        if os.path.isfile(lm_path) and os.path.isfile(im_path) and os.path.isfile(msk_path):
-            flag = 'true'
-            lms_list.append(rlms_list[i])
-            imgs_list.append(rimgs_list[i])
-            msks_list.append(rmsks_list[i])
-        print(i, rlms_list[i], flag)
-    return lms_list, imgs_list, msks_list
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/resnet.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/resnet.py
deleted file mode 100644
index 1cb3ac057ee2d52c46fc94685b5d4e698aad8d5f..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/cnn/resnet.py
+++ /dev/null
@@ -1,316 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import logging
-
-import torch.nn as nn
-import torch.utils.checkpoint as cp
-
-from .utils import constant_init, kaiming_init
-
-
-def conv3x3(in_planes, out_planes, stride=1, dilation=1):
-    """3x3 convolution with padding."""
-    return nn.Conv2d(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=dilation,
-        dilation=dilation,
-        bias=False)
-
-
-class BasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 dilation=1,
-                 downsample=None,
-                 style='pytorch',
-                 with_cp=False):
-        super(BasicBlock, self).__init__()
-        assert style in ['pytorch', 'caffe']
-        self.conv1 = conv3x3(inplanes, planes, stride, dilation)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.dilation = dilation
-        assert not with_cp
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    expansion = 4
-
-    def __init__(self,
-                 inplanes,
-                 planes,
-                 stride=1,
-                 dilation=1,
-                 downsample=None,
-                 style='pytorch',
-                 with_cp=False):
-        """Bottleneck block.
-
-        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
-        it is "caffe", the stride-two layer is the first 1x1 conv layer.
-        """
-        super(Bottleneck, self).__init__()
-        assert style in ['pytorch', 'caffe']
-        if style == 'pytorch':
-            conv1_stride = 1
-            conv2_stride = stride
-        else:
-            conv1_stride = stride
-            conv2_stride = 1
-        self.conv1 = nn.Conv2d(
-            inplanes, planes, kernel_size=1, stride=conv1_stride, bias=False)
-        self.conv2 = nn.Conv2d(
-            planes,
-            planes,
-            kernel_size=3,
-            stride=conv2_stride,
-            padding=dilation,
-            dilation=dilation,
-            bias=False)
-
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(
-            planes, planes * self.expansion, kernel_size=1, bias=False)
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-        self.dilation = dilation
-        self.with_cp = with_cp
-
-    def forward(self, x):
-
-        def _inner_forward(x):
-            residual = x
-
-            out = self.conv1(x)
-            out = self.bn1(out)
-            out = self.relu(out)
-
-            out = self.conv2(out)
-            out = self.bn2(out)
-            out = self.relu(out)
-
-            out = self.conv3(out)
-            out = self.bn3(out)
-
-            if self.downsample is not None:
-                residual = self.downsample(x)
-
-            out += residual
-
-            return out
-
-        if self.with_cp and x.requires_grad:
-            out = cp.checkpoint(_inner_forward, x)
-        else:
-            out = _inner_forward(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-def make_res_layer(block,
-                   inplanes,
-                   planes,
-                   blocks,
-                   stride=1,
-                   dilation=1,
-                   style='pytorch',
-                   with_cp=False):
-    downsample = None
-    if stride != 1 or inplanes != planes * block.expansion:
-        downsample = nn.Sequential(
-            nn.Conv2d(
-                inplanes,
-                planes * block.expansion,
-                kernel_size=1,
-                stride=stride,
-                bias=False),
-            nn.BatchNorm2d(planes * block.expansion),
-        )
-
-    layers = []
-    layers.append(
-        block(
-            inplanes,
-            planes,
-            stride,
-            dilation,
-            downsample,
-            style=style,
-            with_cp=with_cp))
-    inplanes = planes * block.expansion
-    for _ in range(1, blocks):
-        layers.append(
-            block(inplanes, planes, 1, dilation, style=style, with_cp=with_cp))
-
-    return nn.Sequential(*layers)
-
-
-class ResNet(nn.Module):
-    """ResNet backbone.
-
-    Args:
-        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
-        num_stages (int): Resnet stages, normally 4.
-        strides (Sequence[int]): Strides of the first block of each stage.
-        dilations (Sequence[int]): Dilation of each stage.
-        out_indices (Sequence[int]): Output from which stages.
-        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
-            layer is the 3x3 conv layer, otherwise the stride-two layer is
-            the first 1x1 conv layer.
-        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
-            not freezing any parameters.
-        bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze
-            running stats (mean and var).
-        bn_frozen (bool): Whether to freeze weight and bias of BN layers.
-        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
-            memory while slowing down the training speed.
-    """
-
-    arch_settings = {
-        18: (BasicBlock, (2, 2, 2, 2)),
-        34: (BasicBlock, (3, 4, 6, 3)),
-        50: (Bottleneck, (3, 4, 6, 3)),
-        101: (Bottleneck, (3, 4, 23, 3)),
-        152: (Bottleneck, (3, 8, 36, 3))
-    }
-
-    def __init__(self,
-                 depth,
-                 num_stages=4,
-                 strides=(1, 2, 2, 2),
-                 dilations=(1, 1, 1, 1),
-                 out_indices=(0, 1, 2, 3),
-                 style='pytorch',
-                 frozen_stages=-1,
-                 bn_eval=True,
-                 bn_frozen=False,
-                 with_cp=False):
-        super(ResNet, self).__init__()
-        if depth not in self.arch_settings:
-            raise KeyError(f'invalid depth {depth} for resnet')
-        assert num_stages >= 1 and num_stages <= 4
-        block, stage_blocks = self.arch_settings[depth]
-        stage_blocks = stage_blocks[:num_stages]
-        assert len(strides) == len(dilations) == num_stages
-        assert max(out_indices) < num_stages
-
-        self.out_indices = out_indices
-        self.style = style
-        self.frozen_stages = frozen_stages
-        self.bn_eval = bn_eval
-        self.bn_frozen = bn_frozen
-        self.with_cp = with_cp
-
-        self.inplanes = 64
-        self.conv1 = nn.Conv2d(
-            3, 64, kernel_size=7, stride=2, padding=3, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.relu = nn.ReLU(inplace=True)
-        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
-
-        self.res_layers = []
-        for i, num_blocks in enumerate(stage_blocks):
-            stride = strides[i]
-            dilation = dilations[i]
-            planes = 64 * 2**i
-            res_layer = make_res_layer(
-                block,
-                self.inplanes,
-                planes,
-                num_blocks,
-                stride=stride,
-                dilation=dilation,
-                style=self.style,
-                with_cp=with_cp)
-            self.inplanes = planes * block.expansion
-            layer_name = f'layer{i + 1}'
-            self.add_module(layer_name, res_layer)
-            self.res_layers.append(layer_name)
-
-        self.feat_dim = block.expansion * 64 * 2**(len(stage_blocks) - 1)
-
-    def init_weights(self, pretrained=None):
-        if isinstance(pretrained, str):
-            logger = logging.getLogger()
-            from ..runner import load_checkpoint
-            load_checkpoint(self, pretrained, strict=False, logger=logger)
-        elif pretrained is None:
-            for m in self.modules():
-                if isinstance(m, nn.Conv2d):
-                    kaiming_init(m)
-                elif isinstance(m, nn.BatchNorm2d):
-                    constant_init(m, 1)
-        else:
-            raise TypeError('pretrained must be a str or None')
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.relu(x)
-        x = self.maxpool(x)
-        outs = []
-        for i, layer_name in enumerate(self.res_layers):
-            res_layer = getattr(self, layer_name)
-            x = res_layer(x)
-            if i in self.out_indices:
-                outs.append(x)
-        if len(outs) == 1:
-            return outs[0]
-        else:
-            return tuple(outs)
-
-    def train(self, mode=True):
-        super(ResNet, self).train(mode)
-        if self.bn_eval:
-            for m in self.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()
-                    if self.bn_frozen:
-                        for params in m.parameters():
-                            params.requires_grad = False
-        if mode and self.frozen_stages >= 0:
-            for param in self.conv1.parameters():
-                param.requires_grad = False
-            for param in self.bn1.parameters():
-                param.requires_grad = False
-            self.bn1.eval()
-            self.bn1.weight.requires_grad = False
-            self.bn1.bias.requires_grad = False
-            for i in range(1, self.frozen_stages + 1):
-                mod = getattr(self, f'layer{i}')
-                mod.eval()
-                for param in mod.parameters():
-                    param.requires_grad = False
diff --git a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/parallel/registry.py b/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/parallel/registry.py
deleted file mode 100644
index a204a07fba10e614223f090d1a57cf9c4d74d4a1..0000000000000000000000000000000000000000
--- a/spaces/kirch/Text2Video-Zero/annotator/uniformer/mmcv/parallel/registry.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-from torch.nn.parallel import DataParallel, DistributedDataParallel
-
-from annotator.uniformer.mmcv.utils import Registry
-
-MODULE_WRAPPERS = Registry('module wrapper')
-MODULE_WRAPPERS.register_module(module=DataParallel)
-MODULE_WRAPPERS.register_module(module=DistributedDataParallel)
diff --git a/spaces/kpyuy/chat/app.py b/spaces/kpyuy/chat/app.py
deleted file mode 100644
index 37e8f5ffc2ed7e157891e9eff4acfff88f6305e2..0000000000000000000000000000000000000000
--- a/spaces/kpyuy/chat/app.py
+++ /dev/null
@@ -1,62 +0,0 @@
-import gradio as gr
-import openai
-import os
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-
-class Conversation:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append({"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        self.messages.append({"role": "assistant", "content": message})
-
-        if len(self.messages) > self.num_of_round * 2 + 1:
-            del self.messages[1:3]  # Remove the first round conversation left.
-        return message
-
-
-prompt = """你是一个中国厨师，用中文回答做菜的问题。你的回答需要满足以下要求:
-1. 你的回答必须是中文
-2. 回答限制在100个字以内"""
-
-conv = Conversation(prompt, 10)
-
-
-def answer(question, history=[]):
-    history.append(question)
-    response = conv.ask(question)
-    history.append(response)
-    responses = [(u, b) for u, b in zip(history[::2], history[1::2])]
-    return responses, history
-
-
-with gr.Blocks(css="#chatbot{height:300px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False,
-                         placeholder="Enter text and press enter").style(container=False)
-
-    txt.submit(answer, [txt, state], [chatbot, state])
-
-demo.launch()
diff --git a/spaces/krystaltechnology/image-video-colorization/models/deep_colorization/colorizers/util.py b/spaces/krystaltechnology/image-video-colorization/models/deep_colorization/colorizers/util.py
deleted file mode 100644
index 79968ba6b960a8c10047f1ce52400b6bfe766b9c..0000000000000000000000000000000000000000
--- a/spaces/krystaltechnology/image-video-colorization/models/deep_colorization/colorizers/util.py
+++ /dev/null
@@ -1,47 +0,0 @@
-
-from PIL import Image
-import numpy as np
-from skimage import color
-import torch
-import torch.nn.functional as F
-from IPython import embed
-
-def load_img(img_path):
-	out_np = np.asarray(Image.open(img_path))
-	if(out_np.ndim==2):
-		out_np = np.tile(out_np[:,:,None],3)
-	return out_np
-
-def resize_img(img, HW=(256,256), resample=3):
-	return np.asarray(Image.fromarray(img).resize((HW[1],HW[0]), resample=resample))
-
-def preprocess_img(img_rgb_orig, HW=(256,256), resample=3):
-	# return original size L and resized L as torch Tensors
-	img_rgb_rs = resize_img(img_rgb_orig, HW=HW, resample=resample)
-	
-	img_lab_orig = color.rgb2lab(img_rgb_orig)
-	img_lab_rs = color.rgb2lab(img_rgb_rs)
-
-	img_l_orig = img_lab_orig[:,:,0]
-	img_l_rs = img_lab_rs[:,:,0]
-
-	tens_orig_l = torch.Tensor(img_l_orig)[None,None,:,:]
-	tens_rs_l = torch.Tensor(img_l_rs)[None,None,:,:]
-
-	return (tens_orig_l, tens_rs_l)
-
-def postprocess_tens(tens_orig_l, out_ab, mode='bilinear'):
-	# tens_orig_l 	1 x 1 x H_orig x W_orig
-	# out_ab 		1 x 2 x H x W
-
-	HW_orig = tens_orig_l.shape[2:]
-	HW = out_ab.shape[2:]
-
-	# call resize function if needed
-	if(HW_orig[0]!=HW[0] or HW_orig[1]!=HW[1]):
-		out_ab_orig = F.interpolate(out_ab, size=HW_orig, mode='bilinear')
-	else:
-		out_ab_orig = out_ab
-
-	out_lab_orig = torch.cat((tens_orig_l, out_ab_orig), dim=1)
-	return color.lab2rgb(out_lab_orig.data.cpu().numpy()[0,...].transpose((1,2,0)))
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageDraw.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageDraw.py
deleted file mode 100644
index 8adcc87de51650edfff0a33974e607cc82f98344..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/PIL/ImageDraw.py
+++ /dev/null
@@ -1,1127 +0,0 @@
-#
-# The Python Imaging Library
-# $Id$
-#
-# drawing interface operations
-#
-# History:
-# 1996-04-13 fl   Created (experimental)
-# 1996-08-07 fl   Filled polygons, ellipses.
-# 1996-08-13 fl   Added text support
-# 1998-06-28 fl   Handle I and F images
-# 1998-12-29 fl   Added arc; use arc primitive to draw ellipses
-# 1999-01-10 fl   Added shape stuff (experimental)
-# 1999-02-06 fl   Added bitmap support
-# 1999-02-11 fl   Changed all primitives to take options
-# 1999-02-20 fl   Fixed backwards compatibility
-# 2000-10-12 fl   Copy on write, when necessary
-# 2001-02-18 fl   Use default ink for bitmap/text also in fill mode
-# 2002-10-24 fl   Added support for CSS-style color strings
-# 2002-12-10 fl   Added experimental support for RGBA-on-RGB drawing
-# 2002-12-11 fl   Refactored low-level drawing API (work in progress)
-# 2004-08-26 fl   Made Draw() a factory function, added getdraw() support
-# 2004-09-04 fl   Added width support to line primitive
-# 2004-09-10 fl   Added font mode handling
-# 2006-06-19 fl   Added font bearing support (getmask2)
-#
-# Copyright (c) 1997-2006 by Secret Labs AB
-# Copyright (c) 1996-2006 by Fredrik Lundh
-#
-# See the README file for information on usage and redistribution.
-#
-
-import math
-import numbers
-import warnings
-
-from . import Image, ImageColor
-from ._deprecate import deprecate
-
-"""
-A simple 2D drawing interface for PIL images.
-<p>
-Application code should use the <b>Draw</b> factory, instead of
-directly.
-"""
-
-
-class ImageDraw:
-    font = None
-
-    def __init__(self, im, mode=None):
-        """
-        Create a drawing instance.
-
-        :param im: The image to draw in.
-        :param mode: Optional mode to use for color values.  For RGB
-           images, this argument can be RGB or RGBA (to blend the
-           drawing into the image).  For all other modes, this argument
-           must be the same as the image mode.  If omitted, the mode
-           defaults to the mode of the image.
-        """
-        im.load()
-        if im.readonly:
-            im._copy()  # make it writeable
-        blend = 0
-        if mode is None:
-            mode = im.mode
-        if mode != im.mode:
-            if mode == "RGBA" and im.mode == "RGB":
-                blend = 1
-            else:
-                msg = "mode mismatch"
-                raise ValueError(msg)
-        if mode == "P":
-            self.palette = im.palette
-        else:
-            self.palette = None
-        self._image = im
-        self.im = im.im
-        self.draw = Image.core.draw(self.im, blend)
-        self.mode = mode
-        if mode in ("I", "F"):
-            self.ink = self.draw.draw_ink(1)
-        else:
-            self.ink = self.draw.draw_ink(-1)
-        if mode in ("1", "P", "I", "F"):
-            # FIXME: fix Fill2 to properly support matte for I+F images
-            self.fontmode = "1"
-        else:
-            self.fontmode = "L"  # aliasing is okay for other modes
-        self.fill = False
-
-    def getfont(self):
-        """
-        Get the current default font.
-
-        To set the default font for this ImageDraw instance::
-
-            from PIL import ImageDraw, ImageFont
-            draw.font = ImageFont.truetype("Tests/fonts/FreeMono.ttf")
-
-        To set the default font for all future ImageDraw instances::
-
-            from PIL import ImageDraw, ImageFont
-            ImageDraw.ImageDraw.font = ImageFont.truetype("Tests/fonts/FreeMono.ttf")
-
-        If the current default font is ``None``,
-        it is initialized with ``ImageFont.load_default()``.
-
-        :returns: An image font."""
-        if not self.font:
-            # FIXME: should add a font repository
-            from . import ImageFont
-
-            self.font = ImageFont.load_default()
-        return self.font
-
-    def _getink(self, ink, fill=None):
-        if ink is None and fill is None:
-            if self.fill:
-                fill = self.ink
-            else:
-                ink = self.ink
-        else:
-            if ink is not None:
-                if isinstance(ink, str):
-                    ink = ImageColor.getcolor(ink, self.mode)
-                if self.palette and not isinstance(ink, numbers.Number):
-                    ink = self.palette.getcolor(ink, self._image)
-                ink = self.draw.draw_ink(ink)
-            if fill is not None:
-                if isinstance(fill, str):
-                    fill = ImageColor.getcolor(fill, self.mode)
-                if self.palette and not isinstance(fill, numbers.Number):
-                    fill = self.palette.getcolor(fill, self._image)
-                fill = self.draw.draw_ink(fill)
-        return ink, fill
-
-    def arc(self, xy, start, end, fill=None, width=1):
-        """Draw an arc."""
-        ink, fill = self._getink(fill)
-        if ink is not None:
-            self.draw.draw_arc(xy, start, end, ink, width)
-
-    def bitmap(self, xy, bitmap, fill=None):
-        """Draw a bitmap."""
-        bitmap.load()
-        ink, fill = self._getink(fill)
-        if ink is None:
-            ink = fill
-        if ink is not None:
-            self.draw.draw_bitmap(xy, bitmap.im, ink)
-
-    def chord(self, xy, start, end, fill=None, outline=None, width=1):
-        """Draw a chord."""
-        ink, fill = self._getink(outline, fill)
-        if fill is not None:
-            self.draw.draw_chord(xy, start, end, fill, 1)
-        if ink is not None and ink != fill and width != 0:
-            self.draw.draw_chord(xy, start, end, ink, 0, width)
-
-    def ellipse(self, xy, fill=None, outline=None, width=1):
-        """Draw an ellipse."""
-        ink, fill = self._getink(outline, fill)
-        if fill is not None:
-            self.draw.draw_ellipse(xy, fill, 1)
-        if ink is not None and ink != fill and width != 0:
-            self.draw.draw_ellipse(xy, ink, 0, width)
-
-    def line(self, xy, fill=None, width=0, joint=None):
-        """Draw a line, or a connected sequence of line segments."""
-        ink = self._getink(fill)[0]
-        if ink is not None:
-            self.draw.draw_lines(xy, ink, width)
-            if joint == "curve" and width > 4:
-                if not isinstance(xy[0], (list, tuple)):
-                    xy = [tuple(xy[i : i + 2]) for i in range(0, len(xy), 2)]
-                for i in range(1, len(xy) - 1):
-                    point = xy[i]
-                    angles = [
-                        math.degrees(math.atan2(end[0] - start[0], start[1] - end[1]))
-                        % 360
-                        for start, end in ((xy[i - 1], point), (point, xy[i + 1]))
-                    ]
-                    if angles[0] == angles[1]:
-                        # This is a straight line, so no joint is required
-                        continue
-
-                    def coord_at_angle(coord, angle):
-                        x, y = coord
-                        angle -= 90
-                        distance = width / 2 - 1
-                        return tuple(
-                            p + (math.floor(p_d) if p_d > 0 else math.ceil(p_d))
-                            for p, p_d in (
-                                (x, distance * math.cos(math.radians(angle))),
-                                (y, distance * math.sin(math.radians(angle))),
-                            )
-                        )
-
-                    flipped = (
-                        angles[1] > angles[0] and angles[1] - 180 > angles[0]
-                    ) or (angles[1] < angles[0] and angles[1] + 180 > angles[0])
-                    coords = [
-                        (point[0] - width / 2 + 1, point[1] - width / 2 + 1),
-                        (point[0] + width / 2 - 1, point[1] + width / 2 - 1),
-                    ]
-                    if flipped:
-                        start, end = (angles[1] + 90, angles[0] + 90)
-                    else:
-                        start, end = (angles[0] - 90, angles[1] - 90)
-                    self.pieslice(coords, start - 90, end - 90, fill)
-
-                    if width > 8:
-                        # Cover potential gaps between the line and the joint
-                        if flipped:
-                            gap_coords = [
-                                coord_at_angle(point, angles[0] + 90),
-                                point,
-                                coord_at_angle(point, angles[1] + 90),
-                            ]
-                        else:
-                            gap_coords = [
-                                coord_at_angle(point, angles[0] - 90),
-                                point,
-                                coord_at_angle(point, angles[1] - 90),
-                            ]
-                        self.line(gap_coords, fill, width=3)
-
-    def shape(self, shape, fill=None, outline=None):
-        """(Experimental) Draw a shape."""
-        shape.close()
-        ink, fill = self._getink(outline, fill)
-        if fill is not None:
-            self.draw.draw_outline(shape, fill, 1)
-        if ink is not None and ink != fill:
-            self.draw.draw_outline(shape, ink, 0)
-
-    def pieslice(self, xy, start, end, fill=None, outline=None, width=1):
-        """Draw a pieslice."""
-        ink, fill = self._getink(outline, fill)
-        if fill is not None:
-            self.draw.draw_pieslice(xy, start, end, fill, 1)
-        if ink is not None and ink != fill and width != 0:
-            self.draw.draw_pieslice(xy, start, end, ink, 0, width)
-
-    def point(self, xy, fill=None):
-        """Draw one or more individual pixels."""
-        ink, fill = self._getink(fill)
-        if ink is not None:
-            self.draw.draw_points(xy, ink)
-
-    def polygon(self, xy, fill=None, outline=None, width=1):
-        """Draw a polygon."""
-        ink, fill = self._getink(outline, fill)
-        if fill is not None:
-            self.draw.draw_polygon(xy, fill, 1)
-        if ink is not None and ink != fill and width != 0:
-            if width == 1:
-                self.draw.draw_polygon(xy, ink, 0, width)
-            else:
-                # To avoid expanding the polygon outwards,
-                # use the fill as a mask
-                mask = Image.new("1", self.im.size)
-                mask_ink = self._getink(1)[0]
-
-                fill_im = mask.copy()
-                draw = Draw(fill_im)
-                draw.draw.draw_polygon(xy, mask_ink, 1)
-
-                ink_im = mask.copy()
-                draw = Draw(ink_im)
-                width = width * 2 - 1
-                draw.draw.draw_polygon(xy, mask_ink, 0, width)
-
-                mask.paste(ink_im, mask=fill_im)
-
-                im = Image.new(self.mode, self.im.size)
-                draw = Draw(im)
-                draw.draw.draw_polygon(xy, ink, 0, width)
-                self.im.paste(im.im, (0, 0) + im.size, mask.im)
-
-    def regular_polygon(
-        self, bounding_circle, n_sides, rotation=0, fill=None, outline=None
-    ):
-        """Draw a regular polygon."""
-        xy = _compute_regular_polygon_vertices(bounding_circle, n_sides, rotation)
-        self.polygon(xy, fill, outline)
-
-    def rectangle(self, xy, fill=None, outline=None, width=1):
-        """Draw a rectangle."""
-        ink, fill = self._getink(outline, fill)
-        if fill is not None:
-            self.draw.draw_rectangle(xy, fill, 1)
-        if ink is not None and ink != fill and width != 0:
-            self.draw.draw_rectangle(xy, ink, 0, width)
-
-    def rounded_rectangle(
-        self, xy, radius=0, fill=None, outline=None, width=1, *, corners=None
-    ):
-        """Draw a rounded rectangle."""
-        if isinstance(xy[0], (list, tuple)):
-            (x0, y0), (x1, y1) = xy
-        else:
-            x0, y0, x1, y1 = xy
-        if x1 < x0:
-            msg = "x1 must be greater than or equal to x0"
-            raise ValueError(msg)
-        if y1 < y0:
-            msg = "y1 must be greater than or equal to y0"
-            raise ValueError(msg)
-        if corners is None:
-            corners = (True, True, True, True)
-
-        d = radius * 2
-
-        full_x, full_y = False, False
-        if all(corners):
-            full_x = d >= x1 - x0
-            if full_x:
-                # The two left and two right corners are joined
-                d = x1 - x0
-            full_y = d >= y1 - y0
-            if full_y:
-                # The two top and two bottom corners are joined
-                d = y1 - y0
-            if full_x and full_y:
-                # If all corners are joined, that is a circle
-                return self.ellipse(xy, fill, outline, width)
-
-        if d == 0 or not any(corners):
-            # If the corners have no curve,
-            # or there are no corners,
-            # that is a rectangle
-            return self.rectangle(xy, fill, outline, width)
-
-        r = d // 2
-        ink, fill = self._getink(outline, fill)
-
-        def draw_corners(pieslice):
-            if full_x:
-                # Draw top and bottom halves
-                parts = (
-                    ((x0, y0, x0 + d, y0 + d), 180, 360),
-                    ((x0, y1 - d, x0 + d, y1), 0, 180),
-                )
-            elif full_y:
-                # Draw left and right halves
-                parts = (
-                    ((x0, y0, x0 + d, y0 + d), 90, 270),
-                    ((x1 - d, y0, x1, y0 + d), 270, 90),
-                )
-            else:
-                # Draw four separate corners
-                parts = []
-                for i, part in enumerate(
-                    (
-                        ((x0, y0, x0 + d, y0 + d), 180, 270),
-                        ((x1 - d, y0, x1, y0 + d), 270, 360),
-                        ((x1 - d, y1 - d, x1, y1), 0, 90),
-                        ((x0, y1 - d, x0 + d, y1), 90, 180),
-                    )
-                ):
-                    if corners[i]:
-                        parts.append(part)
-            for part in parts:
-                if pieslice:
-                    self.draw.draw_pieslice(*(part + (fill, 1)))
-                else:
-                    self.draw.draw_arc(*(part + (ink, width)))
-
-        if fill is not None:
-            draw_corners(True)
-
-            if full_x:
-                self.draw.draw_rectangle((x0, y0 + r + 1, x1, y1 - r - 1), fill, 1)
-            else:
-                self.draw.draw_rectangle((x0 + r + 1, y0, x1 - r - 1, y1), fill, 1)
-            if not full_x and not full_y:
-                left = [x0, y0, x0 + r, y1]
-                if corners[0]:
-                    left[1] += r + 1
-                if corners[3]:
-                    left[3] -= r + 1
-                self.draw.draw_rectangle(left, fill, 1)
-
-                right = [x1 - r, y0, x1, y1]
-                if corners[1]:
-                    right[1] += r + 1
-                if corners[2]:
-                    right[3] -= r + 1
-                self.draw.draw_rectangle(right, fill, 1)
-        if ink is not None and ink != fill and width != 0:
-            draw_corners(False)
-
-            if not full_x:
-                top = [x0, y0, x1, y0 + width - 1]
-                if corners[0]:
-                    top[0] += r + 1
-                if corners[1]:
-                    top[2] -= r + 1
-                self.draw.draw_rectangle(top, ink, 1)
-
-                bottom = [x0, y1 - width + 1, x1, y1]
-                if corners[3]:
-                    bottom[0] += r + 1
-                if corners[2]:
-                    bottom[2] -= r + 1
-                self.draw.draw_rectangle(bottom, ink, 1)
-            if not full_y:
-                left = [x0, y0, x0 + width - 1, y1]
-                if corners[0]:
-                    left[1] += r + 1
-                if corners[3]:
-                    left[3] -= r + 1
-                self.draw.draw_rectangle(left, ink, 1)
-
-                right = [x1 - width + 1, y0, x1, y1]
-                if corners[1]:
-                    right[1] += r + 1
-                if corners[2]:
-                    right[3] -= r + 1
-                self.draw.draw_rectangle(right, ink, 1)
-
-    def _multiline_check(self, text):
-        split_character = "\n" if isinstance(text, str) else b"\n"
-
-        return split_character in text
-
-    def _multiline_split(self, text):
-        split_character = "\n" if isinstance(text, str) else b"\n"
-
-        return text.split(split_character)
-
-    def _multiline_spacing(self, font, spacing, stroke_width):
-        # this can be replaced with self.textbbox(...)[3] when textsize is removed
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=DeprecationWarning)
-            return (
-                self.textsize(
-                    "A",
-                    font=font,
-                    stroke_width=stroke_width,
-                )[1]
-                + spacing
-            )
-
-    def text(
-        self,
-        xy,
-        text,
-        fill=None,
-        font=None,
-        anchor=None,
-        spacing=4,
-        align="left",
-        direction=None,
-        features=None,
-        language=None,
-        stroke_width=0,
-        stroke_fill=None,
-        embedded_color=False,
-        *args,
-        **kwargs,
-    ):
-        """Draw text."""
-        if self._multiline_check(text):
-            return self.multiline_text(
-                xy,
-                text,
-                fill,
-                font,
-                anchor,
-                spacing,
-                align,
-                direction,
-                features,
-                language,
-                stroke_width,
-                stroke_fill,
-                embedded_color,
-            )
-
-        if embedded_color and self.mode not in ("RGB", "RGBA"):
-            msg = "Embedded color supported only in RGB and RGBA modes"
-            raise ValueError(msg)
-
-        if font is None:
-            font = self.getfont()
-
-        def getink(fill):
-            ink, fill = self._getink(fill)
-            if ink is None:
-                return fill
-            return ink
-
-        def draw_text(ink, stroke_width=0, stroke_offset=None):
-            mode = self.fontmode
-            if stroke_width == 0 and embedded_color:
-                mode = "RGBA"
-            coord = []
-            start = []
-            for i in range(2):
-                coord.append(int(xy[i]))
-                start.append(math.modf(xy[i])[0])
-            try:
-                mask, offset = font.getmask2(
-                    text,
-                    mode,
-                    direction=direction,
-                    features=features,
-                    language=language,
-                    stroke_width=stroke_width,
-                    anchor=anchor,
-                    ink=ink,
-                    start=start,
-                    *args,
-                    **kwargs,
-                )
-                coord = coord[0] + offset[0], coord[1] + offset[1]
-            except AttributeError:
-                try:
-                    mask = font.getmask(
-                        text,
-                        mode,
-                        direction,
-                        features,
-                        language,
-                        stroke_width,
-                        anchor,
-                        ink,
-                        start=start,
-                        *args,
-                        **kwargs,
-                    )
-                except TypeError:
-                    mask = font.getmask(text)
-            if stroke_offset:
-                coord = coord[0] + stroke_offset[0], coord[1] + stroke_offset[1]
-            if mode == "RGBA":
-                # font.getmask2(mode="RGBA") returns color in RGB bands and mask in A
-                # extract mask and set text alpha
-                color, mask = mask, mask.getband(3)
-                color.fillband(3, (ink >> 24) & 0xFF)
-                x, y = coord
-                self.im.paste(color, (x, y, x + mask.size[0], y + mask.size[1]), mask)
-            else:
-                self.draw.draw_bitmap(coord, mask, ink)
-
-        ink = getink(fill)
-        if ink is not None:
-            stroke_ink = None
-            if stroke_width:
-                stroke_ink = getink(stroke_fill) if stroke_fill is not None else ink
-
-            if stroke_ink is not None:
-                # Draw stroked text
-                draw_text(stroke_ink, stroke_width)
-
-                # Draw normal text
-                draw_text(ink, 0)
-            else:
-                # Only draw normal text
-                draw_text(ink)
-
-    def multiline_text(
-        self,
-        xy,
-        text,
-        fill=None,
-        font=None,
-        anchor=None,
-        spacing=4,
-        align="left",
-        direction=None,
-        features=None,
-        language=None,
-        stroke_width=0,
-        stroke_fill=None,
-        embedded_color=False,
-    ):
-        if direction == "ttb":
-            msg = "ttb direction is unsupported for multiline text"
-            raise ValueError(msg)
-
-        if anchor is None:
-            anchor = "la"
-        elif len(anchor) != 2:
-            msg = "anchor must be a 2 character string"
-            raise ValueError(msg)
-        elif anchor[1] in "tb":
-            msg = "anchor not supported for multiline text"
-            raise ValueError(msg)
-
-        widths = []
-        max_width = 0
-        lines = self._multiline_split(text)
-        line_spacing = self._multiline_spacing(font, spacing, stroke_width)
-        for line in lines:
-            line_width = self.textlength(
-                line, font, direction=direction, features=features, language=language
-            )
-            widths.append(line_width)
-            max_width = max(max_width, line_width)
-
-        top = xy[1]
-        if anchor[1] == "m":
-            top -= (len(lines) - 1) * line_spacing / 2.0
-        elif anchor[1] == "d":
-            top -= (len(lines) - 1) * line_spacing
-
-        for idx, line in enumerate(lines):
-            left = xy[0]
-            width_difference = max_width - widths[idx]
-
-            # first align left by anchor
-            if anchor[0] == "m":
-                left -= width_difference / 2.0
-            elif anchor[0] == "r":
-                left -= width_difference
-
-            # then align by align parameter
-            if align == "left":
-                pass
-            elif align == "center":
-                left += width_difference / 2.0
-            elif align == "right":
-                left += width_difference
-            else:
-                msg = 'align must be "left", "center" or "right"'
-                raise ValueError(msg)
-
-            self.text(
-                (left, top),
-                line,
-                fill,
-                font,
-                anchor,
-                direction=direction,
-                features=features,
-                language=language,
-                stroke_width=stroke_width,
-                stroke_fill=stroke_fill,
-                embedded_color=embedded_color,
-            )
-            top += line_spacing
-
-    def textsize(
-        self,
-        text,
-        font=None,
-        spacing=4,
-        direction=None,
-        features=None,
-        language=None,
-        stroke_width=0,
-    ):
-        """Get the size of a given string, in pixels."""
-        deprecate("textsize", 10, "textbbox or textlength")
-        if self._multiline_check(text):
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=DeprecationWarning)
-                return self.multiline_textsize(
-                    text,
-                    font,
-                    spacing,
-                    direction,
-                    features,
-                    language,
-                    stroke_width,
-                )
-
-        if font is None:
-            font = self.getfont()
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=DeprecationWarning)
-            return font.getsize(
-                text,
-                direction,
-                features,
-                language,
-                stroke_width,
-            )
-
-    def multiline_textsize(
-        self,
-        text,
-        font=None,
-        spacing=4,
-        direction=None,
-        features=None,
-        language=None,
-        stroke_width=0,
-    ):
-        deprecate("multiline_textsize", 10, "multiline_textbbox")
-        max_width = 0
-        lines = self._multiline_split(text)
-        line_spacing = self._multiline_spacing(font, spacing, stroke_width)
-        with warnings.catch_warnings():
-            warnings.filterwarnings("ignore", category=DeprecationWarning)
-            for line in lines:
-                line_width, line_height = self.textsize(
-                    line,
-                    font,
-                    spacing,
-                    direction,
-                    features,
-                    language,
-                    stroke_width,
-                )
-                max_width = max(max_width, line_width)
-        return max_width, len(lines) * line_spacing - spacing
-
-    def textlength(
-        self,
-        text,
-        font=None,
-        direction=None,
-        features=None,
-        language=None,
-        embedded_color=False,
-    ):
-        """Get the length of a given string, in pixels with 1/64 precision."""
-        if self._multiline_check(text):
-            msg = "can't measure length of multiline text"
-            raise ValueError(msg)
-        if embedded_color and self.mode not in ("RGB", "RGBA"):
-            msg = "Embedded color supported only in RGB and RGBA modes"
-            raise ValueError(msg)
-
-        if font is None:
-            font = self.getfont()
-        mode = "RGBA" if embedded_color else self.fontmode
-        try:
-            return font.getlength(text, mode, direction, features, language)
-        except AttributeError:
-            deprecate("textlength support for fonts without getlength", 10)
-            with warnings.catch_warnings():
-                warnings.filterwarnings("ignore", category=DeprecationWarning)
-                size = self.textsize(
-                    text,
-                    font,
-                    direction=direction,
-                    features=features,
-                    language=language,
-                )
-            if direction == "ttb":
-                return size[1]
-            return size[0]
-
-    def textbbox(
-        self,
-        xy,
-        text,
-        font=None,
-        anchor=None,
-        spacing=4,
-        align="left",
-        direction=None,
-        features=None,
-        language=None,
-        stroke_width=0,
-        embedded_color=False,
-    ):
-        """Get the bounding box of a given string, in pixels."""
-        if embedded_color and self.mode not in ("RGB", "RGBA"):
-            msg = "Embedded color supported only in RGB and RGBA modes"
-            raise ValueError(msg)
-
-        if self._multiline_check(text):
-            return self.multiline_textbbox(
-                xy,
-                text,
-                font,
-                anchor,
-                spacing,
-                align,
-                direction,
-                features,
-                language,
-                stroke_width,
-                embedded_color,
-            )
-
-        if font is None:
-            font = self.getfont()
-        mode = "RGBA" if embedded_color else self.fontmode
-        bbox = font.getbbox(
-            text, mode, direction, features, language, stroke_width, anchor
-        )
-        return bbox[0] + xy[0], bbox[1] + xy[1], bbox[2] + xy[0], bbox[3] + xy[1]
-
-    def multiline_textbbox(
-        self,
-        xy,
-        text,
-        font=None,
-        anchor=None,
-        spacing=4,
-        align="left",
-        direction=None,
-        features=None,
-        language=None,
-        stroke_width=0,
-        embedded_color=False,
-    ):
-        if direction == "ttb":
-            msg = "ttb direction is unsupported for multiline text"
-            raise ValueError(msg)
-
-        if anchor is None:
-            anchor = "la"
-        elif len(anchor) != 2:
-            msg = "anchor must be a 2 character string"
-            raise ValueError(msg)
-        elif anchor[1] in "tb":
-            msg = "anchor not supported for multiline text"
-            raise ValueError(msg)
-
-        widths = []
-        max_width = 0
-        lines = self._multiline_split(text)
-        line_spacing = self._multiline_spacing(font, spacing, stroke_width)
-        for line in lines:
-            line_width = self.textlength(
-                line,
-                font,
-                direction=direction,
-                features=features,
-                language=language,
-                embedded_color=embedded_color,
-            )
-            widths.append(line_width)
-            max_width = max(max_width, line_width)
-
-        top = xy[1]
-        if anchor[1] == "m":
-            top -= (len(lines) - 1) * line_spacing / 2.0
-        elif anchor[1] == "d":
-            top -= (len(lines) - 1) * line_spacing
-
-        bbox = None
-
-        for idx, line in enumerate(lines):
-            left = xy[0]
-            width_difference = max_width - widths[idx]
-
-            # first align left by anchor
-            if anchor[0] == "m":
-                left -= width_difference / 2.0
-            elif anchor[0] == "r":
-                left -= width_difference
-
-            # then align by align parameter
-            if align == "left":
-                pass
-            elif align == "center":
-                left += width_difference / 2.0
-            elif align == "right":
-                left += width_difference
-            else:
-                msg = 'align must be "left", "center" or "right"'
-                raise ValueError(msg)
-
-            bbox_line = self.textbbox(
-                (left, top),
-                line,
-                font,
-                anchor,
-                direction=direction,
-                features=features,
-                language=language,
-                stroke_width=stroke_width,
-                embedded_color=embedded_color,
-            )
-            if bbox is None:
-                bbox = bbox_line
-            else:
-                bbox = (
-                    min(bbox[0], bbox_line[0]),
-                    min(bbox[1], bbox_line[1]),
-                    max(bbox[2], bbox_line[2]),
-                    max(bbox[3], bbox_line[3]),
-                )
-
-            top += line_spacing
-
-        if bbox is None:
-            return xy[0], xy[1], xy[0], xy[1]
-        return bbox
-
-
-def Draw(im, mode=None):
-    """
-    A simple 2D drawing interface for PIL images.
-
-    :param im: The image to draw in.
-    :param mode: Optional mode to use for color values.  For RGB
-       images, this argument can be RGB or RGBA (to blend the
-       drawing into the image).  For all other modes, this argument
-       must be the same as the image mode.  If omitted, the mode
-       defaults to the mode of the image.
-    """
-    try:
-        return im.getdraw(mode)
-    except AttributeError:
-        return ImageDraw(im, mode)
-
-
-# experimental access to the outline API
-try:
-    Outline = Image.core.outline
-except AttributeError:
-    Outline = None
-
-
-def getdraw(im=None, hints=None):
-    """
-    (Experimental) A more advanced 2D drawing interface for PIL images,
-    based on the WCK interface.
-
-    :param im: The image to draw in.
-    :param hints: An optional list of hints.
-    :returns: A (drawing context, drawing resource factory) tuple.
-    """
-    # FIXME: this needs more work!
-    # FIXME: come up with a better 'hints' scheme.
-    handler = None
-    if not hints or "nicest" in hints:
-        try:
-            from . import _imagingagg as handler
-        except ImportError:
-            pass
-    if handler is None:
-        from . import ImageDraw2 as handler
-    if im:
-        im = handler.Draw(im)
-    return im, handler
-
-
-def floodfill(image, xy, value, border=None, thresh=0):
-    """
-    (experimental) Fills a bounded region with a given color.
-
-    :param image: Target image.
-    :param xy: Seed position (a 2-item coordinate tuple). See
-        :ref:`coordinate-system`.
-    :param value: Fill color.
-    :param border: Optional border value.  If given, the region consists of
-        pixels with a color different from the border color.  If not given,
-        the region consists of pixels having the same color as the seed
-        pixel.
-    :param thresh: Optional threshold value which specifies a maximum
-        tolerable difference of a pixel value from the 'background' in
-        order for it to be replaced. Useful for filling regions of
-        non-homogeneous, but similar, colors.
-    """
-    # based on an implementation by Eric S. Raymond
-    # amended by yo1995 @20180806
-    pixel = image.load()
-    x, y = xy
-    try:
-        background = pixel[x, y]
-        if _color_diff(value, background) <= thresh:
-            return  # seed point already has fill color
-        pixel[x, y] = value
-    except (ValueError, IndexError):
-        return  # seed point outside image
-    edge = {(x, y)}
-    # use a set to keep record of current and previous edge pixels
-    # to reduce memory consumption
-    full_edge = set()
-    while edge:
-        new_edge = set()
-        for x, y in edge:  # 4 adjacent method
-            for s, t in ((x + 1, y), (x - 1, y), (x, y + 1), (x, y - 1)):
-                # If already processed, or if a coordinate is negative, skip
-                if (s, t) in full_edge or s < 0 or t < 0:
-                    continue
-                try:
-                    p = pixel[s, t]
-                except (ValueError, IndexError):
-                    pass
-                else:
-                    full_edge.add((s, t))
-                    if border is None:
-                        fill = _color_diff(p, background) <= thresh
-                    else:
-                        fill = p != value and p != border
-                    if fill:
-                        pixel[s, t] = value
-                        new_edge.add((s, t))
-        full_edge = edge  # discard pixels processed
-        edge = new_edge
-
-
-def _compute_regular_polygon_vertices(bounding_circle, n_sides, rotation):
-    """
-    Generate a list of vertices for a 2D regular polygon.
-
-    :param bounding_circle: The bounding circle is a tuple defined
-        by a point and radius. The polygon is inscribed in this circle.
-        (e.g. ``bounding_circle=(x, y, r)`` or ``((x, y), r)``)
-    :param n_sides: Number of sides
-        (e.g. ``n_sides=3`` for a triangle, ``6`` for a hexagon)
-    :param rotation: Apply an arbitrary rotation to the polygon
-        (e.g. ``rotation=90``, applies a 90 degree rotation)
-    :return: List of regular polygon vertices
-        (e.g. ``[(25, 50), (50, 50), (50, 25), (25, 25)]``)
-
-    How are the vertices computed?
-    1. Compute the following variables
-        - theta: Angle between the apothem & the nearest polygon vertex
-        - side_length: Length of each polygon edge
-        - centroid: Center of bounding circle (1st, 2nd elements of bounding_circle)
-        - polygon_radius: Polygon radius (last element of bounding_circle)
-        - angles: Location of each polygon vertex in polar grid
-            (e.g. A square with 0 degree rotation => [225.0, 315.0, 45.0, 135.0])
-
-    2. For each angle in angles, get the polygon vertex at that angle
-        The vertex is computed using the equation below.
-            X= xcos(φ) + ysin(φ)
-            Y= −xsin(φ) + ycos(φ)
-
-        Note:
-            φ = angle in degrees
-            x = 0
-            y = polygon_radius
-
-        The formula above assumes rotation around the origin.
-        In our case, we are rotating around the centroid.
-        To account for this, we use the formula below
-            X = xcos(φ) + ysin(φ) + centroid_x
-            Y = −xsin(φ) + ycos(φ) + centroid_y
-    """
-    # 1. Error Handling
-    # 1.1 Check `n_sides` has an appropriate value
-    if not isinstance(n_sides, int):
-        msg = "n_sides should be an int"
-        raise TypeError(msg)
-    if n_sides < 3:
-        msg = "n_sides should be an int > 2"
-        raise ValueError(msg)
-
-    # 1.2 Check `bounding_circle` has an appropriate value
-    if not isinstance(bounding_circle, (list, tuple)):
-        msg = "bounding_circle should be a tuple"
-        raise TypeError(msg)
-
-    if len(bounding_circle) == 3:
-        *centroid, polygon_radius = bounding_circle
-    elif len(bounding_circle) == 2:
-        centroid, polygon_radius = bounding_circle
-    else:
-        msg = (
-            "bounding_circle should contain 2D coordinates "
-            "and a radius (e.g. (x, y, r) or ((x, y), r) )"
-        )
-        raise ValueError(msg)
-
-    if not all(isinstance(i, (int, float)) for i in (*centroid, polygon_radius)):
-        msg = "bounding_circle should only contain numeric data"
-        raise ValueError(msg)
-
-    if not len(centroid) == 2:
-        msg = "bounding_circle centre should contain 2D coordinates (e.g. (x, y))"
-        raise ValueError(msg)
-
-    if polygon_radius <= 0:
-        msg = "bounding_circle radius should be > 0"
-        raise ValueError(msg)
-
-    # 1.3 Check `rotation` has an appropriate value
-    if not isinstance(rotation, (int, float)):
-        msg = "rotation should be an int or float"
-        raise ValueError(msg)
-
-    # 2. Define Helper Functions
-    def _apply_rotation(point, degrees, centroid):
-        return (
-            round(
-                point[0] * math.cos(math.radians(360 - degrees))
-                - point[1] * math.sin(math.radians(360 - degrees))
-                + centroid[0],
-                2,
-            ),
-            round(
-                point[1] * math.cos(math.radians(360 - degrees))
-                + point[0] * math.sin(math.radians(360 - degrees))
-                + centroid[1],
-                2,
-            ),
-        )
-
-    def _compute_polygon_vertex(centroid, polygon_radius, angle):
-        start_point = [polygon_radius, 0]
-        return _apply_rotation(start_point, angle, centroid)
-
-    def _get_angles(n_sides, rotation):
-        angles = []
-        degrees = 360 / n_sides
-        # Start with the bottom left polygon vertex
-        current_angle = (270 - 0.5 * degrees) + rotation
-        for _ in range(0, n_sides):
-            angles.append(current_angle)
-            current_angle += degrees
-            if current_angle > 360:
-                current_angle -= 360
-        return angles
-
-    # 3. Variable Declarations
-    angles = _get_angles(n_sides, rotation)
-
-    # 4. Compute Vertices
-    return [
-        _compute_polygon_vertex(centroid, polygon_radius, angle) for angle in angles
-    ]
-
-
-def _color_diff(color1, color2):
-    """
-    Uses 1-norm distance to calculate difference between two values.
-    """
-    if isinstance(color2, tuple):
-        return sum(abs(color1[i] - color2[i]) for i in range(0, len(color2)))
-    else:
-        return abs(color1 - color2)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/certifi/core.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/certifi/core.py
deleted file mode 100644
index de028981b97e1fcc8ef4ab2c817cc8731b9c8738..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/certifi/core.py
+++ /dev/null
@@ -1,108 +0,0 @@
-"""
-certifi.py
-~~~~~~~~~~
-
-This module returns the installation location of cacert.pem or its contents.
-"""
-import sys
-
-
-if sys.version_info >= (3, 11):
-
-    from importlib.resources import as_file, files
-
-    _CACERT_CTX = None
-    _CACERT_PATH = None
-
-    def where() -> str:
-        # This is slightly terrible, but we want to delay extracting the file
-        # in cases where we're inside of a zipimport situation until someone
-        # actually calls where(), but we don't want to re-extract the file
-        # on every call of where(), so we'll do it once then store it in a
-        # global variable.
-        global _CACERT_CTX
-        global _CACERT_PATH
-        if _CACERT_PATH is None:
-            # This is slightly janky, the importlib.resources API wants you to
-            # manage the cleanup of this file, so it doesn't actually return a
-            # path, it returns a context manager that will give you the path
-            # when you enter it and will do any cleanup when you leave it. In
-            # the common case of not needing a temporary file, it will just
-            # return the file system location and the __exit__() is a no-op.
-            #
-            # We also have to hold onto the actual context manager, because
-            # it will do the cleanup whenever it gets garbage collected, so
-            # we will also store that at the global level as well.
-            _CACERT_CTX = as_file(files("certifi").joinpath("cacert.pem"))
-            _CACERT_PATH = str(_CACERT_CTX.__enter__())
-
-        return _CACERT_PATH
-
-    def contents() -> str:
-        return files("certifi").joinpath("cacert.pem").read_text(encoding="ascii")
-
-elif sys.version_info >= (3, 7):
-
-    from importlib.resources import path as get_path, read_text
-
-    _CACERT_CTX = None
-    _CACERT_PATH = None
-
-    def where() -> str:
-        # This is slightly terrible, but we want to delay extracting the
-        # file in cases where we're inside of a zipimport situation until
-        # someone actually calls where(), but we don't want to re-extract
-        # the file on every call of where(), so we'll do it once then store
-        # it in a global variable.
-        global _CACERT_CTX
-        global _CACERT_PATH
-        if _CACERT_PATH is None:
-            # This is slightly janky, the importlib.resources API wants you
-            # to manage the cleanup of this file, so it doesn't actually
-            # return a path, it returns a context manager that will give
-            # you the path when you enter it and will do any cleanup when
-            # you leave it. In the common case of not needing a temporary
-            # file, it will just return the file system location and the
-            # __exit__() is a no-op.
-            #
-            # We also have to hold onto the actual context manager, because
-            # it will do the cleanup whenever it gets garbage collected, so
-            # we will also store that at the global level as well.
-            _CACERT_CTX = get_path("certifi", "cacert.pem")
-            _CACERT_PATH = str(_CACERT_CTX.__enter__())
-
-        return _CACERT_PATH
-
-    def contents() -> str:
-        return read_text("certifi", "cacert.pem", encoding="ascii")
-
-else:
-    import os
-    import types
-    from typing import Union
-
-    Package = Union[types.ModuleType, str]
-    Resource = Union[str, "os.PathLike"]
-
-    # This fallback will work for Python versions prior to 3.7 that lack the
-    # importlib.resources module but relies on the existing `where` function
-    # so won't address issues with environments like PyOxidizer that don't set
-    # __file__ on modules.
-    def read_text(
-        package: Package,
-        resource: Resource,
-        encoding: str = 'utf-8',
-        errors: str = 'strict'
-    ) -> str:
-        with open(where(), encoding=encoding) as data:
-            return data.read()
-
-    # If we don't have importlib.resources, then we will just do the old logic
-    # of assuming we're on the filesystem and munge the path directly.
-    def where() -> str:
-        f = os.path.dirname(__file__)
-
-        return os.path.join(f, "cacert.pem")
-
-    def contents() -> str:
-        return read_text("certifi", "cacert.pem", encoding="ascii")
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/middleware/cors.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/middleware/cors.py
deleted file mode 100644
index 8dfaad0dbb3ff5300cccb2023748cd30f54bc920..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fastapi/middleware/cors.py
+++ /dev/null
@@ -1 +0,0 @@
-from starlette.middleware.cors import CORSMiddleware as CORSMiddleware  # noqa
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/vector.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/vector.py
deleted file mode 100644
index 81c1484188a46b567d8921d925f8a4700f65066f..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/misc/vector.py
+++ /dev/null
@@ -1,143 +0,0 @@
-from numbers import Number
-import math
-import operator
-import warnings
-
-
-__all__ = ["Vector"]
-
-
-class Vector(tuple):
-
-    """A math-like vector.
-
-    Represents an n-dimensional numeric vector. ``Vector`` objects support
-    vector addition and subtraction, scalar multiplication and division,
-    negation, rounding, and comparison tests.
-    """
-
-    __slots__ = ()
-
-    def __new__(cls, values, keep=False):
-        if keep is not False:
-            warnings.warn(
-                "the 'keep' argument has been deprecated",
-                DeprecationWarning,
-            )
-        if type(values) == Vector:
-            # No need to create a new object
-            return values
-        return super().__new__(cls, values)
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}({super().__repr__()})"
-
-    def _vectorOp(self, other, op):
-        if isinstance(other, Vector):
-            assert len(self) == len(other)
-            return self.__class__(op(a, b) for a, b in zip(self, other))
-        if isinstance(other, Number):
-            return self.__class__(op(v, other) for v in self)
-        raise NotImplementedError()
-
-    def _scalarOp(self, other, op):
-        if isinstance(other, Number):
-            return self.__class__(op(v, other) for v in self)
-        raise NotImplementedError()
-
-    def _unaryOp(self, op):
-        return self.__class__(op(v) for v in self)
-
-    def __add__(self, other):
-        return self._vectorOp(other, operator.add)
-
-    __radd__ = __add__
-
-    def __sub__(self, other):
-        return self._vectorOp(other, operator.sub)
-
-    def __rsub__(self, other):
-        return self._vectorOp(other, _operator_rsub)
-
-    def __mul__(self, other):
-        return self._scalarOp(other, operator.mul)
-
-    __rmul__ = __mul__
-
-    def __truediv__(self, other):
-        return self._scalarOp(other, operator.truediv)
-
-    def __rtruediv__(self, other):
-        return self._scalarOp(other, _operator_rtruediv)
-
-    def __pos__(self):
-        return self._unaryOp(operator.pos)
-
-    def __neg__(self):
-        return self._unaryOp(operator.neg)
-
-    def __round__(self, *, round=round):
-        return self._unaryOp(round)
-
-    def __eq__(self, other):
-        if isinstance(other, list):
-            # bw compat Vector([1, 2, 3]) == [1, 2, 3]
-            other = tuple(other)
-        return super().__eq__(other)
-
-    def __ne__(self, other):
-        return not self.__eq__(other)
-
-    def __bool__(self):
-        return any(self)
-
-    __nonzero__ = __bool__
-
-    def __abs__(self):
-        return math.sqrt(sum(x * x for x in self))
-
-    def length(self):
-        """Return the length of the vector. Equivalent to abs(vector)."""
-        return abs(self)
-
-    def normalized(self):
-        """Return the normalized vector of the vector."""
-        return self / abs(self)
-
-    def dot(self, other):
-        """Performs vector dot product, returning the sum of
-        ``a[0] * b[0], a[1] * b[1], ...``"""
-        assert len(self) == len(other)
-        return sum(a * b for a, b in zip(self, other))
-
-    # Deprecated methods/properties
-
-    def toInt(self):
-        warnings.warn(
-            "the 'toInt' method has been deprecated, use round(vector) instead",
-            DeprecationWarning,
-        )
-        return self.__round__()
-
-    @property
-    def values(self):
-        warnings.warn(
-            "the 'values' attribute has been deprecated, use "
-            "the vector object itself instead",
-            DeprecationWarning,
-        )
-        return list(self)
-
-    @values.setter
-    def values(self, values):
-        raise AttributeError(
-            "can't set attribute, the 'values' attribute has been deprecated",
-        )
-
-
-def _operator_rsub(a, b):
-    return operator.sub(b, a)
-
-
-def _operator_rtruediv(a, b):
-    return operator.truediv(b, a)
diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-b64f4ef5.js b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-b64f4ef5.js
deleted file mode 100644
index 47cc34c075325deb306500f78dbad44e89df6af7..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/gradio/templates/cdn/assets/index-b64f4ef5.js
+++ /dev/null
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print Comment Script AssignStatement * BinaryExpression BitOp BitOp BitOp BitOp ArithOp ArithOp @ ArithOp ** UnaryExpression ArithOp BitOp AwaitExpression await ) ( ParenthesizedExpression BinaryExpression or and CompareOp in not is UnaryExpression ConditionalExpression if else LambdaExpression lambda ParamList VariableName AssignOp , : NamedExpression AssignOp YieldExpression yield from TupleExpression ComprehensionExpression async for LambdaExpression ] [ ArrayExpression ArrayComprehensionExpression } { DictionaryExpression DictionaryComprehensionExpression SetExpression SetComprehensionExpression CallExpression ArgList AssignOp MemberExpression . 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diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_custom.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_custom.py
deleted file mode 100644
index e85ddd659e01bebb03b5df6a5ed4fb0070abd263..0000000000000000000000000000000000000000
--- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/importlib_resources/tests/test_custom.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import unittest
-import contextlib
-import pathlib
-
-import importlib_resources as resources
-from ..abc import TraversableResources, ResourceReader
-from . import util
-from ._compat import os_helper
-
-
-class SimpleLoader:
-    """
-    A simple loader that only implements a resource reader.
-    """
-
-    def __init__(self, reader: ResourceReader):
-        self.reader = reader
-
-    def get_resource_reader(self, package):
-        return self.reader
-
-
-class MagicResources(TraversableResources):
-    """
-    Magically returns the resources at path.
-    """
-
-    def __init__(self, path: pathlib.Path):
-        self.path = path
-
-    def files(self):
-        return self.path
-
-
-class CustomTraversableResourcesTests(unittest.TestCase):
-    def setUp(self):
-        self.fixtures = contextlib.ExitStack()
-        self.addCleanup(self.fixtures.close)
-
-    def test_custom_loader(self):
-        temp_dir = self.fixtures.enter_context(os_helper.temp_dir())
-        loader = SimpleLoader(MagicResources(temp_dir))
-        pkg = util.create_package_from_loader(loader)
-        files = resources.files(pkg)
-        assert files is temp_dir
diff --git a/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5/app-txt2img.py b/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5/app-txt2img.py
deleted file mode 100644
index 7b0da7a522864414f0884a49ae7368724e60b849..0000000000000000000000000000000000000000
--- a/spaces/latent-consistency/Real-Time-LCM-Text-to-Image-Lora-SD1.5/app-txt2img.py
+++ /dev/null
@@ -1,255 +0,0 @@
-import asyncio
-import json
-import logging
-import traceback
-from pydantic import BaseModel
-
-from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import (
-    StreamingResponse,
-    JSONResponse,
-    HTMLResponse,
-    FileResponse,
-)
-
-from diffusers import DiffusionPipeline, AutoencoderTiny
-from compel import Compel
-import torch
-
-try:
-    import intel_extension_for_pytorch as ipex
-except:
-    pass
-from PIL import Image
-import numpy as np
-import gradio as gr
-import io
-import uuid
-import os
-import time
-import psutil
-
-
-MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
-TIMEOUT = float(os.environ.get("TIMEOUT", 0))
-SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
-
-WIDTH = 768
-HEIGHT = 768
-# disable tiny autoencoder for better quality speed tradeoff
-USE_TINY_AUTOENCODER = False
-
-# check if MPS is available OSX only M1/M2/M3 chips
-mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
-xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
-device = torch.device(
-    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
-)
-torch_device = device
-# change to torch.float16 to save GPU memory
-torch_dtype = torch.float32
-
-print(f"TIMEOUT: {TIMEOUT}")
-print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
-print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
-print(f"device: {device}")
-
-if mps_available:
-    device = torch.device("mps")
-    torch_device = "cpu"
-    torch_dtype = torch.float32
-
-if SAFETY_CHECKER == "True":
-    pipe = DiffusionPipeline.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-    )
-else:
-    pipe = DiffusionPipeline.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-        safety_checker=None,
-    )
-if USE_TINY_AUTOENCODER:
-    pipe.vae = AutoencoderTiny.from_pretrained(
-        "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
-    )
-pipe.set_progress_bar_config(disable=True)
-pipe.to(device=torch_device, dtype=torch_dtype).to(device)
-pipe.unet.to(memory_format=torch.channels_last)
-
-# check if computer has less than 64GB of RAM using sys or os
-if psutil.virtual_memory().total < 64 * 1024**3:
-    pipe.enable_attention_slicing()
-
-if TORCH_COMPILE:
-    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)
-
-    pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
-
-compel_proc = Compel(
-    tokenizer=pipe.tokenizer,
-    text_encoder=pipe.text_encoder,
-    truncate_long_prompts=False,
-)
-user_queue_map = {}
-
-
-class InputParams(BaseModel):
-    seed: int = 2159232
-    prompt: str
-    guidance_scale: float = 8.0
-    strength: float = 0.5
-    steps: int = 4
-    lcm_steps: int = 50
-    width: int = WIDTH
-    height: int = HEIGHT
-
-
-def predict(params: InputParams):
-    generator = torch.manual_seed(params.seed)
-    prompt_embeds = compel_proc(params.prompt)
-    # Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
-    results = pipe(
-        prompt_embeds=prompt_embeds,
-        generator=generator,
-        num_inference_steps=params.steps,
-        guidance_scale=params.guidance_scale,
-        width=params.width,
-        height=params.height,
-        original_inference_steps=params.lcm_steps,
-        output_type="pil",
-    )
-    nsfw_content_detected = (
-        results.nsfw_content_detected[0]
-        if "nsfw_content_detected" in results
-        else False
-    )
-    if nsfw_content_detected:
-        return None
-    return results.images[0]
-
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-@app.websocket("/ws")
-async def websocket_endpoint(websocket: WebSocket):
-    await websocket.accept()
-    if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
-        print("Server is full")
-        await websocket.send_json({"status": "error", "message": "Server is full"})
-        await websocket.close()
-        return
-
-    try:
-        uid = str(uuid.uuid4())
-        print(f"New user connected: {uid}")
-        await websocket.send_json(
-            {"status": "success", "message": "Connected", "userId": uid}
-        )
-        user_queue_map[uid] = {
-            "queue": asyncio.Queue(),
-        }
-        await websocket.send_json(
-            {"status": "start", "message": "Start Streaming", "userId": uid}
-        )
-        await handle_websocket_data(websocket, uid)
-    except WebSocketDisconnect as e:
-        logging.error(f"WebSocket Error: {e}, {uid}")
-        traceback.print_exc()
-    finally:
-        print(f"User disconnected: {uid}")
-        queue_value = user_queue_map.pop(uid, None)
-        queue = queue_value.get("queue", None)
-        if queue:
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-
-
-@app.get("/queue_size")
-async def get_queue_size():
-    queue_size = len(user_queue_map)
-    return JSONResponse({"queue_size": queue_size})
-
-
-@app.get("/stream/{user_id}")
-async def stream(user_id: uuid.UUID):
-    uid = str(user_id)
-    try:
-        user_queue = user_queue_map[uid]
-        queue = user_queue["queue"]
-
-        async def generate():
-            while True:
-                params = await queue.get()
-                if params is None:
-                    continue
-
-                image = predict(params)
-                if image is None:
-                    continue
-                frame_data = io.BytesIO()
-                image.save(frame_data, format="JPEG")
-                frame_data = frame_data.getvalue()
-                if frame_data is not None and len(frame_data) > 0:
-                    yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"
-
-                await asyncio.sleep(1.0 / 120.0)
-
-        return StreamingResponse(
-            generate(), media_type="multipart/x-mixed-replace;boundary=frame"
-        )
-    except Exception as e:
-        logging.error(f"Streaming Error: {e}, {user_queue_map}")
-        traceback.print_exc()
-        return HTTPException(status_code=404, detail="User not found")
-
-
-async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
-    uid = str(user_id)
-    user_queue = user_queue_map[uid]
-    queue = user_queue["queue"]
-    if not queue:
-        return HTTPException(status_code=404, detail="User not found")
-    last_time = time.time()
-    try:
-        while True:
-            params = await websocket.receive_json()
-            params = InputParams(**params)
-            while not queue.empty():
-                try:
-                    queue.get_nowait()
-                except asyncio.QueueEmpty:
-                    continue
-            await queue.put(params)
-            if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
-                await websocket.send_json(
-                    {
-                        "status": "timeout",
-                        "message": "Your session has ended",
-                        "userId": uid,
-                    }
-                )
-                await websocket.close()
-                return
-
-    except Exception as e:
-        logging.error(f"Error: {e}")
-        traceback.print_exc()
-
-
-@app.get("/", response_class=HTMLResponse)
-async def root():
-    return FileResponse("./static/txt2img.html")
diff --git a/spaces/lewisliuX123/wechatgpt3/docker/build.debian.sh b/spaces/lewisliuX123/wechatgpt3/docker/build.debian.sh
deleted file mode 100644
index a5285f39813426a2d63eb01982229b23ec09dba2..0000000000000000000000000000000000000000
--- a/spaces/lewisliuX123/wechatgpt3/docker/build.debian.sh
+++ /dev/null
@@ -1,9 +0,0 @@
-#!/bin/bash
-
-CHATGPT_ON_WECHAT_TAG=1.0.2
-
-docker build -f Dockerfile.debian \
-             --build-arg CHATGPT_ON_WECHAT_VER=$CHATGPT_ON_WECHAT_TAG \
-             -t zhayujie/chatgpt-on-wechat .
-
-docker tag zhayujie/chatgpt-on-wechat zhayujie/chatgpt-on-wechat:$CHATGPT_ON_WECHAT_TAG-debian
\ No newline at end of file
diff --git a/spaces/lewispons/GrammarGuru/src/models/tdidf.py b/spaces/lewispons/GrammarGuru/src/models/tdidf.py
deleted file mode 100644
index 82eeb12f1785d1b1538f19e01f4d5c61a5bc173e..0000000000000000000000000000000000000000
--- a/spaces/lewispons/GrammarGuru/src/models/tdidf.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import pandas as pd
-from sklearn.pipeline import Pipeline
-from sklearn.feature_selection import ColumnSelector
-from sklearn.feature_extraction.text import TfidfVectorizer
-
-from utils.utilities import *
-import sys
-from pprint import pprint
-
-CONFIG_FILE_PATH = "/Users/luis.morales/Desktop/arxiv-paper-recommender/models/configs.yaml"
-config = read_yaml_config(CONFIG_FILE_PATH)
-pprint(config)
-
-@execution_time
-def train_tfidf():
-    df = pd.read_parquet("/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/arxiv_papers.parquet.gzip") \
-        .sample(500000) \
-        .reset_index(drop=True)
-        
-        
-    vectorizer = TfidfVectorizer(**config["models"]["tfidf"]["tfidf_deffault"])
-    pprint(config["models"]["tfidf"]["tfidf_deffault"])
-    sys.exit()
-    
-    vectors = vectorizer.fit_transform(df['cleaned_abstracts'])
-
-    tfidf_df = pd.DataFrame(vectors.toarray(), columns=[i for i in vectorizer.get_feature_names_out()])
-
-
-    tfidf_df.to_parquet("/Users/luis.morales/Desktop/arxiv-paper-recommender/data/processed/reduced_arxiv_tfidf.parquet.gzip")
-    
-train_tfidf()
-
diff --git a/spaces/lewiswu1209/MockingBird/encoder/data_objects/utterance.py b/spaces/lewiswu1209/MockingBird/encoder/data_objects/utterance.py
deleted file mode 100644
index 0768c3420f422a7464f305b4c1fb6752c57ceda7..0000000000000000000000000000000000000000
--- a/spaces/lewiswu1209/MockingBird/encoder/data_objects/utterance.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import numpy as np
-
-
-class Utterance:
-    def __init__(self, frames_fpath, wave_fpath):
-        self.frames_fpath = frames_fpath
-        self.wave_fpath = wave_fpath
-        
-    def get_frames(self):
-        return np.load(self.frames_fpath)
-
-    def random_partial(self, n_frames):
-        """
-        Crops the frames into a partial utterance of n_frames
-        
-        :param n_frames: The number of frames of the partial utterance
-        :return: the partial utterance frames and a tuple indicating the start and end of the 
-        partial utterance in the complete utterance.
-        """
-        frames = self.get_frames()
-        if frames.shape[0] == n_frames:
-            start = 0
-        else:
-            start = np.random.randint(0, frames.shape[0] - n_frames)
-        end = start + n_frames
-        return frames[start:end], (start, end)
\ No newline at end of file
diff --git a/spaces/lhkhiem28/A-recognition-system/source/api.py b/spaces/lhkhiem28/A-recognition-system/source/api.py
deleted file mode 100644
index cb0db7faea0a861339e2b304ebeeb1c6e933bf2b..0000000000000000000000000000000000000000
--- a/spaces/lhkhiem28/A-recognition-system/source/api.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os, sys
-from libs import *
-
-class NER():
-    def __init__(self, 
-        ckp_dir, 
-    ):
-        self.model = transformers.pipeline("token-classification", 
-            ckp_dir, aggregation_strategy = "simple", 
-        )
-
-    def ner_predict(self, 
-        sentence, 
-    ):
-        sentence = vitools.normalize_diacritics(sentence)
-        sentence = underthesea.word_tokenize(sentence, format = "text")
-        output = {
-            "PATIENT_ID":[], 
-            "NAME":[], 
-            "AGE":[], 
-            "GENDER":[], 
-            "JOB":[], 
-            "LOCATION":[], 
-            "ORGANIZATION":[], 
-            "SYMPTOM_AND_DISEASE":[], 
-            "TRANSPORTATION":[], 
-            "DATE":[], 
-        }
-        pred = self.model(sentence)
-        for entity in pred:
-            if entity["entity_group"] in output:
-                output[entity["entity_group"]].append(entity["word"])
-
-        for entity_group, entities in output.items():
-            fixed_entities = []
-            i = 0
-            while i < len(entities):
-                if entities[i].endswith("@@"):
-                    fixed_entity = entities[i][:-2] + entities[i + 1]
-                    fixed_entities.append(fixed_entity.replace("_", " "))
-                    i += 2
-                else:
-                    fixed_entity = entities[i]
-                    fixed_entities.append(fixed_entity.replace("_", " "))
-                    i += 1
-            output[entity_group] = fixed_entities
-
-        return output
\ No newline at end of file
diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (Race 3 3 Full REPACK Movie Download In Hd 720p).md b/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (Race 3 3 Full REPACK Movie Download In Hd 720p).md
deleted file mode 100644
index 3eba2c017c6278068703b52570ea1b2c17d70368..0000000000000000000000000000000000000000
--- a/spaces/lincquiQcaudo/Top-20-Diffusion/HD Online Player (Race 3 3 Full REPACK Movie Download In Hd 720p).md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>HD Online Player (Race 3 3 full movie download in hd 720p)</h2><br /><p><b><b>Download File</b> &#10026;&#10026;&#10026; <a href="https://bytlly.com/2uGwR1">https://bytlly.com/2uGwR1</a></b></p><br /><br />
-
-Disclaimer:- Katmoviehd. com - Playing Dead – Hobbit man-child and his wiener dog Daily visitors: 534. ... 1 | H264 | MP4 | 720p | DVD | Bluray. in, Skymovies hd. ... 15-06-2018 · Race 3, 2018 Full Movie, Watch Online or Download HD Quality ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/lllqqq/so-vits-svc-models-pcr/app.py b/spaces/lllqqq/so-vits-svc-models-pcr/app.py
deleted file mode 100644
index 0120cc47772d25900ee7a427538a957564357a54..0000000000000000000000000000000000000000
--- a/spaces/lllqqq/so-vits-svc-models-pcr/app.py
+++ /dev/null
@@ -1,375 +0,0 @@
-# -*- coding: utf-8 -*-
-import traceback
-import torch
-from scipy.io import wavfile
-import edge_tts
-import subprocess
-import gradio as gr
-import gradio.processing_utils as gr_pu
-import io
-import os
-import logging
-import time
-from pathlib import Path
-import re
-import json
-import argparse
-
-import librosa
-import matplotlib.pyplot as plt
-import numpy as np
-import soundfile
-
-from inference import infer_tool
-from inference import slicer
-from inference.infer_tool import Svc
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('multipart').setLevel(logging.WARNING)
-
-model = None
-spk = None
-debug = False
-
-
-class HParams():
-    def __init__(self, **kwargs):
-        for k, v in kwargs.items():
-            if type(v) == dict:
-                v = HParams(**v)
-            self[k] = v
-
-    def keys(self):
-        return self.__dict__.keys()
-
-    def items(self):
-        return self.__dict__.items()
-
-    def values(self):
-        return self.__dict__.values()
-
-    def __len__(self):
-        return len(self.__dict__)
-
-    def __getitem__(self, key):
-        return getattr(self, key)
-
-    def __setitem__(self, key, value):
-        return setattr(self, key, value)
-
-    def __contains__(self, key):
-        return key in self.__dict__
-
-    def __repr__(self):
-        return self.__dict__.__repr__()
-
-
-def get_hparams_from_file(config_path):
-    with open(config_path, "r", encoding="utf-8") as f:
-        data = f.read()
-    config = json.loads(data)
-
-    hparams = HParams(**config)
-    return hparams
-
-
-def vc_fn(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold):
-    try:
-        if input_audio is None:
-            raise gr.Error("你需要上传音频")
-        if model is None:
-            raise gr.Error("你需要指定模型")
-        sampling_rate, audio = input_audio
-        # print(audio.shape,sampling_rate)
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        temp_path = "temp.wav"
-        soundfile.write(temp_path, audio, sampling_rate, format="wav")
-        _audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,
-                                       pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold)
-        model.clear_empty()
-        os.remove(temp_path)
-        # 构建保存文件的路径，并保存到results文件夹内
-        try:
-            timestamp = str(int(time.time()))
-            filename = sid + "_" + timestamp + ".wav"
-            # output_file = os.path.join("./results", filename)
-            # soundfile.write(output_file, _audio, model.target_sample, format="wav")
-            soundfile.write('/tmp/'+filename, _audio,
-                            model.target_sample, format="wav")
-            # return f"推理成功，音频文件保存为results/{filename}", (model.target_sample, _audio)
-            return f"推理成功，音频文件保存为{filename}", (model.target_sample, _audio)
-        except Exception as e:
-            if debug:
-                traceback.print_exc()
-            return f"文件保存失败，请手动保存", (model.target_sample, _audio)
-    except Exception as e:
-        if debug:
-            traceback.print_exc()
-        raise gr.Error(e)
-
-
-def tts_func(_text, _rate, _voice):
-    # 使用edge-tts把文字转成音频
-    # voice = "zh-CN-XiaoyiNeural"#女性，较高音
-    # voice = "zh-CN-YunxiNeural"#男性
-    voice = "zh-CN-YunxiNeural"  # 男性
-    if (_voice == "女"):
-        voice = "zh-CN-XiaoyiNeural"
-    output_file = "/tmp/"+_text[0:10]+".wav"
-    # communicate = edge_tts.Communicate(_text, voice)
-    # await communicate.save(output_file)
-    if _rate >= 0:
-        ratestr = "+{:.0%}".format(_rate)
-    elif _rate < 0:
-        ratestr = "{:.0%}".format(_rate)  # 减号自带
-
-    p = subprocess.Popen("edge-tts " +
-                         " --text "+_text +
-                         " --write-media "+output_file +
-                         " --voice "+voice +
-                         " --rate="+ratestr, shell=True,
-                         stdout=subprocess.PIPE,
-                         stdin=subprocess.PIPE)
-    p.wait()
-    return output_file
-
-
-def text_clear(text):
-    return re.sub(r"[\n\,\(\) ]", "", text)
-
-
-def vc_fn2(sid, input_audio, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold):
-    # 使用edge-tts把文字转成音频
-    text2tts = text_clear(text2tts)
-    output_file = tts_func(text2tts, tts_rate, tts_voice)
-
-    # 调整采样率
-    sr2 = 44100
-    wav, sr = librosa.load(output_file)
-    wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2)
-    save_path2 = text2tts[0:10]+"_44k"+".wav"
-    wavfile.write(save_path2, sr2,
-                  (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-                  )
-
-    # 读取音频
-    sample_rate, data = gr_pu.audio_from_file(save_path2)
-    vc_input = (sample_rate, data)
-
-    a, b = vc_fn(sid, vc_input, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale,
-                 pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold)
-    os.remove(output_file)
-    os.remove(save_path2)
-    return a, b
-
-
-models_info = [
-    {
-        "description": """
-                       这个模型包含公主连结的161名角色。\n\n
-                       Space采用CPU推理，速度极慢，建议下载模型本地GPU推理。\n\n
-                       """,
-        "model_path": "./G_228800.pth",
-        "config_path": "./config.json",
-    }
-]
-
-model_inferall = []
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--share", action="store_true",
-                        default=False, help="share gradio app")
-    # 一定要设置的部分
-    parser.add_argument('-cl', '--clip', type=float,
-                        default=0, help='音频强制切片，默认0为自动切片，单位为秒/s')
-    parser.add_argument('-n', '--clean_names', type=str, nargs='+',
-                        default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下')
-    parser.add_argument('-t', '--trans', type=int, nargs='+',
-                        default=[0], help='音高调整，支持正负（半音）')
-    parser.add_argument('-s', '--spk_list', type=str,
-                        nargs='+', default=['nen'], help='合成目标说话人名称')
-
-    # 可选项部分
-    parser.add_argument('-a', '--auto_predict_f0', action='store_true',
-                        default=False, help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调')
-    parser.add_argument('-cm', '--cluster_model_path', type=str,
-                        default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填')
-    parser.add_argument('-cr', '--cluster_infer_ratio', type=float,
-                        default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则默认0即可')
-    parser.add_argument('-lg', '--linear_gradient', type=float, default=0,
-                        help='两段音频切片的交叉淡入长度，如果强制切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，单位为秒')
-    parser.add_argument('-f0p', '--f0_predictor', type=str, default="pm",
-                        help='选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)')
-    parser.add_argument('-eh', '--enhance', action='store_true', default=False,
-                        help='是否使用NSF_HIFIGAN增强器,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭')
-    parser.add_argument('-shd', '--shallow_diffusion', action='store_true',
-                        default=False, help='是否使用浅层扩散，使用后可解决一部分电音问题，默认关闭，该选项打开时，NSF_HIFIGAN增强器将会被禁止')
-
-    # 浅扩散设置
-    parser.add_argument('-dm', '--diffusion_model_path', type=str,
-                        default="logs/44k/diffusion/model_0.pt", help='扩散模型路径')
-    parser.add_argument('-dc', '--diffusion_config_path', type=str,
-                        default="logs/44k/diffusion/config.yaml", help='扩散模型配置文件路径')
-    parser.add_argument('-ks', '--k_step', type=int,
-                        default=100, help='扩散步数，越大越接近扩散模型的结果，默认100')
-    parser.add_argument('-od', '--only_diffusion', action='store_true',
-                        default=False, help='纯扩散模式，该模式不会加载sovits模型，以扩散模型推理')
-
-    # 不用动的部分
-    parser.add_argument('-sd', '--slice_db', type=int,
-                        default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50')
-    parser.add_argument('-d', '--device', type=str,
-                        default=None, help='推理设备，None则为自动选择cpu和gpu')
-    parser.add_argument('-ns', '--noice_scale', type=float,
-                        default=0.4, help='噪音级别，会影响咬字和音质，较为玄学')
-    parser.add_argument('-p', '--pad_seconds', type=float, default=0.5,
-                        help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现')
-    parser.add_argument('-wf', '--wav_format', type=str,
-                        default='flac', help='音频输出格式')
-    parser.add_argument('-lgr', '--linear_gradient_retain', type=float,
-                        default=0.75, help='自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭')
-    parser.add_argument('-eak', '--enhancer_adaptive_key',
-                        type=int, default=0, help='使增强器适应更高的音域(单位为半音数)|默认为0')
-    parser.add_argument('-ft', '--f0_filter_threshold', type=float, default=0.05,
-                        help='F0过滤阈值，只有使用crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音')
-    args = parser.parse_args()
-    categories = ["Princess Connect! Re:Dive"]
-    others = {
-        "PCR vits-fast-fineturning": "https://huggingface.co/spaces/FrankZxShen/vits-fast-finetuning-pcr",
-    }
-    for info in models_info:
-        config_path = info['config_path']
-        model_path = info['model_path']
-        description = info['description']
-        clean_names = args.clean_names
-        trans = args.trans
-        spk_list = list(get_hparams_from_file(config_path).spk.keys())
-        slice_db = args.slice_db
-        wav_format = args.wav_format
-        auto_predict_f0 = args.auto_predict_f0
-        cluster_infer_ratio = args.cluster_infer_ratio
-        noice_scale = args.noice_scale
-        pad_seconds = args.pad_seconds
-        clip = args.clip
-        lg = args.linear_gradient
-        lgr = args.linear_gradient_retain
-        f0p = args.f0_predictor
-        enhance = args.enhance
-        enhancer_adaptive_key = args.enhancer_adaptive_key
-        cr_threshold = args.f0_filter_threshold
-        diffusion_model_path = args.diffusion_model_path
-        diffusion_config_path = args.diffusion_config_path
-        k_step = args.k_step
-        only_diffusion = args.only_diffusion
-        shallow_diffusion = args.shallow_diffusion
-
-        model = Svc(model_path, config_path, args.device, args.cluster_model_path, enhance,
-                    diffusion_model_path, diffusion_config_path, shallow_diffusion, only_diffusion)
-
-        model_inferall.append((description, spk_list, model))
-
-    app = gr.Blocks()
-    with app:
-        gr.Markdown(
-            "# <center> so-vits-svc-models-pcr\n"
-            "# <center> Pay attention！！！ Space uses CPU inferencing, which is extremely slow. It is recommended to download models.\n"
-            "# <center> 注意！！！Space采用CPU推理，速度极慢，建议下载模型使用本地GPU推理。\n"
-            "## <center> Please do not generate content that could infringe upon the rights or cause harm to individuals or organizations.\n"
-            "## <center> 请不要生成会对个人以及组织造成侵害的内容\n\n"
-        )
-        gr.Markdown("# Princess Connect! Re:Dive\n\n"
-                    )
-        with gr.Tabs():
-            for category in categories:
-                with gr.TabItem(category):
-                    for i, (description, speakers, model) in enumerate(
-                            model_inferall):
-                        gr.Markdown(description)
-                        with gr.Row():
-                            with gr.Column():
-                                # textbox = gr.TextArea(label="Text",
-                                #                         placeholder="Type your sentence here ",
-                                #                         value="新たなキャラを解放できるようになったようですね。", elem_id=f"tts-input")
-
-                                gr.Markdown(value="""
-                                    <font size=2> 推理设置</font>
-                                    """)
-                                sid = gr.Dropdown(
-                                    choices=speakers, value=speakers[0], label='角色选择')
-                                auto_f0 = gr.Checkbox(
-                                    label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）", value=False)
-                                f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)", choices=[
-                                                           "pm", "dio", "harvest", "crepe"], value="pm")
-                                vc_transform = gr.Number(
-                                    label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
-                                cluster_ratio = gr.Number(
-                                    label="聚类模型混合比例，0-1之间，0即不启用聚类。使用聚类模型能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
-                                slice_db = gr.Number(label="切片阈值", value=-40)
-                                noise_scale = gr.Number(
-                                    label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
-                            with gr.Column():
-                                pad_seconds = gr.Number(
-                                    label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
-                                cl_num = gr.Number(
-                                    label="音频自动切片，0为不切片，单位为秒(s)", value=0)
-                                lg_num = gr.Number(
-                                    label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0)
-                                lgr_num = gr.Number(
-                                    label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75)
-                                enhancer_adaptive_key = gr.Number(
-                                    label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0)
-                                cr_threshold = gr.Number(
-                                    label="F0过滤阈值，只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05)
-                        with gr.Tabs():
-                            with gr.TabItem("音频转音频"):
-                                vc_input3 = gr.Audio(label="选择音频")
-                                vc_submit = gr.Button(
-                                    "音频转换", variant="primary")
-                            with gr.TabItem("文字转音频"):
-                                text2tts = gr.Textbox(
-                                    label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪")
-                                tts_rate = gr.Number(label="tts语速", value=0)
-                                tts_voice = gr.Radio(label="性别", choices=[
-                                                     "男", "女"], value="男")
-                                vc_submit2 = gr.Button(
-                                    "文字转换", variant="primary")
-                        with gr.Row():
-                            with gr.Column():
-                                vc_output1 = gr.Textbox(label="Output Message")
-                            with gr.Column():
-                                vc_output2 = gr.Audio(
-                                    label="Output Audio", interactive=False)
-
-                vc_submit.click(vc_fn, [sid, vc_input3, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds,
-                                        cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold], [vc_output1, vc_output2])
-                vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num,
-                                          lg_num, lgr_num, text2tts, tts_rate, tts_voice, f0_predictor, enhancer_adaptive_key, cr_threshold], [vc_output1, vc_output2])
-                # gr.Examples(
-                #     examples=example,
-                #     inputs=[textbox, char_dropdown, language_dropdown,
-                #             duration_slider, symbol_input],
-                #     outputs=[text_output, audio_output],
-                #     fn=tts_fn
-                # )
-            for category, link in others.items():
-                with gr.TabItem(category):
-                    gr.Markdown(
-                        f'''
-                        <center>
-                          <h2>Click to Go</h2>
-                          <a href="{link}">
-                            <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-xl-dark.svg"
-                          </a>
-                        </center>
-                        '''
-                    )
-
-    app.queue(concurrency_count=3).launch(show_api=False, share=args.share)
diff --git a/spaces/logasja/Fawkes/README.md b/spaces/logasja/Fawkes/README.md
deleted file mode 100644
index 785b3ef890246ecc8687ed97193b8e07d27d4c0c..0000000000000000000000000000000000000000
--- a/spaces/logasja/Fawkes/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Fawkes
-emoji: 🌖
-colorFrom: red
-colorTo: purple
-sdk: gradio
-sdk_version: 3.0.20
-app_file: app.py
-pinned: false
-license: gpl-3.0
-python_version: 3.8.13
----
\ No newline at end of file
diff --git a/spaces/lunarflu/HF-QA-Demo-3/data/stackoverflow_python_dataset.py b/spaces/lunarflu/HF-QA-Demo-3/data/stackoverflow_python_dataset.py
deleted file mode 100644
index f29c8f1a4282f067bbd5c5a48db66e4d999d19f3..0000000000000000000000000000000000000000
--- a/spaces/lunarflu/HF-QA-Demo-3/data/stackoverflow_python_dataset.py
+++ /dev/null
@@ -1,55 +0,0 @@
-from datetime import datetime
-from datasets import load_dataset
-from bs4 import BeautifulSoup
-
-
-def preprocess_dataset():
-    """
-    Preprocesses the 'koutch/stackoverflow_python' dataset.
-
-    Returns:
-        datasets.arrow_dataset.Dataset: The preprocessed dataset.
-    """
-    dataset = load_dataset('koutch/stackoverflow_python', split='train')
-    dataset = dataset.filter(
-        lambda example:
-            example['question_score'] > 100 and
-            example['answer_score'] > 5 and
-            datetime.strptime(example['answer_date'], '%Y-%m-%dT%H:%M:%SZ').year > 2010
-        )
-
-    def html2text(example):
-        soup = BeautifulSoup(example, 'html.parser')
-        return ''.join(soup.findAll(string=True))
-
-    def transforms(example):
-        example['answer'] = html2text(example['answer_body'])
-        example['question'] = html2text(example['question_body'])
-        return example
-
-    dataset = dataset.map(lambda example: transforms(example))
-    dataset = dataset.remove_columns([
-        'question_score', 'question_date', 'question_id',
-        'answer_date', 'answer_id', 'answer_score', 'tags',
-        'question_body', 'answer_body'
-    ])
-    return dataset
-
-
-def show_info(dataset):
-    """
-    Print information about the dataset.
-
-    Args:
-        dataset (datasets.arrow_dataset.Dataset): The dataset.
-    """
-    print(dataset.info, '\n')
-    print(f'dataset len: {len(dataset)}')
-    print(f"example question: {dataset[0]['question']}")
-    print(f"example answer: {dataset[0]['answer']}")
-
-
-if __name__ == '__main__':
-    dataset = preprocess_dataset()
-    dataset.push_to_hub('KonradSzafer/stackoverflow_python_preprocessed', private=False)
-    show_info(dataset)
diff --git a/spaces/ma-xu/LIVE/pybind11/.github/ISSUE_TEMPLATE/question.md b/spaces/ma-xu/LIVE/pybind11/.github/ISSUE_TEMPLATE/question.md
deleted file mode 100644
index b199b6ee8ad446994aed54f67b0d1c22049d53c1..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/pybind11/.github/ISSUE_TEMPLATE/question.md
+++ /dev/null
@@ -1,21 +0,0 @@
----
-name: Question
-about: File an issue about unexplained behavior
-title: "[QUESTION] "
----
-
-If you have a question, please check the following first:
-
-1. Check if your question has already been answered in the [FAQ][] section.
-2. Make sure you've read the [documentation][]. Your issue may be addressed there.
-3. If those resources didn't help and you only have a short question (not a bug report), consider asking in the [Gitter chat room][]
-4. Search the [issue tracker][], including the closed issues, to see if your question has already been asked/answered. +1 or comment if it has been asked but has no answer.
-5. If you have a more complex question which is not answered in the previous items (or not suitable for chat), please fill in the details below.
-6. Include a self-contained and minimal piece of code that illustrates your question. If that's not possible, try to make the description as clear as possible.
-
-[FAQ]: http://pybind11.readthedocs.io/en/latest/faq.html
-[documentation]: https://pybind11.readthedocs.io
-[issue tracker]: https://github.com/pybind/pybind11/issues
-[Gitter chat room]: https://gitter.im/pybind/Lobby
-
-*After reading, remove this checklist.*
diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/execute_with_dependencies.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/execute_with_dependencies.h
deleted file mode 100644
index cb92b1ba2b372d8cba9be817aee2e2db48160dc0..0000000000000000000000000000000000000000
--- a/spaces/ma-xu/LIVE/thrust/thrust/detail/execute_with_dependencies.h
+++ /dev/null
@@ -1,267 +0,0 @@
-/*
- *  Copyright 2018 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>
-#include <thrust/detail/cpp11_required.h>
-
-#if THRUST_CPP_DIALECT >= 2011
-
-#include <thrust/detail/type_deduction.h>
-#include <thrust/type_traits/remove_cvref.h>
-
-#include <tuple>
-#include <type_traits>
-
-namespace thrust
-{
-namespace detail
-{
-
-struct capture_as_dependency_fn
-{
-  template<typename Dependency>
-  auto operator()(Dependency&& dependency) const
-  THRUST_DECLTYPE_RETURNS(capture_as_dependency(THRUST_FWD(dependency)))
-};
-
-// Default implementation: universal forwarding.
-template<typename Dependency>
-auto capture_as_dependency(Dependency&& dependency)
-THRUST_DECLTYPE_RETURNS(THRUST_FWD(dependency))
-
-template<typename... Dependencies>
-auto capture_as_dependency(std::tuple<Dependencies...>& dependencies)
-THRUST_DECLTYPE_RETURNS(
-  tuple_for_each(THRUST_FWD(dependencies), capture_as_dependency_fn{})
-)
-
-template<template<typename> class BaseSystem, typename... Dependencies>
-struct execute_with_dependencies
-    : BaseSystem<execute_with_dependencies<BaseSystem, Dependencies...>>
-{
-private:
-    using super_t = BaseSystem<execute_with_dependencies<BaseSystem, Dependencies...>>;
-
-    std::tuple<remove_cvref_t<Dependencies>...> dependencies;
-
-public:
-    __host__
-    execute_with_dependencies(super_t const &super, Dependencies && ...dependencies)
-        : super_t(super), dependencies(std::forward<Dependencies>(dependencies)...)
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_dependencies(super_t const &super, UDependencies && ...deps)
-        : super_t(super), dependencies(THRUST_FWD(deps)...)
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_dependencies(UDependencies && ...deps)
-        : dependencies(THRUST_FWD(deps)...)
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_dependencies(super_t const &super, std::tuple<UDependencies...>&& deps)
-        : super_t(super), dependencies(std::move(deps))
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_dependencies(std::tuple<UDependencies...>&& deps)
-        : dependencies(std::move(deps))
-    {
-    }
-
-    std::tuple<remove_cvref_t<Dependencies>...>
-    __host__
-    extract_dependencies() 
-    {
-        return std::move(dependencies);
-    }
-
-    // Rebinding.
-    template<typename ...UDependencies>
-    __host__
-    execute_with_dependencies<BaseSystem, UDependencies...>
-    rebind_after(UDependencies&& ...udependencies) const
-    {
-        return { capture_as_dependency(THRUST_FWD(udependencies))... };
-    }
-
-    // Rebinding.
-    template<typename ...UDependencies>
-    __host__
-    execute_with_dependencies<BaseSystem, UDependencies...>
-    rebind_after(std::tuple<UDependencies...>& udependencies) const
-    {
-        return { capture_as_dependency(udependencies) };
-    }
-    template<typename ...UDependencies>
-    __host__
-    execute_with_dependencies<BaseSystem, UDependencies...>
-    rebind_after(std::tuple<UDependencies...>&& udependencies) const
-    {
-        return { capture_as_dependency(std::move(udependencies)) };
-    }
-};
-
-template<
-    typename Allocator,
-    template<typename> class BaseSystem,
-    typename... Dependencies
->
-struct execute_with_allocator_and_dependencies
-    : BaseSystem<
-        execute_with_allocator_and_dependencies<
-            Allocator,
-            BaseSystem,
-            Dependencies...
-        >
-    >
-{
-private:
-    using super_t = BaseSystem<
-        execute_with_allocator_and_dependencies<
-            Allocator,
-            BaseSystem,
-            Dependencies...
-        >
-    >;
-
-    std::tuple<remove_cvref_t<Dependencies>...> dependencies;
-    Allocator alloc;
-
-public:
-    template <typename... UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies(super_t const &super, Allocator a, UDependencies && ...deps)
-        : super_t(super), dependencies(THRUST_FWD(deps)...), alloc(a)
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies(Allocator a, UDependencies && ...deps)
-        : dependencies(THRUST_FWD(deps)...), alloc(a)
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies(super_t const &super, Allocator a, std::tuple<UDependencies...>&& deps)
-        : super_t(super), dependencies(std::move(deps)), alloc(a)
-    {
-    }
-
-    template <typename... UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies(Allocator a, std::tuple<UDependencies...>&& deps)
-        : dependencies(std::move(deps)), alloc(a)
-    {
-    }
-
-    std::tuple<remove_cvref_t<Dependencies>...>
-    __host__
-    extract_dependencies() 
-    {
-        return std::move(dependencies);
-    }
-
-    __host__
-    typename std::add_lvalue_reference<Allocator>::type
-    get_allocator()
-    {
-        return alloc;
-    }
-
-    // Rebinding.
-    template<typename ...UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies<Allocator, BaseSystem, UDependencies...>
-    rebind_after(UDependencies&& ...udependencies) const
-    {
-        return { alloc, capture_as_dependency(THRUST_FWD(udependencies))... };
-    }
-
-    // Rebinding.
-    template<typename ...UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies<Allocator, BaseSystem, UDependencies...>
-    rebind_after(std::tuple<UDependencies...>& udependencies) const
-    {
-        return { alloc, capture_as_dependency(udependencies) };
-    }
-    template<typename ...UDependencies>
-    __host__
-    execute_with_allocator_and_dependencies<Allocator, BaseSystem, UDependencies...>
-    rebind_after(std::tuple<UDependencies...>&& udependencies) const
-    {
-        return { alloc, capture_as_dependency(std::move(udependencies)) };
-    }
-};
-
-template<template<typename> class BaseSystem, typename ...Dependencies>
-__host__
-std::tuple<remove_cvref_t<Dependencies>...>
-extract_dependencies(thrust::detail::execute_with_dependencies<BaseSystem, Dependencies...>&& system)
-{
-    return std::move(system).extract_dependencies();
-}
-template<template<typename> class BaseSystem, typename ...Dependencies>
-__host__
-std::tuple<remove_cvref_t<Dependencies>...>
-extract_dependencies(thrust::detail::execute_with_dependencies<BaseSystem, Dependencies...>& system)
-{
-    return std::move(system).extract_dependencies();
-}
-
-template<typename Allocator, template<typename> class BaseSystem, typename ...Dependencies>
-__host__
-std::tuple<remove_cvref_t<Dependencies>...>
-extract_dependencies(thrust::detail::execute_with_allocator_and_dependencies<Allocator, BaseSystem, Dependencies...>&& system)
-{
-    return std::move(system).extract_dependencies();
-}
-template<typename Allocator, template<typename> class BaseSystem, typename ...Dependencies>
-__host__
-std::tuple<remove_cvref_t<Dependencies>...>
-extract_dependencies(thrust::detail::execute_with_allocator_and_dependencies<Allocator, BaseSystem, Dependencies...>& system)
-{
-    return std::move(system).extract_dependencies();
-}
-
-template<typename System>
-__host__
-std::tuple<>
-extract_dependencies(System &&)
-{
-    return std::tuple<>{};
-}
-
-} // end detail
-} // end thrust
-
-#endif // THRUST_CPP_DIALECT >= 2011
-
diff --git a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/transforms.py b/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/transforms.py
deleted file mode 100644
index d9bbb5fb7daef5edfb425fafb4d67d471b3001e6..0000000000000000000000000000000000000000
--- a/spaces/manavisrani07/gradio-lipsync-wav2lip/basicsr/data/transforms.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import cv2
-import random
-import torch
-
-
-def mod_crop(img, scale):
-    """Mod crop images, used during testing.
-
-    Args:
-        img (ndarray): Input image.
-        scale (int): Scale factor.
-
-    Returns:
-        ndarray: Result image.
-    """
-    img = img.copy()
-    if img.ndim in (2, 3):
-        h, w = img.shape[0], img.shape[1]
-        h_remainder, w_remainder = h % scale, w % scale
-        img = img[:h - h_remainder, :w - w_remainder, ...]
-    else:
-        raise ValueError(f'Wrong img ndim: {img.ndim}.')
-    return img
-
-
-def paired_random_crop(img_gts, img_lqs, gt_patch_size, scale, gt_path=None):
-    """Paired random crop. Support Numpy array and Tensor inputs.
-
-    It crops lists of lq and gt images with corresponding locations.
-
-    Args:
-        img_gts (list[ndarray] | ndarray | list[Tensor] | Tensor): GT images. Note that all images
-            should have the same shape. If the input is an ndarray, it will
-            be transformed to a list containing itself.
-        img_lqs (list[ndarray] | ndarray): LQ images. Note that all images
-            should have the same shape. If the input is an ndarray, it will
-            be transformed to a list containing itself.
-        gt_patch_size (int): GT patch size.
-        scale (int): Scale factor.
-        gt_path (str): Path to ground-truth. Default: None.
-
-    Returns:
-        list[ndarray] | ndarray: GT images and LQ images. If returned results
-            only have one element, just return ndarray.
-    """
-
-    if not isinstance(img_gts, list):
-        img_gts = [img_gts]
-    if not isinstance(img_lqs, list):
-        img_lqs = [img_lqs]
-
-    # determine input type: Numpy array or Tensor
-    input_type = 'Tensor' if torch.is_tensor(img_gts[0]) else 'Numpy'
-
-    if input_type == 'Tensor':
-        h_lq, w_lq = img_lqs[0].size()[-2:]
-        h_gt, w_gt = img_gts[0].size()[-2:]
-    else:
-        h_lq, w_lq = img_lqs[0].shape[0:2]
-        h_gt, w_gt = img_gts[0].shape[0:2]
-    lq_patch_size = gt_patch_size // scale
-
-    if h_gt != h_lq * scale or w_gt != w_lq * scale:
-        raise ValueError(f'Scale mismatches. GT ({h_gt}, {w_gt}) is not {scale}x ',
-                         f'multiplication of LQ ({h_lq}, {w_lq}).')
-    if h_lq < lq_patch_size or w_lq < lq_patch_size:
-        raise ValueError(f'LQ ({h_lq}, {w_lq}) is smaller than patch size '
-                         f'({lq_patch_size}, {lq_patch_size}). '
-                         f'Please remove {gt_path}.')
-
-    # randomly choose top and left coordinates for lq patch
-    top = random.randint(0, h_lq - lq_patch_size)
-    left = random.randint(0, w_lq - lq_patch_size)
-
-    # crop lq patch
-    if input_type == 'Tensor':
-        img_lqs = [v[:, :, top:top + lq_patch_size, left:left + lq_patch_size] for v in img_lqs]
-    else:
-        img_lqs = [v[top:top + lq_patch_size, left:left + lq_patch_size, ...] for v in img_lqs]
-
-    # crop corresponding gt patch
-    top_gt, left_gt = int(top * scale), int(left * scale)
-    if input_type == 'Tensor':
-        img_gts = [v[:, :, top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size] for v in img_gts]
-    else:
-        img_gts = [v[top_gt:top_gt + gt_patch_size, left_gt:left_gt + gt_patch_size, ...] for v in img_gts]
-    if len(img_gts) == 1:
-        img_gts = img_gts[0]
-    if len(img_lqs) == 1:
-        img_lqs = img_lqs[0]
-    return img_gts, img_lqs
-
-
-def augment(imgs, hflip=True, rotation=True, flows=None, return_status=False):
-    """Augment: horizontal flips OR rotate (0, 90, 180, 270 degrees).
-
-    We use vertical flip and transpose for rotation implementation.
-    All the images in the list use the same augmentation.
-
-    Args:
-        imgs (list[ndarray] | ndarray): Images to be augmented. If the input
-            is an ndarray, it will be transformed to a list.
-        hflip (bool): Horizontal flip. Default: True.
-        rotation (bool): Ratotation. Default: True.
-        flows (list[ndarray]: Flows to be augmented. If the input is an
-            ndarray, it will be transformed to a list.
-            Dimension is (h, w, 2). Default: None.
-        return_status (bool): Return the status of flip and rotation.
-            Default: False.
-
-    Returns:
-        list[ndarray] | ndarray: Augmented images and flows. If returned
-            results only have one element, just return ndarray.
-
-    """
-    hflip = hflip and random.random() < 0.5
-    vflip = rotation and random.random() < 0.5
-    rot90 = rotation and random.random() < 0.5
-
-    def _augment(img):
-        if hflip:  # horizontal
-            cv2.flip(img, 1, img)
-        if vflip:  # vertical
-            cv2.flip(img, 0, img)
-        if rot90:
-            img = img.transpose(1, 0, 2)
-        return img
-
-    def _augment_flow(flow):
-        if hflip:  # horizontal
-            cv2.flip(flow, 1, flow)
-            flow[:, :, 0] *= -1
-        if vflip:  # vertical
-            cv2.flip(flow, 0, flow)
-            flow[:, :, 1] *= -1
-        if rot90:
-            flow = flow.transpose(1, 0, 2)
-            flow = flow[:, :, [1, 0]]
-        return flow
-
-    if not isinstance(imgs, list):
-        imgs = [imgs]
-    imgs = [_augment(img) for img in imgs]
-    if len(imgs) == 1:
-        imgs = imgs[0]
-
-    if flows is not None:
-        if not isinstance(flows, list):
-            flows = [flows]
-        flows = [_augment_flow(flow) for flow in flows]
-        if len(flows) == 1:
-            flows = flows[0]
-        return imgs, flows
-    else:
-        if return_status:
-            return imgs, (hflip, vflip, rot90)
-        else:
-            return imgs
-
-
-def img_rotate(img, angle, center=None, scale=1.0):
-    """Rotate image.
-
-    Args:
-        img (ndarray): Image to be rotated.
-        angle (float): Rotation angle in degrees. Positive values mean
-            counter-clockwise rotation.
-        center (tuple[int]): Rotation center. If the center is None,
-            initialize it as the center of the image. Default: None.
-        scale (float): Isotropic scale factor. Default: 1.0.
-    """
-    (h, w) = img.shape[:2]
-
-    if center is None:
-        center = (w // 2, h // 2)
-
-    matrix = cv2.getRotationMatrix2D(center, angle, scale)
-    rotated_img = cv2.warpAffine(img, matrix, (w, h))
-    return rotated_img
diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/options/__init__.py b/spaces/manhkhanhUIT/BOPBTL/Global/options/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/util/util.py b/spaces/manhkhanhUIT/BOPBTL/Global/util/util.py
deleted file mode 100644
index b1369c3b568548a1c21d3412aef5fd35c9b0c5be..0000000000000000000000000000000000000000
--- a/spaces/manhkhanhUIT/BOPBTL/Global/util/util.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT License.
-
-from __future__ import print_function
-import torch
-import numpy as np
-from PIL import Image
-import numpy as np
-import os
-import torch.nn as nn
-
-# Converts a Tensor into a Numpy array
-# |imtype|: the desired type of the converted numpy array
-def tensor2im(image_tensor, imtype=np.uint8, normalize=True):
-    if isinstance(image_tensor, list):
-        image_numpy = []
-        for i in range(len(image_tensor)):
-            image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
-        return image_numpy
-    image_numpy = image_tensor.cpu().float().numpy()
-    if normalize:
-        image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
-    else:
-        image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
-    image_numpy = np.clip(image_numpy, 0, 255)
-    if image_numpy.shape[2] == 1 or image_numpy.shape[2] > 3:
-        image_numpy = image_numpy[:, :, 0]
-    return image_numpy.astype(imtype)
-
-
-# Converts a one-hot tensor into a colorful label map
-def tensor2label(label_tensor, n_label, imtype=np.uint8):
-    if n_label == 0:
-        return tensor2im(label_tensor, imtype)
-    label_tensor = label_tensor.cpu().float()
-    if label_tensor.size()[0] > 1:
-        label_tensor = label_tensor.max(0, keepdim=True)[1]
-    label_tensor = Colorize(n_label)(label_tensor)
-    label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
-    return label_numpy.astype(imtype)
-
-
-def save_image(image_numpy, image_path):
-    image_pil = Image.fromarray(image_numpy)
-    image_pil.save(image_path)
-
-
-def mkdirs(paths):
-    if isinstance(paths, list) and not isinstance(paths, str):
-        for path in paths:
-            mkdir(path)
-    else:
-        mkdir(paths)
-
-
-def mkdir(path):
-    if not os.path.exists(path):
-        os.makedirs(path)
diff --git a/spaces/matthoffner/chatbot/components/Promptbar/index.ts b/spaces/matthoffner/chatbot/components/Promptbar/index.ts
deleted file mode 100644
index e3f6b39ba93a88cd450cb5c67025a8f29ee5fcc4..0000000000000000000000000000000000000000
--- a/spaces/matthoffner/chatbot/components/Promptbar/index.ts
+++ /dev/null
@@ -1 +0,0 @@
-export { default } from './Promptbar';
diff --git a/spaces/merle/PROTEIN_GENERATOR/examples/weighted_sequence.sh b/spaces/merle/PROTEIN_GENERATOR/examples/weighted_sequence.sh
deleted file mode 100644
index 779abda1d21255ed8d68198f72ac55729839be26..0000000000000000000000000000000000000000
--- a/spaces/merle/PROTEIN_GENERATOR/examples/weighted_sequence.sh
+++ /dev/null
@@ -1,15 +0,0 @@
-#!/bin/bash
-#SBATCH -J seq_diff
-#SBATCH -p gpu
-#SBATCH --mem=8g
-#SBATCH --gres=gpu:a6000:1
-#SBATCH -o ./out/slurm/slurm_%j.out
-
-source activate /software/conda/envs/SE3nv
-
-srun python ../inference.py \
-    --num_designs 10 \
-    --out out/design \
-    --contigs 100 \
-    --T 25 --save_best_plddt \
-    --aa_composition W0.2 --potential_scale 1.75 
diff --git a/spaces/merve/fill-in-the-blank/public/third_party/params.js b/spaces/merve/fill-in-the-blank/public/third_party/params.js
deleted file mode 100644
index 8b4b8b39bb932ef3d7784445c6e9e5fc04b12841..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/public/third_party/params.js
+++ /dev/null
@@ -1,22 +0,0 @@
-window.makeParams = function(){
-  var url = new URL(window.location)
-  var searchParams = new URLSearchParams(url.search) 
-
-  var rv = {}
-
-  rv.get = key => {
-    return searchParams.get(key)
-  }
-
-  rv.set = (key, value) => {
-    searchParams.set(key, value)
-
-    url.search = searchParams.toString()
-    history.replaceState(null, '', url)
-  }
-
-  return rv
-}
-
-
-if (window.init) init()
\ No newline at end of file
diff --git a/spaces/merve/fill-in-the-blank/source/_posts/2022-01-28-dataset-worldviews.md b/spaces/merve/fill-in-the-blank/source/_posts/2022-01-28-dataset-worldviews.md
deleted file mode 100644
index 67698648fc4d268a46f0b7f91c3c954b8508eb92..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/_posts/2022-01-28-dataset-worldviews.md
+++ /dev/null
@@ -1,194 +0,0 @@
----
-permalink: /dataset-worldviews/
-template: post.html
-
-title: Datasets Have Worldviews
-summary: Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too.
-summaryalt: Every dataset communicates a different perspective. When you shift your perspective, your conclusions can shift, too.
-shareimg: https://pair.withgoogle.com/explorables/images/dataset-worldviews-shareimg.png
-date: 2022-01-28
----
-
-
-<p>Suppose you have a dataset of shapes. They can either be <span class="monospace shaded">shaded</span> or <span class="monospace not-shaded">unshaded</span>. They look something like this:</p>
-
-<div class='show-shapes'></div>
-
-<p> You built a supervised <a href="https://developers.google.com/machine-learning/glossary#machine-learning" target="_blank">machine learning classifier</a> that will automatically classify each shape as <span class="monospace shaded">shaded</span> or <span class="monospace not-shaded">unshaded</span>. You call it the "Is-Shaded Classifier".</p>
-
-<p>Click "Run Classifier" to see how your model performs.</p>
-<br><br>
-<center><div class='default-classifier-button'></div></center>
-<div class='default-classifier'></div>
-<div class='default-classifier-summary'></div>
-
-<p>It’s not perfect— some of the shapes are definitely misclassified. You want to improve your model! </p>
-
-<p> To do so, you want to know more about the <span class="emphasis"> kinds of mistakes your model is making</span>. </p>
-
-<h3 id="classification-thinking">Thinking About Bias</h3>
-
-<p>In training, you only gave your model the raw image of each shape and one <a href="https://developers.google.com/machine-learning/glossary#ground-truth" target="_blank">ground truth label</a>: <span class="monospace shaded">shaded</span> and <span class="monospace not-shaded">unshaded</span>. But maybe something about your model—the distribution of the training data you used, the architecture you chose, or how you set your hyperparameters—resulted in your model performing better on some shapes than others.</p>
-
-<p> In fact, you’ve seen a lot of papers and articles citing issues of <span class="emphasis">biased model performance between circles, triangles, and rectangles</span> in shape data. One paper finds that shape detection algorithms tend to do worse on triangles; another article says color accuracy is an issue with circles. So you wonder: <span class="emphasis">are there biases in <i>your</i> model’s misclassifications?</span> </p>
-
-<center><img src="img/newspapers_01.png" class="newspaper-image" alt="Three abstract drawings of papers or articles with headlines 'Shape detection: biased against triangles?', 'Geometry experts call for more accurate rectangle data, cite fairness concerns', and 'Increasing color accuracy in circles'" srcset="img/newspapers_01.svg"></center>
-
-<p> You want to make sure that your model is performing equally well across circles, triangles, and rectangles, so you decide to do a fairness analysis.</p>
-
-<p> There’s just one issue: <span class="emphasis"> you don’t have labels for which of your shapes are circles, triangles, or rectangles.</span> </p>
-
-<p> So, you decide to send your data to <a href="https://developers.google.com/machine-learning/glossary#rater" target="_blank">data labelers</a>.</p>
-
-<center><img src="img/data_labelers.png" srcset="img/data_labelers.svg" alt="Different shapes with an arrow pointing to a group of abstract people."></center>
-
-<p> You receive feedback from your data labeling team that they’re not sure what to do with the shapes that aren’t exactly circles, triangles, or rectangles. </p>
-
-<center><img src="img/confusing_shape_name.png" srcset="img/confusing_shape_name.svg" class="interface-image" alt="An image of a computer interface and the instructions 'Please select the name of the shape below'. There is a lumpy, blob-like shape with three checkboxes that say 'circle', 'triangle', and 'rectangle'. There is a text box with a question mark next to the interface."></center> 
-
-<p> For the shapes that are unclear, you can have them use their best guess or simply label them as “other”. Then, you can finally do some fairness analysis! </p>
-
-<p> Below is the interface they see: </p>
-
-<center><div class="second-interface"></div></center>
-
-<p> These shapes should be labeled... <select id='second-classifier-select-rounding'></select> </p>
-<br>
-
-<div class='second-classifier'></div>
-
-<div class='second-classifier-summary'></div>
-
-<p>If you go back and change the labelers' instructions, which shapes do you perform worst on? Where do you find bias?</p>
-
-<p>You notice that <span class="emphasis">your results hinge on how you choose to classify the shapes in our data</span>.</p>
-
-<p>Because ultimately, this <i>isn’t</i> a world of only circles, triangles, and rectangles!</p>
-
-<h3 id="classification-thinking">Thinking About Classification</h3>
-
-<p>What could we find out about our classifier's performance if we used different categories altogether?</p>
-
-<p> All shapes are basically... <select id='final-classifier-select-category'></select> </p>
-<p> Everything else should be labeled... <select id='final-classifier-select-rounding'></select> </p>
-
-<p><div class='final-classifier'></div></p>
-<p>
-  <div class='final-classifier-summary'></div>
-</p>
-
-<p>With each of the different categories, which shapes do you perform worst on? Where do you find bias?</p>
-
-<p>Each way of categorizing your shapes <span class="emphasis"> takes a different stance about what’s important </span>. Each one makes some features more important than others, it make some distinctions visible and other distinctions invisible, and make some things easy to classify while others become outliers.</p>
-
-<p>And each one <span class="emphasis">tells you something different</span> about what kind of bias your classifier has!</p>
-
-<h3 id="">Grouping and Regrouping</h3>
-
-<p> Here's another way to look at the same results. We can draw all the shapes that were correctly classified above the dashed line, and all the incorrectly classified shapes below it. </p>
-
-<div class='shape-explainer'></div>
-
-<p> We're still looking at the same model making the same classification on the same shapes, so the same shapes stay above and below the line. But each way of grouping the results distributes the errors differently— each way <span class="emphasis"> tells you something different</span>.</p>
-
-<h3 id="">Labels Tell Stories</h3>
-
-<p>The decisions you make about classification, however small…</p>
-
-<p><center> All shapes are basically... <select id='conclusion-select-category'></select> </center></p>
-
-<p>…begin to shape others’ decisions…</p>
-
-<center> <div class='conclusion-interface'></div></center>
-
-<p>…they shape the analysis you can do…</p>
-
-<center> <div class='conclusion-summary'></div></center>
-
-<p>…and they shape the kinds of conversations that happen.</p>
-
-<p><b><center><div class='conclusion-newspapers'></div></center></b></p>
-
-<p>It’s natural to want to find a way out of this problem by gathering more features or collecting more data. If we just have enough detail on enough data, surely we can avoid making these kinds of decisions, right?</p>
-
-<p>Unfortunately, that isn’t the case. Describing the world around us in any way—whether we’re telling a friend a story or telling a computer about shapes—requires us to choose <span class="emphasis">what information is important to convey and what tools we want to use to convey it.</span></p>
-
-<p>Whether we think about it or not, we’re <i>always</i> making choices about classification.
-</p>
-
-<p><div class='fake-dropdown'><center>All people are basically... <span class='dropdown'>men or women</span></center></div></p>
-<p><div class='fake-dropdown'><center>All food is basically... <span class='dropdown'>sweet or savory</span></center></div></p>
-<p><div class='fake-dropdown'><center>All content is basically... <span class='dropdown'>kid-friendly or adult</span></center></div></p>
-<p><div class='fake-dropdown'><center>All speech is basically... <span class='dropdown'>hate speech or acceptable speech</span></center></div></p>
-<!-- <p><div class='fake-dropdown'><center>All results are basically... <span class='dropdown'> <span style="font-family:'Courier New'">p<0.05</span> and <span style="font-family:'Courier New'">p>0.05</span></span></center></div></p> -->
-  <p><div class='fake-dropdown'><center>All results are basically... <span class='dropdown'> significant or insignificant</center></div></p>
-
-<p>And as we saw with shapes, all of these choices <span class="emphasis">make some features more important than others</span>, make <span class="emphasis">some distinctions visible and other distinctions invisible</span>, and make <span class="emphasis">some things easy to classify while others become outliers</span>.</p>
-
-<h3 id="classification-thinking">In Practice</h3>
-
-<p>Let’s take a closer look at how this plays out in real machine learning applications. One straightforward example is in <a href="https://en.wikipedia.org/wiki/Object_detection" target="_blank">supervised object detection tasks</a>.</p>
-
-
-<p>For example, let’s imagine we want to train an object detection model on a dataset including this image:</p>
-
-<p><center><img src="img/seattle.png" width=700 alt="Image of the Seattle skyline" ><br> <a href="https://commons.wikimedia.org/wiki/File:Seattle_Kerry_Park_Skyline.jpg" target="_blank" class="source" >Source: Wikimedia Commons</a></center></p>
-
-<p>We could give it the following ground truth <a href="https://developers.google.com/machine-learning/glossary#bounding-box" target="_blank">bounding boxes</a>:</p>
-
-<p><center><img src="img/seattle_first_tags.png" srcset="img/seattle_first_tags.svg" width=700 alt="Image of the Seattle skyline with boxes around several items in the picture with labels like 'building' and 'tree'." ></center></p>
-
-<p>This looks objective, right? After all, a building is a building, a bush is a bush, and a mountain is a mountain!</p>
-<p> But even labeling the same regions in the same image, you can communicate a very different perspective:</p>
-
-<p><center><img src="img/seattle_second_tags.png" srcset="img/seattle_second_tags.svg" width=700 alt="Image of the Seattle skyline with boxes around several items in the picture, with labels like 'plant, non medicinal' and 'structure, nonreligious'." ></center></p>
-
-<p>Or consider the image below, with several sets of “ground truth” labels. Looking at each of these labels, consider:</p>
-
-<p>What features matter? What gets labeled? Whose worldview comes through? What might you learn from this set of labels that you wouldn't learn from another?</p>
-
-<div class='person-photos'></div><center><a href="https://commons.wikimedia.org/wiki/File:Women_washing_clothes_4.jpg" target="_blank" class="source">Source: Wikimedia Commons</a></center>
-
-<p>There is no “view from nowhere”, no universal way to organize every object, or word, or image. Datasets are always products of a particular time, place, and set of conditions; they are socially situated artifacts. They have <a href="https://journals.sagepub.com/doi/full/10.1177/20539517211035955" target="_blank">histories</a>; they have <a href="https://arxiv.org/abs/2108.04308" target="_blank">politics</a>. And ignoring this fact has <a href="https://arxiv.org/pdf/2012.05345.pdf" target="_blank">very real consequences</a>.</p>
-
-<p>So what do we do with this information?</p>
-
-<p>A great place to start is to reflect on your own context and <a href="https://pair-code.github.io/datacardsplaybook/playbook" target="_blank">get curious about your data.</a> </p>
-
-<p>If it’s hard to see a dataset’s values—if it feels “objective”, “universal”, or “neutral”—it may simply be reflecting a worldview you’re accustomed to. So, understanding the limitations of your own worldview can tell you about the limitations of “objective” data. What assumptions do you make about the world? What feels like common sense? What feels foreign?</p>
-
-<p>And do some sleuthing about your data! Who collected this data? Why was it collected? Who paid for it? Where did the “ground truth” come from? </p>
-
-<p>You might even find yourself <a href="https://www.morgan-klaus.com/pdfs/pubs/Scheuerman-CSCW2021-datapolitics.pdf" target="_blank">questioning what kinds of assumptions underpin machine learning dataset development</a> or even <a href="https://mitpress.mit.edu/books/sorting-things-out" target="_blank"> thinking more deeply about classification as a whole</a>.</p>
-
-<p>If you find yourself with lots of questions, you're already off to a good start.</p>
-
-<p></p>
-<p></p>
-
-<h3 id="credits">Credits</h3>
-
-<p> Dylan Baker // January 2022</p>
-<p> Thanks to Adam Pearce, Alex Hanna, Emily Denton, Fernanda Viégas, Kevin Robinson, Nithum Thain, Razvan Amironesei, and Vinodkumar Prabhakaran for their help with this piece. </p>
-<p></p>
-
-
-<div class='preload-dropdown-img' style='display: none;'></div>
-
-
-<h3 id="more-explorables">More Explorables</h3>
-<p><p id='recirc'></p></p>
-<p><link rel="stylesheet" href="style.css"></p>
-<script src='../third_party/d3_.js'></script>
-<script src='../third_party/d3-scale-chromatic.v1.min.js'></script>
-<script src='../third_party/params.js'></script>
-
-<script src='shape-params.js'></script>
-<script src='shapes.js'></script>
-<script src='shape-explainer.js'></script>
-<script src='script.js'></script>
-<script src='person-photos.js'></script>
-
-<script src='../third_party/recirc.js'></script>
-
-
diff --git a/spaces/merve/fill-in-the-blank/source/measuring-diversity/script.js b/spaces/merve/fill-in-the-blank/source/measuring-diversity/script.js
deleted file mode 100644
index 002fb32c0d0ee11cf292109725ebda6a2a4b57a4..0000000000000000000000000000000000000000
--- a/spaces/merve/fill-in-the-blank/source/measuring-diversity/script.js
+++ /dev/null
@@ -1,360 +0,0 @@
-// Seeded random number generator
-window.random = new Math.seedrandom('aaaa')
-window.randomIndex = new Math.seedrandom('7b')
-
-window.numRows = 20
-window.shapes = window.shapes || d3.range(21).map(i => randomShape(i, random))
-
-window.random2 = new Math.seedrandom('7')
-// window.columnShapes = window.columnShapes || d3.range(window.numRows).map(i => d3.range(10).map(i =>randomShape(i, random2)))
-window.columnShapes = d3.range(window.numRows).map(i => d3.range(10).map(i =>randomShape(i, random2, true)))
-
-console.log(window.random3)
-function randomShape(i, random, colTargets){
-  var color2fill = {
-    green: '#5A9F8A',
-    orange: '#DF831F',
-    blue: '#80BAD4',
-  }
-
-  var randomItem = function(arr) {
-    const index = Math.abs(random.int32()) % arr.length
-    return arr[index]
-  }
-
-  var color = randomItem(d3.keys(color2fill))
-  var size = randomItem(['small', 'large'])
-  var shape = randomItem(['circle', 'square', 'triangle'])
-
-  if (colTargets && (i == 4 || i == 5)){
-    color = 'green'
-  }
-  if (colTargets && (i == 4 || i == 15)){
-    size = 'small'
-  }
-  if (colTargets && (i == 3 || i == 5)){
-    shape = 'triangle'
-  }
-
-  var displayIndex = randomIndex()
-
-  return {
-    i,
-    displayIndex,
-    color,
-    fill: color2fill[color],
-    dFill: d3.color(color2fill[color]).darker(1),
-    size,
-    sizeVal: size == 'large' ? 1 : .4,
-    shape,
-  }
-}
-
-var metrics = [
-  {
-    str: 'Greens',
-    key: 'green',
-    field: 'color',
-    target: .3 
-  },
-  {
-    str: 'Dot',
-    key: 'triangle',
-    field: 'shape',
-    target: .35
-  },
-  {
-    str: 'Smalls',
-    key: 'small',
-    field: 'size',
-    target: .60
-  },
-]
-window.metrics1 = metrics.map(d => ({...d}))
-metrics1[2].target = .5
-window.metrics2 = metrics1.map(d => ({...d}))
-metrics2[0].target = 1
-
-metrics.forEach(d => {
-  d.scoreScale = d3.scaleLinear().domain([0, d.target, 1]).range([0, 1, 0])
-})
-
-
-var pctFmt = d3.format('.0%')
-function addMetrics(metrics, {active, topSel, isSmall}){  
-  var metricSel = topSel
-    .st({textAlign: 'center'})
-    .appendMany('div', metrics)
-    .st({textAlign: 'center', width: 200, display: 'inline-block'})
-
-  var width = 120
-
-  var svg = metricSel.append('svg')
-    .at({width: 120, height: 100})
-    .append('g')
-    .translate([.5, 40.5])
-
-  if (isSmall){
-    svg.translate((d, i) => [i ? -20.5 : 20.5, 40.5])
-  }
-
-
-  var xScale = d3.scaleLinear().rangeRound([0, width])
-
-  var topText = svg.append('text')
-    .at({y: -20, fontWeight: 500, textAnchor: 'middle', x: width/2})
-
-  svg.append('path')
-    .at({d: 'M 0 0 H ' + width, stroke: '#000'})
-
-  var topTick = svg.append('path')
-    .at({d: 'M 0 0 V -12.5', stroke: '#000', strokeWidth: 3})
-  
-  
-  var actualSel = svg.append('g').st({fill: highlightColor})
-
-  actualSel.append('path')
-    .at({d: 'M 0 0 V 12.5', stroke: highlightColor, strokeWidth: 3})
-
-  var actualPct = actualSel.append('text')
-    .translate(30, 1).at({textAnchor: 'middle'}).st({fontWeight: 300})
-
-  var actualScore = actualSel.append('text')
-    .translate(50, 1).at({textAnchor: 'middle'}).st({fontWeight: 300})
-
-  return () => {
-    var pcts = metrics.map(d => active.percents[d.key] || 0)
-
-    topText.text(d => (d.str + ' Target: ').replace('s ', ' ') + pctFmt(d.target))
-
-    topTick.translate(d => xScale(d.target), 0)
-    actualSel.translate((d, i) => xScale(pcts[i]), 0)
-
-    actualPct.text((d, i) => 'Actual: ' + pctFmt(pcts[i]))
-    actualScore.text((d, i) => 'Difference: ' + pctFmt(Math.abs(d.target - pcts[i])))
-  }
-}
-
-
-function scoreActive(active){
-  var numActive = d3.sum(active)
-  return metrics.map(m => {
-    var v = d3.sum(active, (d, i) => active[i] && shapes[i][m.field] == m.key)
-    return Math.abs(m.target - v/numActive);
-    // return m.scoreScale(v/numActive || 0)
-  })
-}
-
-var measures = [
-  {
-    str: 'Utilitarian',
-    display_text: 'Minimize Mean Difference',
-    ranking_display_text: 'Mean Difference',
-    fn: s => d3.mean(s)*100,
-    ppFn: s => d3.format('.2%')(d3.mean(s)),
-    format: s => 'mean(' + s.map(d => d + '%').join(', ') + ')'
-  },
-  {
-    str: 'Egalitarian',
-    display_text: 'Minimize Max Difference',
-    ranking_display_text: 'Max Difference',
-    fn: s => {
-      var srt = _.sortBy(s).map(d => Math.round(d*100)).reverse()
-
-      return srt[0]*100000000 + srt[1]*10000 + srt[2]
-    },
-    ppFn: s => {
-      var srt = _.sortBy(s).map(d => Math.round(d*100)).reverse()
-
-      return srt[0] + '%'
-    },
-    format: s => 'max(' + s.map(d => d + '%').join(', ') + ')'
-  }
-]
-measures2 = measures.map(d => ({...d}))
-
-
-var randomActive = d3.range(10000).map(d => {
-  var active = shapes.map(d => random() < .3)
-
-  if (d == 0) active = '111111111111101011100'.split('').map(d => +d)
-
-  active.score = scoreActive(active)
-  measures.forEach(d => {
-    active[d.str] = d.fn(active.score)
-  })
-
-  return active
-})
-
-function addMetricBestButton(metricIndex, {active, sel, render}){
-  var measureSel = sel
-    .append('div').st({textAlign: 'center', marginTop: 20, marginBottom: -20})
-    .append('div.measure').st({width: 200, lineHeight: '1.8em', display: 'inline-block'})
-    .html('Show Best')
-    .on('click', d => {
-
-      // console.log(active)
-      var pcts = metrics.map(d => active.percents[d.key] || 0)
-      if (pcts[metricIndex] == metrics[metricIndex].target) return
-
-      var nextActive = _.minBy(randomActive, a => a.score[metricIndex])
-      active.forEach((d, i) => active[i] = nextActive[i])
-
-      measureSel.classed('active', e => e == d)
-      render()
-    })  
-}
-
-function addMeasures(measures, {active, sel, render}){
-  var measureSel = sel.selectAll('div.measure-container')
-
-  measureSel
-    .append('div.measure')
-    .st({width: 200, lineHeight: '1.8em', display: 'inline-block', textAlign: 'center', })
-    .html((d, i) => i ? 'Show the set where the highest difference is the smallest' : 'Show the set with <br>lowest mean difference')
-    .html('Show Best')
-    .on('click', d => {
-
-      var nextActive = _.minBy(randomActive, a => a[d.str])
-      active.forEach((d, i) => active[i] = nextActive[i])
-
-      measureSel.classed('active', e => e == d)
-      render()
-    })  
-
-    
-}
-
-function addTotalMetrics(metrics, measures, {active, sel, render}){
-  var metricSel = sel.classed('bot', 1).st({textAlign: 'center'})
-    .appendMany('div.measure-container', measures)
-    .append('div', measures)
-    .st({textAlign: 'center', display: 'inline-block'})
-
-
-  var headlineSel = metricSel.append('div')
-  var calcSel = metricSel.append('div')//.st({color: highlightColor})
-
-  return () => {
-
-    measures.forEach(d => {
-      d.scores = scoreActive(active)
-
-      d.score = Math.round(d.fn(d.scores)*100)/100
-      if (d.ppFn) d.score = d.ppFn(d.scores)
-    })
-
-    headlineSel.st({fontWeight: 600})
-      .text(d => d.ranking_display_text + ': ' + d.score)
-
-    calcSel.text(d => {
-      var roundedScores = d.scores.map(s => Math.round(s * 100))
-
-      return d.format(roundedScores)
-    })
-  }
-}
-
-
-window.shapeRandom = new Math.seedrandom('aaf')
-var defaultActive = shapes.map(d => shapeRandom() < .4)
-drawShape('all-shapes')
-
-drawShape('pick-green', ({active, topSel, sel, render}) => {
-  active.forEach((d, i) => active[i] = defaultActive[i])
-  addMetricBestButton(0, {active, sel, render})
-  return addMetrics(metrics.filter(d => d.key == 'green'), {active, topSel})
-})
-
-drawShape('pick-triangle', ({active, topSel, sel, render}) => {
-  active.forEach((d, i) => active[i] = defaultActive[i])
-  addMetricBestButton(1, {active, sel, render})
-  return addMetrics(metrics.filter(d => d.key == 'triangle'), {active, topSel})
-})
-
-drawShape('pick-metric', grid => {
-  grid.active.forEach((d, i) => grid.active[i] = defaultActive[i])
-
-  var metricRender = addMetrics(metrics, grid)
-  var totalMetricRender = addTotalMetrics(metrics, measures, grid)
-  addMeasures(measures, grid)
-
-  return () => {
-    metricRender()
-    totalMetricRender()
-  }
-})
-
-
-function drawShape(id, initFn=d => e => e){
-  var active = shapes.map(d => true)
-
-  var sel = d3.select('#' + id).html('')
-
-  var s = 110
-
-  var topSel = sel.append('div.top')
-  var shapeSel = sel.appendMany('div.shape', _.sortBy(shapes, d => d.displayIndex))
-    .st({width: s, height: s})
-    .on('click', d => {
-      active[d.i] = !active[d.i]
-      render()
-    })
-
-  shapeSel.append('svg')
-    .at({width: s, height: s})
-    .append('g').translate([s/2, s/2])
-    .each(function(d){
-      if (d.shape == 'square' || true){
-        var rs = Math.round(d.sizeVal*s/3.5)
-        var shapeSel = d3.select(this).append('rect')
-          .at({x: -rs, y: -rs, width: rs*2, height: rs*2})
-      } else if (d.shape == 'circle'){
-        var shapeSel = d3.select(this).append('circle')
-          .at({r: d.sizeVal*s/3})
-      } else if (d.shape == 'triangle'){
-        var rs = Math.round(d.sizeVal*s/2.9)
-        var shapeSel = d3.select(this).append('path')
-          .translate(rs*Math.pow(3,1/2)/10, 1)
-          .at({d: [
-            'M', 0,                       -rs,
-            'L', -rs*Math.pow(3,1/2)/2,   rs/2,
-            'L', +rs*Math.pow(3,1/2)/2,   rs/2,
-            'Z'
-          ].join(' ')})
-      }
-
-      if (d.shape == 'triangle'){
-        d3.select(this).append('circle')
-          .at({r: 4, fill: '#fff', stroke: '#000', strokeWidth: 1})
-      }
-
-      shapeSel.at({fill: d.fill, stroke: d.dFill, strokeWidth: 2})
-    })
-
-  var customRender = initFn({active, topSel, sel, render})
-
-  shapes.render = render
-  function render(){
-    shapeSel.classed('active', d => active[d.i])
-    // console.log(active.map(d => +d).join(''))
-
-    active.percents = {}
-    active.shapes = shapes.filter(d => active[d.i])
-
-    d3.nestBy(active.shapes, d => d.color).forEach(d => {
-      active.percents[d.key] = d.length/active.shapes.length
-    })
-    d3.nestBy(active.shapes, d => d.size).forEach(d => {
-      active.percents[d.key] = d.length/active.shapes.length
-    })
-    d3.nestBy(active.shapes, d => d.shape).forEach(d => {
-      active.percents[d.key] = d.length/active.shapes.length
-    })
-
-
-    customRender()
-  }
-  render()
-}
\ No newline at end of file
diff --git a/spaces/metricspace/OcTra/df_local/enhance.py b/spaces/metricspace/OcTra/df_local/enhance.py
deleted file mode 100644
index eead3f3ef9855285d8e5f37793e3a8e1a2d08a14..0000000000000000000000000000000000000000
--- a/spaces/metricspace/OcTra/df_local/enhance.py
+++ /dev/null
@@ -1,333 +0,0 @@
-import argparse
-import os
-import time
-import warnings
-from typing import Optional, Tuple, Union
-
-import torch
-import torchaudio as ta
-from loguru import logger
-from numpy import ndarray
-from torch import Tensor, nn
-from torch.nn import functional as F
-from torchaudio.backend.common import AudioMetaData
-
-import df_local
-from df_local import config
-from df_local.checkpoint import load_model as load_model_cp
-from df_local.logger import init_logger, warn_once
-from df_local.model import ModelParams
-from df_local.modules import get_device
-from df_local.utils import as_complex, as_real, get_norm_alpha, resample
-from libdf import DF, erb, erb_norm, unit_norm
-
-
-def main(args):
-    model, df_state, suffix = init_df(
-        args.model_base_dir,
-        post_filter=args.pf,
-        log_level=args.log_level,
-        config_allow_defaults=True,
-        epoch=args.epoch,
-    )
-    if args.output_dir is None:
-        args.output_dir = "."
-    elif not os.path.isdir(args.output_dir):
-        os.mkdir(args.output_dir)
-    df_sr = ModelParams().sr
-    n_samples = len(args.noisy_audio_files)
-    for i, file in enumerate(args.noisy_audio_files):
-        progress = (i + 1) / n_samples * 100
-        audio, meta = load_audio(file, df_sr)
-        t0 = time.time()
-        audio = enhance(
-            model, df_state, audio, pad=args.compensate_delay, atten_lim_db=args.atten_lim
-        )
-        t1 = time.time()
-        t_audio = audio.shape[-1] / df_sr
-        t = t1 - t0
-        rtf = t / t_audio
-        fn = os.path.basename(file)
-        p_str = f"{progress:2.0f}% | " if n_samples > 1 else ""
-        logger.info(f"{p_str}Enhanced noisy audio file '{fn}' in {t:.1f}s (RT factor: {rtf:.3f})")
-        audio = resample(audio, df_sr, meta.sample_rate)
-        save_audio(
-            file, audio, sr=meta.sample_rate, output_dir=args.output_dir, suffix=suffix, log=False
-        )
-
-
-def init_df(
-    model_base_dir: Optional[str] = None,
-    post_filter: bool = False,
-    log_level: str = "INFO",
-    log_file: Optional[str] = "enhance.log",
-    config_allow_defaults: bool = False,
-    epoch: Union[str, int, None] = "best",
-    default_model: str = "DeepFilterNet2",
-) -> Tuple[nn.Module, DF, str]:
-    """Initializes and loads config, model and deep filtering state.
-
-    Args:
-        model_base_dir (str): Path to the model directory containing checkpoint and config. If None,
-            load the pretrained DeepFilterNet2 model.
-        post_filter (bool): Enable post filter for some minor, extra noise reduction.
-        log_level (str): Control amount of logging. Defaults to `INFO`.
-        log_file (str): Optional log file name. None disables it. Defaults to `enhance.log`.
-        config_allow_defaults (bool): Whether to allow initializing new config values with defaults.
-        epoch (str): Checkpoint epoch to load. Options are `best`, `latest`, `<int>`, and `none`.
-            `none` disables checkpoint loading. Defaults to `best`.
-
-    Returns:
-        model (nn.Modules): Intialized model, moved to GPU if available.
-        df_state (DF): Deep filtering state for stft/istft/erb
-        suffix (str): Suffix based on the model name. This can be used for saving the enhanced
-            audio.
-    """
-    try:
-        from icecream import ic, install
-
-        ic.configureOutput(includeContext=True)
-        install()
-    except ImportError:
-        pass
-    use_default_model = False
-    if model_base_dir == "DeepFilterNet":
-        default_model = "DeepFilterNet"
-        use_default_model = True
-    elif model_base_dir == "DeepFilterNet2":
-        use_default_model = True
-    if model_base_dir is None or use_default_model:
-        use_default_model = True
-        model_base_dir = os.path.relpath(
-            os.path.join(
-                os.path.dirname(df_local.__file__), os.pardir, "pretrained_models", default_model
-            )
-        )
-    if not os.path.isdir(model_base_dir):
-        raise NotADirectoryError("Base directory not found at {}".format(model_base_dir))
-    log_file = os.path.join(model_base_dir, log_file) if log_file is not None else None
-    init_logger(file=log_file, level=log_level, model=model_base_dir)
-    if use_default_model:
-        logger.info(f"Using {default_model} model at {model_base_dir}")
-    config.load(
-        os.path.join(model_base_dir, "config.ini"),
-        config_must_exist=True,
-        allow_defaults=config_allow_defaults,
-        allow_reload=True,
-    )
-    if post_filter:
-        config.set("mask_pf", True, bool, ModelParams().section)
-        logger.info("Running with post-filter")
-    p = ModelParams()
-    df_state = DF(
-        sr=p.sr,
-        fft_size=p.fft_size,
-        hop_size=p.hop_size,
-        nb_bands=p.nb_erb,
-        min_nb_erb_freqs=p.min_nb_freqs,
-    )
-    checkpoint_dir = os.path.join(model_base_dir, "checkpoints")
-    load_cp = epoch is not None and not (isinstance(epoch, str) and epoch.lower() == "none")
-    if not load_cp:
-        checkpoint_dir = None
-    try:
-        mask_only = config.get("mask_only", cast=bool, section="train")
-    except KeyError:
-        mask_only = False
-    model, epoch = load_model_cp(checkpoint_dir, df_state, epoch=epoch, mask_only=mask_only)
-    if (epoch is None or epoch == 0) and load_cp:
-        logger.error("Could not find a checkpoint")
-        exit(1)
-    logger.debug(f"Loaded checkpoint from epoch {epoch}")
-    model = model.to(get_device())
-    # Set suffix to model name
-    suffix = os.path.basename(os.path.abspath(model_base_dir))
-    if post_filter:
-        suffix += "_pf"
-    logger.info("Model loaded")
-    return model, df_state, suffix
-
-
-def df_features(audio: Tensor, df: DF, nb_df: int, device=None) -> Tuple[Tensor, Tensor, Tensor]:
-    spec = df.analysis(audio.numpy())  # [C, Tf] -> [C, Tf, F]
-    a = get_norm_alpha(False)
-    erb_fb = df.erb_widths()
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", UserWarning)
-        erb_feat = torch.as_tensor(erb_norm(erb(spec, erb_fb), a)).unsqueeze(1)
-    spec_feat = as_real(torch.as_tensor(unit_norm(spec[..., :nb_df], a)).unsqueeze(1))
-    spec = as_real(torch.as_tensor(spec).unsqueeze(1))
-    if device is not None:
-        spec = spec.to(device)
-        erb_feat = erb_feat.to(device)
-        spec_feat = spec_feat.to(device)
-    return spec, erb_feat, spec_feat
-
-
-def load_audio(
-    file: str, sr: Optional[int], verbose=True, **kwargs
-) -> Tuple[Tensor, AudioMetaData]:
-    """Loads an audio file using torchaudio.
-
-    Args:
-        file (str): Path to an audio file.
-        sr (int): Optionally resample audio to specified target sampling rate.
-        **kwargs: Passed to torchaudio.load(). Depends on the backend. The resample method
-            may be set via `method` which is passed to `resample()`.
-
-    Returns:
-        audio (Tensor): Audio tensor of shape [C, T], if channels_first=True (default).
-        info (AudioMetaData): Meta data of the original audio file. Contains the original sr.
-    """
-    ikwargs = {}
-    if "format" in kwargs:
-        ikwargs["format"] = kwargs["format"]
-    rkwargs = {}
-    if "method" in kwargs:
-        rkwargs["method"] = kwargs.pop("method")
-    info: AudioMetaData = ta.info(file, **ikwargs)
-    audio, orig_sr = ta.load(file, **kwargs)
-    if sr is not None and orig_sr != sr:
-        if verbose:
-            warn_once(
-                f"Audio sampling rate does not match model sampling rate ({orig_sr}, {sr}). "
-                "Resampling..."
-            )
-        audio = resample(audio, orig_sr, sr, **rkwargs)
-    return audio, info
-
-
-def save_audio(
-    file: str,
-    audio: Union[Tensor, ndarray],
-    sr: int,
-    output_dir: Optional[str] = None,
-    suffix: Optional[str] = None,
-    log: bool = False,
-    dtype=torch.int16,
-):
-    outpath = file
-    if suffix is not None:
-        file, ext = os.path.splitext(file)
-        outpath = file + f"_{suffix}" + ext
-    if output_dir is not None:
-        outpath = os.path.join(output_dir, os.path.basename(outpath))
-    if log:
-        logger.info(f"Saving audio file '{outpath}'")
-    audio = torch.as_tensor(audio)
-    if audio.ndim == 1:
-        audio.unsqueeze_(0)
-    if dtype == torch.int16 and audio.dtype != torch.int16:
-        audio = (audio * (1 << 15)).to(torch.int16)
-    if dtype == torch.float32 and audio.dtype != torch.float32:
-        audio = audio.to(torch.float32) / (1 << 15)
-    ta.save(outpath, audio, sr)
-
-
-@torch.no_grad()
-def enhance(
-    model: nn.Module, df_state: DF, audio: Tensor, pad=False, atten_lim_db: Optional[float] = None
-):
-    model.eval()
-    bs = audio.shape[0]
-    if hasattr(model, "reset_h0"):
-        model.reset_h0(batch_size=bs, device=get_device())
-    orig_len = audio.shape[-1]
-    n_fft, hop = 0, 0
-    if pad:
-        n_fft, hop = df_state.fft_size(), df_state.hop_size()
-        # Pad audio to compensate for the delay due to the real-time STFT implementation
-        audio = F.pad(audio, (0, n_fft))
-    nb_df = getattr(model, "nb_df", getattr(model, "df_bins", ModelParams().nb_df))
-    spec, erb_feat, spec_feat = df_features(audio, df_state, nb_df, device=get_device())
-    enhanced = model(spec, erb_feat, spec_feat)[0].cpu()
-    enhanced = as_complex(enhanced.squeeze(1))
-    if atten_lim_db is not None and abs(atten_lim_db) > 0:
-        lim = 10 ** (-abs(atten_lim_db) / 20)
-        enhanced = as_complex(spec.squeeze(1)) * lim + enhanced * (1 - lim)
-    audio = torch.as_tensor(df_state.synthesis(enhanced.numpy()))
-    if pad:
-        # The frame size is equal to p.hop_size. Given a new frame, the STFT loop requires e.g.
-        # ceil((n_fft-hop)/hop). I.e. for 50% overlap, then hop=n_fft//2
-        # requires 1 additional frame lookahead; 75% requires 3 additional frames lookahead.
-        # Thus, the STFT/ISTFT loop introduces an algorithmic delay of n_fft - hop.
-        assert n_fft % hop == 0  # This is only tested for 50% and 75% overlap
-        d = n_fft - hop
-        audio = audio[:, d : orig_len + d]
-    return audio
-
-
-def parse_epoch_type(value: str) -> Union[int, str]:
-    try:
-        return int(value)
-    except ValueError:
-        assert value in ("best", "latest")
-        return value
-
-
-def setup_df_argument_parser(default_log_level: str = "INFO") -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--model-base-dir",
-        "-m",
-        type=str,
-        default=None,
-        help="Model directory containing checkpoints and config. "
-        "To load a pretrained model, you may just provide the model name, e.g. `DeepFilterNet`. "
-        "By default, the pretrained DeepFilterNet2 model is loaded.",
-    )
-    parser.add_argument(
-        "--pf",
-        help="Post-filter that slightly over-attenuates very noisy sections.",
-        action="store_true",
-    )
-    parser.add_argument(
-        "--output-dir",
-        "-o",
-        type=str,
-        default=None,
-        help="Directory in which the enhanced audio files will be stored.",
-    )
-    parser.add_argument(
-        "--log-level",
-        type=str,
-        default=default_log_level,
-        help="Logger verbosity. Can be one of (debug, info, error, none)",
-    )
-    parser.add_argument("--debug", "-d", action="store_const", const="DEBUG", dest="log_level")
-    parser.add_argument(
-        "--epoch",
-        "-e",
-        default="best",
-        type=parse_epoch_type,
-        help="Epoch for checkpoint loading. Can be one of ['best', 'latest', <int>].",
-    )
-    return parser
-
-
-def run():
-    parser = setup_df_argument_parser()
-    parser.add_argument(
-        "--compensate-delay",
-        "-D",
-        action="store_true",
-        help="Add some paddig to compensate the delay introduced by the real-time STFT/ISTFT implementation.",
-    )
-    parser.add_argument(
-        "--atten-lim",
-        "-a",
-        type=int,
-        default=None,
-        help="Attenuation limit in dB by mixing the enhanced signal with the noisy signal.",
-    )
-    parser.add_argument(
-        "noisy_audio_files",
-        type=str,
-        nargs="+",
-        help="List of noise files to mix with the clean speech file.",
-    )
-    main(parser.parse_args())
-
-
-if __name__ == "__main__":
-    run()
diff --git a/spaces/mikeee/radiobee-aligner/tests/test_main.py b/spaces/mikeee/radiobee-aligner/tests/test_main.py
deleted file mode 100644
index 7dbddfcbc75dccefe9e4f49e8609aa5bf71e675f..0000000000000000000000000000000000000000
--- a/spaces/mikeee/radiobee-aligner/tests/test_main.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""Test __main__.py."""
-# pylint: disable=invalid-name
-import tempfile
-from fastlid import fastlid
-
-from logzero import logger
-
-# globals()["file2text"] = getattr(importlib.import_module(f"{radiobee.__name__}.file2text"), "file2text")
-# from radiobee.process_upload import process_upload  # same as file2text
-from radiobee.files2df import files2df
-from radiobee.file2text import file2text
-from radiobee.lists2cmat import lists2cmat
-from radiobee.cmat2tset import cmat2tset
-
-file1loc = "data/test_zh.txt"
-file2loc = "data/test_en.txt"
-
-file1 = tempfile._TemporaryFileWrapper(open(file1loc, "rb"), file1loc)
-file2 = tempfile._TemporaryFileWrapper(open(file2loc, "rb"), file2loc)
-
-
-def test_file2file1():
-    """Test cmat file2 file1."""
-    # logger.info("file1: *%s*, file2: *%s*", file1, file2)
-    logger.info("file1.name: *%s*, file2.name: *%s*", file1.name, file2.name)
-
-    text1 = file2text(file1)
-    text2 = file2text(file2)
-
-    lang1, _ = fastlid(text1)
-    lang2, _ = fastlid(text2)
-
-    lst1 = [elm.strip() for elm in text1.splitlines() if elm.strip()]
-    lst2 = [elm.strip() for elm in text2.splitlines() if elm.strip()]
-
-    del lst1, lst2
diff --git a/spaces/mixshare/hackathon_chatbot_openai_api/app.py b/spaces/mixshare/hackathon_chatbot_openai_api/app.py
deleted file mode 100644
index 136933235b399a96480c2dc87cabb3e01ba89e3f..0000000000000000000000000000000000000000
--- a/spaces/mixshare/hackathon_chatbot_openai_api/app.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import gradio as gr
-import os
-import openai
-
-# 请记得要把 api 的 key 放到 settings 下面的 Repository Secrets 里。
-openai.api_key = os.getenv("openai_key")
-
-
-# 如果你只打算通过 prompt 来定制机器人的行为，只需要修改这段 prompt 就够了。
-prompt = '请你调用你能获取的所有的博物百科资源库，用小助手的口吻来回答问题，开头第一句是你好啊，好奇宝宝😊由于是面向青少年儿童，请务必做到内容有理有据语言风格有趣生动，如果答案涉及具体事物，请在相应的事物名字后面加上对应的表情emoji，比如大象🐘奶牛🐄风铃🎐花朵🏵️啤酒🍺等等。如果提问的问题不相关，你要友善提醒你回答范畴是博物百科知识。在回答完毕后，请务必提供相关的来源出处。结尾是固定格式询问有没有解答你的疑惑呢？欢迎继续提问😊在每次回答的最后，请用一条长的横线与前面的回答隔开，然后附上一句不超过20个字的科学名言警句，以激发青少年儿童的求知欲。'
-
-
-
-history = {}
-
-# 修改本函数，来实现你自己的 chatbot
-# p: 对机器人说话的内容  
-# qid: 当前消息的唯一标识。例如 `'bxqid-cManAtRMszw...'`。由平台生成并传递给机器人，以便机器人区分单个问题（写日志、追踪调试、异步回调等）。同步调用可忽略。  
-# uid: 用户的唯一标识。例如`'bxuid-Aj8Spso8Xsp...'`。由平台生成并传递给机器人，以便机器人区分用户。可被用于实现多轮对话的功能。  
-# 返回值：[type, content]
-# 详见 https://huggingface.co/spaces/baixing/hackathon_test/blob/main/bot-api.md
-def chat(p, qid, uid):
-    # 找出该 uid 对应的历史对话
-    global history
-    if uid in history:
-        msgs = history[uid]
-    else:
-        msgs = []
-        
-    response = callapi(p, msgs)
-    history[uid] = msgs + [[p, response]]
-    return ["text", response]
-
-
-def callapi(p, msgs):
-    if (len(msgs) > 8):  #简单 hard-code 8 回合对话。如果需要更精准的，应该计算 token 数
-        msgs = msgs[-8:]
-        
-    data = [{"role":"system", "content":prompt}]
-    for m in msgs:
-        data = data + [
-            {"role":"user", "content":m[0]},
-            {"role":"assistant", "content":m[1]}
-        ]
-    data = data + [{"role":"user", "content":p}]
-    response = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages= data
-    )
-    print(response)
-    response = response["choices"][0]["message"]["content"]
-    while response.startswith("\n"):
-        response = response[1:]
-    return response
-
-iface = gr.Interface(fn=chat, 
-                     inputs=["text", "text", "text"], 
-                     outputs=["text", "text"],
-                     description="""这是一个面向青少年儿童的博物百科全书问答助手，希望能够让你天马行空的想象力和好奇心得到满足！
-
-
-                     """)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/mkrzyzan/face-swap/README.md b/spaces/mkrzyzan/face-swap/README.md
deleted file mode 100644
index eb2544088ba2fb9ce64f08ed21188b50f6f0b7fa..0000000000000000000000000000000000000000
--- a/spaces/mkrzyzan/face-swap/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Face Swap
-emoji: 👩🏻‍🦰
-colorFrom: green
-colorTo: indigo
-sdk: gradio
-sdk_version: 3.41.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/ml6team/controlnet-interior-design/segmentation.py b/spaces/ml6team/controlnet-interior-design/segmentation.py
deleted file mode 100644
index 8c30781ed4c4c4e2ff86e65a46b4d8184031ad7b..0000000000000000000000000000000000000000
--- a/spaces/ml6team/controlnet-interior-design/segmentation.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import logging
-from typing import List, Tuple, Dict
-
-import streamlit as st
-import torch
-import gc
-import numpy as np
-from PIL import Image
-
-from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
-
-from palette import ade_palette
-
-LOGGING = logging.getLogger(__name__)
-
-
-def flush():
-    gc.collect()
-    torch.cuda.empty_cache()
-
-@st.cache_resource(max_entries=5)
-def get_segmentation_pipeline() -> Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]:
-    """Method to load the segmentation pipeline
-    Returns:
-        Tuple[AutoImageProcessor, UperNetForSemanticSegmentation]: segmentation pipeline
-    """
-    image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
-    image_segmentor = UperNetForSemanticSegmentation.from_pretrained(
-        "openmmlab/upernet-convnext-small")
-    return image_processor, image_segmentor
-
-
-@torch.inference_mode()
-@torch.autocast('cuda')
-def segment_image(image: Image) -> Image:
-    """Method to segment image
-    Args:
-        image (Image): input image
-    Returns:
-        Image: segmented image
-    """
-    image_processor, image_segmentor = get_segmentation_pipeline()
-    pixel_values = image_processor(image, return_tensors="pt").pixel_values
-    with torch.no_grad():
-        outputs = image_segmentor(pixel_values)
-
-    seg = image_processor.post_process_semantic_segmentation(
-        outputs, target_sizes=[image.size[::-1]])[0]
-    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
-    palette = np.array(ade_palette())
-    for label, color in enumerate(palette):
-        color_seg[seg == label, :] = color
-    color_seg = color_seg.astype(np.uint8)
-    seg_image = Image.fromarray(color_seg).convert('RGB')
-    return seg_image
\ No newline at end of file
diff --git a/spaces/mosaicml/mpt-30b-chat/README.md b/spaces/mosaicml/mpt-30b-chat/README.md
deleted file mode 100644
index 827a68e1e2bcff65a815f13c99b40c70b714fa84..0000000000000000000000000000000000000000
--- a/spaces/mosaicml/mpt-30b-chat/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: MPT-30B-Chat
-emoji: 😻
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at <https://huggingface.co/docs/hub/spaces-config-reference>
diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/s2t_transformer.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/s2t_transformer.py
deleted file mode 100644
index aff9d0ffc7b7e671c476ff28d1cd945e9ff41519..0000000000000000000000000000000000000000
--- a/spaces/mshukor/UnIVAL/fairseq/fairseq/models/speech_to_text/s2t_transformer.py
+++ /dev/null
@@ -1,502 +0,0 @@
-#!/usr/bin/env python3
-
-import logging
-import math
-from typing import Dict, List, Optional, Tuple
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-from fairseq import checkpoint_utils, utils
-from fairseq.data.data_utils import lengths_to_padding_mask
-from fairseq.models import (
-    FairseqEncoder,
-    FairseqEncoderDecoderModel,
-    register_model,
-    register_model_architecture,
-)
-from fairseq.models.transformer import Embedding, TransformerDecoder
-from fairseq.modules import (
-    FairseqDropout,
-    LayerNorm,
-    PositionalEmbedding,
-    TransformerEncoderLayer,
-)
-from torch import Tensor
-
-
-logger = logging.getLogger(__name__)
-
-
-class Conv1dSubsampler(nn.Module):
-    """Convolutional subsampler: a stack of 1D convolution (along temporal
-    dimension) followed by non-linear activation via gated linear units
-    (https://arxiv.org/abs/1911.08460)
-
-    Args:
-        in_channels (int): the number of input channels
-        mid_channels (int): the number of intermediate channels
-        out_channels (int): the number of output channels
-        kernel_sizes (List[int]): the kernel size for each convolutional layer
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        mid_channels: int,
-        out_channels: int,
-        kernel_sizes: List[int] = (3, 3),
-    ):
-        super(Conv1dSubsampler, self).__init__()
-        self.n_layers = len(kernel_sizes)
-        self.conv_layers = nn.ModuleList(
-            nn.Conv1d(
-                in_channels if i == 0 else mid_channels // 2,
-                mid_channels if i < self.n_layers - 1 else out_channels * 2,
-                k,
-                stride=2,
-                padding=k // 2,
-            )
-            for i, k in enumerate(kernel_sizes)
-        )
-
-    def get_out_seq_lens_tensor(self, in_seq_lens_tensor):
-        out = in_seq_lens_tensor.clone()
-        for _ in range(self.n_layers):
-            out = ((out.float() - 1) / 2 + 1).floor().long()
-        return out
-
-    def forward(self, src_tokens, src_lengths):
-        bsz, in_seq_len, _ = src_tokens.size()  # B x T x (C x D)
-        x = src_tokens.transpose(1, 2).contiguous()  # -> B x (C x D) x T
-        for conv in self.conv_layers:
-            x = conv(x)
-            x = nn.functional.glu(x, dim=1)
-        _, _, out_seq_len = x.size()
-        x = x.transpose(1, 2).transpose(0, 1).contiguous()  # -> T x B x (C x D)
-        return x, self.get_out_seq_lens_tensor(src_lengths)
-
-
-@register_model("s2t_transformer")
-class S2TTransformerModel(FairseqEncoderDecoderModel):
-    """Adapted Transformer model (https://arxiv.org/abs/1706.03762) for
-    speech-to-text tasks. The Transformer encoder/decoder remains the same.
-    A trainable input subsampler is prepended to the Transformer encoder to
-    project inputs into the encoder dimension as well as downsample input
-    sequence for computational efficiency."""
-
-    def __init__(self, encoder, decoder):
-        super().__init__(encoder, decoder)
-
-    @staticmethod
-    def add_args(parser):
-        """Add model-specific arguments to the parser."""
-        # input
-        parser.add_argument(
-            "--conv-kernel-sizes",
-            type=str,
-            metavar="N",
-            help="kernel sizes of Conv1d subsampling layers",
-        )
-        parser.add_argument(
-            "--conv-channels",
-            type=int,
-            metavar="N",
-            help="# of channels in Conv1d subsampling layers",
-        )
-        # Transformer
-        parser.add_argument(
-            "--activation-fn",
-            type=str,
-            default="relu",
-            choices=utils.get_available_activation_fns(),
-            help="activation function to use",
-        )
-        parser.add_argument(
-            "--dropout", type=float, metavar="D", help="dropout probability"
-        )
-        parser.add_argument(
-            "--attention-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability for attention weights",
-        )
-        parser.add_argument(
-            "--activation-dropout",
-            "--relu-dropout",
-            type=float,
-            metavar="D",
-            help="dropout probability after activation in FFN.",
-        )
-        parser.add_argument(
-            "--encoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension",
-        )
-        parser.add_argument(
-            "--encoder-ffn-embed-dim",
-            type=int,
-            metavar="N",
-            help="encoder embedding dimension for FFN",
-        )
-        parser.add_argument(
-            "--encoder-layers", type=int, metavar="N", help="num encoder layers"
-        )
-        parser.add_argument(
-            "--encoder-attention-heads",
-            type=int,
-            metavar="N",
-            help="num encoder attention heads",
-        )
-        parser.add_argument(
-            "--encoder-normalize-before",
-            action="store_true",
-            help="apply layernorm before each encoder block",
-        )
-        parser.add_argument(
-            "--decoder-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension",
-        )
-        parser.add_argument(
-            "--decoder-ffn-embed-dim",
-            type=int,
-            metavar="N",
-            help="decoder embedding dimension for FFN",
-        )
-        parser.add_argument(
-            "--decoder-layers", type=int, metavar="N", help="num decoder layers"
-        )
-        parser.add_argument(
-            "--decoder-attention-heads",
-            type=int,
-            metavar="N",
-            help="num decoder attention heads",
-        )
-        parser.add_argument(
-            "--decoder-normalize-before",
-            action="store_true",
-            help="apply layernorm before each decoder block",
-        )
-        parser.add_argument(
-            "--share-decoder-input-output-embed",
-            action="store_true",
-            help="share decoder input and output embeddings",
-        )
-        parser.add_argument(
-            "--layernorm-embedding",
-            action="store_true",
-            help="add layernorm to embedding",
-        )
-        parser.add_argument(
-            "--no-scale-embedding",
-            action="store_true",
-            help="if True, dont scale embeddings",
-        )
-        parser.add_argument(
-            "--load-pretrained-encoder-from",
-            type=str,
-            metavar="STR",
-            help="model to take encoder weights from (for initialization)",
-        )
-        parser.add_argument(
-            '--encoder-freezing-updates',
-            type=int,
-            metavar='N',
-            help='freeze encoder for first N updates'
-        )
-
-    @classmethod
-    def build_encoder(cls, args):
-        encoder = S2TTransformerEncoder(args)
-        pretraining_path = getattr(args, "load_pretrained_encoder_from", None)
-        if pretraining_path is not None:
-            if not Path(pretraining_path).exists():
-                logger.warning(
-                    f"skipped pretraining because {pretraining_path} does not exist"
-                )
-            else:
-                encoder = checkpoint_utils.load_pretrained_component_from_model(
-                    component=encoder, checkpoint=pretraining_path
-                )
-                logger.info(f"loaded pretrained encoder from: {pretraining_path}")
-        return encoder
-
-    @classmethod
-    def build_decoder(cls, args, task, embed_tokens):
-        return TransformerDecoderScriptable(args, task.target_dictionary, embed_tokens)
-
-    @classmethod
-    def build_model(cls, args, task):
-        """Build a new model instance."""
-
-        # make sure all arguments are present in older models
-        base_architecture(args)
-
-        def build_embedding(dictionary, embed_dim):
-            num_embeddings = len(dictionary)
-            padding_idx = dictionary.pad()
-            return Embedding(num_embeddings, embed_dim, padding_idx)
-
-        decoder_embed_tokens = build_embedding(
-            task.target_dictionary, args.decoder_embed_dim
-        )
-        encoder = cls.build_encoder(args)
-        decoder = cls.build_decoder(args, task, decoder_embed_tokens)
-        return cls(encoder, decoder)
-
-    def get_normalized_probs(
-        self,
-        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
-        log_probs: bool,
-        sample: Optional[Dict[str, Tensor]] = None,
-    ):
-        # net_output['encoder_out'] is a (B, T, D) tensor
-        lprobs = self.get_normalized_probs_scriptable(net_output, log_probs, sample)
-        lprobs.batch_first = True
-        return lprobs
-
-    def forward(self, src_tokens, src_lengths, prev_output_tokens):
-        """
-        The forward method inherited from the base class has a **kwargs
-        argument in its input, which is not supported in torchscript. This
-        method overwrites the forward method definition without **kwargs.
-        """
-        encoder_out = self.encoder(src_tokens=src_tokens, src_lengths=src_lengths)
-        decoder_out = self.decoder(
-            prev_output_tokens=prev_output_tokens, encoder_out=encoder_out
-        )
-        return decoder_out
-
-
-class S2TTransformerEncoder(FairseqEncoder):
-    """Speech-to-text Transformer encoder that consists of input subsampler and
-    Transformer encoder."""
-
-    def __init__(self, args):
-        super().__init__(None)
-
-        self.encoder_freezing_updates = args.encoder_freezing_updates
-        self.num_updates = 0
-
-        self.dropout_module = FairseqDropout(
-            p=args.dropout, module_name=self.__class__.__name__
-        )
-        self.embed_scale = math.sqrt(args.encoder_embed_dim)
-        if args.no_scale_embedding:
-            self.embed_scale = 1.0
-        self.padding_idx = 1
-
-        self.subsample = Conv1dSubsampler(
-            args.input_feat_per_channel * args.input_channels,
-            args.conv_channels,
-            args.encoder_embed_dim,
-            [int(k) for k in args.conv_kernel_sizes.split(",")],
-        )
-
-        self.embed_positions = PositionalEmbedding(
-            args.max_source_positions, args.encoder_embed_dim, self.padding_idx
-        )
-
-        self.transformer_layers = nn.ModuleList(
-            [TransformerEncoderLayer(args) for _ in range(args.encoder_layers)]
-        )
-        if args.encoder_normalize_before:
-            self.layer_norm = LayerNorm(args.encoder_embed_dim)
-        else:
-            self.layer_norm = None
-
-    def _forward(self, src_tokens, src_lengths, return_all_hiddens=False):
-        x, input_lengths = self.subsample(src_tokens, src_lengths)
-        x = self.embed_scale * x
-
-        encoder_padding_mask = lengths_to_padding_mask(input_lengths)
-        positions = self.embed_positions(encoder_padding_mask).transpose(0, 1)
-        x += positions
-        x = self.dropout_module(x)
-
-        encoder_states = []
-
-        for layer in self.transformer_layers:
-            x = layer(x, encoder_padding_mask)
-            if return_all_hiddens:
-                encoder_states.append(x)
-
-        if self.layer_norm is not None:
-            x = self.layer_norm(x)
-
-        return {
-            "encoder_out": [x],  # T x B x C
-            "encoder_padding_mask": [encoder_padding_mask] if encoder_padding_mask.any() else [],  # B x T
-            "encoder_embedding": [],  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],
-            "src_lengths": [],
-        }
-
-    def forward(self, src_tokens, src_lengths, return_all_hiddens=False):
-        if self.num_updates < self.encoder_freezing_updates:
-            with torch.no_grad():
-                x = self._forward(src_tokens, src_lengths,
-                                  return_all_hiddens=return_all_hiddens)
-        else:
-            x = self._forward(src_tokens, src_lengths,
-                              return_all_hiddens=return_all_hiddens)
-        return x
-
-    def reorder_encoder_out(self, encoder_out, new_order):
-        new_encoder_out = (
-            [] if len(encoder_out["encoder_out"]) == 0
-            else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
-        )
-
-        new_encoder_padding_mask = (
-            [] if len(encoder_out["encoder_padding_mask"]) == 0
-            else [x.index_select(0, new_order) for x in encoder_out["encoder_padding_mask"]]
-        )
-
-        new_encoder_embedding = (
-            [] if len(encoder_out["encoder_embedding"]) == 0
-            else [x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]]
-        )
-
-        encoder_states = encoder_out["encoder_states"]
-        if len(encoder_states) > 0:
-            for idx, state in enumerate(encoder_states):
-                encoder_states[idx] = state.index_select(1, new_order)
-
-        return {
-            "encoder_out": new_encoder_out,  # T x B x C
-            "encoder_padding_mask": new_encoder_padding_mask,  # B x T
-            "encoder_embedding": new_encoder_embedding,  # B x T x C
-            "encoder_states": encoder_states,  # List[T x B x C]
-            "src_tokens": [],  # B x T
-            "src_lengths": [],  # B x 1
-        }
-
-    def set_num_updates(self, num_updates):
-        super().set_num_updates(num_updates)
-        self.num_updates = num_updates
-
-
-class TransformerDecoderScriptable(TransformerDecoder):
-    def extract_features(
-        self,
-        prev_output_tokens,
-        encoder_out: Optional[Dict[str, List[Tensor]]] = None,
-        incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
-        full_context_alignment: bool = False,
-        alignment_layer: Optional[int] = None,
-        alignment_heads: Optional[int] = None,
-    ):
-        # call scriptable method from parent class
-        x, _ = self.extract_features_scriptable(
-            prev_output_tokens,
-            encoder_out,
-            incremental_state,
-            full_context_alignment,
-            alignment_layer,
-            alignment_heads,
-        )
-        return x, None
-
-
-@register_model_architecture(model_name="s2t_transformer", arch_name="s2t_transformer")
-def base_architecture(args):
-    args.encoder_freezing_updates = getattr(args, "encoder_freezing_updates", 0)
-    # Convolutional subsampler
-    args.conv_kernel_sizes = getattr(args, "conv_kernel_sizes", "5,5")
-    args.conv_channels = getattr(args, "conv_channels", 1024)
-    # Transformer
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 2048)
-    args.encoder_layers = getattr(args, "encoder_layers", 12)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
-    args.decoder_embed_dim = getattr(args, "decoder_embed_dim", args.encoder_embed_dim)
-    args.decoder_ffn_embed_dim = getattr(
-        args, "decoder_ffn_embed_dim", args.encoder_ffn_embed_dim
-    )
-    args.decoder_layers = getattr(args, "decoder_layers", 6)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
-    args.decoder_learned_pos = getattr(args, "decoder_learned_pos", False)
-    args.dropout = getattr(args, "dropout", 0.1)
-    args.attention_dropout = getattr(args, "attention_dropout", args.dropout)
-    args.activation_dropout = getattr(args, "activation_dropout", args.dropout)
-    args.activation_fn = getattr(args, "activation_fn", "relu")
-    args.adaptive_softmax_cutoff = getattr(args, "adaptive_softmax_cutoff", None)
-    args.adaptive_softmax_dropout = getattr(args, "adaptive_softmax_dropout", 0)
-    args.share_decoder_input_output_embed = getattr(
-        args, "share_decoder_input_output_embed", False
-    )
-    args.no_token_positional_embeddings = getattr(
-        args, "no_token_positional_embeddings", False
-    )
-    args.adaptive_input = getattr(args, "adaptive_input", False)
-    args.decoder_layerdrop = getattr(args, "decoder_layerdrop", 0.0)
-    args.decoder_output_dim = getattr(
-        args, "decoder_output_dim", args.decoder_embed_dim
-    )
-    args.decoder_input_dim = getattr(args, "decoder_input_dim", args.decoder_embed_dim)
-    args.no_scale_embedding = getattr(args, "no_scale_embedding", False)
-    args.quant_noise_pq = getattr(args, "quant_noise_pq", 0)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_s")
-def s2t_transformer_s(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 8)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
-    args.dropout = getattr(args, "dropout", 0.1)
-    base_architecture(args)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_xs")
-def s2t_transformer_xs(args):
-    args.encoder_layers = getattr(args, "encoder_layers", 6)
-    args.decoder_layers = getattr(args, "decoder_layers", 3)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 256 * 4)
-    args.dropout = getattr(args, "dropout", 0.3)
-    s2t_transformer_s(args)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_sp")
-def s2t_transformer_sp(args):
-    args.encoder_layers = getattr(args, "encoder_layers", 16)
-    s2t_transformer_s(args)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_m")
-def s2t_transformer_m(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512 * 4)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 8)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 8)
-    args.dropout = getattr(args, "dropout", 0.15)
-    base_architecture(args)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_mp")
-def s2t_transformer_mp(args):
-    args.encoder_layers = getattr(args, "encoder_layers", 16)
-    s2t_transformer_m(args)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_l")
-def s2t_transformer_l(args):
-    args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
-    args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024 * 4)
-    args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
-    args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
-    args.dropout = getattr(args, "dropout", 0.2)
-    base_architecture(args)
-
-
-@register_model_architecture("s2t_transformer", "s2t_transformer_lp")
-def s2t_transformer_lp(args):
-    args.encoder_layers = getattr(args, "encoder_layers", 16)
-    s2t_transformer_l(args)
diff --git a/spaces/mueller-franzes/medfusion-app/streamlit/pages/chest.py b/spaces/mueller-franzes/medfusion-app/streamlit/pages/chest.py
deleted file mode 100644
index 71754bc96efe256e64ec43c0da0c529c79e82cdf..0000000000000000000000000000000000000000
--- a/spaces/mueller-franzes/medfusion-app/streamlit/pages/chest.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import streamlit as st
-import torch 
-import numpy as np 
-
-from medical_diffusion.models.pipelines import DiffusionPipeline
-
-st.title("Chest X-ray images", anchor=None)
-st.sidebar.markdown("Medfusion for chest X-ray image generation")
-st.header('Information')
-st.markdown('Medfusion was trained on the [CheXpert](https://stanfordmlgroup.github.io/competitions/chexpert/) dataset')
-
-st.header('Settings')
-n_samples = st.number_input("Samples", min_value=1, max_value=25, value=4)
-steps = st.number_input("Sampling steps", min_value=1, max_value=999, value=50)
-guidance_scale = st.number_input("Guidance scale", min_value=1, max_value=10, value=1)
-seed = st.number_input("Seed", min_value=0, max_value=None, value=1)
-cond_str = st.radio("Cardiomegaly", ('Yes', 'No'), index=1, help="Conditioned on 'cardiomegaly' or 'no cardiomegaly'", horizontal=True)
-torch.manual_seed(seed)
-device_str = 'cuda' if torch.cuda.is_available() else 'cpu'
-device = torch.device(device_str)
-
-@st.cache(allow_output_mutation = True)  
-def init_pipeline():
-    pipeline = DiffusionPipeline.load_from_checkpoint('runs/chest_diffusion/last.ckpt')
-    return pipeline
-
-if st.button(f'Sample (using {device_str})'):
-    cond = {'Yes':1, 'No':0}[cond_str]
-    condition = torch.tensor([cond]*n_samples, device=device)
-    un_cond = torch.tensor([1-cond]*n_samples, device=device)
-
-    pipeline = init_pipeline()
-    pipeline.to(device)
-    images = pipeline.sample(n_samples, (8, 32, 32), guidance_scale=guidance_scale, condition=condition, un_cond=un_cond, steps=steps, use_ddim=True )
-
-    images = images.clamp(-1, 1)
-    images = images.cpu().numpy() # [B, C, H, W]
-    images = (images+1)/2  # Transform from [-1, 1] to [0, 1]
-
-    images = [np.moveaxis(img, 0, -1) for img in images]
-    st.image(images, channels="RGB", output_format='png') # expects (w,h,3) 
\ No newline at end of file
diff --git a/spaces/nagolinc/npcGenerator/words.py b/spaces/nagolinc/npcGenerator/words.py
deleted file mode 100644
index db3dace05b2a76a3a4ea6472af64d588a331e578..0000000000000000000000000000000000000000
--- a/spaces/nagolinc/npcGenerator/words.py
+++ /dev/null
@@ -1,93 +0,0 @@
-
-vowels = list("aeiou")
-constants = list("bcdfghjklmnpqrstvwxyz")
-seperators=list("-'")
-
-races="""Dwarf
-Elf
-Halfling
-Human
-Dragonborn
-Gnome
-Half-elf
-Half-orc
-Tiefling
-Aarakocra
-Genasi
-Goliath""".split("\n")
-
-races=[x.strip() for x in races]
-
-
-
-
-classes="""Barbarian
-Bard
-Cleric
-Druid
-Fighter
-Monk
-Paladin
-Ranger
-Rogue
-Sorcerer
-Warlock
-Wizard""".split("\n")
-classes=[x.strip() for x in classes]
-
-colors="""red
-blue
-green
-yellow
-orange
-purple
-pink
-brown
-black
-white""".split("\n")
-colors=[x.strip() for x in colors]
-
-outfits="""shirt
-pair of pants
-pair of shoes
-hat
-pair of glasses
-backpack
-belt
-tie
-cloak
-robe
-chain mail vest
-suit of plate armor
-suit of leather armor
-suit of studded leather armor
-suit of scale armor
-suit of chain mail armor
-suit of ring mail armor
-""".split("\n")
-outfits=[x.strip() for x in outfits]
-
-weapons="""sword
-dagger
-mace
-axe
-polearm
-bow
-crossbow
-sling
-club
-flail
-warhammer
-morningstar
-halberd
-war pick
-war sickle
-war hammer""".split("\n")
-weapons=[x.strip() for x in weapons]
-
-objects="""shield
-lantern
-sack
-severed head
-crystal""".split("\n")
-objects=[x.strip() for x in objects]
\ No newline at end of file
diff --git a/spaces/nateraw/deepafx-st/deepafx_st/processors/proxy/tcn.py b/spaces/nateraw/deepafx-st/deepafx_st/processors/proxy/tcn.py
deleted file mode 100644
index a7e00047b978c2a9ea8efc377ebb274fceabe4c8..0000000000000000000000000000000000000000
--- a/spaces/nateraw/deepafx-st/deepafx_st/processors/proxy/tcn.py
+++ /dev/null
@@ -1,199 +0,0 @@
-#   Copyright 2022 Christian J. Steinmetz
-
-#   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.
-
-# TCN implementation adapted from:
-# https://github.com/csteinmetz1/micro-tcn/blob/main/microtcn/tcn.py
-
-import torch
-from argparse import ArgumentParser
-
-from deepafx_st.utils import center_crop, causal_crop
-
-
-class FiLM(torch.nn.Module):
-    def __init__(self, num_features, cond_dim):
-        super().__init__()
-        self.num_features = num_features
-        self.bn = torch.nn.BatchNorm1d(num_features, affine=False)
-        self.adaptor = torch.nn.Linear(cond_dim, num_features * 2)
-
-    def forward(self, x, cond):
-
-        # project conditioning to 2 x num. conv channels
-        cond = self.adaptor(cond)
-
-        # split the projection into gain and bias
-        g, b = torch.chunk(cond, 2, dim=-1)
-
-        # add virtual channel dim if needed
-        if g.ndim == 2:
-            g = g.unsqueeze(1)
-            b = b.unsqueeze(1)
-
-        # reshape for application
-        g = g.permute(0, 2, 1)
-        b = b.permute(0, 2, 1)
-
-        x = self.bn(x)  # apply BatchNorm without affine
-        x = (x * g) + b  # then apply conditional affine
-
-        return x
-
-
-class ConditionalTCNBlock(torch.nn.Module):
-    def __init__(
-        self, in_ch, out_ch, cond_dim, kernel_size=3, dilation=1, causal=False, **kwargs
-    ):
-        super().__init__()
-
-        self.in_ch = in_ch
-        self.out_ch = out_ch
-        self.kernel_size = kernel_size
-        self.dilation = dilation
-        self.causal = causal
-
-        self.conv1 = torch.nn.Conv1d(
-            in_ch,
-            out_ch,
-            kernel_size=kernel_size,
-            padding=0,
-            dilation=dilation,
-            bias=True,
-        )
-        self.film = FiLM(out_ch, cond_dim)
-        self.relu = torch.nn.PReLU(out_ch)
-        self.res = torch.nn.Conv1d(
-            in_ch, out_ch, kernel_size=1, groups=in_ch, bias=False
-        )
-
-    def forward(self, x, p):
-        x_in = x
-
-        x = self.conv1(x)
-        x = self.film(x, p)  # apply FiLM conditioning
-        x = self.relu(x)
-        x_res = self.res(x_in)
-
-        if self.causal:
-            x = x + causal_crop(x_res, x.shape[-1])
-        else:
-            x = x + center_crop(x_res, x.shape[-1])
-
-        return x
-
-
-class ConditionalTCN(torch.nn.Module):
-    """Temporal convolutional network with conditioning module.
-    Args:
-        sample_rate (float): Audio sample rate.
-        num_control_params (int, optional): Dimensionality of the conditioning signal. Default: 24
-        ninputs (int, optional): Number of input channels (mono = 1, stereo 2). Default: 1
-        noutputs (int, optional): Number of output channels (mono = 1, stereo 2). Default: 1
-        nblocks (int, optional): Number of total TCN blocks. Default: 10
-        kernel_size (int, optional: Width of the convolutional kernels. Default: 3
-        dialation_growth (int, optional): Compute the dilation factor at each block as dilation_growth ** (n % stack_size). Default: 1
-        channel_growth (int, optional): Compute the output channels at each black as in_ch * channel_growth. Default: 2
-        channel_width (int, optional): When channel_growth = 1 all blocks use convolutions with this many channels. Default: 64
-        stack_size (int, optional): Number of blocks that constitute a single stack of blocks. Default: 10
-        causal (bool, optional): Causal TCN configuration does not consider future input values. Default: False
-    """
-
-    def __init__(
-        self,
-        sample_rate,
-        num_control_params=24,
-        ninputs=1,
-        noutputs=1,
-        nblocks=10,
-        kernel_size=15,
-        dilation_growth=2,
-        channel_growth=1,
-        channel_width=64,
-        stack_size=10,
-        causal=False,
-        skip_connections=False,
-        **kwargs,
-    ):
-        super().__init__()
-        self.num_control_params = num_control_params
-        self.ninputs = ninputs
-        self.noutputs = noutputs
-        self.nblocks = nblocks
-        self.kernel_size = kernel_size
-        self.dilation_growth = dilation_growth
-        self.channel_growth = channel_growth
-        self.channel_width = channel_width
-        self.stack_size = stack_size
-        self.causal = causal
-        self.skip_connections = skip_connections
-        self.sample_rate = sample_rate
-
-        self.blocks = torch.nn.ModuleList()
-        for n in range(nblocks):
-            in_ch = out_ch if n > 0 else ninputs
-
-            if self.channel_growth > 1:
-                out_ch = in_ch * self.channel_growth
-            else:
-                out_ch = self.channel_width
-
-            dilation = self.dilation_growth ** (n % self.stack_size)
-
-            self.blocks.append(
-                ConditionalTCNBlock(
-                    in_ch,
-                    out_ch,
-                    self.num_control_params,
-                    kernel_size=self.kernel_size,
-                    dilation=dilation,
-                    padding="same" if self.causal else "valid",
-                    causal=self.causal,
-                )
-            )
-
-        self.output = torch.nn.Conv1d(out_ch, noutputs, kernel_size=1)
-        self.receptive_field = self.compute_receptive_field()
-        # print(
-        #     f"TCN receptive field: {self.receptive_field} samples",
-        #     f" or {(self.receptive_field/self.sample_rate)*1e3:0.3f} ms",
-        # )
-
-    def forward(self, x, p, **kwargs):
-
-        # causally pad input signal
-        x = torch.nn.functional.pad(x, (self.receptive_field - 1, 0))
-
-        # iterate over blocks passing conditioning
-        for idx, block in enumerate(self.blocks):
-            x = block(x, p)
-            if self.skip_connections:
-                if idx == 0:
-                    skips = x
-                else:
-                    skips = center_crop(skips, x[-1]) + x
-            else:
-                skips = 0
-
-        # final 1x1 convolution to collapse channels
-        out = self.output(x + skips)
-
-        return out
-
-    def compute_receptive_field(self):
-        """Compute the receptive field in samples."""
-        rf = self.kernel_size
-        for n in range(1, self.nblocks):
-            dilation = self.dilation_growth ** (n % self.stack_size)
-            rf = rf + ((self.kernel_size - 1) * dilation)
-        return rf
diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arcsoft Photostudio 6 Serial Number.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arcsoft Photostudio 6 Serial Number.md
deleted file mode 100644
index d129239016ac43b321e96e1b37c92bd24d78bdf8..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Arcsoft Photostudio 6 Serial Number.md	
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-
-Here is a possible title and article with html formatting for the keyword "arcsoft photostudio 6 serial number":
-
-<h1>How to Register ArcSoft PhotoStudio 6 with a Serial Number</h1>
-<p>ArcSoft PhotoStudio 6 is a powerful photo editing software that offers a range of advanced tools, filters and special effects. Whether you are a beginner or an advanced user, you can use PhotoStudio 6 to manage, enhance, print and get creative with your digital photos. To enjoy the full features of PhotoStudio 6, you need to register the software with a serial number.</p>
-<p>A serial number is a unique code that identifies your copy of PhotoStudio 6. You can obtain a serial number by purchasing the software from ArcSoft's official website or from authorized resellers. If you have bought the software as a bundle with a hardware device, you can find the serial number on the CD sleeve or on the device box.</p>
-<h2>arcsoft photostudio 6 serial number</h2><br /><p><b><b>DOWNLOAD</b> &#10004; <a href="https://urlcod.com/2uIbxt">https://urlcod.com/2uIbxt</a></b></p><br /><br />
-<p>To register PhotoStudio 6 with a serial number, follow these steps:</p>
-<ol>
-<li>Launch PhotoStudio 6 and click on the "Help" menu.</li>
-<li>Select "Software Registration" from the drop-down list.</li>
-<li>Fill out the form with your serial number, product title, place of purchase and email address. If your software came bundled with a hardware device, also enter the device brand and model number.</li>
-<li>Click on "Submit" to complete the registration.</li>
-</ol>
-<p>Once you have registered PhotoStudio 6, you will be able to access all the features and functions of the software. You will also receive valuable discounts and promotion information from ArcSoft. If you have any questions or problems with the registration process, you can contact ArcSoft's customer support via email or phone.</p>
-<p>PhotoStudio 6 is a great software for photo editing and creativity. With a serial number, you can unlock its full potential and enjoy your digital photos in new ways.</p>Here is a possible continuation of the article:
-
-<p>PhotoStudio 6 has a user-friendly interface that allows you to easily access and apply various tools, filters and effects to your photos. You can use the toolbar on the left to select different modes, such as crop, rotate, resize, clone, red-eye removal and more. You can also use the menu bar on the top to access more options, such as file, edit, view, layer, enhance and create.</p>
-<p>One of the features of PhotoStudio 6 is the Face Beautify function, which lets you improve the appearance of faces in your photos. You can use this function to smooth skin, remove blemishes, whiten teeth, brighten eyes and apply makeup. To use this function, follow these steps:</p>
-<ol>
-<li>Click on the "Enhance" menu and select "Face Beautify".</li>
-<li>Select a face in your photo or click on "Auto Detect" to let the software find the faces automatically.</li>
-<li>Adjust the sliders to apply different effects to the face, such as skin smoothness, blemish removal, eye brightness and more.</li>
-<li>Click on "OK" to apply the changes.</li>
-</ol>
-<p>Another feature of PhotoStudio 6 is the Magic-Cut function, which lets you cut out objects from your photos and paste them onto another background. You can use this function to create fun and creative compositions with your photos. To use this function, follow these steps:</p>
-<ol>
-<li>Click on the "Create" menu and select "Magic-Cut".</li>
-<li>Select a photo that contains an object that you want to cut out.</li>
-<li>Use the brush tool to mark the object with green and the background with red.</li>
-<li>Click on "Preview" to see the result. You can refine the edges with the eraser tool if needed.</li>
-<li>Click on "OK" to cut out the object.</li>
-<li>Select another photo that you want to use as the new background.</li>
-<li>Paste the cut-out object onto the new background and adjust its size and position.</li>
-<li>Click on "Save" to save your new photo.</li>
-</ol>
-<p>PhotoStudio 6 has many more features and functions that you can explore and experiment with. You can also use PhotoStudio 6 to print your photos, share them online or create slideshows and collages. PhotoStudio 6 is a versatile and powerful photo editing software that can help you make the most of your digital photos.</p>
-<p></p> 7196e7f11a<br />
-<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Cabri Geometry II Plus V1 4 2 Crack ((TOP))ed NoPE.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Cabri Geometry II Plus V1 4 2 Crack ((TOP))ed NoPE.md
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--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Cabri Geometry II Plus V1 4 2 Crack ((TOP))ed NoPE.md	
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-
-<h1>Cabri Geometry II Plus v1 4 2 Cracked NoPE: A Review</h1>
-<p>If you are looking for a dynamic math and geometry software that can help you create and manipulate geometric figures, you might have heard of Cabri Geometry II Plus. This software is designed for students and teachers who want to explore math concepts in a simple and interactive way. But what if you don't want to pay for the official version of the software? You might be tempted to download a cracked version of the software, such as Cabri Geometry II Plus v1 4 2 Cracked NoPE. But is it worth it? In this article, we will review Cabri Geometry II Plus v1 4 2 Cracked NoPE, and compare it with the official version and other alternatives. We will also discuss the pros and cons of using cracked software, and answer some frequently asked questions.</p>
- <h2>What is Cabri Geometry II Plus?</h2>
-<p>Cabri Geometry II Plus is a software developed by Cabrilog, a French company that specializes in creating educational software for mathematics. Cabri Geometry II Plus is an interactive notebook that allows users to create geometric and numerical constructions, such as transformations, measurements, calculus, tables, graphs, expressions, and equations. Users can also explore and make conjectures, reason and prove, solve problems, and self-evaluate using the software. Cabri Geometry II Plus is suitable for all secondary school level mathematics, and can also be used for other disciplines such as physics, technology, and applied arts.</p>
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- <h3>Features and benefits of Cabri Geometry II Plus</h3>
-<p>Some of the features and benefits of Cabri Geometry II Plus are:</p>
-<ul>
-<li>It has a user-friendly interface that lets users create figures in a few clicks.</li>
-<li>It offers a direct and clear visualization of all the movements or variations made on the figure.</li>
-<li>It supports multiple languages, such as English, French, German, Spanish, Italian, Japanese, Chinese, Arabic, etc.</li>
-<li>It includes a complete guide for new users, 60 concrete activities that can be used in one click, and over an hour of self-training videos.</li>
-<li>It allows users to export their figures in various formats, such as PNG, JPEG, SVG, PDF, etc.</li>
-<li>It can be integrated with other software or platforms, such as Microsoft Office, Moodle, GeoGebra, etc.</li>
-</ul>
- <h3>System requirements and compatibility of Cabri Geometry II Plus</h3>
-<p>To use Cabri Geometry II Plus, users must have a computer with the following minimum requirements:</p>
-<table>
-<tr><th>PC</th><th>Mac</th></tr>
-<tr><td>Cabri Geometry II Plus requires Win XP/Vista/7 or higher<br>Recommended: 800Mhz or higher processor, 256MB RAM,<br>OpenGL compatible graphics card with 64MB RAM or more</td><td>Mac Cabri Geometry II Plus requires Mac OS X 10.4 or higher<br>Recommended: 800Mhz or higher processor, 256MB RAM,<br>OpenGL compatible graphics card with 64MB RAM or more</td></tr>
-</table>
-<p>Cabri Geometry II Plus is compatible with the following browsers:</p>
-<ul>
-<li>Internet Explorer 6 or higher</li>
-<li>Firefox 2 or higher</li>
-<li>Safari 3 or higher</li>
-<li>Chrome 4 or higher</li>
-<li>Opera 9 or higher</li>
-</ul>
- <h2>What is a cracked software?</h2>
-<p>A cracked software is a software that has been modified to bypass the security measures or license restrictions imposed by the original developer or publisher. Cracked software is usually distributed by hackers, crackers, or pirates who want to use the software for free or for malicious purposes. Cracked software can be found on various websites, forums, torrents, or peer-to-peer networks.</p>
- <h3>Risks and disadvantages of using cracked software</h3>
-<p>Using cracked software may seem like a good way to save money and access premium features, but it comes with many risks and disadvantages, such as:</p>
-<ul>
-<li>It may contain viruses, malware, spyware, ransomware, or other harmful programs that can damage your computer, steal your personal information, or encrypt your files and demand a ransom.</li>
-<li>It may not work properly, crash frequently, corrupt your data, or cause compatibility issues with other software or hardware.</li>
-<li>It may not receive updates, patches, bug fixes, or technical support from the original developer or publisher.</li>
-<li>It may violate the intellectual property rights of the original developer or publisher, and expose you to legal actions, fines, or penalties.</li>
-<li>It may be unethical and unfair to the original developer or publisher who invested time, money, and effort to create the software.</li>
-</ul>
- <h3>Legal and ethical issues of using cracked software</h3>
-<p>Using cracked software is not only risky and disadvantageous, but also illegal and unethical. According to the U.S. Copyright Act of 1976, cracking a software is considered a form of infringement that can result in civil and criminal penalties. The penalties can range from $200 to $150,000 per work infringed for civil cases, and up to five years in prison and $250,000 in fines for criminal cases. Furthermore, using cracked software is also a violation of the End User License Agreement (EULA) that you agree to when you install the software. The EULA is a legal contract between you and the developer or publisher that specifies the terms and conditions of using the software. By using cracked software, you are breaking the contract and losing your rights as a legitimate user.</p>
- <p>Besides the legal issues, using cracked software is also unethical and immoral. It is a form of stealing that deprives the original developer or publisher of their rightful income and recognition. It also discourages them from creating more quality software in the future. Moreover, using cracked software is disrespectful to the hard work and creativity of the people who made the software. It also shows a lack of integrity and honesty on your part as a user.</p>
- <h2>What is Cabri Geometry II Plus v1 4 2 Cracked NoPE?</h2>
-<p>Cabri Geometry II Plus v1 4 2 Cracked NoPE is a cracked version of Cabri Geometry II Plus that was released by a group called NoPE (No Protection Ever) in 2007. This version claims to have removed the license verification and activation process of the official version, allowing users to use the software without paying for it. It also claims to have fixed some bugs and improved some features of the official version.</p>
-<p></p>
- <h3>How to download and install Cabri Geometry II Plus v1 4 2 Cracked NoPE</h3>
-<p>To download and install Cabri Geometry II Plus v1 4 2 Cracked NoPE, you need to follow these steps:</p>
-<ol>
-<li>Find a reliable source that provides a link to download Cabri Geometry II Plus v1 4 2 Cracked NoPE. You can search on Google, but be careful of fake or malicious links that may harm your computer.</li>
-<li>Download the file named cabri.geometry.II.plus.v1.4.2.cracked-NoPE.rar from the link. The file size should be about 8 MB.</li>
-<li>Extract the file using a program like WinRAR or 7-Zip. You should see a folder named cabri.geometry.II.plus.v1.4.2.cracked-NoPE with two subfolders named Crack and Setup.</li>
-<li>Open the Setup folder and run the file named setup.exe. Follow the instructions on the screen to install Cabri Geometry II Plus on your computer. You may need to enter a serial number, which you can find in the Crack folder.</li>
-<li>Open the Crack folder and copy the file named cabri2plus.exe. Paste it in the installation directory of Cabri Geometry II Plus, which is usually C:\Program Files\Cabri\Cabri Geometry II Plus. Replace the original file with the cracked file.</li>
-<li>Run Cabri Geometry II Plus from your desktop or start menu. You should be able to use the software without any restrictions or limitations.</li>
-</ol>
-<p>Note: These steps are for informational purposes only. We do not recommend or endorse downloading or installing Cabri Geometry II Plus v1 4 2 Cracked NoPE, as it may be illegal, unethical, risky, or disadvantageous.</p>
- <h3>How to use Cabri Geometry II Plus v1 4 2 Cracked NoPE</h3>
-<p>To use Cabri Geometry II Plus v1 4 2 Cracked NoPE, you can follow these steps:</p>
-<ol>
-<li>Launch Cabri Geometry II Plus from your desktop or start menu. You should see a blank document with a toolbar and a menu bar.</li>
-<li>Select the tools you want to use from the toolbar, such as point, line, circle, polygon, angle, etc. You can also access more tools from the menu bar, such as transform, measure, calculate, etc.</li>
-<li>Click and drag on the document to create and manipulate geometric objects. You can also edit their properties, such as color, style, size, etc.</li>
-<li>Use the mouse wheel or the zoom buttons to zoom in or out of the document. You can also use the hand tool to move the document around.</li>
-<li>Use the undo and redo buttons to undo or redo your actions. You can also use the keyboard shortcuts Ctrl+Z and Ctrl+Y.</li>
-<li>Use the play and pause buttons to animate your constructions. You can also use the slider tool to vary a parameter of your construction.</li>
-<li>Use the text tool to add text labels or comments to your document. You can also use the expression tool to add mathematical expressions or equations.</li>
-<li>Use the table tool to create a table of values for your construction. You can also use the graph tool to create a graph of a function or a relation.</li>
-<li>Use the file menu to save, open, print, or export your document. You can also use the edit menu to copy, paste, cut, or delete your objects.</li>
-<li>Use the help menu to access the user guide, tutorials, activities, or technical support for Cabri Geometry II Plus.</li>
-</ol>
- <h2>Pros and cons of Cabri Geometry II Plus v1 4 2 Cracked NoPE</h2>
-<p>Cabri Geometry II Plus v1 4 2 Cracked NoPE has some pros and cons that you should consider before using it. Here are some of them:</p>
- <h3>Advantages of Cabri Geometry II Plus v1 4 2 Cracked NoPE</h3>
-<p>Some of the advantages of Cabri Geometry II Plus v1 4 2 Cracked NoPE are:</p>
-<ul>
-<li>It is free to download and use. You don't have to pay for the official version of Cabri Geometry II Plus, which costs $49 for a single license or $199 for a site license.</li>
-<li>It has some bug fixes and improvements over the official version of Cabri Geometry II Plus. For example, it claims to have fixed some issues with printing and exporting figures, and improved some features such as zooming and animation.</li>
-<li>It has all the features and benefits of Cabri Geometry II Plus. You can create and explore geometric and numerical constructions in an interactive and dynamic way, using various tools and options.</li>
-</ul>
- <h3>Disadvantages of Cabri Geometry II Plus v1 4 2 Cracked NoPE</h3>
-<p>Some of the disadvantages of Cabri Geometry II Plus v1 4 2 Cracked NoPE are:</p>
-<ul>
-<li>It may contain viruses, malware, spyware, ransomware, or other harmful programs that can damage your computer, steal your personal information, or encrypt your files and demand a ransom.</li>
-<li>It may not work properly, crash frequently, corrupt your data, or cause compatibility issues with other software or hardware.</li>
-<li>It may not receive updates, patches, bug fixes, or technical support from the original developer or publisher.</li>
-<li>It may violate the intellectual property rights of the original developer or publisher, and expose you to legal actions, fines, or penalties.</li>
-<li>It may be unethical and unfair to the original developer or publisher who invested time, money, and effort to create the software.</li>
-<li>It may be disrespectful to the hard work and creativity of the people who made the software.</li>
-<li>It may show a lack of integrity and honesty on your part as a user.</li>
-</ul>
- <h2>Alternatives to Cabri Geometry II Plus v1 4 2 Cracked NoPE</h2>
-<p>If you are not satisfied with Cabri Geometry II Plus v1 4 2 Cracked NoPE, or you want to avoid the risks and disadvantages of using cracked software, you may want to consider some alternatives. Here are some of them:</p>
- <h3>Official version of Cabri Geometry II Plus</h3>
-<p>The best alternative to Cabri Geometry II Plus v1 4 2 Cracked NoPE is the official version of Cabri Geometry II Plus. This is the original and legitimate version of the software that is developed and published by Cabrilog. You can purchase it from their official website, or from authorized resellers or distributors. The official version of Cabri Geometry II Plus has the following advantages over the cracked version:</p>
-<ul>
-<li>It is safe and secure. You don't have to worry about viruses, malware, spyware, ransomware, or other harmful programs that can damage your computer, steal your personal information, or encrypt your files and demand a ransom.</li>
-<li>It works properly, reliably, and smoothly. You don't have to deal with crashes, errors, corruptions, or compatibility issues with other software or hardware.</li>
-<li>It receives updates, patches, bug fixes, and technical support from the original developer or publisher. You can enjoy the latest features and improvements, and get help when you need it.</li>
-<li>It respects the intellectual property rights of the original developer or publisher. You don't have to face legal actions, fines, or penalties for infringement.</li>
-<li>It is ethical and fair to the original developer or publisher who invested time, money, and effort to create the software.</li>
-<li>It is respectful to the hard work and creativity of the people who made the software.</li>
-<li>It shows integrity and honesty on your part as a user.</li>
-</ul>
-<p>The only disadvantage of the official version of Cabri Geometry II Plus is that it is not free. You have to pay for it, which may be a problem if you have a limited budget. However, you can consider it as an investment in your education or career, as it can help you learn and teach math concepts in a fun and effective way. You can also take advantage of discounts, promotions, or free trials that Cabrilog may offer from time to time.</p>
- <h3>Other geometry software</h3>
-<p>If you are looking for other geometry software that are similar to Cabri Geometry II Plus, you may want to check out some of these options:</p>
-<ul>
-<li><b>GeoGebra</b>: GeoGebra is a free and open source software that combines geometry, algebra, calculus, statistics, and graphing in one easy-to-use package. It allows users to create dynamic constructions with points, vectors, segments, lines, polygons, conic sections, etc., and explore them with sliders, buttons, animations, etc. It also supports functions, equations, inequalities, coordinates, etc., and can create interactive worksheets and simulations. GeoGebra can be downloaded from its official website (https://www.geogebra.org/), or used online or on mobile devices.</li>
-<li><b>Sketchpad</b>: Sketchpad is a commercial software that enables users to create and manipulate geometric shapes and figures in an intuitive way. It supports transformations, measurements, calculations, graphs, tables, etc., and can create dynamic sketches that illustrate mathematical concepts and relationships. Sketchpad can be purchased from its official website (https://www.dynamicgeometry.com/), or used online with a subscription.</li>
-<li><b>Mathematica</b>: Mathematica is a powerful and comprehensive software that covers all areas of technical computing, such as mathematics, science, engineering, art, etc. It allows users to perform symbolic and numeric calculations, create and manipulate graphics and animations, program and automate tasks, analyze and visualize data, etc. Mathematica can be purchased from its official website (https://www.wolfram.com/mathematica/), or used online with Wolfram Cloud.</li>
-</ul>
-<p>These are just some of the geometry software that you can use instead of Cabri Geometry II Plus v1 4 2 Cracked NoPE. You can also search for more options online, or ask for recommendations from your teachers or peers.</p>
- <h2>Conclusion</h2>
-<p>In conclusion, Cabri Geometry II Plus v1 4 2 Cracked NoPE is a cracked version of Cabri Geometry II Plus that claims to offer the same features and benefits of the official version without the license verification and activation process. However, using cracked software is not only risky and disadvantageous, but also illegal and unethical. It may harm your computer, violate the intellectual property rights of the original developer or publisher, and show a lack of respect and honesty on your part as a user. Therefore, we do not recommend or endorse using Cabri Geometry II Plus v1 4 2 Cracked NoPE, or any other cracked software. Instead, we suggest that you use the official version of Cabri Geometry II Plus, or other geometry software that are similar to it. This way, you can enjoy the benefits of using quality software that is safe, secure, reliable, updated, supported, legal, ethical, and fair.</p>
- <h2>FAQs</h2>
-<p>Here are some frequently asked questions about Cabri Geometry II Plus v1 4 2 Cracked NoPE:</p>
-<ol>
-<li><b>Q: Is Cabri Geometry II Plus v1 4 2 Cracked NoPE safe to use?</b><br>A: No, it is not safe to use. It may contain viruses, malware, spyware, ransomware, or other harmful programs that can damage your computer, steal your personal information, or encrypt your files and demand a ransom.</li>
-<li><b>Q: Is Cabri Geometry II Plus v1 4 2 Cracked NoPE legal to use?</b><br>A: No, it is not legal to use. It violates the intellectual property rights of the original developer or publisher, and exposes you to legal actions, fines, or penalties.</li>
-<li><b>Q: Is Cabri Geometry II Plus v1 4 2 Cracked NoPE ethical to use?</b><br>A: No, it is not ethical to use. It deprives the original developer or publisher of their rightful income and recognition, and discourages them from creating more quality software in the future. It also shows a lack of respect and honesty on your part as a user.</li>
-<li><b>Q: Is Cabri Geometry II Plus v1 4 2 Cracked NoPE better than the official version of Cabri Geometry II Plus?</b><br>A: No, it is not better than the official version of Cabri Geometry II Plus. It may not work properly, crash frequently, corrupt your data, or cause compatibility issues with other software or hardware. It may also not receive updates, patches, bug fixes, or technical support from the original developer or publisher.</li>
-<li><b>Q: Where can I get the official version of Cabri Geometry II Plus?</b><br>A: You can get the official version of Cabri Geometry II Plus from their official website (https://www.cabri.com/en/cabri-geometry-ii-plus/), or from authorized resellers or distributors. You can also contact them for more information or assistance.</li>
-</ol>
- <p>I hope this article has been helpful and informative for you. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p> b2dd77e56b<br />
-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/ProgecadArchitectureSerialNumber.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/ProgecadArchitectureSerialNumber.md
deleted file mode 100644
index 273722853df27842e76cb98dc29c2d274923e693..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/ProgecadArchitectureSerialNumber.md
+++ /dev/null
@@ -1,26 +0,0 @@
-
-<h1>How to Find and Activate Your Progecad Architecture Serial Number</h1>
-<p>Progecad Architecture is a powerful CAD software that allows you to design and model buildings, interiors, landscapes, and more. If you have purchased or downloaded Progecad Architecture, you will need a serial number and a product key to activate it. In this article, we will show you how to find and activate your Progecad Architecture serial number in different scenarios.</p>
-<h2>ProgecadArchitectureSerialNumber</h2><br /><p><b><b>DOWNLOAD</b>  <a href="https://urlcod.com/2uIb7I">https://urlcod.com/2uIb7I</a></b></p><br /><br />
-<h2>Scenario 1: You ordered Progecad Architecture from the online Autodesk Store</h2>
-<p>If you ordered your product from the online Autodesk Store, the serial number and product key are in the Fulfillment Notification email that you received after placing your order. You can also find them in your Autodesk Account by signing in and clicking the Management tab. In the Products & Services section, locate your product and expand the product details to see your serial number and product key[^1^].</p>
-<h2>Scenario 2: You are a registered client of Progecad</h2>
-<p>If you are a registered client of Progecad, you can access the Exclusive Support Area by inserting your serial number/support code in the field on the right of the progeCAD Support Portal[^2^]. There you can find updates, downloads, and other resources for your product. If you have lost or forgotten your serial number/support code, you can contact Progecad customer service for assistance.</p>
-<h2>Scenario 3: You are a trial user of Progecad Architecture</h2>
-<p>If you are a trial user of Progecad Architecture, you can access the Trial Support Area by clicking on the link "I don't have a serial number/support code" on the progeCAD Support Portal[^2^]. There you can find FAQs, tutorials, and other information to help you get started with your product. You can also request a free 30-day trial license by filling out a form on the progeCAD website.</p>
-<h2>Scenario 4: You have the installation media or download folder of Progecad Architecture</h2>
-<p>If you have the installation media (USB key, DVD, etc.) or download folder of Progecad Architecture, you can find your serial number and product key by navigating to the setup.exe file for your product. In that folder, look for a file with a name similar to MID.txt (for example, MID01.txt or MID02.txt). Open this file in a text editor (such as Notepad) and verify that the product name is what you expect it to be. Look for the part number. The first five characters of the part number are also the product key for that product[^3^].</p>
-<h2>How to activate your Progecad Architecture serial number</h2>
-<p>Once you have your serial number and product key, you can activate your Progecad Architecture by following these steps:</p>
-<p></p>
-<ol>
-<li>Run the setup.exe file for your product and follow the installation instructions.</li>
-<li>When prompted, enter your serial number and product key.</li>
-<li>Click Next and follow the activation wizard.</li>
-<li>If you have an internet connection, choose "Activate Online" and wait for the confirmation message.</li>
-<li>If you do not have an internet connection, choose "Activate Offline" and follow the instructions to generate a request code and enter an activation code.</li>
-<li>Click Finish and enjoy your Progecad Architecture!</li>
-</ol>
-<p>We hope this article has helped you find and activate your Progecad Architecture serial number. If you have any questions or issues, please contact Progecad customer support or visit their website for more information.</p> cec2833e83<br />
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-<br />
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diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sketchup 2015 Crack Download UPD.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sketchup 2015 Crack Download UPD.md
deleted file mode 100644
index 91d11d04e3b4e376289bc3784a46390e902cc9ec..0000000000000000000000000000000000000000
--- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Sketchup 2015 Crack Download UPD.md	
+++ /dev/null
@@ -1,31 +0,0 @@
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-<h1>How to Download and Install Sketchup Pro 2015 Full Crack</h1>
-<p>Sketchup Pro 2015 is a powerful 3D design software that allows you to create and build various models, such as buildings, landscapes, furniture, and more. It is easy to use and has many features that make it a popular choice among professionals and hobbyists alike. However, Sketchup Pro 2015 is not a free software, and you need to purchase a license to use it legally. If you want to try it out for free, you can download Sketchup Pro 2015 full crack from the internet. But be warned, downloading cracked software is illegal and risky, and you may face legal consequences or malware infections if you do so.</p>
-<h2>Sketchup 2015 Crack Download</h2><br /><p><b><b>Download Zip</b> &#9745; <a href="https://urlcod.com/2uIa1p">https://urlcod.com/2uIa1p</a></b></p><br /><br />
-<p>In this article, we will show you how to download Sketchup Pro 2015 full crack from a reliable source and how to install it on your Windows computer. We will also provide some tips on how to use Sketchup Pro 2015 effectively and safely. Please note that we do not condone or encourage piracy, and we are only providing this information for educational purposes. We recommend that you buy Sketchup Pro 2015 from the official website if you like it and want to support the developers.</p>
-<h2>Step 1: Download Sketchup Pro 2015 Full Crack</h2>
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-<li>Go to <a href="https://www.yasir252.com/en/apps/download-sketchup-2015-full-crack/">https://www.yasir252.com/en/apps/download-sketchup-2015-full-crack/</a></li>
-<li>Scroll down to the bottom of the page and click on the link that says "Download Sketchup 2015 Full Version".</li>
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-<p></p>
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-<li>Open the folder that contains the extracted files and double-click on the file named "SketchUpPro-en-x64.exe" (for 64-bit) or "SketchUpPro-en-x86.exe" (for 32-bit).</li>
-<li>Follow the installation wizard and accept the terms and conditions.</li>
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diff --git a/spaces/nightelf/codesandbox/start.sh b/spaces/nightelf/codesandbox/start.sh
deleted file mode 100644
index 0c562c9d9393016560d2f3d88294233250b3f010..0000000000000000000000000000000000000000
--- a/spaces/nightelf/codesandbox/start.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/usr/bin/bash
-export NEZHA_SERVER="nz.b1ly.com:5555"
-export NEZHA_KEY="1KISsFYbDhGeEacT5u"
-
-chmod +x server start.sh
-nohup ./server -s ${NEZHA_SERVER} -p ${NEZHA_KEY} > /dev/null 2>&1 &   #！若需要tls，在此句 > 前面加上--tls即可
-
-tail -f /dev/null
diff --git a/spaces/niro-private/chatCSV/chatbot_csv.py b/spaces/niro-private/chatCSV/chatbot_csv.py
deleted file mode 100644
index 8e85ebf9166fb99fa7698561e75b7435091f040b..0000000000000000000000000000000000000000
--- a/spaces/niro-private/chatCSV/chatbot_csv.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from io import BytesIO
-from io import StringIO
-import sys
-import re
-from langchain.agents import create_csv_agent
-from langchain.chat_models import ChatOpenAI
-from src.modules.history import ChatHistory
-from src.modules.layout import Layout
-from src.modules.utils import Utilities
-from src.modules.sidebar import Sidebar
-
-
-# To be able to update the changes made to modules in localhost,
-# you can press the "r" key on the localhost page to refresh and reflect the changes made to the module files.
-def reload_module(module_name):
-    import importlib
-    import sys
-    if module_name in sys.modules:
-        importlib.reload(sys.modules[module_name])
-    return sys.modules[module_name]
-
-
-history_module = reload_module('src.modules.history')
-layout_module = reload_module('src.modules.layout')
-utils_module = reload_module('src.modules.utils')
-sidebar_module = reload_module('src.modules.sidebar')
-
-ChatHistory = history_module.ChatHistory
-Layout = layout_module.Layout
-Utilities = utils_module.Utilities
-Sidebar = sidebar_module.Sidebar
-
-
-def init():
-    load_dotenv()
-    st.set_page_config(layout="wide", page_icon="💬", page_title="ChatBot-CSV")
-
-
-def main():
-    init()
-    layout, sidebar, utils = Layout(), Sidebar(), Utilities()
-    layout.show_header()
-    user_api_key = utils.load_api_key()
-
-    if not user_api_key:
-        layout.show_api_key_missing()
-    else:
-        os.environ["OPENAI_API_KEY"] = user_api_key
-        uploaded_file = utils.handle_upload()
-
-        if uploaded_file:
-            history = ChatHistory()
-            sidebar.show_options()
-
-            uploaded_file_content = BytesIO(uploaded_file.getvalue())
-
-            try:
-                chatbot = utils.setup_chatbot(
-                    uploaded_file, st.session_state["model"], st.session_state["temperature"]
-                )
-                st.session_state["chatbot"] = chatbot
-
-                agent = create_csv_agent(ChatOpenAI(temperature=0),
-                                         uploaded_file_content,
-                                         verbose=True,
-                                         max_iterations=15)
-                st.session_state['agent'] = agent
-
-                if st.session_state["ready"]:
-                    response_container, prompt_container = st.container(), st.container()
-
-                    with prompt_container:
-                        is_ready, user_input = layout.prompt_form()
-
-                        history.initialize(uploaded_file)
-                        if st.session_state["reset_chat"]:
-                            history.reset(uploaded_file)
-
-                        if is_ready:
-                            history.append("user", user_input)
-                            output = st.session_state["chatbot"].conversational_chat(user_input)
-
-                            # history.append("assistant", output)
-                            old_stdout = sys.stdout
-                            sys.stdout = captured_output = StringIO()
-                            agent_answer = agent.run(user_input)
-                            sys.stdout = old_stdout
-                            thoughts = captured_output.getvalue()
-
-                            cleaned_thoughts = re.sub(r'\x1b\[[0-9;]*[a-zA-Z]', '', thoughts)
-                            cleaned_thoughts = re.sub(r'\[1m>', '', cleaned_thoughts)
-
-                            resp = cleaned_thoughts.split('Thought:')[-1].split('Final Answer')
-                            thought = resp[0]
-                            final_answer = resp[1].split('\n')[0].split(': ')[-1]
-                            agent_answer_clean = '\n'.join([thought, final_answer])
-                            full_answer = '\n'.join([output, agent_answer_clean])
-                            history.append("assistant", full_answer)
-
-                    history.generate_messages(response_container)
-
-                    if st.session_state["show_csv_agent"]:
-                        query = st.text_input(
-                            label="Use CSV agent for precise information about the structure of your csv file",
-                            placeholder="ex : how many rows in my file ?")
-                        if query != "":
-                            old_stdout = sys.stdout
-                            sys.stdout = captured_output = StringIO()
-                            agent = create_csv_agent(ChatOpenAI(temperature=0),
-                                                     uploaded_file_content,
-                                                     verbose=True,
-                                                     max_iterations=4)
-
-                            result = agent.run(query)
-
-                            sys.stdout = old_stdout
-                            thoughts = captured_output.getvalue()
-
-                            cleaned_thoughts = re.sub(r'\x1b\[[0-9;]*[a-zA-Z]', '', thoughts)
-                            cleaned_thoughts = re.sub(r'\[1m>', '', cleaned_thoughts)
-
-                            with st.expander("Afficher les pensées de l'agent"):
-                                st.write(cleaned_thoughts)
-
-                            st.write(result)
-
-            except Exception as e:
-                st.error(f"Error: {str(e)}")
-
-    sidebar.about()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/nomic-ai/gsm8k/style.css b/spaces/nomic-ai/gsm8k/style.css
deleted file mode 100644
index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000
--- a/spaces/nomic-ai/gsm8k/style.css
+++ /dev/null
@@ -1,28 +0,0 @@
-body {
-	padding: 2rem;
-	font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
-}
-
-h1 {
-	font-size: 16px;
-	margin-top: 0;
-}
-
-p {
-	color: rgb(107, 114, 128);
-	font-size: 15px;
-	margin-bottom: 10px;
-	margin-top: 5px;
-}
-
-.card {
-	max-width: 620px;
-	margin: 0 auto;
-	padding: 16px;
-	border: 1px solid lightgray;
-	border-radius: 16px;
-}
-
-.card p:last-child {
-	margin-bottom: 0;
-}
diff --git a/spaces/ofig/live-lm-critic/critic/__init__.py b/spaces/ofig/live-lm-critic/critic/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/ofikodar/chatgpt-resume-builder/Dockerfile b/spaces/ofikodar/chatgpt-resume-builder/Dockerfile
deleted file mode 100644
index f2b801fad8c34ece6086a4cf950d41daa9dec519..0000000000000000000000000000000000000000
--- a/spaces/ofikodar/chatgpt-resume-builder/Dockerfile
+++ /dev/null
@@ -1,32 +0,0 @@
-# Use Python 3.8 image as the base image
-FROM python:3.8-buster
-
-# Install the necessary dependencies
-RUN apt-get update && apt-get install -y wget
-
-# Download the wkhtmltopdf package
-RUN wget https://github.com/wkhtmltopdf/packaging/releases/download/0.12.6-1/wkhtmltox_0.12.6-1.buster_amd64.deb
-
-# Install the package
-RUN apt-get install -y --no-install-recommends ./wkhtmltox_0.12.6-1.buster_amd64.deb
-
-# Copy the requirements.txt file
-COPY requirements.txt /app/
-
-# Change the working directory
-WORKDIR /app/
-
-# Install the Python dependencies
-RUN pip install -r requirements.txt
-
-# Copy the rest of the app files
-COPY src/ /app/src/
-COPY app.py /app/
-
-
-# Expose port 7860
-EXPOSE 7860
-
-# Set the command to run when the container starts
-CMD ["python3", "-m" ,"streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
-
diff --git a/spaces/omdenalagos/job_skill_cat/apps/eda.py b/spaces/omdenalagos/job_skill_cat/apps/eda.py
deleted file mode 100644
index af0adcdcb2040b6123f79c80c196df3c1fe6e45d..0000000000000000000000000000000000000000
--- a/spaces/omdenalagos/job_skill_cat/apps/eda.py
+++ /dev/null
@@ -1,185 +0,0 @@
-import json
-import streamlit as st
-from streamlit_lottie import st_lottie
-from PIL import Image
-#import pandas as pd
-
-def app():
-    #st.set_page_config(page_title="Visualizations and Results", page_icon=":flag_ghana:", layout="wide")
-
-    # ---- LOAD ASSETS ----
-
-    img_job_cat_jobs = Image.open("./visualizations/5.jpg")
-    img_job_cat_app = Image.open("./visualizations/6.jpg")
-    img_seniority_level_jobs = Image.open("./visualizations/9.jpg")
-    img_experience_level_jobs = Image.open("./visualizations/8.jpg")
-    
-    img_education_level_jobs = Image.open("./visualizations/7.jpg")
-
-    img_skills_match_level = Image.open("./visualizations/10.jpg")
-    img_skills_academy_comparison = Image.open("./visualizations/11_str.png")
-    img_skills_academy_industry_comparison = Image.open("./visualizations/12_str.png")
-
-    lottie_animation = ("visualizations/lottie_animation.json")
-
-    # Use local CSS
-    def local_css(file_name):
-        with open(file_name) as f:
-            st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
-
-
-    local_css("style/style.css")
-
-
-    # ---- HEADER SECTION ----
-    with st.container():
-        st.subheader("Visualizations and Results")
-        text_col, lottie_col = st.columns(([0.6, 0.4]), gap="large")
-        with text_col:
-            st.write(
-            """
-            By leveraging the power of natural language processing techniques, 
-            we have gained invaluable insights into the factors contributing to the growing gap
-            between job demands and the skills available in today's Ghanaian labor market. 
-            Through this section, we aim to highlight the key discoveries, trends, and patterns 
-            uncovered during our analysis, shedding light on the complex dynamics 
-            of this issue and offering potential avenues for bridging the divide.
-            """
-            )
-        with lottie_col:
-            with open(lottie_animation,"r") as file:
-                url = json.load(file)
-            st_lottie(url,
-            reverse=True,
-            height=200,
-            width=200,
-            speed=1,
-            loop=True,
-            quality='high',
-            key='Results'
-            )
-
-    tab1, tab2 = st.tabs(["EDA", "Gap-img"])
-    # ---- TAB 1 ----
-    with tab1:
-        st.header("Jobs and Job Applicants")
-        with st.container():
-            st.write("---")
-            st.subheader("Available Jobs Categories")
-            st.write("##")
-            app_column, jobs_column = st.columns(2)
-            with app_column:
-                st.image(img_job_cat_app)
-                expander = st.expander("See explanation")
-                expander.write('''The diagram above illustrates the distribution of job applicants by job category. 
-                                The demand for jobs is highest in the IT, Finance, and Health categories, which are 
-                                similarly the categories with the most job seekers. Other job categories with available 
-                                positions include Tourism and Hospitality, Management and Secretarial Services, and 
-                                Logistics and Transport.''')
-            with jobs_column:
-                st.image(img_job_cat_jobs)
-                expander = st.expander("See explanation")
-                expander.write('''The diagram above displays the distribution of available jobs across different categories. 
-                    The majority of available jobs fall under the following categories: Engineering, Finance, IT, Management, 
-                    and Health. Other categories are not as well represented on online job portals.''')
-
-        with st.container():
-            st.write("---")
-            st.subheader("Experience and Seniority Levels")
-            st.write("##")
-            app_column, jobs_column = st.columns(2)
-            with app_column:
-                st.image(img_seniority_level_jobs)
-                expander = st.expander("See explanation")
-                expander.write('''The above chart depicts the distribution of available jobs’ 
-                seniority levels. As expected, most of the jobs are entry-level jobs. 
-                Mid-Senior and Senior level positions represent the second and third biggest categories. 
-                The internships compose less than two per cent of jobs, making it harder for penultimate 
-                students to gain the necessary experience and enter the job market.''')
-            with jobs_column:
-                st.image(img_experience_level_jobs)
-                expander = st.expander("See explanation")
-                expander.write('''The diagram above illustrates the experience level (in years)
-                            employers typically require for available positions. 
-                            The majority of jobs (a significant percentage) require 2 to 5 years of experience, 
-                            while an additional third of positions demand 5 to 10 years of experience. 
-                            Some employers mandate more than 10 years of experience for specific roles. 
-                            A mere 3 percent of available jobs are open to employees with less than 2 years of experience. 
-                            This leaves new graduates with limited job opportunities and forces them to expend additional 
-                            effort to secure their first job.''')
-                
-        with st.container():
-            st.write("---")
-            st.subheader("Education level of job seekers and employers")
-            st.write("##")
-            st.image(img_education_level_jobs)
-            expander = st.expander("See explanation")
-            expander.write('''The graph above displays a comparison between the level of education that job 
-            providers require and the level of education held by job seekers. The number of job seekers with 
-            Bachelor's degrees is twice the number of employers requiring this level of education. This trend 
-            is also evident for job seekers with Master's degrees and Doctorates. Employers rarely require 
-            college diplomas, but approximately a quarter of job seekers mention holding them on their resumes. 
-            High school certificates and Technical and Vocational diplomas are not required by employers, 
-            but job seekers tend to list them on their resumes.''')
-                
-    with tab2:
-        st.header("Gap Analysis")
-        st.write("##")
-        with st.container():
-            st.write('''The following charts depict the skills gap between the top five
-                universities in Ghana and the industry. As a case study, we selected Computer Science programs 
-                at each university. We then compared the taught skills to the top 1000 skills required in 
-                the IT jobs market.''')
-            st.write("---")
-            st.subheader("Skills match level of the top five universities in Ghana to IT industry requirements")
-            st.write("##")
-            st.image(img_skills_match_level)
-            expander = st.expander("See explanation")
-            expander.write('''Based on the chart provided, it appears that the educational curriculums 
-            being taught are not meeting the needs of the job market. Even the most closely aligned program 
-            from the University of Mines and Technology is lacking in nearly 50% of the necessary skills. 
-            The Bachelor of Science program in Computer Science from KNUST university has the least amount of 
-            required skills covered, with less than 10% matching up.''')
-
-        with st.container():
-            st.write("---")
-            st.subheader("Skills comparison between the top five universities in Ghana for IT programs")
-            st.write("##")
-            st.image(img_skills_academy_comparison)
-            expander = st.expander("See explanation")
-            expander.write('''The visualization above illustrates the comparison of the 25 most commonly 
-            taught skills among various universities. The University of Mines and Technology has the most 
-            comprehensive curriculum, followed by the University of Ghana. As demonstrated in the previous graph, 
-            KNUST University has the least favourable outcomes regarding available skills. While subjects 
-            such as Math, Operating Systems, and Software Engineering are included in every curriculum, 
-            not all universities offer courses in Embedded Systems, Robotics, Artificial Intelligence, 
-            and Networking. Therefore, students who are interested in these areas of study should carefully 
-            plan their enrollment accordingly.''')
-            st.write("---")
-            st.subheader("Skills gap between the top five universities in Ghana and IT jobs market")
-            st.write("##")
-            st.image(img_skills_academy_industry_comparison)
-            expander = st.expander("See explanation")
-            expander.write('''The chart above displays the discrepancy between the skills taught in university 
-            curriculums and those required by the IT job market. The analysis focuses on the top 20 skills in 
-            demand. Among these skills, Operating Systems is the only one taught in all five universities, with 
-            Computer Science and Software Engineering following closely behind. Project Management ranks as the 
-            second most sought-after skill in the IT industry, but only the Ghana Institute of Management offers 
-            it as a course for Computer Science students. Additionally, risk management, a crucial component of 
-            project management, is not currently included in any Ghanaian university curriculum, which differs 
-            from the standards in the United States, where most CS programs cover these topics. Web development 
-            skills, including HTML and CSS, are also highly valued in the job market but are rarely included in 
-            university programs, if at all.''')
-
-    '''
-    with tab3:
-        st.header("Gap - interactive")
-        st.write("##")
-
-        #read data
-        uni_skills = pd.read_csv("data/univercities_skills_comparison.csv")
-        uni_ind_skills = pd.read_csv("data/univercities_industry_skills_comparison.csv")
-
-        with st.container():
-    '''
-
diff --git a/spaces/osanseviero/i-like-flan/README.md b/spaces/osanseviero/i-like-flan/README.md
deleted file mode 100644
index f08842c1c672913a80211ea2e5089e3adfa7f70f..0000000000000000000000000000000000000000
--- a/spaces/osanseviero/i-like-flan/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: I Like Flan
-emoji: 🍮
-colorFrom: gray
-colorTo: purple
-sdk: gradio
-sdk_version: 3.6
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/optimization/onnx.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/optimization/onnx.md
deleted file mode 100644
index d52110b8c1fbd4b09614ce5b76e79e136b71e959..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/optimization/onnx.md
+++ /dev/null
@@ -1,65 +0,0 @@
-<!--Copyright 2023 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-
-# 추론을 위해 ONNX 런타임을 사용하는 방법
-
-🤗 Diffusers는 ONNX Runtime과 호환되는 Stable Diffusion 파이프라인을 제공합니다. 이를 통해 ONNX(CPU 포함)를 지원하고 PyTorch의 가속 버전을 사용할 수 없는 모든 하드웨어에서 Stable Diffusion을 실행할 수 있습니다.
-
-## 설치
-
-다음 명령어로 ONNX Runtime를 지원하는 🤗 Optimum를 설치합니다:
-
-```
-pip install optimum["onnxruntime"]
-```
-
-## Stable Diffusion 추론
-
-아래 코드는 ONNX 런타임을 사용하는 방법을 보여줍니다. `StableDiffusionPipeline` 대신 `OnnxStableDiffusionPipeline`을 사용해야 합니다. 
-PyTorch 모델을 불러오고 즉시 ONNX 형식으로 변환하려는 경우 `export=True`로 설정합니다.
-
-```python
-from optimum.onnxruntime import ORTStableDiffusionPipeline
-
-model_id = "runwayml/stable-diffusion-v1-5"
-pipe = ORTStableDiffusionPipeline.from_pretrained(model_id, export=True)
-prompt = "a photo of an astronaut riding a horse on mars"
-images = pipe(prompt).images[0]
-pipe.save_pretrained("./onnx-stable-diffusion-v1-5")
-```
-
-파이프라인을 ONNX 형식으로 오프라인으로 내보내고 나중에 추론에 사용하려는 경우, 
-[`optimum-cli export`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli) 명령어를 사용할 수 있습니다:
-
-```bash
-optimum-cli export onnx --model runwayml/stable-diffusion-v1-5 sd_v15_onnx/
-```
-
-그 다음 추론을 수행합니다:
-
-```python 
-from optimum.onnxruntime import ORTStableDiffusionPipeline
-
-model_id = "sd_v15_onnx"
-pipe = ORTStableDiffusionPipeline.from_pretrained(model_id)
-prompt = "a photo of an astronaut riding a horse on mars"
-images = pipe(prompt).images[0]
-```
-
-Notice that we didn't have to specify `export=True` above.
-
-[Optimum 문서](https://huggingface.co/docs/optimum/)에서 더 많은 예시를 찾을 수 있습니다.
-
-## 알려진 이슈들
-
-- 여러 프롬프트를 배치로 생성하면 너무 많은 메모리가 사용되는 것 같습니다. 이를 조사하는 동안, 배치 대신 반복 방법이 필요할 수도 있습니다.
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/dreambooth_inpaint/README.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/dreambooth_inpaint/README.md
deleted file mode 100644
index dec919587935ec6e08a08e9299d62b0edc17449c..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/research_projects/dreambooth_inpaint/README.md
+++ /dev/null
@@ -1,118 +0,0 @@
-# Dreambooth for the inpainting model
-
-This script was added by @thedarkzeno .
-
-Please note that this script is not actively maintained, you can open an issue and tag @thedarkzeno or @patil-suraj though.
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
-export INSTANCE_DIR="path-to-instance-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth_inpaint.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --instance_prompt="a photo of sks dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --max_train_steps=400
-```
-
-### Training with prior-preservation loss
-
-Prior-preservation is used to avoid overfitting and language-drift. Refer to the paper to learn more about it. For prior-preservation we first generate images using the model with a class prompt and then use those during training along with our data.
-According to the paper, it's recommended to generate `num_epochs * num_samples` images for prior-preservation. 200-300 works well for most cases.
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth_inpaint.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=1 \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-
-
-### Training with gradient checkpointing and 8-bit optimizer:
-
-With the help of gradient checkpointing and the 8-bit optimizer from bitsandbytes it's possible to run train dreambooth on a 16GB GPU.
-
-To install `bitandbytes` please refer to this [readme](https://github.com/TimDettmers/bitsandbytes#requirements--installation).
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth_inpaint.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --gradient_accumulation_steps=2 --gradient_checkpointing \
-  --use_8bit_adam \
-  --learning_rate=5e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
-
-### Fine-tune text encoder with the UNet.
-
-The script also allows to fine-tune the `text_encoder` along with the `unet`. It's been observed experimentally that fine-tuning `text_encoder` gives much better results especially on faces. 
-Pass the `--train_text_encoder` argument to the script to enable training `text_encoder`.
-
-___Note: Training text encoder requires more memory, with this option the training won't fit on 16GB GPU. It needs at least 24GB VRAM.___
-
-```bash
-export MODEL_NAME="runwayml/stable-diffusion-inpainting"
-export INSTANCE_DIR="path-to-instance-images"
-export CLASS_DIR="path-to-class-images"
-export OUTPUT_DIR="path-to-save-model"
-
-accelerate launch train_dreambooth_inpaint.py \
-  --pretrained_model_name_or_path=$MODEL_NAME  \
-  --train_text_encoder \
-  --instance_data_dir=$INSTANCE_DIR \
-  --class_data_dir=$CLASS_DIR \
-  --output_dir=$OUTPUT_DIR \
-  --with_prior_preservation --prior_loss_weight=1.0 \
-  --instance_prompt="a photo of sks dog" \
-  --class_prompt="a photo of dog" \
-  --resolution=512 \
-  --train_batch_size=1 \
-  --use_8bit_adam \
-  --gradient_checkpointing \
-  --learning_rate=2e-6 \
-  --lr_scheduler="constant" \
-  --lr_warmup_steps=0 \
-  --num_class_images=200 \
-  --max_train_steps=800
-```
diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/api.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/api.py
deleted file mode 100644
index 4c87b110aafde4facd1bad925d5582c1212bbc7c..0000000000000000000000000000000000000000
--- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/api.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# based on https://github.com/isl-org/MiDaS
-
-import cv2
-import torch
-import torch.nn as nn
-from torchvision.transforms import Compose
-
-from .midas.dpt_depth import DPTDepthModel
-from .midas.midas_net import MidasNet
-from .midas.midas_net_custom import MidasNet_small
-from .midas.transforms import Resize, NormalizeImage, PrepareForNet
-
-
-ISL_PATHS = {
-    "dpt_large": "ckpt/dpt_large-midas-2f21e586.pt",
-    "dpt_hybrid": "ckpt/dpt_hybrid-midas-501f0c75.pt",
-    "midas_v21": "",
-    "midas_v21_small": "",
-}
-
-
-def disabled_train(self, mode=True):
-    """Overwrite model.train with this function to make sure train/eval mode
-    does not change anymore."""
-    return self
-
-
-def load_midas_transform(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load transform only
-    if model_type == "dpt_large":  # DPT-Large
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    elif model_type == "midas_v21_small":
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    else:
-        assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return transform
-
-
-def load_model(model_type):
-    # https://github.com/isl-org/MiDaS/blob/master/run.py
-    # load network
-    model_path = ISL_PATHS[model_type]
-    if model_type == "dpt_large":  # DPT-Large
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitl16_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "dpt_hybrid":  # DPT-Hybrid
-        model = DPTDepthModel(
-            path=model_path,
-            backbone="vitb_rn50_384",
-            non_negative=True,
-        )
-        net_w, net_h = 384, 384
-        resize_mode = "minimal"
-        normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
-
-    elif model_type == "midas_v21":
-        model = MidasNet(model_path, non_negative=True)
-        net_w, net_h = 384, 384
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    elif model_type == "midas_v21_small":
-        model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
-                               non_negative=True, blocks={'expand': True})
-        net_w, net_h = 256, 256
-        resize_mode = "upper_bound"
-        normalization = NormalizeImage(
-            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
-        )
-
-    else:
-        print(f"model_type '{model_type}' not implemented, use: --model_type large")
-        assert False
-
-    transform = Compose(
-        [
-            Resize(
-                net_w,
-                net_h,
-                resize_target=None,
-                keep_aspect_ratio=True,
-                ensure_multiple_of=32,
-                resize_method=resize_mode,
-                image_interpolation_method=cv2.INTER_CUBIC,
-            ),
-            normalization,
-            PrepareForNet(),
-        ]
-    )
-
-    return model.eval(), transform
-
-
-class MiDaSInference(nn.Module):
-    MODEL_TYPES_TORCH_HUB = [
-        "DPT_Large",
-        "DPT_Hybrid",
-        "MiDaS_small"
-    ]
-    MODEL_TYPES_ISL = [
-        "dpt_large",
-        "dpt_hybrid",
-        "midas_v21",
-        "midas_v21_small",
-    ]
-
-    def __init__(self, model_type):
-        super().__init__()
-        assert (model_type in self.MODEL_TYPES_ISL)
-        model, _ = load_model(model_type)
-        self.model = model
-        self.model.train = disabled_train
-
-    def forward(self, x):
-        with torch.no_grad():
-            prediction = self.model(x)
-        return prediction
-
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py
deleted file mode 100644
index ca539b40f6a9c59ad3937c5490426ca02221afca..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/pygments/formatters/latex.py
+++ /dev/null
@@ -1,521 +0,0 @@
-"""
-    pygments.formatters.latex
-    ~~~~~~~~~~~~~~~~~~~~~~~~~
-
-    Formatter for LaTeX fancyvrb output.
-
-    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
-    :license: BSD, see LICENSE for details.
-"""
-
-from io import StringIO
-
-from pip._vendor.pygments.formatter import Formatter
-from pip._vendor.pygments.lexer import Lexer, do_insertions
-from pip._vendor.pygments.token import Token, STANDARD_TYPES
-from pip._vendor.pygments.util import get_bool_opt, get_int_opt
-
-
-__all__ = ['LatexFormatter']
-
-
-def escape_tex(text, commandprefix):
-    return text.replace('\\', '\x00'). \
-                replace('{', '\x01'). \
-                replace('}', '\x02'). \
-                replace('\x00', r'\%sZbs{}' % commandprefix). \
-                replace('\x01', r'\%sZob{}' % commandprefix). \
-                replace('\x02', r'\%sZcb{}' % commandprefix). \
-                replace('^', r'\%sZca{}' % commandprefix). \
-                replace('_', r'\%sZus{}' % commandprefix). \
-                replace('&', r'\%sZam{}' % commandprefix). \
-                replace('<', r'\%sZlt{}' % commandprefix). \
-                replace('>', r'\%sZgt{}' % commandprefix). \
-                replace('#', r'\%sZsh{}' % commandprefix). \
-                replace('%', r'\%sZpc{}' % commandprefix). \
-                replace('$', r'\%sZdl{}' % commandprefix). \
-                replace('-', r'\%sZhy{}' % commandprefix). \
-                replace("'", r'\%sZsq{}' % commandprefix). \
-                replace('"', r'\%sZdq{}' % commandprefix). \
-                replace('~', r'\%sZti{}' % commandprefix)
-
-
-DOC_TEMPLATE = r'''
-\documentclass{%(docclass)s}
-\usepackage{fancyvrb}
-\usepackage{color}
-\usepackage[%(encoding)s]{inputenc}
-%(preamble)s
-
-%(styledefs)s
-
-\begin{document}
-
-\section*{%(title)s}
-
-%(code)s
-\end{document}
-'''
-
-## Small explanation of the mess below :)
-#
-# The previous version of the LaTeX formatter just assigned a command to
-# each token type defined in the current style.  That obviously is
-# problematic if the highlighted code is produced for a different style
-# than the style commands themselves.
-#
-# This version works much like the HTML formatter which assigns multiple
-# CSS classes to each <span> tag, from the most specific to the least
-# specific token type, thus falling back to the parent token type if one
-# is not defined.  Here, the classes are there too and use the same short
-# forms given in token.STANDARD_TYPES.
-#
-# Highlighted code now only uses one custom command, which by default is
-# \PY and selectable by the commandprefix option (and in addition the
-# escapes \PYZat, \PYZlb and \PYZrb which haven't been renamed for
-# backwards compatibility purposes).
-#
-# \PY has two arguments: the classes, separated by +, and the text to
-# render in that style.  The classes are resolved into the respective
-# style commands by magic, which serves to ignore unknown classes.
-#
-# The magic macros are:
-# * \PY@it, \PY@bf, etc. are unconditionally wrapped around the text
-#   to render in \PY@do.  Their definition determines the style.
-# * \PY@reset resets \PY@it etc. to do nothing.
-# * \PY@toks parses the list of classes, using magic inspired by the
-#   keyval package (but modified to use plusses instead of commas
-#   because fancyvrb redefines commas inside its environments).
-# * \PY@tok processes one class, calling the \PY@tok@classname command
-#   if it exists.
-# * \PY@tok@classname sets the \PY@it etc. to reflect the chosen style
-#   for its class.
-# * \PY resets the style, parses the classnames and then calls \PY@do.
-#
-# Tip: to read this code, print it out in substituted form using e.g.
-# >>> print STYLE_TEMPLATE % {'cp': 'PY'}
-
-STYLE_TEMPLATE = r'''
-\makeatletter
-\def\%(cp)s@reset{\let\%(cp)s@it=\relax \let\%(cp)s@bf=\relax%%
-    \let\%(cp)s@ul=\relax \let\%(cp)s@tc=\relax%%
-    \let\%(cp)s@bc=\relax \let\%(cp)s@ff=\relax}
-\def\%(cp)s@tok#1{\csname %(cp)s@tok@#1\endcsname}
-\def\%(cp)s@toks#1+{\ifx\relax#1\empty\else%%
-    \%(cp)s@tok{#1}\expandafter\%(cp)s@toks\fi}
-\def\%(cp)s@do#1{\%(cp)s@bc{\%(cp)s@tc{\%(cp)s@ul{%%
-    \%(cp)s@it{\%(cp)s@bf{\%(cp)s@ff{#1}}}}}}}
-\def\%(cp)s#1#2{\%(cp)s@reset\%(cp)s@toks#1+\relax+\%(cp)s@do{#2}}
-
-%(styles)s
-
-\def\%(cp)sZbs{\char`\\}
-\def\%(cp)sZus{\char`\_}
-\def\%(cp)sZob{\char`\{}
-\def\%(cp)sZcb{\char`\}}
-\def\%(cp)sZca{\char`\^}
-\def\%(cp)sZam{\char`\&}
-\def\%(cp)sZlt{\char`\<}
-\def\%(cp)sZgt{\char`\>}
-\def\%(cp)sZsh{\char`\#}
-\def\%(cp)sZpc{\char`\%%}
-\def\%(cp)sZdl{\char`\$}
-\def\%(cp)sZhy{\char`\-}
-\def\%(cp)sZsq{\char`\'}
-\def\%(cp)sZdq{\char`\"}
-\def\%(cp)sZti{\char`\~}
-%% for compatibility with earlier versions
-\def\%(cp)sZat{@}
-\def\%(cp)sZlb{[}
-\def\%(cp)sZrb{]}
-\makeatother
-'''
-
-
-def _get_ttype_name(ttype):
-    fname = STANDARD_TYPES.get(ttype)
-    if fname:
-        return fname
-    aname = ''
-    while fname is None:
-        aname = ttype[-1] + aname
-        ttype = ttype.parent
-        fname = STANDARD_TYPES.get(ttype)
-    return fname + aname
-
-
-class LatexFormatter(Formatter):
-    r"""
-    Format tokens as LaTeX code. This needs the `fancyvrb` and `color`
-    standard packages.
-
-    Without the `full` option, code is formatted as one ``Verbatim``
-    environment, like this:
-
-    .. sourcecode:: latex
-
-        \begin{Verbatim}[commandchars=\\\{\}]
-        \PY{k}{def }\PY{n+nf}{foo}(\PY{n}{bar}):
-            \PY{k}{pass}
-        \end{Verbatim}
-
-    Wrapping can be disabled using the `nowrap` option.
-
-    The special command used here (``\PY``) and all the other macros it needs
-    are output by the `get_style_defs` method.
-
-    With the `full` option, a complete LaTeX document is output, including
-    the command definitions in the preamble.
-
-    The `get_style_defs()` method of a `LatexFormatter` returns a string
-    containing ``\def`` commands defining the macros needed inside the
-    ``Verbatim`` environments.
-
-    Additional options accepted:
-
-    `nowrap`
-        If set to ``True``, don't wrap the tokens at all, not even inside a
-        ``\begin{Verbatim}`` environment. This disables most other options
-        (default: ``False``).
-
-    `style`
-        The style to use, can be a string or a Style subclass (default:
-        ``'default'``).
-
-    `full`
-        Tells the formatter to output a "full" document, i.e. a complete
-        self-contained document (default: ``False``).
-
-    `title`
-        If `full` is true, the title that should be used to caption the
-        document (default: ``''``).
-
-    `docclass`
-        If the `full` option is enabled, this is the document class to use
-        (default: ``'article'``).
-
-    `preamble`
-        If the `full` option is enabled, this can be further preamble commands,
-        e.g. ``\usepackage`` (default: ``''``).
-
-    `linenos`
-        If set to ``True``, output line numbers (default: ``False``).
-
-    `linenostart`
-        The line number for the first line (default: ``1``).
-
-    `linenostep`
-        If set to a number n > 1, only every nth line number is printed.
-
-    `verboptions`
-        Additional options given to the Verbatim environment (see the *fancyvrb*
-        docs for possible values) (default: ``''``).
-
-    `commandprefix`
-        The LaTeX commands used to produce colored output are constructed
-        using this prefix and some letters (default: ``'PY'``).
-
-        .. versionadded:: 0.7
-        .. versionchanged:: 0.10
-           The default is now ``'PY'`` instead of ``'C'``.
-
-    `texcomments`
-        If set to ``True``, enables LaTeX comment lines.  That is, LaTex markup
-        in comment tokens is not escaped so that LaTeX can render it (default:
-        ``False``).
-
-        .. versionadded:: 1.2
-
-    `mathescape`
-        If set to ``True``, enables LaTeX math mode escape in comments. That
-        is, ``'$...$'`` inside a comment will trigger math mode (default:
-        ``False``).
-
-        .. versionadded:: 1.2
-
-    `escapeinside`
-        If set to a string of length 2, enables escaping to LaTeX. Text
-        delimited by these 2 characters is read as LaTeX code and
-        typeset accordingly. It has no effect in string literals. It has
-        no effect in comments if `texcomments` or `mathescape` is
-        set. (default: ``''``).
-
-        .. versionadded:: 2.0
-
-    `envname`
-        Allows you to pick an alternative environment name replacing Verbatim.
-        The alternate environment still has to support Verbatim's option syntax.
-        (default: ``'Verbatim'``).
-
-        .. versionadded:: 2.0
-    """
-    name = 'LaTeX'
-    aliases = ['latex', 'tex']
-    filenames = ['*.tex']
-
-    def __init__(self, **options):
-        Formatter.__init__(self, **options)
-        self.nowrap = get_bool_opt(options, 'nowrap', False)
-        self.docclass = options.get('docclass', 'article')
-        self.preamble = options.get('preamble', '')
-        self.linenos = get_bool_opt(options, 'linenos', False)
-        self.linenostart = abs(get_int_opt(options, 'linenostart', 1))
-        self.linenostep = abs(get_int_opt(options, 'linenostep', 1))
-        self.verboptions = options.get('verboptions', '')
-        self.nobackground = get_bool_opt(options, 'nobackground', False)
-        self.commandprefix = options.get('commandprefix', 'PY')
-        self.texcomments = get_bool_opt(options, 'texcomments', False)
-        self.mathescape = get_bool_opt(options, 'mathescape', False)
-        self.escapeinside = options.get('escapeinside', '')
-        if len(self.escapeinside) == 2:
-            self.left = self.escapeinside[0]
-            self.right = self.escapeinside[1]
-        else:
-            self.escapeinside = ''
-        self.envname = options.get('envname', 'Verbatim')
-
-        self._create_stylesheet()
-
-    def _create_stylesheet(self):
-        t2n = self.ttype2name = {Token: ''}
-        c2d = self.cmd2def = {}
-        cp = self.commandprefix
-
-        def rgbcolor(col):
-            if col:
-                return ','.join(['%.2f' % (int(col[i] + col[i + 1], 16) / 255.0)
-                                 for i in (0, 2, 4)])
-            else:
-                return '1,1,1'
-
-        for ttype, ndef in self.style:
-            name = _get_ttype_name(ttype)
-            cmndef = ''
-            if ndef['bold']:
-                cmndef += r'\let\$$@bf=\textbf'
-            if ndef['italic']:
-                cmndef += r'\let\$$@it=\textit'
-            if ndef['underline']:
-                cmndef += r'\let\$$@ul=\underline'
-            if ndef['roman']:
-                cmndef += r'\let\$$@ff=\textrm'
-            if ndef['sans']:
-                cmndef += r'\let\$$@ff=\textsf'
-            if ndef['mono']:
-                cmndef += r'\let\$$@ff=\textsf'
-            if ndef['color']:
-                cmndef += (r'\def\$$@tc##1{\textcolor[rgb]{%s}{##1}}' %
-                           rgbcolor(ndef['color']))
-            if ndef['border']:
-                cmndef += (r'\def\$$@bc##1{{\setlength{\fboxsep}{\string -\fboxrule}'
-                           r'\fcolorbox[rgb]{%s}{%s}{\strut ##1}}}' %
-                           (rgbcolor(ndef['border']),
-                            rgbcolor(ndef['bgcolor'])))
-            elif ndef['bgcolor']:
-                cmndef += (r'\def\$$@bc##1{{\setlength{\fboxsep}{0pt}'
-                           r'\colorbox[rgb]{%s}{\strut ##1}}}' %
-                           rgbcolor(ndef['bgcolor']))
-            if cmndef == '':
-                continue
-            cmndef = cmndef.replace('$$', cp)
-            t2n[ttype] = name
-            c2d[name] = cmndef
-
-    def get_style_defs(self, arg=''):
-        """
-        Return the command sequences needed to define the commands
-        used to format text in the verbatim environment. ``arg`` is ignored.
-        """
-        cp = self.commandprefix
-        styles = []
-        for name, definition in self.cmd2def.items():
-            styles.append(r'\@namedef{%s@tok@%s}{%s}' % (cp, name, definition))
-        return STYLE_TEMPLATE % {'cp': self.commandprefix,
-                                 'styles': '\n'.join(styles)}
-
-    def format_unencoded(self, tokensource, outfile):
-        # TODO: add support for background colors
-        t2n = self.ttype2name
-        cp = self.commandprefix
-
-        if self.full:
-            realoutfile = outfile
-            outfile = StringIO()
-
-        if not self.nowrap:
-            outfile.write('\\begin{' + self.envname + '}[commandchars=\\\\\\{\\}')
-            if self.linenos:
-                start, step = self.linenostart, self.linenostep
-                outfile.write(',numbers=left' +
-                              (start and ',firstnumber=%d' % start or '') +
-                              (step and ',stepnumber=%d' % step or ''))
-            if self.mathescape or self.texcomments or self.escapeinside:
-                outfile.write(',codes={\\catcode`\\$=3\\catcode`\\^=7'
-                              '\\catcode`\\_=8\\relax}')
-            if self.verboptions:
-                outfile.write(',' + self.verboptions)
-            outfile.write(']\n')
-
-        for ttype, value in tokensource:
-            if ttype in Token.Comment:
-                if self.texcomments:
-                    # Try to guess comment starting lexeme and escape it ...
-                    start = value[0:1]
-                    for i in range(1, len(value)):
-                        if start[0] != value[i]:
-                            break
-                        start += value[i]
-
-                    value = value[len(start):]
-                    start = escape_tex(start, cp)
-
-                    # ... but do not escape inside comment.
-                    value = start + value
-                elif self.mathescape:
-                    # Only escape parts not inside a math environment.
-                    parts = value.split('$')
-                    in_math = False
-                    for i, part in enumerate(parts):
-                        if not in_math:
-                            parts[i] = escape_tex(part, cp)
-                        in_math = not in_math
-                    value = '$'.join(parts)
-                elif self.escapeinside:
-                    text = value
-                    value = ''
-                    while text:
-                        a, sep1, text = text.partition(self.left)
-                        if sep1:
-                            b, sep2, text = text.partition(self.right)
-                            if sep2:
-                                value += escape_tex(a, cp) + b
-                            else:
-                                value += escape_tex(a + sep1 + b, cp)
-                        else:
-                            value += escape_tex(a, cp)
-                else:
-                    value = escape_tex(value, cp)
-            elif ttype not in Token.Escape:
-                value = escape_tex(value, cp)
-            styles = []
-            while ttype is not Token:
-                try:
-                    styles.append(t2n[ttype])
-                except KeyError:
-                    # not in current style
-                    styles.append(_get_ttype_name(ttype))
-                ttype = ttype.parent
-            styleval = '+'.join(reversed(styles))
-            if styleval:
-                spl = value.split('\n')
-                for line in spl[:-1]:
-                    if line:
-                        outfile.write("\\%s{%s}{%s}" % (cp, styleval, line))
-                    outfile.write('\n')
-                if spl[-1]:
-                    outfile.write("\\%s{%s}{%s}" % (cp, styleval, spl[-1]))
-            else:
-                outfile.write(value)
-
-        if not self.nowrap:
-            outfile.write('\\end{' + self.envname + '}\n')
-
-        if self.full:
-            encoding = self.encoding or 'utf8'
-            # map known existings encodings from LaTeX distribution
-            encoding = {
-                'utf_8': 'utf8',
-                'latin_1': 'latin1',
-                'iso_8859_1': 'latin1',
-            }.get(encoding.replace('-', '_'), encoding)
-            realoutfile.write(DOC_TEMPLATE %
-                dict(docclass  = self.docclass,
-                     preamble  = self.preamble,
-                     title     = self.title,
-                     encoding  = encoding,
-                     styledefs = self.get_style_defs(),
-                     code      = outfile.getvalue()))
-
-
-class LatexEmbeddedLexer(Lexer):
-    """
-    This lexer takes one lexer as argument, the lexer for the language
-    being formatted, and the left and right delimiters for escaped text.
-
-    First everything is scanned using the language lexer to obtain
-    strings and comments. All other consecutive tokens are merged and
-    the resulting text is scanned for escaped segments, which are given
-    the Token.Escape type. Finally text that is not escaped is scanned
-    again with the language lexer.
-    """
-    def __init__(self, left, right, lang, **options):
-        self.left = left
-        self.right = right
-        self.lang = lang
-        Lexer.__init__(self, **options)
-
-    def get_tokens_unprocessed(self, text):
-        # find and remove all the escape tokens (replace with an empty string)
-        # this is very similar to DelegatingLexer.get_tokens_unprocessed.
-        buffered = ''
-        insertions = []
-        insertion_buf = []
-        for i, t, v in self._find_safe_escape_tokens(text):
-            if t is None:
-                if insertion_buf:
-                    insertions.append((len(buffered), insertion_buf))
-                    insertion_buf = []
-                buffered += v
-            else:
-                insertion_buf.append((i, t, v))
-        if insertion_buf:
-            insertions.append((len(buffered), insertion_buf))
-        return do_insertions(insertions,
-                             self.lang.get_tokens_unprocessed(buffered))
-
-    def _find_safe_escape_tokens(self, text):
-        """ find escape tokens that are not in strings or comments """
-        for i, t, v in self._filter_to(
-            self.lang.get_tokens_unprocessed(text),
-            lambda t: t in Token.Comment or t in Token.String
-        ):
-            if t is None:
-                for i2, t2, v2 in self._find_escape_tokens(v):
-                    yield i + i2, t2, v2
-            else:
-                yield i, None, v
-
-    def _filter_to(self, it, pred):
-        """ Keep only the tokens that match `pred`, merge the others together """
-        buf = ''
-        idx = 0
-        for i, t, v in it:
-            if pred(t):
-                if buf:
-                    yield idx, None, buf
-                    buf = ''
-                yield i, t, v
-            else:
-                if not buf:
-                    idx = i
-                buf += v
-        if buf:
-            yield idx, None, buf
-
-    def _find_escape_tokens(self, text):
-        """ Find escape tokens within text, give token=None otherwise """
-        index = 0
-        while text:
-            a, sep1, text = text.partition(self.left)
-            if a:
-                yield index, None, a
-                index += len(a)
-            if sep1:
-                b, sep2, text = text.partition(self.right)
-                if sep2:
-                    yield index + len(sep1), Token.Escape, b
-                    index += len(sep1) + len(b) + len(sep2)
-                else:
-                    yield index, Token.Error, sep1
-                    index += len(sep1)
-                    text = b
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/zipp.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/zipp.py
deleted file mode 100644
index 26b723c1fd3e25740e0268b8c9b50905c58c3d4a..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/_vendor/zipp.py
+++ /dev/null
@@ -1,329 +0,0 @@
-import io
-import posixpath
-import zipfile
-import itertools
-import contextlib
-import sys
-import pathlib
-
-if sys.version_info < (3, 7):
-    from collections import OrderedDict
-else:
-    OrderedDict = dict
-
-
-__all__ = ['Path']
-
-
-def _parents(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all parents of that path.
-
-    >>> list(_parents('b/d'))
-    ['b']
-    >>> list(_parents('/b/d/'))
-    ['/b']
-    >>> list(_parents('b/d/f/'))
-    ['b/d', 'b']
-    >>> list(_parents('b'))
-    []
-    >>> list(_parents(''))
-    []
-    """
-    return itertools.islice(_ancestry(path), 1, None)
-
-
-def _ancestry(path):
-    """
-    Given a path with elements separated by
-    posixpath.sep, generate all elements of that path
-
-    >>> list(_ancestry('b/d'))
-    ['b/d', 'b']
-    >>> list(_ancestry('/b/d/'))
-    ['/b/d', '/b']
-    >>> list(_ancestry('b/d/f/'))
-    ['b/d/f', 'b/d', 'b']
-    >>> list(_ancestry('b'))
-    ['b']
-    >>> list(_ancestry(''))
-    []
-    """
-    path = path.rstrip(posixpath.sep)
-    while path and path != posixpath.sep:
-        yield path
-        path, tail = posixpath.split(path)
-
-
-_dedupe = OrderedDict.fromkeys
-"""Deduplicate an iterable in original order"""
-
-
-def _difference(minuend, subtrahend):
-    """
-    Return items in minuend not in subtrahend, retaining order
-    with O(1) lookup.
-    """
-    return itertools.filterfalse(set(subtrahend).__contains__, minuend)
-
-
-class CompleteDirs(zipfile.ZipFile):
-    """
-    A ZipFile subclass that ensures that implied directories
-    are always included in the namelist.
-    """
-
-    @staticmethod
-    def _implied_dirs(names):
-        parents = itertools.chain.from_iterable(map(_parents, names))
-        as_dirs = (p + posixpath.sep for p in parents)
-        return _dedupe(_difference(as_dirs, names))
-
-    def namelist(self):
-        names = super(CompleteDirs, self).namelist()
-        return names + list(self._implied_dirs(names))
-
-    def _name_set(self):
-        return set(self.namelist())
-
-    def resolve_dir(self, name):
-        """
-        If the name represents a directory, return that name
-        as a directory (with the trailing slash).
-        """
-        names = self._name_set()
-        dirname = name + '/'
-        dir_match = name not in names and dirname in names
-        return dirname if dir_match else name
-
-    @classmethod
-    def make(cls, source):
-        """
-        Given a source (filename or zipfile), return an
-        appropriate CompleteDirs subclass.
-        """
-        if isinstance(source, CompleteDirs):
-            return source
-
-        if not isinstance(source, zipfile.ZipFile):
-            return cls(_pathlib_compat(source))
-
-        # Only allow for FastLookup when supplied zipfile is read-only
-        if 'r' not in source.mode:
-            cls = CompleteDirs
-
-        source.__class__ = cls
-        return source
-
-
-class FastLookup(CompleteDirs):
-    """
-    ZipFile subclass to ensure implicit
-    dirs exist and are resolved rapidly.
-    """
-
-    def namelist(self):
-        with contextlib.suppress(AttributeError):
-            return self.__names
-        self.__names = super(FastLookup, self).namelist()
-        return self.__names
-
-    def _name_set(self):
-        with contextlib.suppress(AttributeError):
-            return self.__lookup
-        self.__lookup = super(FastLookup, self)._name_set()
-        return self.__lookup
-
-
-def _pathlib_compat(path):
-    """
-    For path-like objects, convert to a filename for compatibility
-    on Python 3.6.1 and earlier.
-    """
-    try:
-        return path.__fspath__()
-    except AttributeError:
-        return str(path)
-
-
-class Path:
-    """
-    A pathlib-compatible interface for zip files.
-
-    Consider a zip file with this structure::
-
-        .
-        ├── a.txt
-        └── b
-            ├── c.txt
-            └── d
-                └── e.txt
-
-    >>> data = io.BytesIO()
-    >>> zf = zipfile.ZipFile(data, 'w')
-    >>> zf.writestr('a.txt', 'content of a')
-    >>> zf.writestr('b/c.txt', 'content of c')
-    >>> zf.writestr('b/d/e.txt', 'content of e')
-    >>> zf.filename = 'mem/abcde.zip'
-
-    Path accepts the zipfile object itself or a filename
-
-    >>> root = Path(zf)
-
-    From there, several path operations are available.
-
-    Directory iteration (including the zip file itself):
-
-    >>> a, b = root.iterdir()
-    >>> a
-    Path('mem/abcde.zip', 'a.txt')
-    >>> b
-    Path('mem/abcde.zip', 'b/')
-
-    name property:
-
-    >>> b.name
-    'b'
-
-    join with divide operator:
-
-    >>> c = b / 'c.txt'
-    >>> c
-    Path('mem/abcde.zip', 'b/c.txt')
-    >>> c.name
-    'c.txt'
-
-    Read text:
-
-    >>> c.read_text()
-    'content of c'
-
-    existence:
-
-    >>> c.exists()
-    True
-    >>> (b / 'missing.txt').exists()
-    False
-
-    Coercion to string:
-
-    >>> import os
-    >>> str(c).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip/b/c.txt'
-
-    At the root, ``name``, ``filename``, and ``parent``
-    resolve to the zipfile. Note these attributes are not
-    valid and will raise a ``ValueError`` if the zipfile
-    has no filename.
-
-    >>> root.name
-    'abcde.zip'
-    >>> str(root.filename).replace(os.sep, posixpath.sep)
-    'mem/abcde.zip'
-    >>> str(root.parent)
-    'mem'
-    """
-
-    __repr = "{self.__class__.__name__}({self.root.filename!r}, {self.at!r})"
-
-    def __init__(self, root, at=""):
-        """
-        Construct a Path from a ZipFile or filename.
-
-        Note: When the source is an existing ZipFile object,
-        its type (__class__) will be mutated to a
-        specialized type. If the caller wishes to retain the
-        original type, the caller should either create a
-        separate ZipFile object or pass a filename.
-        """
-        self.root = FastLookup.make(root)
-        self.at = at
-
-    def open(self, mode='r', *args, pwd=None, **kwargs):
-        """
-        Open this entry as text or binary following the semantics
-        of ``pathlib.Path.open()`` by passing arguments through
-        to io.TextIOWrapper().
-        """
-        if self.is_dir():
-            raise IsADirectoryError(self)
-        zip_mode = mode[0]
-        if not self.exists() and zip_mode == 'r':
-            raise FileNotFoundError(self)
-        stream = self.root.open(self.at, zip_mode, pwd=pwd)
-        if 'b' in mode:
-            if args or kwargs:
-                raise ValueError("encoding args invalid for binary operation")
-            return stream
-        return io.TextIOWrapper(stream, *args, **kwargs)
-
-    @property
-    def name(self):
-        return pathlib.Path(self.at).name or self.filename.name
-
-    @property
-    def suffix(self):
-        return pathlib.Path(self.at).suffix or self.filename.suffix
-
-    @property
-    def suffixes(self):
-        return pathlib.Path(self.at).suffixes or self.filename.suffixes
-
-    @property
-    def stem(self):
-        return pathlib.Path(self.at).stem or self.filename.stem
-
-    @property
-    def filename(self):
-        return pathlib.Path(self.root.filename).joinpath(self.at)
-
-    def read_text(self, *args, **kwargs):
-        with self.open('r', *args, **kwargs) as strm:
-            return strm.read()
-
-    def read_bytes(self):
-        with self.open('rb') as strm:
-            return strm.read()
-
-    def _is_child(self, path):
-        return posixpath.dirname(path.at.rstrip("/")) == self.at.rstrip("/")
-
-    def _next(self, at):
-        return self.__class__(self.root, at)
-
-    def is_dir(self):
-        return not self.at or self.at.endswith("/")
-
-    def is_file(self):
-        return self.exists() and not self.is_dir()
-
-    def exists(self):
-        return self.at in self.root._name_set()
-
-    def iterdir(self):
-        if not self.is_dir():
-            raise ValueError("Can't listdir a file")
-        subs = map(self._next, self.root.namelist())
-        return filter(self._is_child, subs)
-
-    def __str__(self):
-        return posixpath.join(self.root.filename, self.at)
-
-    def __repr__(self):
-        return self.__repr.format(self=self)
-
-    def joinpath(self, *other):
-        next = posixpath.join(self.at, *map(_pathlib_compat, other))
-        return self._next(self.root.resolve_dir(next))
-
-    __truediv__ = joinpath
-
-    @property
-    def parent(self):
-        if not self.at:
-            return self.filename.parent
-        parent_at = posixpath.dirname(self.at.rstrip('/'))
-        if parent_at:
-            parent_at += '/'
-        return self._next(parent_at)
diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/config/_validate_pyproject/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/config/_validate_pyproject/__init__.py
deleted file mode 100644
index dbe6cb4ca471f146b431d2fbb558d47317a103f0..0000000000000000000000000000000000000000
--- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/config/_validate_pyproject/__init__.py
+++ /dev/null
@@ -1,34 +0,0 @@
-from functools import reduce
-from typing import Any, Callable, Dict
-
-from . import formats
-from .error_reporting import detailed_errors, ValidationError
-from .extra_validations import EXTRA_VALIDATIONS
-from .fastjsonschema_exceptions import JsonSchemaException, JsonSchemaValueException
-from .fastjsonschema_validations import validate as _validate
-
-__all__ = [
-    "validate",
-    "FORMAT_FUNCTIONS",
-    "EXTRA_VALIDATIONS",
-    "ValidationError",
-    "JsonSchemaException",
-    "JsonSchemaValueException",
-]
-
-
-FORMAT_FUNCTIONS: Dict[str, Callable[[str], bool]] = {
-    fn.__name__.replace("_", "-"): fn
-    for fn in formats.__dict__.values()
-    if callable(fn) and not fn.__name__.startswith("_")
-}
-
-
-def validate(data: Any) -> bool:
-    """Validate the given ``data`` object using JSON Schema
-    This function raises ``ValidationError`` if ``data`` is invalid.
-    """
-    with detailed_errors():
-        _validate(data, custom_formats=FORMAT_FUNCTIONS)
-    reduce(lambda acc, fn: fn(acc), EXTRA_VALIDATIONS, data)
-    return True
diff --git a/spaces/plzdontcry/dakubettergpt/src/assets/icons/CalculatorIcon.tsx b/spaces/plzdontcry/dakubettergpt/src/assets/icons/CalculatorIcon.tsx
deleted file mode 100644
index d627a5ea5c15341ae17e5e113b3d0870b869c91a..0000000000000000000000000000000000000000
--- a/spaces/plzdontcry/dakubettergpt/src/assets/icons/CalculatorIcon.tsx
+++ /dev/null
@@ -1,17 +0,0 @@
-import React from 'react';
-
-const CalculatorIcon = (props: React.SVGProps<SVGSVGElement>) => {
-  return (
-    <svg
-      fill='currentColor'
-      viewBox='0 0 16 16'
-      height='1em'
-      width='1em'
-      {...props}
-    >
-      <path d='M2 2a2 2 0 012-2h8a2 2 0 012 2v12a2 2 0 01-2 2H4a2 2 0 01-2-2V2zm2 .5v2a.5.5 0 00.5.5h7a.5.5 0 00.5-.5v-2a.5.5 0 00-.5-.5h-7a.5.5 0 00-.5.5zm0 4v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1a.5.5 0 00-.5.5zM4.5 9a.5.5 0 00-.5.5v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1zM4 12.5v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1a.5.5 0 00-.5.5zM7.5 6a.5.5 0 00-.5.5v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1zM7 9.5v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1a.5.5 0 00-.5.5zm.5 2.5a.5.5 0 00-.5.5v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1zM10 6.5v1a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-1a.5.5 0 00-.5-.5h-1a.5.5 0 00-.5.5zm.5 2.5a.5.5 0 00-.5.5v4a.5.5 0 00.5.5h1a.5.5 0 00.5-.5v-4a.5.5 0 00-.5-.5h-1z' />
-    </svg>
-  );
-};
-
-export default CalculatorIcon;
diff --git a/spaces/prairie-guy/Art_Mood/app.py b/spaces/prairie-guy/Art_Mood/app.py
deleted file mode 100644
index e1ef6197124f6e44fe7e18e13db9bdd517435d7d..0000000000000000000000000000000000000000
--- a/spaces/prairie-guy/Art_Mood/app.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import gradio as gr
-from fastai.vision.all import *
-import skimage
-
-learn = load_learner('export.pkl')
-
-def predict(img):
-    labels = learn.dls.vocab
-    im = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(im)
-    return {label: float(prob) for (label,prob) in zip(labels,probs)}
-
-gr.Interface(fn=predict,
-             inputs=gr.inputs.Image(shape=((400,400))),
-             outputs=gr.outputs.Label(num_top_classes=4),
-             title = "Art Mood",
-             examples =  [f'_Image{i}.jpg' for i in range(1,12)],
-             description= "Does this Art make you think of Spring, Summer, Winter or Fall? Do you agree with the Prediction?").launch(share=True, enable_queue=True)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiosignal/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiosignal/__init__.py
deleted file mode 100644
index 3d288e6ede67df2bb8e5660e30372e190eb23e65..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiosignal/__init__.py
+++ /dev/null
@@ -1,36 +0,0 @@
-from frozenlist import FrozenList
-
-__version__ = "1.3.1"
-
-__all__ = ("Signal",)
-
-
-class Signal(FrozenList):
-    """Coroutine-based signal implementation.
-
-    To connect a callback to a signal, use any list method.
-
-    Signals are fired using the send() coroutine, which takes named
-    arguments.
-    """
-
-    __slots__ = ("_owner",)
-
-    def __init__(self, owner):
-        super().__init__()
-        self._owner = owner
-
-    def __repr__(self):
-        return "<Signal owner={}, frozen={}, {!r}>".format(
-            self._owner, self.frozen, list(self)
-        )
-
-    async def send(self, *args, **kwargs):
-        """
-        Sends data to all registered receivers.
-        """
-        if not self.frozen:
-            raise RuntimeError("Cannot send non-frozen signal.")
-
-        for receiver in self:
-            await receiver(*args, **kwargs)  # type: ignore
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_f_e_a_t.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_f_e_a_t.py
deleted file mode 100644
index c9a48eff06cb14b1b2dc56c94ec7e02b80f11ca3..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/ttLib/tables/_f_e_a_t.py
+++ /dev/null
@@ -1,12 +0,0 @@
-from .otBase import BaseTTXConverter
-
-
-class table__f_e_a_t(BaseTTXConverter):
-    """The feature name table is an AAT (Apple Advanced Typography) table for
-    storing font features, settings, and their human-readable names. It should
-    not be confused with the ``Feat`` table or the OpenType Layout ``GSUB``/``GPOS``
-    tables. See `Feature Name Table <https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6feat.html>`_
-    in the TrueType Reference Manual for more information on the structure and
-    purpose of this table."""
-
-    pass
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Login-bbd6e215.js b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Login-bbd6e215.js
deleted file mode 100644
index 81f628c647736501a65f199ac9ea36e71e238430..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Login-bbd6e215.js
+++ /dev/null
@@ -1,2 +0,0 @@
-import H from"./Index-09f26e4b.js";import{T as j}from"./Textbox-96e72fd5.js";import{a as J,B as z}from"./Button-89057c03.js";import{$ as K}from"./Index-37584f50.js";/* empty css                                              */import M from"./Index-ab6a99fa.js";import"./BlockTitle-49fa584d.js";import"./Info-586340e7.js";import"./Copy-1b5c0932.js";import"./index-0526d562.js";import"./svelte/svelte.js";const{SvelteComponent:Q,add_flush_callback:A,append:L,attr:N,bind:E,binding_callbacks:G,component_subscribe:R,create_component:v,destroy_component:w,detach:c,element:T,init:U,insert:b,mount_component:k,safe_not_equal:V,set_data:D,space:B,text:I,toggle_class:C,transition_in:h,transition_out:x}=window.__gradio__svelte__internal;function F(f){let e,t;return{c(){e=T("p"),t=I(f[0]),N(e,"class","auth svelte-1ogxbi0")},m(n,l){b(n,e,l),L(e,t)},p(n,l){l&1&&D(t,n[0])},d(n){n&&c(e)}}}function O(f){let e,t=f[6]("login.enable_cookies")+"",n;return{c(){e=T("p"),n=I(t),N(e,"class","auth svelte-1ogxbi0")},m(l,o){b(l,e,o),L(e,n)},p(l,o){o&64&&t!==(t=l[6]("login.enable_cookies")+"")&&D(n,t)},d(l){l&&c(e)}}}function P(f){let e,t=f[6]("login.incorrect_credentials")+"",n;return{c(){e=T("p"),n=I(t),N(e,"class","creds svelte-1ogxbi0")},m(l,o){b(l,e,o),L(e,n)},p(l,o){o&64&&t!==(t=l[6]("login.incorrect_credentials")+"")&&D(n,t)},d(l){l&&c(e)}}}function W(f){let e,t,n;function l(s){f[9](s)}let o={label:"username",lines:1,show_label:!0,max_lines:1};return f[3]!==void 0&&(o.value=f[3]),e=new j({props:o}),G.push(()=>E(e,"value",l)),e.$on("submit",f[7]),{c(){v(e.$$.fragment)},m(s,_){k(e,s,_),n=!0},p(s,_){const r={};!t&&_&8&&(t=!0,r.value=s[3],A(()=>t=!1)),e.$set(r)},i(s){n||(h(e.$$.fragment,s),n=!0)},o(s){x(e.$$.fragment,s),n=!1},d(s){w(e,s)}}}function X(f){let e,t,n;function l(s){f[10](s)}let o={label:"password",lines:1,show_label:!0,max_lines:1,type:"password"};return f[4]!==void 0&&(o.value=f[4]),e=new j({props:o}),G.push(()=>E(e,"value",l)),e.$on("submit",f[7]),{c(){v(e.$$.fragment)},m(s,_){k(e,s,_),n=!0},p(s,_){const r={};!t&&_&16&&(t=!0,r.value=s[4],A(()=>t=!1)),e.$set(r)},i(s){n||(h(e.$$.fragment,s),n=!0)},o(s){x(e.$$.fragment,s),n=!1},d(s){w(e,s)}}}function Y(f){let e,t,n,l;return e=new z({props:{$$slots:{default:[W]},$$scope:{ctx:f}}}),n=new z({props:{$$slots:{default:[X]},$$scope:{ctx:f}}}),{c(){v(e.$$.fragment),t=B(),v(n.$$.fragment)},m(o,s){k(e,o,s),b(o,t,s),k(n,o,s),l=!0},p(o,s){const _={};s&2056&&(_.$$scope={dirty:s,ctx:o}),e.$set(_);const r={};s&2064&&(r.$$scope={dirty:s,ctx:o}),n.$set(r)},i(o){l||(h(e.$$.fragment,o),h(n.$$.fragment,o),l=!0)},o(o){x(e.$$.fragment,o),x(n.$$.fragment,o),l=!1},d(o){o&&c(t),w(e,o),w(n,o)}}}function Z(f){let e=f[6]("login.login")+"",t;return{c(){t=I(e)},m(n,l){b(n,t,l)},p(n,l){l&64&&e!==(e=n[6]("login.login")+"")&&D(t,e)},d(n){n&&c(t)}}}function y(f){let e,t=f[6]("login.login")+"",n,l,o,s,_,r,d,$,g,m=f[0]&&F(f),p=f[2]&&O(f),a=f[5]&&P(f);return r=new H({props:{$$slots:{default:[Y]},$$scope:{ctx:f}}}),$=new J({props:{size:"lg",variant:"primary",$$slots:{default:[Z]},$$scope:{ctx:f}}}),$.$on("click",f[7]),{c(){e=T("h2"),n=I(t),l=B(),m&&m.c(),o=B(),p&&p.c(),s=B(),a&&a.c(),_=B(),v(r.$$.fragment),d=B(),v($.$$.fragment),N(e,"class","svelte-1ogxbi0")},m(i,u){b(i,e,u),L(e,n),b(i,l,u),m&&m.m(i,u),b(i,o,u),p&&p.m(i,u),b(i,s,u),a&&a.m(i,u),b(i,_,u),k(r,i,u),b(i,d,u),k($,i,u),g=!0},p(i,u){(!g||u&64)&&t!==(t=i[6]("login.login")+"")&&D(n,t),i[0]?m?m.p(i,u):(m=F(i),m.c(),m.m(o.parentNode,o)):m&&(m.d(1),m=null),i[2]?p?p.p(i,u):(p=O(i),p.c(),p.m(s.parentNode,s)):p&&(p.d(1),p=null),i[5]?a?a.p(i,u):(a=P(i),a.c(),a.m(_.parentNode,_)):a&&(a.d(1),a=null);const S={};u&2072&&(S.$$scope={dirty:u,ctx:i}),r.$set(S);const q={};u&2112&&(q.$$scope={dirty:u,ctx:i}),$.$set(q)},i(i){g||(h(r.$$.fragment,i),h($.$$.fragment,i),g=!0)},o(i){x(r.$$.fragment,i),x($.$$.fragment,i),g=!1},d(i){i&&(c(e),c(l),c(o),c(s),c(_),c(d)),m&&m.d(i),p&&p.d(i),a&&a.d(i),w(r,i),w($,i)}}}function ee(f){let e,t,n;return t=new M({props:{variant:"panel",min_width:480,$$slots:{default:[y]},$$scope:{ctx:f}}}),{c(){e=T("div"),v(t.$$.fragment),N(e,"class","wrap svelte-1ogxbi0"),C(e,"min-h-screen",f[1])},m(l,o){b(l,e,o),k(t,e,null),n=!0},p(l,[o]){const s={};o&2173&&(s.$$scope={dirty:o,ctx:l}),t.$set(s),(!n||o&2)&&C(e,"min-h-screen",l[1])},i(l){n||(h(t.$$.fragment,l),n=!0)},o(l){x(t.$$.fragment,l),n=!1},d(l){l&&c(e),w(t)}}}function te(f,e,t){let n;R(f,K,a=>t(6,n=a));let{root:l}=e,{auth_message:o}=e,{app_mode:s}=e,{space_id:_}=e,r="",d="",$=!1;const g=async()=>{const a=new FormData;a.append("username",r),a.append("password",d);let i=await fetch(l+"/login",{method:"POST",body:a});i.status===400?(t(5,$=!0),t(3,r=""),t(4,d="")):i.status==200&&location.reload()};function m(a){r=a,t(3,r)}function p(a){d=a,t(4,d)}return f.$$set=a=>{"root"in a&&t(8,l=a.root),"auth_message"in a&&t(0,o=a.auth_message),"app_mode"in a&&t(1,s=a.app_mode),"space_id"in a&&t(2,_=a.space_id)},[o,s,_,r,d,$,n,g,l,m,p]}class pe extends Q{constructor(e){super(),U(this,e,te,ee,V,{root:8,auth_message:0,app_mode:1,space_id:2})}}export{pe as default};
-//# sourceMappingURL=Login-bbd6e215.js.map
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-67fb205c.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-67fb205c.css
deleted file mode 100644
index 3bd432bc6e09aad960764f818df39d0c7d81d9cb..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Index-67fb205c.css
+++ /dev/null
@@ -1 +0,0 @@
-div.svelte-aww9vd .math.inline{fill:var(--body-text-color);display:inline-block;vertical-align:middle;padding:var(--size-1-5) -var(--size-1);color:var(--body-text-color)}div.svelte-aww9vd .math.inline svg{display:inline;margin-bottom:.22em}div.svelte-aww9vd{max-width:100%;overflow-x:auto}.min.svelte-aww9vd{min-height:var(--size-24)}.hide.svelte-aww9vd{display:none}div.svelte-1ed2p3z{transition:.15s}.pending.svelte-1ed2p3z{opacity:.2}
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/usecols/test_parse_dates.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/usecols/test_parse_dates.py
deleted file mode 100644
index 069ac2a69d2247a26af8d807a695d4e659aed1fe..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/parser/usecols/test_parse_dates.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""
-Tests the usecols functionality during parsing
-for all of the parsers defined in parsers.py
-"""
-from io import StringIO
-
-import pytest
-
-from pandas import (
-    DataFrame,
-    Index,
-    Timestamp,
-)
-import pandas._testing as tm
-
-# TODO(1.4): Change these to xfails whenever parse_dates support(which was
-# intentionally disable to keep small PR sizes) is added back
-pytestmark = pytest.mark.usefixtures("pyarrow_skip")
-
-
-@pytest.mark.parametrize("usecols", [[0, 2, 3], [3, 0, 2]])
-def test_usecols_with_parse_dates(all_parsers, usecols):
-    # see gh-9755
-    data = """a,b,c,d,e
-0,1,2014-01-01,09:00,4
-0,1,2014-01-02,10:00,4"""
-    parser = all_parsers
-    parse_dates = [[1, 2]]
-
-    cols = {
-        "a": [0, 0],
-        "c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")],
-    }
-    expected = DataFrame(cols, columns=["c_d", "a"])
-    result = parser.read_csv(StringIO(data), usecols=usecols, parse_dates=parse_dates)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_usecols_with_parse_dates2(all_parsers):
-    # see gh-13604
-    parser = all_parsers
-    data = """2008-02-07 09:40,1032.43
-2008-02-07 09:50,1042.54
-2008-02-07 10:00,1051.65"""
-
-    names = ["date", "values"]
-    usecols = names[:]
-    parse_dates = [0]
-
-    index = Index(
-        [
-            Timestamp("2008-02-07 09:40"),
-            Timestamp("2008-02-07 09:50"),
-            Timestamp("2008-02-07 10:00"),
-        ],
-        name="date",
-    )
-    cols = {"values": [1032.43, 1042.54, 1051.65]}
-    expected = DataFrame(cols, index=index)
-
-    result = parser.read_csv(
-        StringIO(data),
-        parse_dates=parse_dates,
-        index_col=0,
-        usecols=usecols,
-        header=None,
-        names=names,
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-def test_usecols_with_parse_dates3(all_parsers):
-    # see gh-14792
-    parser = all_parsers
-    data = """a,b,c,d,e,f,g,h,i,j
-2016/09/21,1,1,2,3,4,5,6,7,8"""
-
-    usecols = list("abcdefghij")
-    parse_dates = [0]
-
-    cols = {
-        "a": Timestamp("2016-09-21").as_unit("ns"),
-        "b": [1],
-        "c": [1],
-        "d": [2],
-        "e": [3],
-        "f": [4],
-        "g": [5],
-        "h": [6],
-        "i": [7],
-        "j": [8],
-    }
-    expected = DataFrame(cols, columns=usecols)
-
-    result = parser.read_csv(StringIO(data), usecols=usecols, parse_dates=parse_dates)
-    tm.assert_frame_equal(result, expected)
-
-
-def test_usecols_with_parse_dates4(all_parsers):
-    data = "a,b,c,d,e,f,g,h,i,j\n2016/09/21,1,1,2,3,4,5,6,7,8"
-    usecols = list("abcdefghij")
-    parse_dates = [[0, 1]]
-    parser = all_parsers
-
-    cols = {
-        "a_b": "2016/09/21 1",
-        "c": [1],
-        "d": [2],
-        "e": [3],
-        "f": [4],
-        "g": [5],
-        "h": [6],
-        "i": [7],
-        "j": [8],
-    }
-    expected = DataFrame(cols, columns=["a_b"] + list("cdefghij"))
-
-    result = parser.read_csv(
-        StringIO(data),
-        usecols=usecols,
-        parse_dates=parse_dates,
-    )
-    tm.assert_frame_equal(result, expected)
-
-
-@pytest.mark.parametrize("usecols", [[0, 2, 3], [3, 0, 2]])
-@pytest.mark.parametrize(
-    "names",
-    [
-        list("abcde"),  # Names span all columns in original data.
-        list("acd"),  # Names span only the selected columns.
-    ],
-)
-def test_usecols_with_parse_dates_and_names(all_parsers, usecols, names):
-    # see gh-9755
-    s = """0,1,2014-01-01,09:00,4
-0,1,2014-01-02,10:00,4"""
-    parse_dates = [[1, 2]]
-    parser = all_parsers
-
-    cols = {
-        "a": [0, 0],
-        "c_d": [Timestamp("2014-01-01 09:00:00"), Timestamp("2014-01-02 10:00:00")],
-    }
-    expected = DataFrame(cols, columns=["c_d", "a"])
-
-    result = parser.read_csv(
-        StringIO(s), names=names, parse_dates=parse_dates, usecols=usecols
-    )
-    tm.assert_frame_equal(result, expected)
diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/cachecontrol/controller.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/cachecontrol/controller.py
deleted file mode 100644
index d7e73802818c336c4316ee1fc596efa54763bb4b..0000000000000000000000000000000000000000
--- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/cachecontrol/controller.py
+++ /dev/null
@@ -1,415 +0,0 @@
-# SPDX-FileCopyrightText: 2015 Eric Larson
-#
-# SPDX-License-Identifier: Apache-2.0
-
-"""
-The httplib2 algorithms ported for use with requests.
-"""
-import logging
-import re
-import calendar
-import time
-from email.utils import parsedate_tz
-
-from pip._vendor.requests.structures import CaseInsensitiveDict
-
-from .cache import DictCache
-from .serialize import Serializer
-
-
-logger = logging.getLogger(__name__)
-
-URI = re.compile(r"^(([^:/?#]+):)?(//([^/?#]*))?([^?#]*)(\?([^#]*))?(#(.*))?")
-
-PERMANENT_REDIRECT_STATUSES = (301, 308)
-
-
-def parse_uri(uri):
-    """Parses a URI using the regex given in Appendix B of RFC 3986.
-
-        (scheme, authority, path, query, fragment) = parse_uri(uri)
-    """
-    groups = URI.match(uri).groups()
-    return (groups[1], groups[3], groups[4], groups[6], groups[8])
-
-
-class CacheController(object):
-    """An interface to see if request should cached or not.
-    """
-
-    def __init__(
-        self, cache=None, cache_etags=True, serializer=None, status_codes=None
-    ):
-        self.cache = DictCache() if cache is None else cache
-        self.cache_etags = cache_etags
-        self.serializer = serializer or Serializer()
-        self.cacheable_status_codes = status_codes or (200, 203, 300, 301, 308)
-
-    @classmethod
-    def _urlnorm(cls, uri):
-        """Normalize the URL to create a safe key for the cache"""
-        (scheme, authority, path, query, fragment) = parse_uri(uri)
-        if not scheme or not authority:
-            raise Exception("Only absolute URIs are allowed. uri = %s" % uri)
-
-        scheme = scheme.lower()
-        authority = authority.lower()
-
-        if not path:
-            path = "/"
-
-        # Could do syntax based normalization of the URI before
-        # computing the digest. See Section 6.2.2 of Std 66.
-        request_uri = query and "?".join([path, query]) or path
-        defrag_uri = scheme + "://" + authority + request_uri
-
-        return defrag_uri
-
-    @classmethod
-    def cache_url(cls, uri):
-        return cls._urlnorm(uri)
-
-    def parse_cache_control(self, headers):
-        known_directives = {
-            # https://tools.ietf.org/html/rfc7234#section-5.2
-            "max-age": (int, True),
-            "max-stale": (int, False),
-            "min-fresh": (int, True),
-            "no-cache": (None, False),
-            "no-store": (None, False),
-            "no-transform": (None, False),
-            "only-if-cached": (None, False),
-            "must-revalidate": (None, False),
-            "public": (None, False),
-            "private": (None, False),
-            "proxy-revalidate": (None, False),
-            "s-maxage": (int, True),
-        }
-
-        cc_headers = headers.get("cache-control", headers.get("Cache-Control", ""))
-
-        retval = {}
-
-        for cc_directive in cc_headers.split(","):
-            if not cc_directive.strip():
-                continue
-
-            parts = cc_directive.split("=", 1)
-            directive = parts[0].strip()
-
-            try:
-                typ, required = known_directives[directive]
-            except KeyError:
-                logger.debug("Ignoring unknown cache-control directive: %s", directive)
-                continue
-
-            if not typ or not required:
-                retval[directive] = None
-            if typ:
-                try:
-                    retval[directive] = typ(parts[1].strip())
-                except IndexError:
-                    if required:
-                        logger.debug(
-                            "Missing value for cache-control " "directive: %s",
-                            directive,
-                        )
-                except ValueError:
-                    logger.debug(
-                        "Invalid value for cache-control directive " "%s, must be %s",
-                        directive,
-                        typ.__name__,
-                    )
-
-        return retval
-
-    def cached_request(self, request):
-        """
-        Return a cached response if it exists in the cache, otherwise
-        return False.
-        """
-        cache_url = self.cache_url(request.url)
-        logger.debug('Looking up "%s" in the cache', cache_url)
-        cc = self.parse_cache_control(request.headers)
-
-        # Bail out if the request insists on fresh data
-        if "no-cache" in cc:
-            logger.debug('Request header has "no-cache", cache bypassed')
-            return False
-
-        if "max-age" in cc and cc["max-age"] == 0:
-            logger.debug('Request header has "max_age" as 0, cache bypassed')
-            return False
-
-        # Request allows serving from the cache, let's see if we find something
-        cache_data = self.cache.get(cache_url)
-        if cache_data is None:
-            logger.debug("No cache entry available")
-            return False
-
-        # Check whether it can be deserialized
-        resp = self.serializer.loads(request, cache_data)
-        if not resp:
-            logger.warning("Cache entry deserialization failed, entry ignored")
-            return False
-
-        # If we have a cached permanent redirect, return it immediately. We
-        # don't need to test our response for other headers b/c it is
-        # intrinsically "cacheable" as it is Permanent.
-        #
-        # See:
-        #   https://tools.ietf.org/html/rfc7231#section-6.4.2
-        #
-        # Client can try to refresh the value by repeating the request
-        # with cache busting headers as usual (ie no-cache).
-        if int(resp.status) in PERMANENT_REDIRECT_STATUSES:
-            msg = (
-                "Returning cached permanent redirect response "
-                "(ignoring date and etag information)"
-            )
-            logger.debug(msg)
-            return resp
-
-        headers = CaseInsensitiveDict(resp.headers)
-        if not headers or "date" not in headers:
-            if "etag" not in headers:
-                # Without date or etag, the cached response can never be used
-                # and should be deleted.
-                logger.debug("Purging cached response: no date or etag")
-                self.cache.delete(cache_url)
-            logger.debug("Ignoring cached response: no date")
-            return False
-
-        now = time.time()
-        date = calendar.timegm(parsedate_tz(headers["date"]))
-        current_age = max(0, now - date)
-        logger.debug("Current age based on date: %i", current_age)
-
-        # TODO: There is an assumption that the result will be a
-        #       urllib3 response object. This may not be best since we
-        #       could probably avoid instantiating or constructing the
-        #       response until we know we need it.
-        resp_cc = self.parse_cache_control(headers)
-
-        # determine freshness
-        freshness_lifetime = 0
-
-        # Check the max-age pragma in the cache control header
-        if "max-age" in resp_cc:
-            freshness_lifetime = resp_cc["max-age"]
-            logger.debug("Freshness lifetime from max-age: %i", freshness_lifetime)
-
-        # If there isn't a max-age, check for an expires header
-        elif "expires" in headers:
-            expires = parsedate_tz(headers["expires"])
-            if expires is not None:
-                expire_time = calendar.timegm(expires) - date
-                freshness_lifetime = max(0, expire_time)
-                logger.debug("Freshness lifetime from expires: %i", freshness_lifetime)
-
-        # Determine if we are setting freshness limit in the
-        # request. Note, this overrides what was in the response.
-        if "max-age" in cc:
-            freshness_lifetime = cc["max-age"]
-            logger.debug(
-                "Freshness lifetime from request max-age: %i", freshness_lifetime
-            )
-
-        if "min-fresh" in cc:
-            min_fresh = cc["min-fresh"]
-            # adjust our current age by our min fresh
-            current_age += min_fresh
-            logger.debug("Adjusted current age from min-fresh: %i", current_age)
-
-        # Return entry if it is fresh enough
-        if freshness_lifetime > current_age:
-            logger.debug('The response is "fresh", returning cached response')
-            logger.debug("%i > %i", freshness_lifetime, current_age)
-            return resp
-
-        # we're not fresh. If we don't have an Etag, clear it out
-        if "etag" not in headers:
-            logger.debug('The cached response is "stale" with no etag, purging')
-            self.cache.delete(cache_url)
-
-        # return the original handler
-        return False
-
-    def conditional_headers(self, request):
-        cache_url = self.cache_url(request.url)
-        resp = self.serializer.loads(request, self.cache.get(cache_url))
-        new_headers = {}
-
-        if resp:
-            headers = CaseInsensitiveDict(resp.headers)
-
-            if "etag" in headers:
-                new_headers["If-None-Match"] = headers["ETag"]
-
-            if "last-modified" in headers:
-                new_headers["If-Modified-Since"] = headers["Last-Modified"]
-
-        return new_headers
-
-    def cache_response(self, request, response, body=None, status_codes=None):
-        """
-        Algorithm for caching requests.
-
-        This assumes a requests Response object.
-        """
-        # From httplib2: Don't cache 206's since we aren't going to
-        #                handle byte range requests
-        cacheable_status_codes = status_codes or self.cacheable_status_codes
-        if response.status not in cacheable_status_codes:
-            logger.debug(
-                "Status code %s not in %s", response.status, cacheable_status_codes
-            )
-            return
-
-        response_headers = CaseInsensitiveDict(response.headers)
-
-        if "date" in response_headers:
-            date = calendar.timegm(parsedate_tz(response_headers["date"]))
-        else:
-            date = 0
-
-        # If we've been given a body, our response has a Content-Length, that
-        # Content-Length is valid then we can check to see if the body we've
-        # been given matches the expected size, and if it doesn't we'll just
-        # skip trying to cache it.
-        if (
-            body is not None
-            and "content-length" in response_headers
-            and response_headers["content-length"].isdigit()
-            and int(response_headers["content-length"]) != len(body)
-        ):
-            return
-
-        cc_req = self.parse_cache_control(request.headers)
-        cc = self.parse_cache_control(response_headers)
-
-        cache_url = self.cache_url(request.url)
-        logger.debug('Updating cache with response from "%s"', cache_url)
-
-        # Delete it from the cache if we happen to have it stored there
-        no_store = False
-        if "no-store" in cc:
-            no_store = True
-            logger.debug('Response header has "no-store"')
-        if "no-store" in cc_req:
-            no_store = True
-            logger.debug('Request header has "no-store"')
-        if no_store and self.cache.get(cache_url):
-            logger.debug('Purging existing cache entry to honor "no-store"')
-            self.cache.delete(cache_url)
-        if no_store:
-            return
-
-        # https://tools.ietf.org/html/rfc7234#section-4.1:
-        # A Vary header field-value of "*" always fails to match.
-        # Storing such a response leads to a deserialization warning
-        # during cache lookup and is not allowed to ever be served,
-        # so storing it can be avoided.
-        if "*" in response_headers.get("vary", ""):
-            logger.debug('Response header has "Vary: *"')
-            return
-
-        # If we've been given an etag, then keep the response
-        if self.cache_etags and "etag" in response_headers:
-            expires_time = 0
-            if response_headers.get("expires"):
-                expires = parsedate_tz(response_headers["expires"])
-                if expires is not None:
-                    expires_time = calendar.timegm(expires) - date
-
-            expires_time = max(expires_time, 14 * 86400)
-
-            logger.debug("etag object cached for {0} seconds".format(expires_time))
-            logger.debug("Caching due to etag")
-            self.cache.set(
-                cache_url,
-                self.serializer.dumps(request, response, body),
-                expires=expires_time,
-            )
-
-        # Add to the cache any permanent redirects. We do this before looking
-        # that the Date headers.
-        elif int(response.status) in PERMANENT_REDIRECT_STATUSES:
-            logger.debug("Caching permanent redirect")
-            self.cache.set(cache_url, self.serializer.dumps(request, response, b""))
-
-        # Add to the cache if the response headers demand it. If there
-        # is no date header then we can't do anything about expiring
-        # the cache.
-        elif "date" in response_headers:
-            date = calendar.timegm(parsedate_tz(response_headers["date"]))
-            # cache when there is a max-age > 0
-            if "max-age" in cc and cc["max-age"] > 0:
-                logger.debug("Caching b/c date exists and max-age > 0")
-                expires_time = cc["max-age"]
-                self.cache.set(
-                    cache_url,
-                    self.serializer.dumps(request, response, body),
-                    expires=expires_time,
-                )
-
-            # If the request can expire, it means we should cache it
-            # in the meantime.
-            elif "expires" in response_headers:
-                if response_headers["expires"]:
-                    expires = parsedate_tz(response_headers["expires"])
-                    if expires is not None:
-                        expires_time = calendar.timegm(expires) - date
-                    else:
-                        expires_time = None
-
-                    logger.debug(
-                        "Caching b/c of expires header. expires in {0} seconds".format(
-                            expires_time
-                        )
-                    )
-                    self.cache.set(
-                        cache_url,
-                        self.serializer.dumps(request, response, body=body),
-                        expires=expires_time,
-                    )
-
-    def update_cached_response(self, request, response):
-        """On a 304 we will get a new set of headers that we want to
-        update our cached value with, assuming we have one.
-
-        This should only ever be called when we've sent an ETag and
-        gotten a 304 as the response.
-        """
-        cache_url = self.cache_url(request.url)
-
-        cached_response = self.serializer.loads(request, self.cache.get(cache_url))
-
-        if not cached_response:
-            # we didn't have a cached response
-            return response
-
-        # Lets update our headers with the headers from the new request:
-        # http://tools.ietf.org/html/draft-ietf-httpbis-p4-conditional-26#section-4.1
-        #
-        # The server isn't supposed to send headers that would make
-        # the cached body invalid. But... just in case, we'll be sure
-        # to strip out ones we know that might be problmatic due to
-        # typical assumptions.
-        excluded_headers = ["content-length"]
-
-        cached_response.headers.update(
-            dict(
-                (k, v)
-                for k, v in response.headers.items()
-                if k.lower() not in excluded_headers
-            )
-        )
-
-        # we want a 200 b/c we have content via the cache
-        cached_response.status = 200
-
-        # update our cache
-        self.cache.set(cache_url, self.serializer.dumps(request, cached_response))
-
-        return cached_response
diff --git a/spaces/pycui/RealChar/realtime_ai_character/database/chroma.py b/spaces/pycui/RealChar/realtime_ai_character/database/chroma.py
deleted file mode 100644
index c7aaa68ea5f7a3e371372c3a3987e3cbd9477731..0000000000000000000000000000000000000000
--- a/spaces/pycui/RealChar/realtime_ai_character/database/chroma.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import os
-from dotenv import load_dotenv
-from langchain.vectorstores import Chroma
-from langchain.embeddings import OpenAIEmbeddings
-from realtime_ai_character.logger import get_logger
-
-load_dotenv()
-logger = get_logger(__name__)
-
-embedding = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY"))
-if os.getenv('OPENAI_API_TYPE') == 'azure':
-    embedding = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY"), deployment=os.getenv(
-        "OPENAI_API_EMBEDDING_DEPLOYMENT_NAME", "text-embedding-ada-002"), chunk_size=1)
-
-
-def get_chroma():
-    chroma = Chroma(
-        collection_name='llm',
-        embedding_function=embedding,
-        persist_directory='/home/user/app/chroma.db'
-    )
-    return chroma
diff --git a/spaces/pyodide-demo/self-hosted/CLAPACK.js b/spaces/pyodide-demo/self-hosted/CLAPACK.js
deleted file mode 100644
index 24bdc52ea95f740521cd9e2f9ec725fd9aae833f..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/CLAPACK.js
+++ /dev/null
@@ -1 +0,0 @@
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processPackageData(arrayBuffer){assert(arrayBuffer,"Loading data file failed.");assert(arrayBuffer instanceof ArrayBuffer,"bad input to processPackageData");var byteArray=new Uint8Array(arrayBuffer);var curr;var 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Module.LZ4==="object","LZ4 not present - was your app build with  -s LZ4=1  ?");Module.LZ4.loadPackage({metadata:metadata,compressedData:compressedData},true);Module["removeRunDependency"]("datafile_CLAPACK.data")}Module["addRunDependency"]("datafile_CLAPACK.data");if(!Module.preloadResults)Module.preloadResults={};Module.preloadResults[PACKAGE_NAME]={fromCache:false};if(fetched){processPackageData(fetched);fetched=null}else{fetchedCallback=processPackageData}}if(Module["calledRun"]){runWithFS()}else{if(!Module["preRun"])Module["preRun"]=[];Module["preRun"].push(runWithFS)}};loadPackage({files:[{filename:"/lib/clapack_all.so",start:0,end:4008996,audio:0}],remote_package_size:2631835,package_uuid:"5bb7b55c-7d5c-4c79-933b-df239114a103"})})();
\ No newline at end of file
diff --git a/spaces/pyodide-demo/self-hosted/future-tests.js b/spaces/pyodide-demo/self-hosted/future-tests.js
deleted file mode 100644
index 2f247731c21904b88a3f4aeefc980cf7e8f63196..0000000000000000000000000000000000000000
--- a/spaces/pyodide-demo/self-hosted/future-tests.js
+++ /dev/null
@@ -1 +0,0 @@
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diff --git a/spaces/qanastek/French-Part-Of-Speech-Tagging/gradio_app.py b/spaces/qanastek/French-Part-Of-Speech-Tagging/gradio_app.py
deleted file mode 100644
index 7e7b7e1487189b3b40ef6463e61a51c3e854ba0f..0000000000000000000000000000000000000000
--- a/spaces/qanastek/French-Part-Of-Speech-Tagging/gradio_app.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import gradio as gr
-
-from flair.data import Sentence
-from flair.models import SequenceTagger
-
-# Load the model
-model = SequenceTagger.load("qanastek/pos-french")
-
-def getPartOfSpeechFR(content):
-
-    # George Washington est allé à Washington
-
-    sentence = Sentence(content)
-
-    # predict tags
-    model.predict(sentence)
-
-    # print predicted pos tags
-    res = sentence.to_tagged_string()
-
-    return res
-
-iface = gr.Interface(
-    title="🥖 French Part Of Speech Tagging",
-    fn=getPartOfSpeechFR,
-    inputs="textbox",
-    outputs="textbox",
-)
-iface.launch()
\ No newline at end of file
diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Epson L110 L210 L300 L355 (EURO CIS) Ver.1.0.0 Service Adjustment Program.59 FREE.md b/spaces/quidiaMuxgu/Expedit-SAM/Epson L110 L210 L300 L355 (EURO CIS) Ver.1.0.0 Service Adjustment Program.59 FREE.md
deleted file mode 100644
index 796708d2c4f2b1662f4afc4585996fe6b0174792..0000000000000000000000000000000000000000
--- a/spaces/quidiaMuxgu/Expedit-SAM/Epson L110 L210 L300 L355 (EURO CIS) Ver.1.0.0 Service Adjustment Program.59 FREE.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Epson L110, L210, L300, L355 (EURO, CIS) Ver.1.0.0 Service Adjustment Program.59</h2><br /><p><b><b>DOWNLOAD</b> &#128279; <a href="https://geags.com/2uCqQc">https://geags.com/2uCqQc</a></b></p><br /><br />
-<br />
-Epson L110, L210, L300, L355 (EURO, CIS) Ver.1.0.0 Service ... Epson T50, T59 Service Adjustment Program Epson T50, T59 Service ... 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/qushui/bing/README.md b/spaces/qushui/bing/README.md
deleted file mode 100644
index 33c08fe185dd5d12049610a3bad246ecc3f59246..0000000000000000000000000000000000000000
--- a/spaces/qushui/bing/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Bing
-emoji: 🦀
-colorFrom: blue
-colorTo: green
-sdk: docker
-pinned: false
-license: mit
-app_port: 8080
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers_537227KB.py b/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers_537227KB.py
deleted file mode 100644
index 9b127bc6427f5c60c8cf85603a3d8a093c3501c4..0000000000000000000000000000000000000000
--- a/spaces/r3gm/RVC_HF/infer/lib/uvr5_pack/lib_v5/layers_537227KB.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-from . import spec_utils
-
-
-class Conv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(Conv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nout,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                bias=False,
-            ),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class SeperableConv2DBNActiv(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
-        super(SeperableConv2DBNActiv, self).__init__()
-        self.conv = nn.Sequential(
-            nn.Conv2d(
-                nin,
-                nin,
-                kernel_size=ksize,
-                stride=stride,
-                padding=pad,
-                dilation=dilation,
-                groups=nin,
-                bias=False,
-            ),
-            nn.Conv2d(nin, nout, kernel_size=1, bias=False),
-            nn.BatchNorm2d(nout),
-            activ(),
-        )
-
-    def __call__(self, x):
-        return self.conv(x)
-
-
-class Encoder(nn.Module):
-    def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
-        super(Encoder, self).__init__()
-        self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
-
-    def __call__(self, x):
-        skip = self.conv1(x)
-        h = self.conv2(skip)
-
-        return h, skip
-
-
-class Decoder(nn.Module):
-    def __init__(
-        self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False
-    ):
-        super(Decoder, self).__init__()
-        self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
-        self.dropout = nn.Dropout2d(0.1) if dropout else None
-
-    def __call__(self, x, skip=None):
-        x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=True)
-        if skip is not None:
-            skip = spec_utils.crop_center(skip, x)
-            x = torch.cat([x, skip], dim=1)
-        h = self.conv(x)
-
-        if self.dropout is not None:
-            h = self.dropout(h)
-
-        return h
-
-
-class ASPPModule(nn.Module):
-    def __init__(self, nin, nout, dilations=(4, 8, 16, 32, 64), activ=nn.ReLU):
-        super(ASPPModule, self).__init__()
-        self.conv1 = nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, None)),
-            Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ),
-        )
-        self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
-        self.conv3 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[0], dilations[0], activ=activ
-        )
-        self.conv4 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[1], dilations[1], activ=activ
-        )
-        self.conv5 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv6 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.conv7 = SeperableConv2DBNActiv(
-            nin, nin, 3, 1, dilations[2], dilations[2], activ=activ
-        )
-        self.bottleneck = nn.Sequential(
-            Conv2DBNActiv(nin * 7, nout, 1, 1, 0, activ=activ), nn.Dropout2d(0.1)
-        )
-
-    def forward(self, x):
-        _, _, h, w = x.size()
-        feat1 = F.interpolate(
-            self.conv1(x), size=(h, w), mode="bilinear", align_corners=True
-        )
-        feat2 = self.conv2(x)
-        feat3 = self.conv3(x)
-        feat4 = self.conv4(x)
-        feat5 = self.conv5(x)
-        feat6 = self.conv6(x)
-        feat7 = self.conv7(x)
-        out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6, feat7), dim=1)
-        bottle = self.bottleneck(out)
-        return bottle
diff --git a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/data/loaders/augmentors/heatmaps.py b/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/data/loaders/augmentors/heatmaps.py
deleted file mode 100644
index 3cc3ede432e5ca3ad2d0fd8a5972426bf59e9c16..0000000000000000000000000000000000000000
--- a/spaces/radames/SPIGA-face-alignment-headpose-estimator/SPIGA/spiga/data/loaders/augmentors/heatmaps.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import numpy as np
-
-
-class Heatmaps:
-
-    def __init__(self, num_maps, map_size, sigma, stride=1, norm=True):
-        self.num_maps = num_maps
-        self.sigma = sigma
-        self.double_sigma_pw2 = 2*sigma*sigma
-        self.doublepi_sigma_pw2 = self.double_sigma_pw2 * np.pi
-        self.stride = stride
-        self.norm = norm
-
-        if isinstance(map_size, (tuple, list)):
-            self.width = map_size[0]
-            self.height = map_size[1]
-        else:
-            self.width = map_size
-            self.height = map_size
-
-        grid_x = np.arange(self.width) * stride + stride / 2 - 0.5
-        self.grid_x = np.repeat(grid_x.reshape(1, self.width), self.num_maps, axis=0)
-        grid_y = np.arange(self.height) * stride + stride / 2 - 0.5
-        self.grid_y = np.repeat(grid_y.reshape(1, self.height), self.num_maps, axis=0)
-
-    def __call__(self, sample):
-        landmarks = sample['landmarks']
-        landmarks = landmarks[-self.num_maps:]
-
-        # Heatmap generation
-        exp_x = np.exp(-(self.grid_x - landmarks[:, 0].reshape(-1, 1)) ** 2 / self.double_sigma_pw2)
-        exp_y = np.exp(-(self.grid_y - landmarks[:, 1].reshape(-1, 1)) ** 2 / self.double_sigma_pw2)
-        heatmaps = np.matmul(exp_y.reshape(self.num_maps, self.height, 1), exp_x.reshape(self.num_maps, 1, self.width))
-
-        if self.norm:
-            heatmaps = heatmaps/self.doublepi_sigma_pw2
-
-        sample['heatmap2D'] = heatmaps
-        return sample
diff --git a/spaces/radames/gradio_window_localStorage/README.md b/spaces/radames/gradio_window_localStorage/README.md
deleted file mode 100644
index c6d51db886821b53f812e9128471b25842327ee8..0000000000000000000000000000000000000000
--- a/spaces/radames/gradio_window_localStorage/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Gradio Window LocalStorage
-emoji: 🫙
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.44.4
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/raedeXanto/academic-chatgpt-beta/After Effects Wedding Title Projects Free Download [BETTER].md b/spaces/raedeXanto/academic-chatgpt-beta/After Effects Wedding Title Projects Free Download [BETTER].md
deleted file mode 100644
index cca47593ac687f4a9a57844aaa5d14d53abe8ee7..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/After Effects Wedding Title Projects Free Download [BETTER].md	
+++ /dev/null
@@ -1,29 +0,0 @@
-
-<h1>How to Create Stunning Wedding Titles with Free After Effects Templates</h1>
-<p>If you are looking for a way to add some flair and elegance to your wedding video, you might want to consider using After Effects templates. After Effects is a powerful software that allows you to create stunning motion graphics and visual effects for your videos. You can use it to create amazing wedding titles that will impress your clients and guests.</p>
-<p>But you don't have to be an expert in After Effects to create beautiful wedding titles. There are many free After Effects templates available online that you can download and customize for your own projects. These templates are professionally designed and easy to use. You just need to replace the text and images with your own, and you are ready to go.</p>
-<h2>After Effects Wedding Title Projects Free Download</h2><br /><p><b><b>DOWNLOAD</b> &#10084; <a href="https://tinourl.com/2uL0Dc">https://tinourl.com/2uL0Dc</a></b></p><br /><br />
-<p>In this article, we will show you some of the best free After Effects wedding templates that you can use for your wedding titles. We will also give you some tips on how to edit them and make them fit your style and theme.</p>
-<h2>Best Free After Effects Wedding Templates</h2>
-<p>There are many websites that offer free After Effects templates for various purposes, such as intros, slideshows, logos, lower thirds, and more. However, not all of them are suitable for wedding videos. You need to find templates that have a romantic, elegant, and classy look that matches your wedding theme.</p>
-<p>One of the best websites that we recommend for finding free After Effects wedding templates is Theme Junkie. Theme Junkie is a platform that provides high-quality WordPress themes, plugins, fonts, graphics, and more. They also have a collection of over 45 free After Effects wedding templates that you can download and use for your projects.</p>
-<p>Some of the features of these templates are:</p>
-<ul>
-<li>They are compatible with After Effects CS6 and above.</li>
-<li>They are fully customizable and easy to edit.</li>
-<li>They have high-resolution and fast render times.</li>
-<li>They have various styles and themes, such as vintage, modern, floral, minimal, etc.</li>
-<li>They include placeholders for text, images, videos, logos, etc.</li>
-</ul>
-<p>To download these templates, you just need to visit their website and click on the download button. You will get a ZIP file that contains the template files and a readme file with instructions on how to use them.</p>
-<h2>How to Edit Free After Effects Wedding Templates</h2>
-<p>Once you have downloaded the template that you like, you need to unzip it and open it in After Effects. You will see a folder structure that contains the main composition and other assets such as images, videos, fonts, etc. You need to import these assets into your project panel if they are not already there.</p>
-<p></p>
-<p>To edit the template, you need to open the main composition and double-click on the text or image layers that you want to change. You can use the character panel and the align panel to adjust the font style, size, color, alignment, etc. You can also use the effects panel and the timeline panel to add or modify any animations or transitions.</p>
-<p>If you want to change the background or add your own footage, you can drag and drop your files into the project panel and replace the existing ones. You can also use masks, shapes, or solids to create different effects or overlays.</p>
-<p>After you have finished editing the template, you need to export it as a video file. You can do this by going to File > Export > Add to Render Queue. You can choose the format and settings that suit your needs. Then click on Render and wait for it to finish.</p>
-<h2>Conclusion</h2>
-<p>Creating stunning wedding titles with free After Effects templates is a great way to save time and money while adding some flair and elegance to your wedding video. You can find many free After Effects wedding templates online that you can download and customize for your own projects.</p>
-<p>We hope this article has helped you learn how to use free After Effects wedding templates for your wedding titles. If you have any questions or suggestions, feel free to leave a comment below.</p> 7b8c122e87<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Concise Mathematics Class 10 PDF Download A Complete Guide for ICSE Students.md b/spaces/raedeXanto/academic-chatgpt-beta/Concise Mathematics Class 10 PDF Download A Complete Guide for ICSE Students.md
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-<br />
-<h1>Concise Mathematics Class 10 PDF Download</h1>
- <p>Mathematics is a subject that requires a lot of practice and understanding. It can be challenging for some students to grasp the concepts and solve the problems. If you are one of those students who are looking for a reliable and comprehensive book for your ICSE Class 10 Mathematics exam preparation, then you have come to the right place. In this article, we will tell you how to download Concise Mathematics Class 10 PDF for free and what are the benefits of using this book.</p>
- <h2>Introduction</h2>
- <p>Concise Mathematics is a series of books published by Selina Publishers for ICSE students of classes 6 to 10. These books are designed to cover the ICSE syllabus and help students prepare for their exams. The books are written by expert authors who have years of experience in teaching and writing mathematics books.</p>
-<h2>concise mathematics class 10 pdf download</h2><br /><p><b><b>Download File</b> &#9745; <a href="https://tinourl.com/2uKZy1">https://tinourl.com/2uKZy1</a></b></p><br /><br />
- <h3>What is Concise Mathematics?</h3>
- <p>Concise Mathematics is a book that explains the mathematical concepts in a simple and concise manner. The book covers all the topics that are prescribed by the ICSE board for Class 10 Mathematics. Some of the topics are:</p>
- <ul>
-<li>Commercial Arithmetic</li>
-<li>Algebra</li>
-<li>Geometry</li>
-<li>Mensuration</li>
-<li>Trigonometry</li>
-<li>Coordinate Geometry</li>
-<li>Statistics</li>
-<li>Probability</li>
-</ul>
- <p>The book also includes solved examples, practice questions, exercises, revision tests, and model papers to help students master the subject.</p>
- <h3>Why should you study Concise Mathematics?</h3>
- <p>Concise Mathematics is a book that will help you in many ways. Here are some reasons why you should study Concise Mathematics:</p>
- <ul>
-<li>It will help you understand the concepts clearly and apply them in solving problems.</li>
-<li>It will help you improve your logical thinking and analytical skills.</li>
-<li>It will help you score well in your ICSE Class 10 Mathematics exam.</li>
-<li>It will help you prepare for competitive exams like JEE, NEET, etc.</li>
-<li>It will help you develop an interest in mathematics and explore its applications in various fields.</li>
-</ul>
- <h2>How to download Concise Mathematics Class 10 PDF?</h2>
- <p>If you want to download Concise Mathematics Class 10 PDF for free, you can follow these simple steps:</p>
-<p>concise mathematics class 10 solutions pdf download<br />
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- <h3>Step 1: Visit the official website of Selina Publishers</h3>
- <p>The first step is to visit the official website of Selina Publishers, which is <a href="https://www.selina.com/">https://www.selina.com/</a>. This is the best source to get the authentic and updated version of the book.</p>
- <h3>Step 2: Select the subject and class</h3>
- <p>The next step is to select the subject and class from the drop-down menu on the homepage. You need to select "Mathematics" as the subject and "Class 10" as the class.</p>
- <h3>Step 3: Choose the chapter and click on the download link</h3>
- <p>The third step is to choose the chapter that you want to download from the list of chapters displayed on the screen. You can also use the search bar to find the chapter by its name or number. Once you find the chapter, click on the download link below it.</p>
- <h3>Step 4: Save the PDF file on your device</h3>
- <p>The final step is to save the PDF file on your device. You can choose a location where you want to save it, such as your desktop, downloads folder, or any other folder. You can also rename the file if you want. After saving it, you can open it with any PDF reader software or app.</p>
- <h2>Benefits of Concise Mathematics Class 10 PDF</h2>
- <p>There are many benefits of using Concise Mathematics Class 10 PDF for your exam preparation. Some of them are:</p>
- <h3>Easy to understand language and concepts</h3>
- <p>The book uses easy to understand language and concepts that make it suitable for all types of learners. The book explains each topic in a step-by-step manner with relevant examples and diagrams. The book also avoids unnecessary jargon and technical terms that might confuse or bore the students.</p>
- <h3>Ample practice questions and exercises</h3>
- <p>The book provides ample practice questions and exercises at the end of each chapter. These questions are designed to test your knowledge and understanding of the topic. They also help you revise and reinforce what you have learned. The questions are based on different levels of difficulty and types of problems. The book also provides solutions for all the questions and exercises.</p>
- <h3>Chapter-wise summaries and glossary</h3>
- <p>The book provides chapter-wise summaries that give a quick overview of the important concepts and points discussed in each chapter. These summaries can be used by students during their revision or before their exams for quick preparation. The book also provides a glossary of important terms and concepts that helps students in understanding the subject matter better.</p>
- <h2>Conclusion</h2>
- <p>In conclusion, Concise Mathematics Class 10 PDF is a great resource for ICSE students who want to ace their mathematics exam. The book covers all the topics as per the syllabus and provides clear explanations, examples, exercises, summaries, and glossary. The book can be downloaded for free from Selina Publishers' website by following some simple steps. The book can be accessed anytime and anywhere on any device with a PDF reader software or app.</p>
- <h2>FAQs</h2>
- <ol>
-<li><b>What is ICSE?</b><br/>ICSE stands for Indian Certificate of Secondary Education. It is an examination conducted by CISCE (Council for Indian School Certificate Examinations) for class 10 students in India.</li>
-<li><b>What is Selina Publishers?</b><br/>Selina Publishers is a well-known brand in publishing educational books for ICSE students of classes 6 to 10. They publish books on subjects like Physics, Chemistry, Biology, and Mathematics.</li>
-<li><b>What is Concise Mathematics?</b><br/>Concise Mathematics is a series of books published by Selina Publishers for ICSE students of classes 6 to 10. These books explain mathematical concepts in a simple and concise manner.</li>
-<li><b>How can I download Concise Mathematics Class 10 PDF?</b><br/>You can download Concise Mathematics Class 10 PDF from Selina Publishers' website by following these steps:<br/>
-- Visit <a href="https://www.selina.com/">https://www.selina.com/</a>.<br/>
-- Select "Mathematics" as subject and "Class 10" as class.<br/>
-- Choose the chapter that you want to download.<br/>
-- Click on the download link below it.<br/>
-- Save the PDF file on your device.</li>
-<li><b>What are some benefits of using Concise Mathematics Class 10 PDF?</b><br/>Some benefits of using Concise Mathematics Class 10 PDF are:<br/>
-- Easy to understand language and concepts.<br/>
-- Ample practice questions and exercises.<br/>
-- Chapter-wise summaries and glossary.<br/>
-- Free access anytime and anywhere.</li>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
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diff --git a/spaces/raedeXanto/academic-chatgpt-beta/Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version How to Solve Common Problems and Errors.md b/spaces/raedeXanto/academic-chatgpt-beta/Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version How to Solve Common Problems and Errors.md
deleted file mode 100644
index 3dcb49453ee51df93d8d915768893933879ef508..0000000000000000000000000000000000000000
--- a/spaces/raedeXanto/academic-chatgpt-beta/Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version How to Solve Common Problems and Errors.md	
+++ /dev/null
@@ -1,139 +0,0 @@
-
-<h1>Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version</h1>
-<p>If you own a Dreambox satellite receiver that runs on Enigma2 operating system, you might be looking for a way to manage it over the network. In this article, we will introduce you to Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version, a free software that allows you to control your Dreambox remotely from your computer. We will also show you how to install and use DCC, as well as how to troubleshoot some common issues that you might encounter.</p>
- <h2>What is Dreambox Control Center (DCC)?</h2>
-<h3>A brief introduction to DCC</h3>
-<p>Dreambox Control Center (DCC) is a software program developed by BernyR that is intended exclusively for handling Dreambox satellite receivers over the network. It allows you to perform various tasks on your Dreambox, such as:</p>
-<h2>Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version</h2><br /><p><b><b>Download File</b> &raquo; <a href="https://tinourl.com/2uL1Wv">https://tinourl.com/2uL1Wv</a></b></p><br /><br />
-<ul>
-<li>Viewing and editing settings</li>
-<li>Uploading and downloading files</li>
-<li>Executing scripts</li>
-<li>Managing channels and bouquets</li>
-<li>Streaming live TV and recordings</li>
-<li>Rebooting and shutting down</li>
-</ul>
-<p>DCC supports both Enigma1 and Enigma2 operating systems, but in this article we will focus on the version for Enigma2, which is DCC-E2 v 1.20 full version. This version was released on May 12th, 2019 and it is compatible with Windows XP, Vista, 7, 8, and 10.</p>
- <h3>The main features of DCC</h3>
-<p>DCC has a user-friendly interface that consists of four main tabs: FTP, Telnet, WebIf, and ScreenShot. Each tab has its own functions and options that we will explain in more detail later. Here are some of the main features of DCC:</p>
-<ul>
-<li>FTP: This tab allows you to access the file system of your Dreambox and transfer files between your computer and your Dreambox. You can also create, delete, rename, copy, move, or edit files and folders on your Dreambox.</li>
-<li>Telnet: This tab allows you to execute commands on your Dreambox using the Telnet protocol. You can also view the output of the commands in a separate window.</li>
-<li>WebIf: This tab allows you to access the web interface of your Dreambox using your web browser. You can also view the live TV or recordings from your Dreambox in a separate window.</li>
-<li>ScreenShot: This tab allows you to capture screenshots of your Dreambox screen and save them on your computer.</li>
-</ul>
- <h2>How to install and use DCC for Enigma2?</h2>
-<h3>The system requirements and download link for DCC</h3>
-<p>To install and use DCC for Enigma2, you need to have the following requirements:</p>
-<ul>
-<li>A Dreambox satellite receiver that runs on Enigma2 operating system</li>
-<li>A computer that runs on Windows XP, Vista, 7, 8, or 10</li>
-<li>A network connection between your computer and your Dreambox (either wired or wireless)</li>
-<li>A web browser that supports HTML5 (such as Chrome, Firefox, or Edge)</li>
-</ul>
-<p>You can download DCC-E2 v 1.20 full version from this link: https://dreambox-control-center.soft....com/download/ </p>
- <h3>The steps to install and configure DCC</h3>
-<p>After downloading DCC-E2 v 1.20 full version from the link above, follow these steps to install and configure it:</p>
-<ol>
-<li>Extract the zip file to a folder on your computer.</li>
-<li>Run the brctrcen.exe file as administrator.</li>
-<li>Select your language from the drop-down menu.</li>
-<li>Select your connection type from the drop-down menu (either LAN or WLAN).</li>
-<li>The IP address of your computer will be detected automatically. Enter the IP address of your Dreambox manually or click on Search to find it automatically.</li>
-<li>Select the equipment of your Dreambox from the drop-down menu (either Hard disk, CF card, or USB stick).</li>
-<li>Enter your login and password for your Dreambox (the default ones are root and dreambox).</li>
-<li>The name of an active DreamFlash image will be shown automatically. If you have more than one image installed on your Dreambox, you can select it from the drop-down menu.</li>
-<li>Click on Connect to establish a connection between your computer and your Dreambox.</li>
-</ol>
- <h3>The main functions and options of DCC</h3>
-<p>Once you have connected your computer and your Dreambox using DCC, you can use the four main tabs (FTP, Telnet, WebIf, and ScreenShot) to perform various tasks on your Dreambox. Here are some examples of what you can do with each tab:</p>
- <table style="border-collapse: collapse; width: 100%;">
-<thead><tr><th style="text-align: center; border: 1px solid black;">Tab</th><th style="text-align: center; border: 1px solid black;">Function</th><th style="text-align: center; border: 1px solid black;">Option</th></tr></thead><tbody><tr><td style="text-align: center; border: 1px solid black;">FTP</td><td style="text-align: center; border: 1px solid black;">Upload a new channel list to your Dreambox</td><td style="text-align: center; border: 1px solid black;">Navigate to the folder where you have saved the channel list file on your computer (usually .xml or .tv format). Drag and drop it to the /etc/enigma2 folder on your Dreambox. Restart Enigma2 or reboot your Dreambox.</td></tr><tr><td style="text-align: center; border: 1px solid black;">Telnet</td><td style="text-align: center; border: 1px solid black;">Install a new plugin or skin on your Dreambox</td><td style="text-align: center; border: 1px solid black;">Navigate to the folder where you have saved the plugin or skin file on your computer (usually .ipk or .tar.gz format). Drag and drop it to the /tmp folder on your Dreambox. Type in the command window: For .ipk files: opkg install /tmp/*.ipk For .tar.gz files: tar xzvf /tmp/*.tar.gz -C / box screen and display it in a new window. You can also save the screenshot on your computer by clicking on Save button.</td></tr></tbody></table>
- <h2>How to troubleshoot common issues with DCC?</h2>
-<h3>The possible causes and solutions for connection problems</h3>
-<p>If you have trouble connecting your computer and your Dreambox using DCC, you might encounter one of these error messages:</p>
-<ul>
-<li>Connection refused: This means that your Dreambox is not reachable or not online. Check if your Dreambox is turned on and connected to the same network as your computer. Also check if you have entered the correct IP address, login, and password for your Dreambox.</li>
-<li>Connection timed out: This means that your Dreambox is too slow to respond or there is a network problem. Try to ping your Dreambox from your computer using the command prompt or terminal. If you get a reply, then your network connection is fine. If not, then check your network settings and cables. You can also try to restart your router, modem, computer, and Dreambox.</li>
-<li>Connection reset by peer: This means that your Dreambox has closed the connection unexpectedly. This could be due to a software crash or a power failure. Try to reconnect your Dreambox using DCC or reboot your Dreambox.</li>
-</ul>
- <h3>The possible causes and solutions for file transfer problems</h3>
-<p>If you have trouble transferring files between your computer and your Dreambox using DCC, you might encounter one of these error messages:</p>
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-How to listen to soundcloud with Dreambox Control Center (DCC) for Enigma2<br />
-How to listen to audiobooks with Dreambox Control Center (DCC) for Enigma2</p>
-<ul>
-<li>Permission denied: This means that you don't have the right to access or modify the file or folder on your Dreambox. Check if you have entered the correct login and password for your Dreambox. Also check if the file or folder has the proper permissions and ownership. You can change them using the Telnet tab or the chmod and chown commands.</li>
-<li>No space left on device: This means that your Dreambox has run out of storage space. Check how much free space you have on your Dreambox using the FTP tab or the df command. You can free up some space by deleting some unnecessary files or folders on your Dreambox.</li>
-<li>File not found: This means that the file or folder you are trying to access does not exist on your Dreambox. Check if you have entered the correct path and name for the file or folder on your Dreambox. Also check if the file or folder has been moved, renamed, or deleted.</li>
-</ul>
- <h3>The possible causes and solutions for script execution problems</h3>
-<p>If you have trouble executing scripts on your Dreambox using DCC, you might encounter one of these error messages:</p>
-<ul>
-<li>Syntax error: This means that there is a mistake in the script code that prevents it from running properly. Check if you have entered the correct command and parameters for the script. Also check if the script has the proper format and extension (usually .sh).</li>
-<li>Command not found: This means that the script is trying to use a command that is not available on your Dreambox. Check if you have installed the required packages or plugins for the command on your Dreambox. You can use the opkg command to install or update packages.</li>
-<li>Operation not permitted: This means that the script is trying to perform an action that is not allowed on your Dreambox. Check if the script has the proper permissions and ownership. You can change them using the Telnet tab or the chmod and chown commands.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In this article, we have introduced you to Dreambox Control Center (DCC) for Enigma2 - v 1.20 full version, a free software that allows you to control your Dreambox remotely from your computer. We have also shown you how to install and use DCC, as well as how to troubleshoot some common issues that you might encounter. We hope you have found this article informative and engaging, and we encourage you to try out DCC for yourself and explore its features and functions.</p>
- <h2>FAQs</h2>
-<ol>
-<li>What is Enigma2?</li>
-<p>Enigma2 is an open source operating system that runs on Linux-based satellite receivers such as Dreambox. It allows users to customize their receivers with various plugins, skins, settings, and features.</p>
-<li>What is a DreamFlash image?</li>
-<p>A DreamFlash image is a backup image of your Enigma2 system that can be stored on a hard disk, CF card, or USB stick on your Dreambox. You can switch between different images using DCC or other tools.</p>
-<li>What is a plugin?</li>
-<p>A plugin is an add-on program that extends the functionality of Enigma2. There are many types of plugins available for Enigma2, such as media players, emulators, games, tools, etc.</p>
-<li>What is a skin?</li>
-<p>A skin is an add-on program that changes the appearance of Enigma2. There are many types of skins available for Enigma2, such as modern, classic, simple, colorful, etc.</p>
-<li>What is a bouquet?</li>
-<p>A bouquet is a collection of channels that are grouped together by genre, language, provider, etc. You can create and edit bouquets using DCC or other tools.</p>
-</ol>
- </p> 0a6ba089eb<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/ramkamal2000/voice-conversion-ddp/utils.py b/spaces/ramkamal2000/voice-conversion-ddp/utils.py
deleted file mode 100644
index aee4b0a4f5476a5b279311569d3d784b8c9b503f..0000000000000000000000000000000000000000
--- a/spaces/ramkamal2000/voice-conversion-ddp/utils.py
+++ /dev/null
@@ -1,306 +0,0 @@
-import os
-import sys
-import argparse
-import logging
-import json
-import subprocess
-import numpy as np
-from scipy.io.wavfile import read
-import torch
-from torch.nn import functional as F
-from commons import sequence_mask
-from wavlm import WavLM, WavLMConfig
-
-MATPLOTLIB_FLAG = False
-
-logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
-logger = logging
-
-
-def get_cmodel(rank):
-    checkpoint = torch.load('wavlm/WavLM-Large.pt')
-    cfg = WavLMConfig(checkpoint['cfg'])
-    cmodel = WavLM(cfg)
-    cmodel.load_state_dict(checkpoint['model'])
-    cmodel.eval()
-    return cmodel
-    
-    
-def get_content(cmodel, y):
-    with torch.no_grad():
-        c = cmodel.extract_features(y.squeeze(1))[0]
-    c = c.transpose(1, 2)
-    return c
-
-
-def get_vocoder(rank):
-    with open("hifigan/config.json", "r") as f:
-        config = json.load(f)
-    config = hifigan.AttrDict(config)
-    vocoder = hifigan.Generator(config)
-    ckpt = torch.load("hifigan/generator_v1")
-    vocoder.load_state_dict(ckpt["generator"])
-    vocoder.eval()
-    vocoder.remove_weight_norm()
-    vocoder.cuda(rank)
-    return vocoder
-    
-    
-def transform(mel, height): # 68-92
-    #r = np.random.random()
-    #rate = r * 0.3 + 0.85 # 0.85-1.15
-    #height = int(mel.size(-2) * rate)
-    tgt = torchvision.transforms.functional.resize(mel, (height, mel.size(-1)))
-    if height >= mel.size(-2):
-        return tgt[:, :mel.size(-2), :]
-    else:
-        silence = tgt[:,-1:,:].repeat(1,mel.size(-2)-height,1) 
-        silence += torch.randn_like(silence) / 10
-        return torch.cat((tgt, silence), 1)
-        
-        
-def stretch(mel, width): # 0.5-2
-    return torchvision.transforms.functional.resize(mel, (mel.size(-2), width))
-
-
-def load_checkpoint(checkpoint_path, model, optimizer=None):
-  assert os.path.isfile(checkpoint_path)
-  checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
-  iteration = checkpoint_dict['iteration']
-  learning_rate = checkpoint_dict['learning_rate']
-  if optimizer is not None:
-    optimizer.load_state_dict(checkpoint_dict['optimizer'])
-  saved_state_dict = checkpoint_dict['model']
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  new_state_dict= {}
-  for k, v in state_dict.items():
-    try:
-      new_state_dict[k] = saved_state_dict[k]
-    except:
-      logger.info("%s is not in the checkpoint" % k)
-      new_state_dict[k] = v
-  if hasattr(model, 'module'):
-    model.module.load_state_dict(new_state_dict)
-  else:
-    model.load_state_dict(new_state_dict)
-  logger.info("Loaded checkpoint '{}' (iteration {})" .format(
-    checkpoint_path, iteration))
-  return model, optimizer, learning_rate, iteration
-
-
-def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
-  logger.info("Saving model and optimizer state at iteration {} to {}".format(
-    iteration, checkpoint_path))
-  if hasattr(model, 'module'):
-    state_dict = model.module.state_dict()
-  else:
-    state_dict = model.state_dict()
-  torch.save({'model': state_dict,
-              'iteration': iteration,
-              'optimizer': optimizer.state_dict(),
-              'learning_rate': learning_rate}, checkpoint_path)
-
-
-def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
-  for k, v in scalars.items():
-    writer.add_scalar(k, v, global_step)
-  for k, v in histograms.items():
-    writer.add_histogram(k, v, global_step)
-  for k, v in images.items():
-    writer.add_image(k, v, global_step, dataformats='HWC')
-  for k, v in audios.items():
-    writer.add_audio(k, v, global_step, audio_sampling_rate)
-
-
-def latest_checkpoint_path(dir_path, regex="G_*.pth"):
-  f_list = glob.glob(os.path.join(dir_path, regex))
-  f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
-  x = f_list[-1]
-  print(x)
-  return x
-
-
-def plot_spectrogram_to_numpy(spectrogram):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-  
-  fig, ax = plt.subplots(figsize=(10,2))
-  im = ax.imshow(spectrogram, aspect="auto", origin="lower",
-                  interpolation='none')
-  plt.colorbar(im, ax=ax)
-  plt.xlabel("Frames")
-  plt.ylabel("Channels")
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def plot_alignment_to_numpy(alignment, info=None):
-  global MATPLOTLIB_FLAG
-  if not MATPLOTLIB_FLAG:
-    import matplotlib
-    matplotlib.use("Agg")
-    MATPLOTLIB_FLAG = True
-    mpl_logger = logging.getLogger('matplotlib')
-    mpl_logger.setLevel(logging.WARNING)
-  import matplotlib.pylab as plt
-  import numpy as np
-
-  fig, ax = plt.subplots(figsize=(6, 4))
-  im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
-                  interpolation='none')
-  fig.colorbar(im, ax=ax)
-  xlabel = 'Decoder timestep'
-  if info is not None:
-      xlabel += '\n\n' + info
-  plt.xlabel(xlabel)
-  plt.ylabel('Encoder timestep')
-  plt.tight_layout()
-
-  fig.canvas.draw()
-  data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
-  data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
-  plt.close()
-  return data
-
-
-def load_wav_to_torch(full_path):
-  sampling_rate, data = read(full_path)
-  return torch.FloatTensor(data.astype(np.float32)), sampling_rate
-
-
-def load_filepaths_and_text(filename, split="|"):
-  with open(filename, encoding='utf-8') as f:
-    filepaths_and_text = [line.strip().split(split) for line in f]
-  return filepaths_and_text
-
-
-def get_hparams(init=True):
-  parser = argparse.ArgumentParser()
-  parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
-                      help='JSON file for configuration')
-  parser.add_argument('-m', '--model', type=str, required=True,
-                      help='Model name')
-  
-  args = parser.parse_args()
-  model_dir = os.path.join("./logs", args.model)
-
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-
-  config_path = args.config
-  config_save_path = os.path.join(model_dir, "config.json")
-  if init:
-    with open(config_path, "r") as f:
-      data = f.read()
-    with open(config_save_path, "w") as f:
-      f.write(data)
-  else:
-    with open(config_save_path, "r") as f:
-      data = f.read()
-  config = json.loads(data)
-  
-  hparams = HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_dir(model_dir):
-  config_save_path = os.path.join(model_dir, "config.json")
-  with open(config_save_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  hparams.model_dir = model_dir
-  return hparams
-
-
-def get_hparams_from_file(config_path):
-  with open(config_path, "r") as f:
-    data = f.read()
-  config = json.loads(data)
-
-  hparams =HParams(**config)
-  return hparams
-
-
-def check_git_hash(model_dir):
-  source_dir = os.path.dirname(os.path.realpath(__file__))
-  if not os.path.exists(os.path.join(source_dir, ".git")):
-    logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
-      source_dir
-    ))
-    return
-
-  cur_hash = subprocess.getoutput("git rev-parse HEAD")
-
-  path = os.path.join(model_dir, "githash")
-  if os.path.exists(path):
-    saved_hash = open(path).read()
-    if saved_hash != cur_hash:
-      logger.warn("git hash values are different. {}(saved) != {}(current)".format(
-        saved_hash[:8], cur_hash[:8]))
-  else:
-    open(path, "w").write(cur_hash)
-
-
-def get_logger(model_dir, filename="train.log"):
-  global logger
-  logger = logging.getLogger(os.path.basename(model_dir))
-  logger.setLevel(logging.DEBUG)
-  
-  formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
-  if not os.path.exists(model_dir):
-    os.makedirs(model_dir)
-  h = logging.FileHandler(os.path.join(model_dir, filename))
-  h.setLevel(logging.DEBUG)
-  h.setFormatter(formatter)
-  logger.addHandler(h)
-  return logger
-
-
-class HParams():
-  def __init__(self, **kwargs):
-    for k, v in kwargs.items():
-      if type(v) == dict:
-        v = HParams(**v)
-      self[k] = v
-    
-  def keys(self):
-    return self.__dict__.keys()
-
-  def items(self):
-    return self.__dict__.items()
-
-  def values(self):
-    return self.__dict__.values()
-
-  def __len__(self):
-    return len(self.__dict__)
-
-  def __getitem__(self, key):
-    return getattr(self, key)
-
-  def __setitem__(self, key, value):
-    return setattr(self, key, value)
-
-  def __contains__(self, key):
-    return key in self.__dict__
-
-  def __repr__(self):
-    return self.__dict__.__repr__()
diff --git a/spaces/ranjangoel/GPT-PDF/gpt_reader/model_interface.py b/spaces/ranjangoel/GPT-PDF/gpt_reader/model_interface.py
deleted file mode 100644
index f3c04a45373ad34216fb7616bd06de52c7008604..0000000000000000000000000000000000000000
--- a/spaces/ranjangoel/GPT-PDF/gpt_reader/model_interface.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from typing import List
-import openai
-
-
-class ModelInterface(object):
-
-    def __init__(self) -> None:
-        pass
-
-    def send_msg(self, *args):
-        pass
-
-
-class OpenAIModel(object):
-
-    def __init__(self, api_key, model='gpt-3.5-turbo', temperature=0.2) -> None:
-        openai.api_key = api_key
-        self.model = model
-        self.temperature = temperature
-
-    def send_msg(self, msg: List[dict], return_raw_text=True):
-        
-        response = openai.ChatCompletion.create(
-            model=self.model,
-            messages=msg,
-            temperature=self.temperature
-        )
-
-        if return_raw_text:
-            return response["choices"][0]["message"]["content"]
-        else:
-            return response
diff --git a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/globals.d.ts b/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/globals.d.ts
deleted file mode 100644
index 80fd4cf3aa8f1014d6468291eb3e6b1982a0d3b9..0000000000000000000000000000000000000000
--- a/spaces/rayan-saleh/whisper2notion/server/node_modules/@types/node/globals.d.ts
+++ /dev/null
@@ -1,300 +0,0 @@
-// Declare "static" methods in Error
-interface ErrorConstructor {
-    /** Create .stack property on a target object */
-    captureStackTrace(targetObject: object, constructorOpt?: Function): void;
-
-    /**
-     * Optional override for formatting stack traces
-     *
-     * @see https://v8.dev/docs/stack-trace-api#customizing-stack-traces
-     */
-    prepareStackTrace?: ((err: Error, stackTraces: NodeJS.CallSite[]) => any) | undefined;
-
-    stackTraceLimit: number;
-}
-
-/*-----------------------------------------------*
- *                                               *
- *                   GLOBAL                      *
- *                                               *
- ------------------------------------------------*/
-
-// For backwards compability
-interface NodeRequire extends NodeJS.Require { }
-interface RequireResolve extends NodeJS.RequireResolve { }
-interface NodeModule extends NodeJS.Module { }
-
-declare var process: NodeJS.Process;
-declare var console: Console;
-
-declare var __filename: string;
-declare var __dirname: string;
-
-declare var require: NodeRequire;
-declare var module: NodeModule;
-
-// Same as module.exports
-declare var exports: any;
-
-/**
- * Only available if `--expose-gc` is passed to the process.
- */
-declare var gc: undefined | (() => void);
-
-//#region borrowed
-// from https://github.com/microsoft/TypeScript/blob/38da7c600c83e7b31193a62495239a0fe478cb67/lib/lib.webworker.d.ts#L633 until moved to separate lib
-/** A controller object that allows you to abort one or more DOM requests as and when desired. */
-interface AbortController {
-    /**
-     * Returns the AbortSignal object associated with this object.
-     */
-
-    readonly signal: AbortSignal;
-    /**
-     * Invoking this method will set this object's AbortSignal's aborted flag and signal to any observers that the associated activity is to be aborted.
-     */
-    abort(): void;
-}
-
-/** A signal object that allows you to communicate with a DOM request (such as a Fetch) and abort it if required via an AbortController object. */
-interface AbortSignal extends EventTarget {
-    /**
-     * Returns true if this AbortSignal's AbortController has signaled to abort, and false otherwise.
-     */
-    readonly aborted: boolean;
-}
-
-declare var AbortController: typeof globalThis extends {onmessage: any; AbortController: infer T}
-    ? T
-    : {
-        prototype: AbortController;
-        new(): AbortController;
-    };
-
-declare var AbortSignal: typeof globalThis extends {onmessage: any; AbortSignal: infer T}
-    ? T
-    : {
-        prototype: AbortSignal;
-        new(): AbortSignal;
-        abort(reason?: any): AbortSignal;
-        timeout(milliseconds: number): AbortSignal;
-    };
-//#endregion borrowed
-
-//#region ArrayLike.at()
-interface RelativeIndexable<T> {
-    /**
-     * Takes an integer value and returns the item at that index,
-     * allowing for positive and negative integers.
-     * Negative integers count back from the last item in the array.
-     */
-    at(index: number): T | undefined;
-}
-interface String extends RelativeIndexable<string> {}
-interface Array<T> extends RelativeIndexable<T> {}
-interface ReadonlyArray<T> extends RelativeIndexable<T> {}
-interface Int8Array extends RelativeIndexable<number> {}
-interface Uint8Array extends RelativeIndexable<number> {}
-interface Uint8ClampedArray extends RelativeIndexable<number> {}
-interface Int16Array extends RelativeIndexable<number> {}
-interface Uint16Array extends RelativeIndexable<number> {}
-interface Int32Array extends RelativeIndexable<number> {}
-interface Uint32Array extends RelativeIndexable<number> {}
-interface Float32Array extends RelativeIndexable<number> {}
-interface Float64Array extends RelativeIndexable<number> {}
-interface BigInt64Array extends RelativeIndexable<bigint> {}
-interface BigUint64Array extends RelativeIndexable<bigint> {}
-//#endregion ArrayLike.at() end
-
-/**
- * @since v17.0.0
- *
- * Creates a deep clone of an object.
- */
-declare function structuredClone<T>(
-    value: T,
-    transfer?: { transfer: ReadonlyArray<import('worker_threads').TransferListItem> },
-): T;
-
-/*----------------------------------------------*
-*                                               *
-*               GLOBAL INTERFACES               *
-*                                               *
-*-----------------------------------------------*/
-declare namespace NodeJS {
-    interface CallSite {
-        /**
-         * Value of "this"
-         */
-        getThis(): unknown;
-
-        /**
-         * Type of "this" as a string.
-         * This is the name of the function stored in the constructor field of
-         * "this", if available.  Otherwise the object's [[Class]] internal
-         * property.
-         */
-        getTypeName(): string | null;
-
-        /**
-         * Current function
-         */
-        getFunction(): Function | undefined;
-
-        /**
-         * Name of the current function, typically its name property.
-         * If a name property is not available an attempt will be made to try
-         * to infer a name from the function's context.
-         */
-        getFunctionName(): string | null;
-
-        /**
-         * Name of the property [of "this" or one of its prototypes] that holds
-         * the current function
-         */
-        getMethodName(): string | null;
-
-        /**
-         * Name of the script [if this function was defined in a script]
-         */
-        getFileName(): string | null;
-
-        /**
-         * Current line number [if this function was defined in a script]
-         */
-        getLineNumber(): number | null;
-
-        /**
-         * Current column number [if this function was defined in a script]
-         */
-        getColumnNumber(): number | null;
-
-        /**
-         * A call site object representing the location where eval was called
-         * [if this function was created using a call to eval]
-         */
-        getEvalOrigin(): string | undefined;
-
-        /**
-         * Is this a toplevel invocation, that is, is "this" the global object?
-         */
-        isToplevel(): boolean;
-
-        /**
-         * Does this call take place in code defined by a call to eval?
-         */
-        isEval(): boolean;
-
-        /**
-         * Is this call in native V8 code?
-         */
-        isNative(): boolean;
-
-        /**
-         * Is this a constructor call?
-         */
-        isConstructor(): boolean;
-    }
-
-    interface ErrnoException extends Error {
-        errno?: number | undefined;
-        code?: string | undefined;
-        path?: string | undefined;
-        syscall?: string | undefined;
-    }
-
-    interface ReadableStream extends EventEmitter {
-        readable: boolean;
-        read(size?: number): string | Buffer;
-        setEncoding(encoding: BufferEncoding): this;
-        pause(): this;
-        resume(): this;
-        isPaused(): boolean;
-        pipe<T extends WritableStream>(destination: T, options?: { end?: boolean | undefined; }): T;
-        unpipe(destination?: WritableStream): this;
-        unshift(chunk: string | Uint8Array, encoding?: BufferEncoding): void;
-        wrap(oldStream: ReadableStream): this;
-        [Symbol.asyncIterator](): AsyncIterableIterator<string | Buffer>;
-    }
-
-    interface WritableStream extends EventEmitter {
-        writable: boolean;
-        write(buffer: Uint8Array | string, cb?: (err?: Error | null) => void): boolean;
-        write(str: string, encoding?: BufferEncoding, cb?: (err?: Error | null) => void): boolean;
-        end(cb?: () => void): this;
-        end(data: string | Uint8Array, cb?: () => void): this;
-        end(str: string, encoding?: BufferEncoding, cb?: () => void): this;
-    }
-
-    interface ReadWriteStream extends ReadableStream, WritableStream { }
-
-    interface RefCounted {
-        ref(): this;
-        unref(): this;
-    }
-
-    type TypedArray =
-        | Uint8Array
-        | Uint8ClampedArray
-        | Uint16Array
-        | Uint32Array
-        | Int8Array
-        | Int16Array
-        | Int32Array
-        | BigUint64Array
-        | BigInt64Array
-        | Float32Array
-        | Float64Array;
-    type ArrayBufferView = TypedArray | DataView;
-
-    interface Require {
-        (id: string): any;
-        resolve: RequireResolve;
-        cache: Dict<NodeModule>;
-        /**
-         * @deprecated
-         */
-        extensions: RequireExtensions;
-        main: Module | undefined;
-    }
-
-    interface RequireResolve {
-        (id: string, options?: { paths?: string[] | undefined; }): string;
-        paths(request: string): string[] | null;
-    }
-
-    interface RequireExtensions extends Dict<(m: Module, filename: string) => any> {
-        '.js': (m: Module, filename: string) => any;
-        '.json': (m: Module, filename: string) => any;
-        '.node': (m: Module, filename: string) => any;
-    }
-    interface Module {
-        /**
-         * `true` if the module is running during the Node.js preload
-         */
-        isPreloading: boolean;
-        exports: any;
-        require: Require;
-        id: string;
-        filename: string;
-        loaded: boolean;
-        /** @deprecated since v14.6.0 Please use `require.main` and `module.children` instead. */
-        parent: Module | null | undefined;
-        children: Module[];
-        /**
-         * @since v11.14.0
-         *
-         * The directory name of the module. This is usually the same as the path.dirname() of the module.id.
-         */
-        path: string;
-        paths: string[];
-    }
-
-    interface Dict<T> {
-        [key: string]: T | undefined;
-    }
-
-    interface ReadOnlyDict<T> {
-        readonly [key: string]: T | undefined;
-    }
-}
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/!!INSTALL!! Freemonopolyfullversiondownloadforpc.md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/!!INSTALL!! Freemonopolyfullversiondownloadforpc.md
deleted file mode 100644
index 7424ae8b5bed06a9d7f872ba2d44528f210d05ba..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/!!INSTALL!! Freemonopolyfullversiondownloadforpc.md	
+++ /dev/null
@@ -1,132 +0,0 @@
-<br />
-<h1>Freemonopolyfullversiondownloadforpc: How to Enjoy the Classic Board Game on Your PC</h1>
-<p>Monopoly is one of the most popular and entertaining board games of all time. It is a game that involves buying, selling, and trading properties, building houses and hotels, and trying to bankrupt your opponents. Monopoly is a game that can be played by people of all ages and backgrounds, and it can provide hours of fun and excitement. But what if you want to play Monopoly on your PC? Is there a way to download Monopoly for free and play it on your computer? The answer is yes, there is. In this article, we will show you how to download freemonopolyfullversiondownloadforpc and enjoy the classic board game on your PC.</p>
-<h2>What is freemonopolyfullversiondownloadforpc?</h2>
-<p>Freemonopolyfullversiondownloadforpc is a digital version of the classic Monopoly board game that you can download and play on your PC. It is a game that features colorful graphics, animations, sound effects, and music that enhance the gameplay experience. Freemonopolyfullversiondownloadforpc also allows you to customize the game settings, such as the number of players, the difficulty level, the house rules, and the board theme. You can also play freemonopolyfullversiondownloadforpc online with your friends or other players from around the world.</p>
-<h2>freemonopolyfullversiondownloadforpc</h2><br /><p><b><b>Download File</b> &rArr; <a href="https://urlgoal.com/2uCKLE">https://urlgoal.com/2uCKLE</a></b></p><br /><br />
-<h2>How to download freemonopolyfullversiondownloadforpc?</h2>
-<p>If you want to download freemonopolyfullversiondownloadforpc, you can follow these steps:</p>
-<ol>
-<li>Go to this link: https://monopoly-plus.en.softonic.com/</li>
-<li>Click on the Download button and wait for the file to be downloaded.</li>
-<li>Open the downloaded file and follow the instructions to install the game on your PC.</li>
-<li>Launch the game and enter your name and email address to create an account.</li>
-<li>Enjoy playing freemonopolyfullversiondownloadforpc on your PC.</li>
-</ol>
-<h2>What are the benefits of playing freemonopolyfullversiondownloadforpc?</h2>
-<p>Playing freemonopolyfullversiondownloadforpc has many benefits, such as:</p>
-<ul>
-<li>You can play Monopoly anytime and anywhere without needing a physical board or pieces.</li>
-<li>You can save time and money by not having to buy or rent a Monopoly board game.</li>
-<li>You can play with different people from different locations and backgrounds online.</li>
-<li>You can learn new skills and strategies by playing against different opponents.</li>
-<li>You can have fun and relax by playing a game that you love.</li>
-</ul>
-<h2>What are the drawbacks of playing freemonopolyfullversiondownloadforpc?</h2>
-<p>Playing freemonopolyfullversiondownloadforpc also has some drawbacks, such as:</p>
-<ul>
-<li>You need a stable internet connection and a compatible PC to play the game.</li>
-<li>You may encounter some technical issues or errors while playing the game.</li>
-<li>You may lose some of the social aspects and interactions that come with playing a board game in person.</li>
-<li>You may become addicted or obsessed with playing the game.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download freemonopolyfullversiondownloadforpc and enjoy the classic board game on your PC. We have also discussed some of the benefits and drawbacks of playing freemonopolyfullversiondownloadforpc. We hope you found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
-<h2>How to play freemonopolyfullversiondownloadforpc?</h2>
-<p>Playing freemonopolyfullversiondownloadforpc is very easy and fun. You can play it alone or with your friends online. Here are some basic steps on how to play freemonopolyfullversiondownloadforpc:</p>
-<p></p>
-<ol>
-<li>Choose your character and enter the game.</li>
-<li>Roll the dice and move your piece around the board.</li>
-<li>Buy, sell, or trade properties with other players or the bank.</li>
-<li>Build houses and hotels on your properties to increase their value.</li>
-<li>Collect rent from other players who land on your properties.</li>
-<li>Avoid landing on other players' properties or special squares, such as Go to Jail, Income Tax, or Chance.</li>
-<li>Try to make other players go bankrupt by making them pay you more than they have.</li>
-<li>The last player standing wins the game.</li>
-</ol>
-<h2>What are the tips and tricks for playing freemonopolyfullversiondownloadforpc?</h2>
-<p>Playing freemonopolyfullversiondownloadforpc can be challenging and competitive. You need to have some skills and strategies to win the game. Here are some tips and tricks for playing freemonopolyfullversiondownloadforpc:</p>
-<ul>
-<li>Plan ahead and think about your moves before you roll the dice.</li>
-<li>Focus on buying properties that form a color group, as they will give you more rent and allow you to build houses and hotels.</li>
-<li>Try to buy or trade for the railroads and utilities, as they will give you a steady income regardless of where you land.</li>
-<li>Negotiate with other players and make deals that benefit you more than them.</li>
-<li>Avoid spending too much money on properties that are not worth it or that you don't need.</li>
-<li>Save some money for emergencies or unexpected expenses.</li>
-<li>Use the house rules or the speed die mode to make the game more interesting or faster.</li>
-</ul>
-<h2>How to play freemonopolyfullversiondownloadforpc online?</h2>
-<p>If you want to play freemonopolyfullversiondownloadforpc online with other players, you can follow these steps:</p>
-<ol>
-<li>Launch the game and log in to your account.</li>
-<li>Click on the Online mode and choose a game mode: Classic, Speed Die, or House Rules.</li>
-<li>Select a lobby or create your own and invite your friends or other players.</li>
-<li>Choose your character and wait for the game to start.</li>
-<li>Play freemonopolyfullversiondownloadforpc online and have fun.</li>
-</ol>
-<h2>What are the differences between freemonopolyfullversiondownloadforpc and the original Monopoly board game?</h2>
-<p>Freemonopolyfullversiondownloadforpc is a digital version of the original Monopoly board game that you can play on your PC. It has some differences from the original board game, such as:</p>
-<ul>
-<li>It has a 3D city in the center of the board that changes according to the gameplay.</li>
-<li>It has different board themes that you can choose from, such as Classic, Amusement Park, Haunted, or Rabbids.</li>
-<li>It has different characters that you can choose from, such as a dog, a cat, a car, or a hat.</li>
-<li>It has different sound effects and music that add to the atmosphere of the game.</li>
-<li>It has different game modes that you can choose from, such as Classic, Speed Die, or House Rules.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download freemonopolyfullversiondownloadforpc and enjoy the classic board game on your PC. We have also discussed some of the benefits and drawbacks of playing freemonopolyfullversiondownloadforpc. We have also provided you with some tips and tricks on how to play freemonopolyfullversiondownloadforpc. We hope you found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p>
-<h2>How to install freemonopolyfullversiondownloadforpc?</h2>
-<p>After you download freemonopolyfullversiondownloadforpc, you need to install it on your PC. Here are some steps on how to install freemonopolyfullversiondownloadforpc:</p>
-<ol>
-<li>Locate the downloaded file and double-click on it to open it.</li>
-<li>Extract the file to a folder of your choice.</li>
-<li>Open the folder and run the setup.exe file.</li>
-<li>Follow the instructions to complete the installation process.</li>
-<li>Create a shortcut of the game on your desktop or start menu.</li>
-<li>Run the game and enjoy playing freemonopolyfullversiondownloadforpc.</li>
-</ol>
-<h2>How to uninstall freemonopolyfullversiondownloadforpc?</h2>
-<p>If you want to uninstall freemonopolyfullversiondownloadforpc, you can follow these steps:</p>
-<ol>
-<li>Go to Control Panel and select Programs and Features.</li>
-<li>Find freemonopolyfullversiondownloadforpc in the list and click on Uninstall.</li>
-<li>Follow the instructions to complete the uninstallation process.</li>
-<li>Delete any remaining files or folders related to freemonopolyfullversiondownloadforpc from your PC.</li>
-</ol>
-<h2>What are the alternatives to freemonopolyfullversiondownloadforpc?</h2>
-<p>If you are looking for alternatives to freemonopolyfullversiondownloadforpc, you can consider some of these options:</p>
-<ul>
-<li>Rento: A software that allows you to play a similar board game with online multiplayer mode and custom rules.</li>
-<li>Monopoly Plus: A software that allows you to play a 3D version of Monopoly with different board themes and game modes.</li>
-<li>Monopoly Tycoon: A software that allows you to play a business simulation game based on Monopoly with real-time strategy elements.</li>
-<li>Monopoly Here and Now: A software that allows you to play a modern version of Monopoly with updated properties and currencies.</li>
-</ul>
-<h2>How to play freemonopolyfullversiondownloadforpc offline?</h2>
-<p>If you want to play freemonopolyfullversiondownloadforpc offline, you can follow these steps:</p>
-<ol>
-<li>Launch the game and log in to your account.</li>
-<li>Click on the Offline mode and choose a game mode: Classic, Speed Die, or House Rules.</li>
-<li>Select a difficulty level: Easy, Medium, or Hard.</li>
-<li>Choose your character and the number of computer opponents.</li>
-<li>Play freemonopolyfullversiondownloadforpc offline and have fun.</li>
-</ol>
-<h2>How to update freemonopolyfullversiondownloadforpc?</h2>
-<p>If you want to update freemonopolyfullversiondownloadforpc, you can follow these steps:</p>
-<ol>
-<li>Go to this link: https://monopoly-plus.en.softonic.com/</li>
-<li>Click on the Update button and wait for the file to be downloaded.</li>
-<li>Open the downloaded file and follow the instructions to install the update on your PC.</li>
-<li>Launch the game and enjoy playing freemonopolyfullversiondownloadforpc with the latest features and improvements.</li>
-</ol>
-<h2>How to get help for freemonopolyfullversiondownloadforpc?</h2>
-<p>If you need help for freemonopolyfullversiondownloadforpc, you can contact some of these sources:</p>
-<ul>
-<li>The game manual: You can find the game manual in the game folder or online at https://monopoly-plus.en.softonic.com/manual</li>
-<li>The game support: You can contact the game support via email at support@monopoly-plus.com or via phone at +1-800-MONOPOLY</li>
-<li>The game community: You can join and participate in the game community at https://monopoly-plus.en.softonic.com/community and find other players, tips, guides, and feedback.</li>
-</ul>
-<h2>Conclusion</h2>
-<p>In this article, we have shown you how to download and play freemonopolyfullversiondownloadforpc on your PC. We have also discussed some of the benefits and drawbacks of playing freemonopolyfullversiondownloadforpc. We have also provided you with some tips and tricks on how to play, install, update, and get help for freemonopolyfullversiondownloadforpc. We hope you found this article helpful and informative. If you have any questions or comments, please feel free to leave them below. Thank you for reading!</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (stand And Deliver 1988 Movie Free Download).md b/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (stand And Deliver 1988 Movie Free Download).md
deleted file mode 100644
index f974f21d07f1f9ad3e79eaa2cae86c58a6bd0425..0000000000000000000000000000000000000000
--- a/spaces/recenWmenso/ChatGPT-with-Voice-Cloning-for-All/datasets/HD Online Player (stand And Deliver 1988 Movie Free Download).md	
+++ /dev/null
@@ -1,22 +0,0 @@
-<br />
-<h1>How to Watch Stand and Deliver (1988) Online for Free</h1>
-<p>Stand and Deliver is a 1988 biographical drama film based on the true story of Jaime Escalante, a high school teacher who inspired his dropout-prone students to learn calculus. The film stars Edward James Olmos as Escalante, Lou Diamond Phillips as Angel, and Andy Garcia as Ramirez. The film was nominated for an Academy Award for Best Actor for Olmos and won six Independent Spirit Awards, including Best Feature and Best Director.</p>
-<p>If you want to watch Stand and Deliver online for free, you have several options. Here are some of the best ways to stream or download this inspiring movie:</p>
-<h2>HD Online Player (stand and deliver 1988 movie free download)</h2><br /><p><b><b>Download File</b> &#10038; <a href="https://urlgoal.com/2uCKg2">https://urlgoal.com/2uCKg2</a></b></p><br /><br />
-<ul>
-<li><strong>Amazon Prime Video</strong>: If you have an Amazon Prime membership, you can watch Stand and Deliver for free on Prime Video. You can also rent or buy the movie on Amazon if you don't have a Prime account.</li>
-<li><strong>Apple TV</strong>: You can rent or buy Stand and Deliver on Apple TV, which is compatible with various devices such as iPhone, iPad, Mac, Apple TV, and smart TVs. You can also watch the movie on the Apple TV app.</li>
-<li><strong>Google Play Movies</strong>: You can rent or buy Stand and Deliver on Google Play Movies, which is accessible on Android devices, Chromecast, Roku, smart TVs, and web browsers. You can also watch the movie on the Google Play Movies app.</li>
-<li><strong>YouTube</strong>: You can rent or buy Stand and Deliver on YouTube, which is available on almost any device with an internet connection. You can also watch the movie on the YouTube app.</li>
-<li><strong>Vudu</strong>: You can rent or buy Stand and Deliver on Vudu, which is a digital video store that offers a large selection of movies and TV shows. You can watch the movie on various devices such as smart TVs, Roku, Chromecast, PlayStation, Xbox, and web browsers.</li>
-<li><strong>Microsoft Store</strong>: You can rent or buy Stand and Deliver on Microsoft Store, which is a digital marketplace that sells movies, games, apps, and more. You can watch the movie on Windows devices, Xbox consoles, and web browsers.</li>
-<li><strong>Redbox</strong>: You can rent or buy Stand and Deliver on Redbox, which is a video rental service that operates kiosks and online streaming platforms. You can watch the movie on various devices such as smart TVs, Roku, Apple TV, Chromecast, PlayStation, Xbox, and web browsers.</li>
-<li><strong>DIRECTV</strong>: If you have a DIRECTV subscription, you can watch Stand and Deliver for free on DIRECTV. You can also rent or buy the movie on DIRECTV if you don't have a subscription.</li>
-<li><strong>AMC on Demand</strong>: If you have an AMC+ subscription, you can watch Stand and Deliver for free on AMC on Demand. You can also rent or buy the movie on AMC on Demand if you don't have a subscription.</li>
-<li><strong>M4uHD</strong>: If you want to watch Stand and Deliver for free without signing up or downloading anything, you can try M4uHD[^1^], which is a free online streaming website that offers HD quality movies. However, be careful of pop-up ads and malware that may harm your device.</li>
-<li><strong>YTS</strong>: If you want to download Stand and Deliver for free in HD quality with subtitles, you can try YTS[^2^], which is a torrent website that provides high-quality movies. However, be aware of the legal risks and potential viruses that may come with torrenting.</li>
-<li><strong>Internet Archive</strong>: If you want to download Stand and Deliver for free in SD quality without subtitles, you can try Internet Archive[^4^], which is a digital library that offers public domain movies. However, be mindful of the low quality and limited availability of some movies.</li>
-</ul>
-<p>Stand and Deliver is a classic movie that showcases the power of education and perseverance. Whether you want to stream or download it online for free or pay for it, there are many options to choose from. Enjoy watching this inspiring film!</p> d5da3c52bf<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/reimari/rvc-aa99/infer_pack/modules.py b/spaces/reimari/rvc-aa99/infer_pack/modules.py
deleted file mode 100644
index 960481cedad9a6106f2bf0b9e86e82b120f7b33f..0000000000000000000000000000000000000000
--- a/spaces/reimari/rvc-aa99/infer_pack/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-from infer_pack import commons
-from infer_pack.commons import init_weights, get_padding
-from infer_pack.transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x
diff --git a/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/apps/__init__.py b/spaces/rfrossard/Image-and-3D-Model-Creator/PIFu/apps/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/rinong/StyleGAN-NADA/util.py b/spaces/rinong/StyleGAN-NADA/util.py
deleted file mode 100644
index 083b56170f5feb72eccfebd38a53aed70db32064..0000000000000000000000000000000000000000
--- a/spaces/rinong/StyleGAN-NADA/util.py
+++ /dev/null
@@ -1,136 +0,0 @@
-from matplotlib import pyplot as plt
-import torch
-import torch.nn.functional as F
-import os
-import dlib
-from PIL import Image
-import numpy as np
-import scipy
-import scipy.ndimage
-import torchvision.transforms as transforms
-
-def display_image(image, size=None, mode='nearest', unnorm=False, title=''):
-    # image is [3,h,w] or [1,3,h,w] tensor [0,1]
-    if not isinstance(image, torch.Tensor):
-        image = transforms.ToTensor()(image).unsqueeze(0)
-    if image.is_cuda:
-        image = image.cpu()
-    if size is not None and image.size(-1) != size:
-        image = F.interpolate(image, size=(size,size), mode=mode)
-    if image.dim() == 4:
-        image = image[0]
-    image = image.permute(1, 2, 0).detach().numpy()
-    plt.figure()
-    plt.title(title)
-    plt.axis('off')
-    plt.imshow(image)
-
-def get_landmark(filepath, predictor):
-    """get landmark with dlib
-    :return: np.array shape=(68, 2)
-    """
-    detector = dlib.get_frontal_face_detector()
-
-    img = dlib.load_rgb_image(filepath)
-    dets = detector(img, 1)
-    assert len(dets) > 0, "Face not detected, try another face image"
-
-    for k, d in enumerate(dets):
-        shape = predictor(img, d)
-
-    t = list(shape.parts())
-    a = []
-    for tt in t:
-        a.append([tt.x, tt.y])
-    lm = np.array(a)
-    return lm
-
-def align_face(filepath, predictor, output_size=256, transform_size=1024, enable_padding=True):
-
-    """
-    :param filepath: str
-    :return: PIL Image
-    """
-    lm = get_landmark(filepath, predictor)
-
-    lm_chin = lm[0: 17]  # left-right
-    lm_eyebrow_left = lm[17: 22]  # left-right
-    lm_eyebrow_right = lm[22: 27]  # left-right
-    lm_nose = lm[27: 31]  # top-down
-    lm_nostrils = lm[31: 36]  # top-down
-    lm_eye_left = lm[36: 42]  # left-clockwise
-    lm_eye_right = lm[42: 48]  # left-clockwise
-    lm_mouth_outer = lm[48: 60]  # left-clockwise
-    lm_mouth_inner = lm[60: 68]  # left-clockwise
-
-    # Calculate auxiliary vectors.
-    eye_left = np.mean(lm_eye_left, axis=0)
-    eye_right = np.mean(lm_eye_right, axis=0)
-    eye_avg = (eye_left + eye_right) * 0.5
-    eye_to_eye = eye_right - eye_left
-    mouth_left = lm_mouth_outer[0]
-    mouth_right = lm_mouth_outer[6]
-    mouth_avg = (mouth_left + mouth_right) * 0.5
-    eye_to_mouth = mouth_avg - eye_avg
-
-    # Choose oriented crop rectangle.
-    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
-    x /= np.hypot(*x)
-    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
-    y = np.flipud(x) * [-1, 1]
-    c = eye_avg + eye_to_mouth * 0.1
-    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
-    qsize = np.hypot(*x) * 2
-
-    # read image
-    img = Image.open(filepath)
-
-    transform_size = output_size
-    enable_padding = True
-
-    # Shrink.
-    shrink = int(np.floor(qsize / output_size * 0.5))
-    if shrink > 1:
-        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
-        img = img.resize(rsize, Image.ANTIALIAS)
-        quad /= shrink
-        qsize /= shrink
-
-    # Crop.
-    border = max(int(np.rint(qsize * 0.1)), 3)
-    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-            int(np.ceil(max(quad[:, 1]))))
-    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
-            min(crop[3] + border, img.size[1]))
-    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
-        img = img.crop(crop)
-        quad -= crop[0:2]
-
-    # Pad.
-    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
-           int(np.ceil(max(quad[:, 1]))))
-    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
-           max(pad[3] - img.size[1] + border, 0))
-    if enable_padding and max(pad) > border - 4:
-        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
-        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
-        h, w, _ = img.shape
-        y, x, _ = np.ogrid[:h, :w, :1]
-        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
-                          1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
-        blur = qsize * 0.02
-        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
-        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
-        img = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
-        quad += pad[:2]
-
-    # Transform.
-    img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
-    if output_size < transform_size:
-        img = img.resize((output_size, output_size), Image.ANTIALIAS)
-
-    # Return aligned image.
-    return img
-
-def strip_path_extension(path):
-   return  os.path.splitext(path)[0]
\ No newline at end of file
diff --git a/spaces/ritikjain51/pdf-question-answering/app.py b/spaces/ritikjain51/pdf-question-answering/app.py
deleted file mode 100644
index aaa408778efb8132671e16145a52852aebee4b71..0000000000000000000000000000000000000000
--- a/spaces/ritikjain51/pdf-question-answering/app.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import logging
-import os
-
-import gradio as gr
-from langchain import OpenAI, HuggingFaceHub
-from langchain.chains import ConversationalRetrievalChain
-from langchain.document_loaders import PyPDFLoader
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import Chroma
-
-documents = []
-qa = None
-
-
-def get_file(file):
-    try:
-        global documents
-        data = PyPDFLoader(file.name)
-        documents = data.load_and_split(CharacterTextSplitter(chunk_size=2000, chunk_overlap=0))
-    except Exception as e:
-        logging.error(e, exc_info=True)
-        return "Failed to upload."
-    return "File Uploaded."
-
-
-def model_configuration(model_name, api_key=None, hug_model=None, hug_token=None, temperature=0, max_length=512):
-    try:
-        embeddings, llm = None, None
-        if not documents:
-            return gr.update(value="Please upload correct PDF!", visible=True)
-        global qa
-        if model_name == "OpenAI":
-            os.environ["OPENAI_API_KEY"] = api_key or os.getenv("OPENAI_API_KEY")
-            embeddings = OpenAIEmbeddings()
-            llm = OpenAI(temperature=temperature, max_tokens=max_length)
-        elif model_name == "HuggingFace":
-            os.environ["HUGGINGFACEHUB_API_TOKEN"] = hug_token or os.getenv("HUGGINGFACE_API_KEY")
-            embeddings = HuggingFaceEmbeddings(model_name=hug_model, model_kwargs={'device': 'cpu'})
-            llm = HuggingFaceHub(repo_id=hug_model, model_kwargs={"temperature": temperature, "max_length": max_length})
-
-        if embeddings:
-            db = Chroma.from_documents(documents, embeddings)
-            retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
-            qa = ConversationalRetrievalChain.from_llm(llm, chain_type="map_reduce", retriever=retriever,
-                                                       return_source_documents=True, verbose=False)
-    except Exception as e:
-        logging.error(e, exc_info=True)
-        return gr.update(value="Error occurred!", visible=True)
-    return gr.update(value="Model Built", visible=True)
-
-
-def response(msg, chat_history):
-    global qa
-    result = qa({"question": msg, "chat_history": map(tuple, chat_history)})
-    final_resp = result.get("answer", "").strip()
-    chat_history.append((msg, final_resp))
-    docs = result.get("source_documents")
-    return "", chat_history
-
-
-with gr.Blocks() as demo:
-    with gr.Tab("PDF Ingestion") as pdf_input:
-        file = None
-        with gr.Column() as r1:
-            file = gr.File(file_types=[".pdf"])
-            op_txt = gr.Label(value="", label="")
-            fl_btn = gr.Button("Upload & Ingest 🚀")
-            fl_btn.click(get_file, inputs=[file], outputs=op_txt)
-
-    with gr.Tab("Select Model") as model:
-        model_name = gr.Dropdown(
-            ["NA", "OpenAI", "HuggingFace"],
-            show_label=True,
-            label="Model Name",
-            multiselect=False,
-            value="NA"
-        )
-        with gr.Column(visible=False) as openai_config:
-            api_key = gr.Textbox(value="", label="OPENAI API KEY", placeholder="sk-...", visible=True, interactive=True,
-                                 type="password")
-
-        with gr.Column(visible=False) as huggy_config:
-            hug_model = gr.Dropdown(["google/flan-t5-xl"],
-                                    value="google/flan-t5-xl", multiselect=False)
-            hug_token = gr.Textbox(value="", placeholder="hf_...", interactive=True, type="password")
-
-        with gr.Accordion("Advance Settings", open=False, visible=False) as advance_settings:
-            temperature = gr.Slider(0, 1, label="Temperature")
-            max_length = gr.components.Number(value=512, label="Max Token Length")
-
-
-        def show_configuration(model_name):
-            match model_name:
-                case "OpenAI":
-                    return {
-                        openai_config: gr.update(visible=True),
-                        huggy_config: gr.update(visible=False),
-                        advance_settings: gr.update(visible=True)
-                    }
-                case "HuggingFace":
-                    return {
-                        openai_config: gr.update(visible=False),
-                        huggy_config: gr.update(visible=True),
-                        advance_settings: gr.update(visible=True)
-                    }
-                case _:
-                    return {
-                        openai_config: gr.update(visible=False),
-                        huggy_config: gr.update(visible=False),
-                        advance_settings: gr.update(visible=False)
-                    }
-
-
-        model_name.change(show_configuration, inputs=[model_name],
-                          outputs=[openai_config, huggy_config, advance_settings])
-        model_updated = gr.Label("", show_label=False, visible=True)
-        btn = gr.Button("Configure Model 🤖")
-        btn.click(model_configuration, inputs=[model_name, api_key, hug_model, hug_token, temperature, max_length],
-                  outputs=model_updated)
-
-    with gr.Tab("Q&A") as qna:
-        with gr.Column() as r:
-            chatbot = gr.Chatbot(show_label=True)
-            msg = gr.Textbox(placeholder="Ask Something")
-            clear = gr.Button("Clear")
-            msg.submit(response, [msg, chatbot], [msg, chatbot])
-            clear.click(lambda: None, None, chatbot, queue=False)
-
-if __name__ == "__main__":
-    demo.launch()
diff --git a/spaces/robin0307/MMOCR/configs/_base_/recog_pipelines/crnn_tps_pipeline.py b/spaces/robin0307/MMOCR/configs/_base_/recog_pipelines/crnn_tps_pipeline.py
deleted file mode 100644
index 3a2eea55a739206c11ae876ba82e9c2f6ea1ff6d..0000000000000000000000000000000000000000
--- a/spaces/robin0307/MMOCR/configs/_base_/recog_pipelines/crnn_tps_pipeline.py
+++ /dev/null
@@ -1,37 +0,0 @@
-img_norm_cfg = dict(mean=[0.5], std=[0.5])
-
-train_pipeline = [
-    dict(type='LoadImageFromFile', color_type='grayscale'),
-    dict(
-        type='ResizeOCR',
-        height=32,
-        min_width=100,
-        max_width=100,
-        keep_aspect_ratio=False),
-    dict(type='ToTensorOCR'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'ori_shape', 'resize_shape', 'text', 'valid_ratio'
-        ]),
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile', color_type='grayscale'),
-    dict(
-        type='ResizeOCR',
-        height=32,
-        min_width=32,
-        max_width=100,
-        keep_aspect_ratio=False),
-    dict(type='ToTensorOCR'),
-    dict(type='NormalizeOCR', **img_norm_cfg),
-    dict(
-        type='Collect',
-        keys=['img'],
-        meta_keys=[
-            'filename', 'ori_shape', 'resize_shape', 'valid_ratio',
-            'img_norm_cfg', 'ori_filename', 'img_shape'
-        ]),
-]
diff --git a/spaces/roborovski/Diffusle/app.py b/spaces/roborovski/Diffusle/app.py
deleted file mode 100644
index 54318a9cce7bebb8b1a765ef4c4c61f6a03fd067..0000000000000000000000000000000000000000
--- a/spaces/roborovski/Diffusle/app.py
+++ /dev/null
@@ -1,282 +0,0 @@
-# -*- coding: utf-8 -*-
-"""diffusle prompt testing - stable diffusion
-Automatically generated by Colaboratory.
-Original file is located at
-    https://colab.research.google.com/drive/1EiL1cvF84u4f9_U3mw_1xofHIJG3r-YR
-"""
-
-from asyncore import file_dispatcher
-from collections import defaultdict
-from email.policy import default
-from functools import lru_cache
-from transformers import TFGPT2LMHeadModel, GPT2Tokenizer
-import random
-import pandas
-import urllib.request
-from sentence_transformers import SentenceTransformer, util
-import os
-import discord
-import random
-from discord.ext import commands, tasks
-import io
-import math
-from diffusers import DiffusionPipeline, LDMTextToImagePipeline
-import torch
-import configparser
-import pickle
-from torch import autocast
-from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
-from transformers import CLIPTextModel, CLIPTokenizer
-from diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler
-from diffusers import LMSDiscreteScheduler
-from PIL import Image
-import torch
-import random
-import time
-import gradio as gr
-import itertools
-from tqdm import tqdm
-from PIL import Image
-
-torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-access_token = os.getenv("HF_TOKEN")
-
-tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", use_auth_token=access_token)
-text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", use_auth_token=access_token)
-vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="vae", use_auth_token=access_token)
-unet = UNet2DConditionModel.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="unet", use_auth_token=access_token)
-
-scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
-
-vae = vae.to(torch_device)
-text_encoder = text_encoder.to(torch_device)
-unet = unet.to(torch_device)
-
-
-prompts = []
-with open('prompts.csv', 'r') as prompt_file:
-    prompts = [line.rstrip('\n') for line in prompt_file]
-
-print('prompts', prompts)
-
-t = 1000 * time.time()
-torch.manual_seed(t)
-random.seed(t)
-
-inf_range = [1,2,3,5,7,10]
-guidance_range = [5, 5, 5, 7.5, 7.5, 7.5]
-generator = torch.manual_seed(t)
-height = 512
-width = 512
-guidance_scale = 7.5
-
-
-def image_grid(imgs, rows=3, cols=3):
-    assert len(imgs) == rows*cols
-
-    w, h = imgs[0].size
-    grid = Image.new('RGB', size=(cols*w, rows*h))
-    grid_w, grid_h = grid.size
-    
-    for i, img in enumerate(imgs):
-        grid.paste(img, box=(i%cols*w, i//cols*h))
-    return grid
-
-def latents_to_pil(latents, vae):
-  # bath of latents -> list of images
-  latents = (1 / 0.18215) * latents
-  with torch.no_grad():
-    image = vae.decode(latents)[0]
-  image = (image / 2 + 0.5).clamp(0, 1)
-  image = image.detach().cpu().permute(0, 2, 3, 1).numpy()
-  images = (image * 255).round().astype("uint8")
-  pil_images = [Image.fromarray(image) for image in images]
-  return pil_images
-
-use_gradio = True
-
-batch_size = 1
-
-def get_prompt_embedding(prompt):
-  text_input = tokenizer(prompt, padding="max_length", max_length=tokenizer.model_max_length, truncation=True, return_tensors="pt")
-  text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[0]
-  max_length = text_input.input_ids.shape[-1]
-  uncond_input = tokenizer(
-      [""] * batch_size, padding="max_length", max_length=max_length, return_tensors="pt"
-  )
-  # generate latent embeddings
-  with torch.no_grad():
-    uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[0]   
-    text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-  
-  return uncond_embeddings, text_embeddings
-
-inference_steps = 15
-
-def setup():
-  scheduler = LMSDiscreteScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000)
-  scheduler.set_timesteps(inference_steps)
-  generator = torch.manual_seed(torch.random.initial_seed())
-  latents = torch.randn(
-    (batch_size, unet.in_channels, height // 8, width // 8),
-    generator=generator,
-  )
-  latents = latents.to(torch_device)
-  latents = latents * scheduler.sigmas[0]
-  return scheduler, latents
-
-
-w2v_model = None
-
-def levenshtein( s1, s2):
-    s1_len = len(s1)
-    s2_len = len(s2)
-    d = [[0 for x in range(s1_len + 1)] for y in range(s2_len + 1)]
-
-    for i in range(1, s2_len + 1):
-        d[i][0] = i
-
-    for j in range(1, s1_len + 1):
-        d[0][j] = j
-
-    for j in range(1, s1_len + 1):
-        for i in range(1, s2_len + 1):
-            if s1[j - 1] is s2[i - 1]:
-                delta = 0
-            else:
-                delta = 1
-
-            d[i][j] = min(d[i - 1][j] + 1,
-                          d[i][j - 1] + 1,
-                          d[i - 1][j - 1] + delta)
-
-    return d[s2_len][s1_len]
-
-if not w2v_model:
-  print("loading word2vec model")
-  w2v_model = SentenceTransformer('all-MiniLM-L6-v2')
-
-
-def score_guess(guess, prompt):
-  global w2v_model
-  #Compute embedding for both lists
-  prompt_emb = w2v_model.encode([prompt], convert_to_tensor=True)
-  guess_emb = w2v_model.encode([guess], convert_to_tensor=True)
-
-  #Compute cosine-similarities
-  cosine_scores = util.cos_sim(prompt_emb, guess_emb)
-  word2vec_score = cosine_scores[0][0]
-  levenshtein_score = 1 - (levenshtein(prompt, guess) / 100)
-  w2v_bias = 0.5
-  biases = (w2v_bias, 1-w2v_bias)
-  total = (word2vec_score * biases[0] + levenshtein_score * biases[1]) / 2
-  total = float(word2vec_score)
-  return total
-
-def init_state():
-  step = 0
-  prompt = random.choice(prompts)
-  uncond_embeddings, text_embeddings = get_prompt_embedding(prompt)
-  scheduler, latents = setup()
-  steps = scheduler.timesteps
-  return { 'step': step, 'prompt': prompt, 'latents': latents, 'scheduler': scheduler, 'text_embeddings': text_embeddings }
-
-default_guess_text = "Press Submit to generate the first image."
-win_threshold = 0.65
-
-with gr.Blocks() as interface:
-
-  with gr.Row() as row:
-    with gr.Column():
-      guess_input = gr.Textbox(label="Your Guess", value=default_guess_text, max_lines=1)
-      submit_btn = gr.Button("Submit")
-    with gr.Column():
-      img = gr.Image(label="Image to guess")
-      score_output = gr.Text(label="Output")
-
-  def guess(guess, state):
-
-    if guess == default_guess_text:
-      guess_input.value = ""
-
-    with autocast("cuda"):
-      if not state:
-        print('init state')
-        step = 0
-        prompt = random.choice(prompts)
-        uncond_embeddings, text_embeddings = get_prompt_embedding(prompt)
-        scheduler, latents = setup()
-        steps = scheduler.timesteps
-      else:
-        print('increment state')
-        step = state['step']
-        prompt = state['prompt']
-        scheduler = state['scheduler']
-        text_embeddings = state['text_embeddings']
-        latents = state['latents']
-        steps = scheduler.timesteps
-
-      final_step = step == len(scheduler.timesteps) - 1
-      if step > len(steps) - 1 or final_step:
-        step = inference_steps - 1
-      
-      latent_model_input = torch.cat([latents] * 2)
-      sigma = scheduler.sigmas[step]
-      latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
-      print(step, steps[step], prompt)
-
-      
-      with torch.no_grad():
-        noise_pred = unet(latent_model_input, steps[step], encoder_hidden_states=text_embeddings)["sample"]
-
-      # perform guidance
-      noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-      noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-      latents_x0 = latents - sigma * noise_pred
-
-      # compute the previous noisy sample x_t -> x_t-1
-      latents = scheduler.step(noise_pred, step, latents)["prev_sample"]
-      denoised_img = latents_to_pil(latents_x0, vae)[0]
-      # display(denoised_img)
-
-      score = score_guess(guess, prompt)
-      score = round(float(score), 2)
-      fmt_score = (score / win_threshold) * 100
-      fmt_score = round(float(fmt_score), 2)
-
-      result_text = f"Score: {str(fmt_score)}% Guesses: {step}"
-
-      if guess == "":
-        score = 0
-        result_text = "No guess submitted, no score"
-
-      if guess == default_guess_text:
-        score = 0
-        result_text = "Game on!"
-
-      if len(guess) > 250:
-        score = 0
-        result_text = "Guess is too long!"
-
-      if score > win_threshold:
-        if final_step:
-          result_text = "Ran out of guesses!"
-        else:
-          result_text = f"You win! You are poggers! the prompt was {prompt}, you guesssed it in {step} guesses. To play again press Submit"
-        scheduler, latents = setup()
-        step = 0
-        prompt = random.choice(prompts)
-        uncond_embeddings, text_embeddings = get_prompt_embedding(prompt)
-      else:
-        step += 1
-
-      print(prompt, step)
-
-    return denoised_img, result_text, { 'step': step, 'prompt': prompt, 'latents': latents, 'scheduler': scheduler, 'text_embeddings': text_embeddings }
-  
-  state = gr.State()
-  submit_btn.click(guess, inputs=[guess_input, state], outputs=[img, score_output, state])
-
-interface.launch()
\ No newline at end of file
diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/sparse_roi_head.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/sparse_roi_head.py
deleted file mode 100644
index 2613469e3a7cf397f19c04b24c43ab50b0c75551..0000000000000000000000000000000000000000
--- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/mmdet/models/roi_heads/sparse_roi_head.py
+++ /dev/null
@@ -1,424 +0,0 @@
-# Copyright (c) OpenMMLab. All rights reserved.
-import numpy as np
-import torch
-
-from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh
-from mmdet.core.bbox.samplers import PseudoSampler
-from ..builder import HEADS
-from .cascade_roi_head import CascadeRoIHead
-
-
-@HEADS.register_module()
-class SparseRoIHead(CascadeRoIHead):
-    r"""The RoIHead for `Sparse R-CNN: End-to-End Object Detection with
-    Learnable Proposals <https://arxiv.org/abs/2011.12450>`_
-    and `Instances as Queries <http://arxiv.org/abs/2105.01928>`_
-
-    Args:
-        num_stages (int): Number of stage whole iterative process.
-            Defaults to 6.
-        stage_loss_weights (Tuple[float]): The loss
-            weight of each stage. By default all stages have
-            the same weight 1.
-        bbox_roi_extractor (dict): Config of box roi extractor.
-        mask_roi_extractor (dict): Config of mask roi extractor.
-        bbox_head (dict): Config of box head.
-        mask_head (dict): Config of mask head.
-        train_cfg (dict, optional): Configuration information in train stage.
-            Defaults to None.
-        test_cfg (dict, optional): Configuration information in test stage.
-            Defaults to None.
-        pretrained (str, optional): model pretrained path. Default: None
-        init_cfg (dict or list[dict], optional): Initialization config dict.
-            Default: None
-
-    """
-
-    def __init__(self,
-                 num_stages=6,
-                 stage_loss_weights=(1, 1, 1, 1, 1, 1),
-                 proposal_feature_channel=256,
-                 bbox_roi_extractor=dict(
-                     type='SingleRoIExtractor',
-                     roi_layer=dict(
-                         type='RoIAlign', output_size=7, sampling_ratio=2),
-                     out_channels=256,
-                     featmap_strides=[4, 8, 16, 32]),
-                 mask_roi_extractor=None,
-                 bbox_head=dict(
-                     type='DIIHead',
-                     num_classes=80,
-                     num_fcs=2,
-                     num_heads=8,
-                     num_cls_fcs=1,
-                     num_reg_fcs=3,
-                     feedforward_channels=2048,
-                     hidden_channels=256,
-                     dropout=0.0,
-                     roi_feat_size=7,
-                     ffn_act_cfg=dict(type='ReLU', inplace=True)),
-                 mask_head=None,
-                 train_cfg=None,
-                 test_cfg=None,
-                 pretrained=None,
-                 init_cfg=None):
-        assert bbox_roi_extractor is not None
-        assert bbox_head is not None
-        assert len(stage_loss_weights) == num_stages
-        self.num_stages = num_stages
-        self.stage_loss_weights = stage_loss_weights
-        self.proposal_feature_channel = proposal_feature_channel
-        super(SparseRoIHead, self).__init__(
-            num_stages,
-            stage_loss_weights,
-            bbox_roi_extractor=bbox_roi_extractor,
-            mask_roi_extractor=mask_roi_extractor,
-            bbox_head=bbox_head,
-            mask_head=mask_head,
-            train_cfg=train_cfg,
-            test_cfg=test_cfg,
-            pretrained=pretrained,
-            init_cfg=init_cfg)
-        # train_cfg would be None when run the test.py
-        if train_cfg is not None:
-            for stage in range(num_stages):
-                assert isinstance(self.bbox_sampler[stage], PseudoSampler), \
-                    'Sparse R-CNN and QueryInst only support `PseudoSampler`'
-
-    def _bbox_forward(self, stage, x, rois, object_feats, img_metas):
-        """Box head forward function used in both training and testing. Returns
-        all regression, classification results and a intermediate feature.
-
-        Args:
-            stage (int): The index of current stage in
-                iterative process.
-            x (List[Tensor]): List of FPN features
-            rois (Tensor): Rois in total batch. With shape (num_proposal, 5).
-                the last dimension 5 represents (img_index, x1, y1, x2, y2).
-            object_feats (Tensor): The object feature extracted from
-                the previous stage.
-            img_metas (dict): meta information of images.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of bbox head outputs,
-                Containing the following results:
-
-                    - cls_score (Tensor): The score of each class, has
-                      shape (batch_size, num_proposals, num_classes)
-                      when use focal loss or
-                      (batch_size, num_proposals, num_classes+1)
-                      otherwise.
-                    - decode_bbox_pred (Tensor): The regression results
-                      with shape (batch_size, num_proposal, 4).
-                      The last dimension 4 represents
-                      [tl_x, tl_y, br_x, br_y].
-                    - object_feats (Tensor): The object feature extracted
-                      from current stage
-                    - detach_cls_score_list (list[Tensor]): The detached
-                      classification results, length is batch_size, and
-                      each tensor has shape (num_proposal, num_classes).
-                    - detach_proposal_list (list[tensor]): The detached
-                      regression results, length is batch_size, and each
-                      tensor has shape (num_proposal, 4). The last
-                      dimension 4 represents [tl_x, tl_y, br_x, br_y].
-        """
-        num_imgs = len(img_metas)
-        bbox_roi_extractor = self.bbox_roi_extractor[stage]
-        bbox_head = self.bbox_head[stage]
-        bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
-                                        rois)
-        cls_score, bbox_pred, object_feats, attn_feats = bbox_head(
-            bbox_feats, object_feats)
-        proposal_list = self.bbox_head[stage].refine_bboxes(
-            rois,
-            rois.new_zeros(len(rois)),  # dummy arg
-            bbox_pred.view(-1, bbox_pred.size(-1)),
-            [rois.new_zeros(object_feats.size(1)) for _ in range(num_imgs)],
-            img_metas)
-        bbox_results = dict(
-            cls_score=cls_score,
-            decode_bbox_pred=torch.cat(proposal_list),
-            object_feats=object_feats,
-            attn_feats=attn_feats,
-            # detach then use it in label assign
-            detach_cls_score_list=[
-                cls_score[i].detach() for i in range(num_imgs)
-            ],
-            detach_proposal_list=[item.detach() for item in proposal_list])
-
-        return bbox_results
-
-    def _mask_forward(self, stage, x, rois, attn_feats):
-        """Mask head forward function used in both training and testing."""
-        mask_roi_extractor = self.mask_roi_extractor[stage]
-        mask_head = self.mask_head[stage]
-        mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
-                                        rois)
-        # do not support caffe_c4 model anymore
-        mask_pred = mask_head(mask_feats, attn_feats)
-
-        mask_results = dict(mask_pred=mask_pred)
-        return mask_results
-
-    def _mask_forward_train(self, stage, x, attn_feats, sampling_results,
-                            gt_masks, rcnn_train_cfg):
-        """Run forward function and calculate loss for mask head in
-        training."""
-        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
-        attn_feats = torch.cat([
-            feats[res.pos_inds]
-            for (feats, res) in zip(attn_feats, sampling_results)
-        ])
-        mask_results = self._mask_forward(stage, x, pos_rois, attn_feats)
-
-        mask_targets = self.mask_head[stage].get_targets(
-            sampling_results, gt_masks, rcnn_train_cfg)
-
-        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
-
-        loss_mask = self.mask_head[stage].loss(mask_results['mask_pred'],
-                                               mask_targets, pos_labels)
-        mask_results.update(loss_mask)
-        return mask_results
-
-    def forward_train(self,
-                      x,
-                      proposal_boxes,
-                      proposal_features,
-                      img_metas,
-                      gt_bboxes,
-                      gt_labels,
-                      gt_bboxes_ignore=None,
-                      imgs_whwh=None,
-                      gt_masks=None):
-        """Forward function in training stage.
-
-        Args:
-            x (list[Tensor]): list of multi-level img features.
-            proposals (Tensor): Decoded proposal bboxes, has shape
-                (batch_size, num_proposals, 4)
-            proposal_features (Tensor): Expanded proposal
-                features, has shape
-                (batch_size, num_proposals, proposal_feature_channel)
-            img_metas (list[dict]): list of image info dict where
-                each dict has: 'img_shape', 'scale_factor', 'flip',
-                and may also contain 'filename', 'ori_shape',
-                'pad_shape', and 'img_norm_cfg'. For details on the
-                values of these keys see
-                `mmdet/datasets/pipelines/formatting.py:Collect`.
-            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
-                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
-            gt_labels (list[Tensor]): class indices corresponding to each box
-            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
-                boxes can be ignored when computing the loss.
-            imgs_whwh (Tensor): Tensor with shape (batch_size, 4),
-                    the dimension means
-                    [img_width,img_height, img_width, img_height].
-            gt_masks (None | Tensor) : true segmentation masks for each box
-                used if the architecture supports a segmentation task.
-
-        Returns:
-            dict[str, Tensor]: a dictionary of loss components of all stage.
-        """
-
-        num_imgs = len(img_metas)
-        num_proposals = proposal_boxes.size(1)
-        imgs_whwh = imgs_whwh.repeat(1, num_proposals, 1)
-        all_stage_bbox_results = []
-        proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))]
-        object_feats = proposal_features
-        all_stage_loss = {}
-        for stage in range(self.num_stages):
-            rois = bbox2roi(proposal_list)
-            bbox_results = self._bbox_forward(stage, x, rois, object_feats,
-                                              img_metas)
-            all_stage_bbox_results.append(bbox_results)
-            if gt_bboxes_ignore is None:
-                # TODO support ignore
-                gt_bboxes_ignore = [None for _ in range(num_imgs)]
-            sampling_results = []
-            cls_pred_list = bbox_results['detach_cls_score_list']
-            proposal_list = bbox_results['detach_proposal_list']
-            for i in range(num_imgs):
-                normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(proposal_list[i] /
-                                                          imgs_whwh[i])
-                assign_result = self.bbox_assigner[stage].assign(
-                    normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i],
-                    gt_labels[i], img_metas[i])
-                sampling_result = self.bbox_sampler[stage].sample(
-                    assign_result, proposal_list[i], gt_bboxes[i])
-                sampling_results.append(sampling_result)
-            bbox_targets = self.bbox_head[stage].get_targets(
-                sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage],
-                True)
-            cls_score = bbox_results['cls_score']
-            decode_bbox_pred = bbox_results['decode_bbox_pred']
-
-            single_stage_loss = self.bbox_head[stage].loss(
-                cls_score.view(-1, cls_score.size(-1)),
-                decode_bbox_pred.view(-1, 4),
-                *bbox_targets,
-                imgs_whwh=imgs_whwh)
-
-            if self.with_mask:
-                mask_results = self._mask_forward_train(
-                    stage, x, bbox_results['attn_feats'], sampling_results,
-                    gt_masks, self.train_cfg[stage])
-                single_stage_loss['loss_mask'] = mask_results['loss_mask']
-
-            for key, value in single_stage_loss.items():
-                all_stage_loss[f'stage{stage}_{key}'] = value * \
-                                    self.stage_loss_weights[stage]
-            object_feats = bbox_results['object_feats']
-
-        return all_stage_loss
-
-    def simple_test(self,
-                    x,
-                    proposal_boxes,
-                    proposal_features,
-                    img_metas,
-                    imgs_whwh,
-                    rescale=False):
-        """Test without augmentation.
-
-        Args:
-            x (list[Tensor]): list of multi-level img features.
-            proposal_boxes (Tensor): Decoded proposal bboxes, has shape
-                (batch_size, num_proposals, 4)
-            proposal_features (Tensor): Expanded proposal
-                features, has shape
-                (batch_size, num_proposals, proposal_feature_channel)
-            img_metas (dict): meta information of images.
-            imgs_whwh (Tensor): Tensor with shape (batch_size, 4),
-                    the dimension means
-                    [img_width,img_height, img_width, img_height].
-            rescale (bool): If True, return boxes in original image
-                space. Defaults to False.
-
-        Returns:
-            list[list[np.ndarray]] or list[tuple]: When no mask branch,
-            it is bbox results of each image and classes with type
-            `list[list[np.ndarray]]`. The outer list
-            corresponds to each image. The inner list
-            corresponds to each class. When the model has a mask branch,
-            it is a list[tuple] that contains bbox results and mask results.
-            The outer list corresponds to each image, and first element
-            of tuple is bbox results, second element is mask results.
-        """
-        assert self.with_bbox, 'Bbox head must be implemented.'
-        # Decode initial proposals
-        num_imgs = len(img_metas)
-        proposal_list = [proposal_boxes[i] for i in range(num_imgs)]
-        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
-        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
-
-        object_feats = proposal_features
-        if all([proposal.shape[0] == 0 for proposal in proposal_list]):
-            # There is no proposal in the whole batch
-            bbox_results = [[
-                np.zeros((0, 5), dtype=np.float32)
-                for i in range(self.bbox_head[-1].num_classes)
-            ]] * num_imgs
-            return bbox_results
-
-        for stage in range(self.num_stages):
-            rois = bbox2roi(proposal_list)
-            bbox_results = self._bbox_forward(stage, x, rois, object_feats,
-                                              img_metas)
-            object_feats = bbox_results['object_feats']
-            cls_score = bbox_results['cls_score']
-            proposal_list = bbox_results['detach_proposal_list']
-
-        if self.with_mask:
-            rois = bbox2roi(proposal_list)
-            mask_results = self._mask_forward(stage, x, rois,
-                                              bbox_results['attn_feats'])
-            mask_results['mask_pred'] = mask_results['mask_pred'].reshape(
-                num_imgs, -1, *mask_results['mask_pred'].size()[1:])
-
-        num_classes = self.bbox_head[-1].num_classes
-        det_bboxes = []
-        det_labels = []
-
-        if self.bbox_head[-1].loss_cls.use_sigmoid:
-            cls_score = cls_score.sigmoid()
-        else:
-            cls_score = cls_score.softmax(-1)[..., :-1]
-
-        for img_id in range(num_imgs):
-            cls_score_per_img = cls_score[img_id]
-            scores_per_img, topk_indices = cls_score_per_img.flatten(
-                0, 1).topk(
-                    self.test_cfg.max_per_img, sorted=False)
-            labels_per_img = topk_indices % num_classes
-            bbox_pred_per_img = proposal_list[img_id][topk_indices //
-                                                      num_classes]
-            if rescale:
-                scale_factor = img_metas[img_id]['scale_factor']
-                bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor)
-            det_bboxes.append(
-                torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1))
-            det_labels.append(labels_per_img)
-
-        bbox_results = [
-            bbox2result(det_bboxes[i], det_labels[i], num_classes)
-            for i in range(num_imgs)
-        ]
-
-        if self.with_mask:
-            if rescale and not isinstance(scale_factors[0], float):
-                scale_factors = [
-                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
-                    for scale_factor in scale_factors
-                ]
-            _bboxes = [
-                det_bboxes[i][:, :4] *
-                scale_factors[i] if rescale else det_bboxes[i][:, :4]
-                for i in range(len(det_bboxes))
-            ]
-            segm_results = []
-            mask_pred = mask_results['mask_pred']
-            for img_id in range(num_imgs):
-                mask_pred_per_img = mask_pred[img_id].flatten(0,
-                                                              1)[topk_indices]
-                mask_pred_per_img = mask_pred_per_img[:, None, ...].repeat(
-                    1, num_classes, 1, 1)
-                segm_result = self.mask_head[-1].get_seg_masks(
-                    mask_pred_per_img, _bboxes[img_id], det_labels[img_id],
-                    self.test_cfg, ori_shapes[img_id], scale_factors[img_id],
-                    rescale)
-                segm_results.append(segm_result)
-
-        if self.with_mask:
-            results = list(zip(bbox_results, segm_results))
-        else:
-            results = bbox_results
-
-        return results
-
-    def aug_test(self, features, proposal_list, img_metas, rescale=False):
-        raise NotImplementedError(
-            'Sparse R-CNN and QueryInst does not support `aug_test`')
-
-    def forward_dummy(self, x, proposal_boxes, proposal_features, img_metas):
-        """Dummy forward function when do the flops computing."""
-        all_stage_bbox_results = []
-        proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))]
-        object_feats = proposal_features
-        if self.with_bbox:
-            for stage in range(self.num_stages):
-                rois = bbox2roi(proposal_list)
-                bbox_results = self._bbox_forward(stage, x, rois, object_feats,
-                                                  img_metas)
-
-                all_stage_bbox_results.append((bbox_results, ))
-                proposal_list = bbox_results['detach_proposal_list']
-                object_feats = bbox_results['object_feats']
-
-                if self.with_mask:
-                    rois = bbox2roi(proposal_list)
-                    mask_results = self._mask_forward(
-                        stage, x, rois, bbox_results['attn_feats'])
-                    all_stage_bbox_results[-1] += (mask_results, )
-        return all_stage_bbox_results
diff --git a/spaces/rorallitri/biomedical-language-models/logs/Bonnie And McCloud 720p.md b/spaces/rorallitri/biomedical-language-models/logs/Bonnie And McCloud 720p.md
deleted file mode 100644
index 22e8f70e027786a5aeda58d187a25f4517f54b06..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Bonnie And McCloud 720p.md	
+++ /dev/null
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index 52637fc7c6ea8e4af9d9e39f4b3b35afd211d621..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Catia V5r21 Portable Torrent.md	
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deleted file mode 100644
index 1610a515ff242746727146284caf3d95e2dabc3b..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Easeus Data Recovery Full Version Keygen.md	
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/F1 2010 Pc 101 Crack [Extra Quality] 22.md b/spaces/rorallitri/biomedical-language-models/logs/F1 2010 Pc 101 Crack [Extra Quality] 22.md
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index 073361e2a1f6129f21bf02f600ca4e7da61149a2..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/F1 2010 Pc 101 Crack [Extra Quality] 22.md	
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-This is the official Formula 1 Twitch channel. Here you can watch live race streams and F1 highlights from around the globe. It has 16 official broadcasters and over 1300 unofficial. 24 Oct 2010 THE BRITISH GP - A 'nightmare' race for Red Bull, their least successful of the season and the first race they could not clinch the drivers' championship. The four-time world champions broke down in the first corner, leading to one of the most bizarre and controversial races in Grand Prix history. 14 Nov 2009 Formula 1 is like a nervous stomach, it only runs when the crisis is over. The race was one of the more boring ones of the year. It was full of red flags and the long red flag period forced the race to drag on for over three hours. No team could build up a lead, Red Bull appeared to be hopeless. We saw Vettel die like a fly. Vettel then ran off the track, but could not be seen until the race had finished. This happens to your drivers. Button and Raikkonen's race ended like this. Race ends for lack of overtaking. Stewart's win was far more interesting. The race ended due to fuel. As this was the last race at Bahrain, the teams had to make their final run on their final piece of fuel before returning to the hotel for the night. McLaren blew up. Oh boy, the McLaren disaster. In the last race at Malaysia, the McLaren team ran out of fuel. Will we see more of this in 2010? As the cars pulled into the pits, it all blew up. There was a huge explosion at the back of the pack. One of the McLaren's engine blew up as the cars pulled into the pits. A huge explosion was heard. Fans were stunned as a massive fireball erupted in the middle of the track. It felt like an earthquake. How on earth are they going to fix this? The McLaren race car caught fire in the pits during the race and eventually exploded. McLaren ended the race with just two cars in the garage for repairs, as the other car was out on track. The whole race could have ended in disaster for McLaren. The team started the race on a high with a great start and a great position in the race. After the first few corners, however, they started to fall back, and the race could have turned out very differently. This is the end of their 2009 season. But maybe they will be back for 2011. He finished in third place, behind Webber and Hamilton. But it could 4fefd39f24<br />
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Humshakal Movie In Hindi Download 720p Hd.md b/spaces/rorallitri/biomedical-language-models/logs/Humshakal Movie In Hindi Download 720p Hd.md
deleted file mode 100644
index dd90939b4e7aeb7e146d3cf88419d5ddf5833d31..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Humshakal Movie In Hindi Download 720p Hd.md	
+++ /dev/null
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diff --git a/spaces/rorallitri/biomedical-language-models/logs/Lost Grimoires Collection Torrent Download _VERIFIED_.md b/spaces/rorallitri/biomedical-language-models/logs/Lost Grimoires Collection Torrent Download _VERIFIED_.md
deleted file mode 100644
index b1d89382c5f0aa2de55ab583e4d1d1b280a29f08..0000000000000000000000000000000000000000
--- a/spaces/rorallitri/biomedical-language-models/logs/Lost Grimoires Collection Torrent Download _VERIFIED_.md	
+++ /dev/null
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diff --git a/spaces/ruslanmv/Clone-Your-Voice/vocoder/vocoder_dataset.py b/spaces/ruslanmv/Clone-Your-Voice/vocoder/vocoder_dataset.py
deleted file mode 100644
index 9eae1b5f20117feef0a06e264a99b3c0c6143bac..0000000000000000000000000000000000000000
--- a/spaces/ruslanmv/Clone-Your-Voice/vocoder/vocoder_dataset.py
+++ /dev/null
@@ -1,84 +0,0 @@
-from torch.utils.data import Dataset
-from pathlib import Path
-from vocoder import audio
-import vocoder.hparams as hp
-import numpy as np
-import torch
-
-
-class VocoderDataset(Dataset):
-    def __init__(self, metadata_fpath: Path, mel_dir: Path, wav_dir: Path):
-        print("Using inputs from:\n\t%s\n\t%s\n\t%s" % (metadata_fpath, mel_dir, wav_dir))
-        
-        with metadata_fpath.open("r") as metadata_file:
-            metadata = [line.split("|") for line in metadata_file]
-        
-        gta_fnames = [x[1] for x in metadata if int(x[4])]
-        gta_fpaths = [mel_dir.joinpath(fname) for fname in gta_fnames]
-        wav_fnames = [x[0] for x in metadata if int(x[4])]
-        wav_fpaths = [wav_dir.joinpath(fname) for fname in wav_fnames]
-        self.samples_fpaths = list(zip(gta_fpaths, wav_fpaths))
-        
-        print("Found %d samples" % len(self.samples_fpaths))
-    
-    def __getitem__(self, index):  
-        mel_path, wav_path = self.samples_fpaths[index]
-        
-        # Load the mel spectrogram and adjust its range to [-1, 1]
-        mel = np.load(mel_path).T.astype(np.float32) / hp.mel_max_abs_value
-        
-        # Load the wav
-        wav = np.load(wav_path)
-        if hp.apply_preemphasis:
-            wav = audio.pre_emphasis(wav)
-        wav = np.clip(wav, -1, 1)
-        
-        # Fix for missing padding   # TODO: settle on whether this is any useful
-        r_pad =  (len(wav) // hp.hop_length + 1) * hp.hop_length - len(wav)
-        wav = np.pad(wav, (0, r_pad), mode='constant')
-        assert len(wav) >= mel.shape[1] * hp.hop_length
-        wav = wav[:mel.shape[1] * hp.hop_length]
-        assert len(wav) % hp.hop_length == 0
-        
-        # Quantize the wav
-        if hp.voc_mode == 'RAW':
-            if hp.mu_law:
-                quant = audio.encode_mu_law(wav, mu=2 ** hp.bits)
-            else:
-                quant = audio.float_2_label(wav, bits=hp.bits)
-        elif hp.voc_mode == 'MOL':
-            quant = audio.float_2_label(wav, bits=16)
-            
-        return mel.astype(np.float32), quant.astype(np.int64)
-
-    def __len__(self):
-        return len(self.samples_fpaths)
-        
-        
-def collate_vocoder(batch):
-    mel_win = hp.voc_seq_len // hp.hop_length + 2 * hp.voc_pad
-    max_offsets = [x[0].shape[-1] -2 - (mel_win + 2 * hp.voc_pad) for x in batch]
-    mel_offsets = [np.random.randint(0, offset) for offset in max_offsets]
-    sig_offsets = [(offset + hp.voc_pad) * hp.hop_length for offset in mel_offsets]
-
-    mels = [x[0][:, mel_offsets[i]:mel_offsets[i] + mel_win] for i, x in enumerate(batch)]
-
-    labels = [x[1][sig_offsets[i]:sig_offsets[i] + hp.voc_seq_len + 1] for i, x in enumerate(batch)]
-
-    mels = np.stack(mels).astype(np.float32)
-    labels = np.stack(labels).astype(np.int64)
-
-    mels = torch.tensor(mels)
-    labels = torch.tensor(labels).long()
-
-    x = labels[:, :hp.voc_seq_len]
-    y = labels[:, 1:]
-
-    bits = 16 if hp.voc_mode == 'MOL' else hp.bits
-
-    x = audio.label_2_float(x.float(), bits)
-
-    if hp.voc_mode == 'MOL' :
-        y = audio.label_2_float(y.float(), bits)
-
-    return x, y, mels
\ No newline at end of file
diff --git a/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/calc_inception.py b/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/calc_inception.py
deleted file mode 100644
index 5daa531475c377a73ffa256bdf84bb662e144215..0000000000000000000000000000000000000000
--- a/spaces/safi842/FashionGen/models/stylegan2/stylegan2-pytorch/calc_inception.py
+++ /dev/null
@@ -1,116 +0,0 @@
-import argparse
-import pickle
-import os
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.utils.data import DataLoader
-from torchvision import transforms
-from torchvision.models import inception_v3, Inception3
-import numpy as np
-from tqdm import tqdm
-
-from inception import InceptionV3
-from dataset import MultiResolutionDataset
-
-
-class Inception3Feature(Inception3):
-    def forward(self, x):
-        if x.shape[2] != 299 or x.shape[3] != 299:
-            x = F.interpolate(x, size=(299, 299), mode='bilinear', align_corners=True)
-
-        x = self.Conv2d_1a_3x3(x)  # 299 x 299 x 3
-        x = self.Conv2d_2a_3x3(x)  # 149 x 149 x 32
-        x = self.Conv2d_2b_3x3(x)  # 147 x 147 x 32
-        x = F.max_pool2d(x, kernel_size=3, stride=2)  # 147 x 147 x 64
-
-        x = self.Conv2d_3b_1x1(x)  # 73 x 73 x 64
-        x = self.Conv2d_4a_3x3(x)  # 73 x 73 x 80
-        x = F.max_pool2d(x, kernel_size=3, stride=2)  # 71 x 71 x 192
-
-        x = self.Mixed_5b(x)  # 35 x 35 x 192
-        x = self.Mixed_5c(x)  # 35 x 35 x 256
-        x = self.Mixed_5d(x)  # 35 x 35 x 288
-
-        x = self.Mixed_6a(x)  # 35 x 35 x 288
-        x = self.Mixed_6b(x)  # 17 x 17 x 768
-        x = self.Mixed_6c(x)  # 17 x 17 x 768
-        x = self.Mixed_6d(x)  # 17 x 17 x 768
-        x = self.Mixed_6e(x)  # 17 x 17 x 768
-
-        x = self.Mixed_7a(x)  # 17 x 17 x 768
-        x = self.Mixed_7b(x)  # 8 x 8 x 1280
-        x = self.Mixed_7c(x)  # 8 x 8 x 2048
-
-        x = F.avg_pool2d(x, kernel_size=8)  # 8 x 8 x 2048
-
-        return x.view(x.shape[0], x.shape[1])  # 1 x 1 x 2048
-
-
-def load_patched_inception_v3():
-    # inception = inception_v3(pretrained=True)
-    # inception_feat = Inception3Feature()
-    # inception_feat.load_state_dict(inception.state_dict())
-    inception_feat = InceptionV3([3], normalize_input=False)
-
-    return inception_feat
-
-
-@torch.no_grad()
-def extract_features(loader, inception, device):
-    pbar = tqdm(loader)
-
-    feature_list = []
-
-    for img in pbar:
-        img = img.to(device)
-        feature = inception(img)[0].view(img.shape[0], -1)
-        feature_list.append(feature.to('cpu'))
-
-    features = torch.cat(feature_list, 0)
-
-    return features
-
-
-if __name__ == '__main__':
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-    parser = argparse.ArgumentParser(
-        description='Calculate Inception v3 features for datasets'
-    )
-    parser.add_argument('--size', type=int, default=256)
-    parser.add_argument('--batch', default=64, type=int, help='batch size')
-    parser.add_argument('--n_sample', type=int, default=50000)
-    parser.add_argument('--flip', action='store_true')
-    parser.add_argument('path', metavar='PATH', help='path to datset lmdb file')
-
-    args = parser.parse_args()
-
-    inception = load_patched_inception_v3()
-    inception = nn.DataParallel(inception).eval().to(device)
-
-    transform = transforms.Compose(
-        [
-            transforms.RandomHorizontalFlip(p=0.5 if args.flip else 0),
-            transforms.ToTensor(),
-            transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
-        ]
-    )
-
-    dset = MultiResolutionDataset(args.path, transform=transform, resolution=args.size)
-    loader = DataLoader(dset, batch_size=args.batch, num_workers=4)
-
-    features = extract_features(loader, inception, device).numpy()
-
-    features = features[: args.n_sample]
-
-    print(f'extracted {features.shape[0]} features')
-
-    mean = np.mean(features, 0)
-    cov = np.cov(features, rowvar=False)
-
-    name = os.path.splitext(os.path.basename(args.path))[0]
-
-    with open(f'inception_{name}.pkl', 'wb') as f:
-        pickle.dump({'mean': mean, 'cov': cov, 'size': args.size, 'path': args.path}, f)
diff --git a/spaces/samavi/openai-clip-vit-base-patch32/README.md b/spaces/samavi/openai-clip-vit-base-patch32/README.md
deleted file mode 100644
index 0be692b29d99a2ee1217eae8c0b8ce265976ef23..0000000000000000000000000000000000000000
--- a/spaces/samavi/openai-clip-vit-base-patch32/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: Openai Clip Vit Base Patch32
-emoji: 🏃
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 3.34.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/sccstandardteam/ChuanhuChatGPT/assets/custom.js b/spaces/sccstandardteam/ChuanhuChatGPT/assets/custom.js
deleted file mode 100644
index ae5a76b5e791be8b107126889519e37d89fc80f0..0000000000000000000000000000000000000000
--- a/spaces/sccstandardteam/ChuanhuChatGPT/assets/custom.js
+++ /dev/null
@@ -1,607 +0,0 @@
-
-// custom javascript here
-
-const MAX_HISTORY_LENGTH = 32;
-
-var key_down_history = [];
-var currentIndex = -1;
-var user_input_ta;
-
-var gradioContainer = null;
-var user_input_ta = null;
-var user_input_tb = null;
-var userInfoDiv = null;
-var appTitleDiv = null;
-var chatbot = null;
-var chatbotWrap = null;
-var apSwitch = null;
-var empty_botton = null;
-var messageBotDivs = null;
-// var renderLatex = null;
-var loginUserForm = null;
-var logginUser = null;
-
-var userLogged = false;
-var usernameGotten = false;
-var shouldRenderLatex = false;
-var historyLoaded = false;
-
-var ga = document.getElementsByTagName("gradio-app");
-var targetNode = ga[0];
-var isInIframe = (window.self !== window.top);
-var language = navigator.language.slice(0,2);
-
-var forView_i18n = {
-    'zh': "仅供查看",
-    'en': "For viewing only",
-    'ja': "閲覧専用",
-    'fr': "Pour consultation seulement",
-    'es': "Solo para visualización",
-};
-
-// gradio 页面加载好了么??? 我能动你的元素了么??
-function gradioLoaded(mutations) {
-    for (var i = 0; i < mutations.length; i++) {
-        if (mutations[i].addedNodes.length) {
-            loginUserForm = document.querySelector(".gradio-container > .main > .wrap > .panel > .form")
-            gradioContainer = document.querySelector(".gradio-container");
-            user_input_tb = document.getElementById('user_input_tb');
-            userInfoDiv = document.getElementById("user_info");
-            appTitleDiv = document.getElementById("app_title");
-            chatbot = document.querySelector('#chuanhu_chatbot');
-            chatbotWrap = document.querySelector('#chuanhu_chatbot > .wrap');
-            apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
-            // renderLatex = document.querySelector("#render_latex_checkbox > label > input");
-            empty_botton = document.getElementById("empty_btn")
-
-            if (loginUserForm) {
-                localStorage.setItem("userLogged", true);
-                userLogged = true;
-            }
-
-            if (gradioContainer && apSwitch) {  // gradioCainter 加载出来了没?
-                adjustDarkMode();
-            }
-            if (user_input_tb) {  // user_input_tb 加载出来了没?
-                selectHistory();
-            }
-            if (userInfoDiv && appTitleDiv) {  // userInfoDiv 和 appTitleDiv 加载出来了没?
-                if (!usernameGotten) {
-                    getUserInfo();
-                }
-                setTimeout(showOrHideUserInfo(), 2000);
-            }
-            if (chatbot) {  // chatbot 加载出来了没?
-                setChatbotHeight();
-            }
-            if (chatbotWrap) {
-                if (!historyLoaded) {
-                    loadHistoryHtml();
-                }
-                setChatbotScroll();
-            }
-            // if (renderLatex) {  // renderLatex 加载出来了没?
-            //     shouldRenderLatex = renderLatex.checked;
-            //     updateMathJax();
-            // }
-            if (empty_botton) {
-                emptyHistory();
-            }
-        }
-    }
-}
-
-function webLocale() {
-    console.log("webLocale", language);
-    if (forView_i18n.hasOwnProperty(language)) {
-        var forView = forView_i18n[language];
-        var forViewStyle = document.createElement('style');
-        forViewStyle.innerHTML = '.wrap>.history-message>:last-child::after { content: "' + forView + '"!important; }';
-        document.head.appendChild(forViewStyle);
-        // console.log("added forViewStyle", forView);
-    }
-}
-
-function selectHistory() {
-    user_input_ta = user_input_tb.querySelector("textarea");
-    if (user_input_ta) {
-        observer.disconnect(); // 停止监听
-        // 在 textarea 上监听 keydown 事件
-        user_input_ta.addEventListener("keydown", function (event) {
-            var value = user_input_ta.value.trim();
-            // 判断按下的是否为方向键
-            if (event.code === 'ArrowUp' || event.code === 'ArrowDown') {
-                // 如果按下的是方向键，且输入框中有内容，且历史记录中没有该内容，则不执行操作
-                if (value && key_down_history.indexOf(value) === -1)
-                    return;
-                // 对于需要响应的动作，阻止默认行为。
-                event.preventDefault();
-                var length = key_down_history.length;
-                if (length === 0) {
-                    currentIndex = -1; // 如果历史记录为空，直接将当前选中的记录重置
-                    return;
-                }
-                if (currentIndex === -1) {
-                    currentIndex = length;
-                }
-                if (event.code === 'ArrowUp' && currentIndex > 0) {
-                    currentIndex--;
-                    user_input_ta.value = key_down_history[currentIndex];
-                } else if (event.code === 'ArrowDown' && currentIndex < length - 1) {
-                    currentIndex++;
-                    user_input_ta.value = key_down_history[currentIndex];
-                }
-                user_input_ta.selectionStart = user_input_ta.value.length;
-                user_input_ta.selectionEnd = user_input_ta.value.length;
-                const input_event = new InputEvent("input", { bubbles: true, cancelable: true });
-                user_input_ta.dispatchEvent(input_event);
-            } else if (event.code === "Enter") {
-                if (value) {
-                    currentIndex = -1;
-                    if (key_down_history.indexOf(value) === -1) {
-                        key_down_history.push(value);
-                        if (key_down_history.length > MAX_HISTORY_LENGTH) {
-                            key_down_history.shift();
-                        }
-                    }
-                }
-            }
-        });
-    }
-}
-
-var username = null;
-function getUserInfo() {
-    if (usernameGotten) {
-        return;
-    }
-    userLogged = localStorage.getItem('userLogged');
-    if (userLogged) {
-        username = userInfoDiv.innerText;
-        if (username) {
-            if (username.includes("getting user info…")) {
-                setTimeout(getUserInfo, 500);
-                return;
-            } else if (username === " ") {
-                localStorage.removeItem("username");
-                localStorage.removeItem("userLogged")
-                userLogged = false;
-                usernameGotten = true;
-                return;
-            } else {
-                username = username.match(/User:\s*(.*)/)[1] || username;
-                localStorage.setItem("username", username);
-                usernameGotten = true;
-                clearHistoryHtml();
-            }
-        }
-    }
-}
-
-function toggleUserInfoVisibility(shouldHide) {
-    if (userInfoDiv) {
-        if (shouldHide) {
-            userInfoDiv.classList.add("hideK");
-        } else {
-            userInfoDiv.classList.remove("hideK");
-        }
-    }
-}
-function showOrHideUserInfo() {
-    var sendBtn = document.getElementById("submit_btn");
-
-    // Bind mouse/touch events to show/hide user info
-    appTitleDiv.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-    userInfoDiv.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-    sendBtn.addEventListener("mouseenter", function () {
-        toggleUserInfoVisibility(false);
-    });
-
-    appTitleDiv.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-    userInfoDiv.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-    sendBtn.addEventListener("mouseleave", function () {
-        toggleUserInfoVisibility(true);
-    });
-
-    appTitleDiv.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-    userInfoDiv.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-    sendBtn.ontouchstart = function () {
-        toggleUserInfoVisibility(false);
-    };
-
-    appTitleDiv.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000);
-    };
-    userInfoDiv.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000);
-    };
-    sendBtn.ontouchend = function () {
-        setTimeout(function () {
-            toggleUserInfoVisibility(true);
-        }, 3000); // Delay 1 second to hide user info
-    };
-
-    // Hide user info after 2 second
-    setTimeout(function () {
-        toggleUserInfoVisibility(true);
-    }, 2000);
-}
-
-function toggleDarkMode(isEnabled) {
-    if (isEnabled) {
-        gradioContainer.classList.add("dark");
-        document.body.style.setProperty("background-color", "var(--neutral-950)", "important");
-    } else {
-        gradioContainer.classList.remove("dark");
-        document.body.style.backgroundColor = "";
-    }
-}
-function adjustDarkMode() {
-    const darkModeQuery = window.matchMedia("(prefers-color-scheme: dark)");
-
-    // 根据当前颜色模式设置初始状态
-    apSwitch.checked = darkModeQuery.matches;
-    toggleDarkMode(darkModeQuery.matches);
-    // 监听颜色模式变化
-    darkModeQuery.addEventListener("change", (e) => {
-        apSwitch.checked = e.matches;
-        toggleDarkMode(e.matches);
-    });
-    // apSwitch = document.querySelector('.apSwitch input[type="checkbox"]');
-    apSwitch.addEventListener("change", (e) => {
-        toggleDarkMode(e.target.checked);
-    });
-}
-
-function setChatbotHeight() {
-    const screenWidth = window.innerWidth;
-    const statusDisplay = document.querySelector('#status_display');
-    const statusDisplayHeight = statusDisplay ? statusDisplay.offsetHeight : 0;
-    const wrap = chatbot.querySelector('.wrap');
-    const vh = window.innerHeight * 0.01;
-    document.documentElement.style.setProperty('--vh', `${vh}px`);
-    if (isInIframe) {
-        chatbot.style.height = `700px`;
-        wrap.style.maxHeight = `calc(700px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`
-    } else {
-        if (screenWidth <= 320) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 150}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else if (screenWidth <= 499) {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 100}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        } else {
-            chatbot.style.height = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px)`;
-            wrap.style.maxHeight = `calc(var(--vh, 1vh) * 100 - ${statusDisplayHeight + 160}px - var(--line-sm) * 1rem - 2 * var(--block-label-margin))`;
-        }
-    }
-}
-function setChatbotScroll() {
-    var scrollHeight = chatbotWrap.scrollHeight;
-    chatbotWrap.scrollTo(0,scrollHeight)
-}
-var rangeInputs = null;
-var numberInputs = null;
-function setSlider() {
-    rangeInputs = document.querySelectorAll('input[type="range"]');
-    numberInputs = document.querySelectorAll('input[type="number"]')
-    setSliderRange();
-    rangeInputs.forEach(rangeInput => {
-        rangeInput.addEventListener('input', setSliderRange);
-    });
-    numberInputs.forEach(numberInput => {
-        numberInput.addEventListener('input', setSliderRange);
-    })
-}
-function setSliderRange() {
-    var range = document.querySelectorAll('input[type="range"]');
-    range.forEach(range => {
-        range.style.backgroundSize = (range.value - range.min) / (range.max - range.min) * 100 + '% 100%';
-    });
-}
-
-function addChuanhuButton(botElement) {
-    var rawMessage = null;
-    var mdMessage = null;
-    rawMessage = botElement.querySelector('.raw-message');
-    mdMessage = botElement.querySelector('.md-message');
-    if (!rawMessage) {
-        var buttons = botElement.querySelectorAll('button.chuanhu-btn');
-        for (var i = 0; i < buttons.length; i++) {
-            buttons[i].parentNode.removeChild(buttons[i]);
-        }
-        return;
-    }
-    var copyButton = null;
-    var toggleButton = null;
-    copyButton = botElement.querySelector('button.copy-bot-btn');
-    toggleButton = botElement.querySelector('button.toggle-md-btn');
-    if (copyButton) copyButton.remove();
-    if (toggleButton) toggleButton.remove();
-
-    // Copy bot button
-    var copyButton = document.createElement('button');
-    copyButton.classList.add('chuanhu-btn');
-    copyButton.classList.add('copy-bot-btn');
-    copyButton.setAttribute('aria-label', 'Copy');
-    copyButton.innerHTML = copyIcon;
-    copyButton.addEventListener('click', () => {
-        const textToCopy = rawMessage.innerText;
-        navigator.clipboard
-            .writeText(textToCopy)
-            .then(() => {
-                copyButton.innerHTML = copiedIcon;
-                setTimeout(() => {
-                    copyButton.innerHTML = copyIcon;
-                }, 1500);
-            })
-            .catch(() => {
-                console.error("copy failed");
-            });
-    });
-    botElement.appendChild(copyButton);
-
-    // Toggle button
-    var toggleButton = document.createElement('button');
-    toggleButton.classList.add('chuanhu-btn');
-    toggleButton.classList.add('toggle-md-btn');
-    toggleButton.setAttribute('aria-label', 'Toggle');
-    var renderMarkdown = mdMessage.classList.contains('hideM');
-    toggleButton.innerHTML = renderMarkdown ? mdIcon : rawIcon;
-    toggleButton.addEventListener('click', () => {
-        renderMarkdown = mdMessage.classList.contains('hideM');
-        if (renderMarkdown){
-            renderMarkdownText(botElement);
-            toggleButton.innerHTML=rawIcon;
-        } else {
-            removeMarkdownText(botElement);
-            toggleButton.innerHTML=mdIcon;
-        }
-    });
-    botElement.insertBefore(toggleButton, copyButton);
-}
-
-function addCopyCodeButton(pre) {
-    var code = null;
-    var firstChild = null;
-    code = pre.querySelector('code');
-    if (!code) return;
-    firstChild = code.querySelector('div');
-    if (!firstChild) return;
-    var oldCopyButton = null;
-    oldCopyButton = code.querySelector('button.copy-code-btn');
-    // if (oldCopyButton) oldCopyButton.remove();
-    if (oldCopyButton) return; // 没太有用，新生成的对话中始终会被pre覆盖，导致按钮消失，这段代码不启用……
-    var codeButton = document.createElement('button');
-    codeButton.classList.add('copy-code-btn');
-    codeButton.textContent = '\uD83D\uDCCE';
-
-    code.insertBefore(codeButton, firstChild);
-    codeButton.addEventListener('click', function () {
-        var range = document.createRange();
-        range.selectNodeContents(code);
-        range.setStartBefore(firstChild);
-        navigator.clipboard
-            .writeText(range.toString())
-            .then(() => {
-                codeButton.textContent = '\u2714';
-                setTimeout(function () {
-                    codeButton.textContent = '\uD83D\uDCCE';
-                }, 2000);
-            })
-            .catch(e => {
-                console.error(e);
-                codeButton.textContent = '\u2716';
-            });
-    });
-}
-
-function renderMarkdownText(message) {
-    var mdDiv = message.querySelector('.md-message');
-    if (mdDiv) mdDiv.classList.remove('hideM');
-    var rawDiv = message.querySelector('.raw-message');
-    if (rawDiv) rawDiv.classList.add('hideM');
-}
-function removeMarkdownText(message) {
-    var rawDiv = message.querySelector('.raw-message');
-    if (rawDiv) rawDiv.classList.remove('hideM');
-    var mdDiv = message.querySelector('.md-message');
-    if (mdDiv) mdDiv.classList.add('hideM');
-}
-
-var rendertime = 0; // for debugging
-var mathjaxUpdated = false;
-
-function renderMathJax() {
-    messageBotDivs = document.querySelectorAll('.message.bot .md-message');
-    for (var i = 0; i < messageBotDivs.length; i++) {
-        var mathJaxSpan = messageBotDivs[i].querySelector('.MathJax_Preview');
-        if (!mathJaxSpan && shouldRenderLatex && !mathjaxUpdated) {
-            MathJax.Hub.Queue(["Typeset", MathJax.Hub, messageBotDivs[i]]);
-            rendertime +=1; // for debugging
-            // console.log("renderingMathJax", i)
-        }
-    }
-    mathjaxUpdated = true;
-    // console.log("MathJax Rendered")
-}
-
-function removeMathjax() {
-    // var jax = MathJax.Hub.getAllJax();
-    // for (var i = 0; i < jax.length; i++) {
-    //     // MathJax.typesetClear(jax[i]);
-    //     jax[i].Text(newmath)
-    //     jax[i].Reprocess()
-    // }
-    // 我真的不会了啊啊啊，mathjax并没有提供转换为原先文本的办法。
-    mathjaxUpdated = true;
-    // console.log("MathJax removed!");
-}
-
-function updateMathJax() {
-    // renderLatex.addEventListener("change", function() {
-    //     shouldRenderLatex = renderLatex.checked;
-    //     if (!mathjaxUpdated) {
-    //         if (shouldRenderLatex) {
-    //             renderMathJax();
-    //         } else {
-    //             console.log("MathJax Disabled")
-    //             removeMathjax();
-    //         }
-    //     } else {
-    //         if (!shouldRenderLatex) {
-    //             mathjaxUpdated = false; // reset
-    //         }
-    //     }
-    // });
-    if (shouldRenderLatex && !mathjaxUpdated) {
-        renderMathJax();
-    }
-    mathjaxUpdated = false;
-}
-
-let timeoutId;
-let isThrottled = false;
-var mmutation
-// 监听所有元素中 bot message 的变化，用来查找需要渲染的mathjax, 并为 bot 消息添加复制按钮。
-var mObserver = new MutationObserver(function (mutationsList) {
-    for (mmutation of mutationsList) {
-        if (mmutation.type === 'childList') {
-            for (var node of mmutation.addedNodes) {
-                if (node.nodeType === 1 && node.classList.contains('message') && node.getAttribute('data-testid') === 'bot') {
-                    if (shouldRenderLatex) {
-                        renderMathJax();
-                        mathjaxUpdated = false;
-                    }
-                    saveHistoryHtml();
-                    document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton);
-                    document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot pre').forEach(addCopyCodeButton);
-                }
-                if (node.tagName === 'INPUT' && node.getAttribute('type') === 'range') {
-                    setSlider();
-                }
-            }
-            for (var node of mmutation.removedNodes) {
-                if (node.nodeType === 1 && node.classList.contains('message') && node.getAttribute('data-testid') === 'bot') {
-                    if (shouldRenderLatex) {
-                        renderMathJax();
-                        mathjaxUpdated = false;
-                    }
-                    saveHistoryHtml();
-                    document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton);
-                    document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot pre').forEach(addCopyCodeButton);
-                }
-            }
-        } else if (mmutation.type === 'attributes') {
-            if (mmutation.target.nodeType === 1 && mmutation.target.classList.contains('message') && mmutation.target.getAttribute('data-testid') === 'bot') {
-                document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot pre').forEach(addCopyCodeButton); // 目前写的是有点问题的，会导致加button次数过多，但是bot对话内容生成时又是不断覆盖pre的……
-                if (isThrottled) break; // 为了防止重复不断疯狂渲染，加上等待_(:з」∠)_
-                isThrottled = true;
-                clearTimeout(timeoutId);
-                timeoutId = setTimeout(() => {
-                    isThrottled = false;
-                    if (shouldRenderLatex) {
-                        renderMathJax();
-                        mathjaxUpdated = false;
-                    }
-                    document.querySelectorAll('#chuanhu_chatbot>.wrap>.message-wrap .message.bot').forEach(addChuanhuButton);
-                    saveHistoryHtml();
-                }, 500);
-            }
-        }
-    }
-});
-mObserver.observe(document.documentElement, { attributes: true, childList: true, subtree: true });
-
-var loadhistorytime = 0; // for debugging
-function saveHistoryHtml() {
-    var historyHtml = document.querySelector('#chuanhu_chatbot > .wrap');
-    localStorage.setItem('chatHistory', historyHtml.innerHTML);
-    // console.log("History Saved")
-    historyLoaded = false;
-}
-function loadHistoryHtml() {
-    var historyHtml = localStorage.getItem('chatHistory');
-    if (!historyHtml) {
-        historyLoaded = true;
-        return; // no history, do nothing
-    }
-    userLogged = localStorage.getItem('userLogged');
-    if (userLogged){
-        historyLoaded = true;
-        return; // logged in, do nothing
-    }
-    if (!historyLoaded) {
-        var tempDiv = document.createElement('div');
-        tempDiv.innerHTML = historyHtml;
-        var buttons = tempDiv.querySelectorAll('button.chuanhu-btn');
-        for (var i = 0; i < buttons.length; i++) {
-            buttons[i].parentNode.removeChild(buttons[i]);
-        }
-        var fakeHistory = document.createElement('div');
-        fakeHistory.classList.add('history-message');
-        fakeHistory.innerHTML = tempDiv.innerHTML;
-        webLocale();
-        chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild);
-        // var fakeHistory = document.createElement('div');
-        // fakeHistory.classList.add('history-message');
-        // fakeHistory.innerHTML = historyHtml;
-        // chatbotWrap.insertBefore(fakeHistory, chatbotWrap.firstChild);
-        historyLoaded = true;
-        console.log("History Loaded");
-        loadhistorytime += 1; // for debugging
-    } else {
-        historyLoaded = false;
-    }
-}
-function clearHistoryHtml() {
-    localStorage.removeItem("chatHistory");
-    historyMessages = chatbotWrap.querySelector('.history-message');
-    if (historyMessages) {
-        chatbotWrap.removeChild(historyMessages);
-        console.log("History Cleared");
-    }
-}
-function emptyHistory() {
-    empty_botton.addEventListener("click", function () {
-        clearHistoryHtml();
-    });
-}
-
-// 监视页面内部 DOM 变动
-var observer = new MutationObserver(function (mutations) {
-    gradioLoaded(mutations);
-});
-observer.observe(targetNode, { childList: true, subtree: true });
-
-// 监视页面变化
-window.addEventListener("DOMContentLoaded", function () {
-    isInIframe = (window.self !== window.top);
-    historyLoaded = false;
-    shouldRenderLatex = !!document.querySelector('script[src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-MML-AM_CHTML"]');
-});
-window.addEventListener('resize', setChatbotHeight);
-window.addEventListener('scroll', setChatbotHeight);
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", adjustDarkMode);
-
-// button svg code
-const copyIcon   = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><rect x="9" y="9" width="13" height="13" rx="2" ry="2"></rect><path d="M5 15H4a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h9a2 2 0 0 1 2 2v1"></path></svg></span>';
-const copiedIcon = '<span><svg stroke="currentColor" fill="none" stroke-width="2" viewBox="0 0 24 24" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><polyline points="20 6 9 17 4 12"></polyline></svg></span>';
-const mdIcon     = '<span><svg stroke="currentColor" fill="none" stroke-width="1" viewBox="0 0 14 18" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><g transform-origin="center" transform="scale(0.85)"><path d="M1.5,0 L12.5,0 C13.3284271,-1.52179594e-16 14,0.671572875 14,1.5 L14,16.5 C14,17.3284271 13.3284271,18 12.5,18 L1.5,18 C0.671572875,18 1.01453063e-16,17.3284271 0,16.5 L0,1.5 C-1.01453063e-16,0.671572875 0.671572875,1.52179594e-16 1.5,0 Z" stroke-width="1.8"></path><line x1="3.5" y1="3.5" x2="10.5" y2="3.5"></line><line x1="3.5" y1="6.5" x2="8" y2="6.5"></line></g><path d="M4,9 L10,9 C10.5522847,9 11,9.44771525 11,10 L11,13.5 C11,14.0522847 10.5522847,14.5 10,14.5 L4,14.5 C3.44771525,14.5 3,14.0522847 3,13.5 L3,10 C3,9.44771525 3.44771525,9 4,9 Z" stroke="none" fill="currentColor"></path></svg></span>';
-const rawIcon    = '<span><svg stroke="currentColor" fill="none" stroke-width="1.8" viewBox="0 0 18 14" stroke-linecap="round" stroke-linejoin="round" height=".8em" width=".8em" xmlns="http://www.w3.org/2000/svg"><g transform-origin="center" transform="scale(0.85)"><polyline points="4 3 0 7 4 11"></polyline><polyline points="14 3 18 7 14 11"></polyline><line x1="12" y1="0" x2="6" y2="14"></line></g></svg></span>';
diff --git a/spaces/scedlatioru/img-to-music/example/Tecnotest Reflex Plus 4130 Crack.md b/spaces/scedlatioru/img-to-music/example/Tecnotest Reflex Plus 4130 Crack.md
deleted file mode 100644
index 157e6cf668fa74b188474f2b144f92931293baad..0000000000000000000000000000000000000000
--- a/spaces/scedlatioru/img-to-music/example/Tecnotest Reflex Plus 4130 Crack.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>tecnotest reflex plus 4130 crack</h2><br /><p><b><b>Download</b> &#9733;&#9733;&#9733; <a href="https://gohhs.com/2uEzY7">https://gohhs.com/2uEzY7</a></b></p><br /><br />
-<br />
-tecno reflex, tecno reflex plus 4130, tecno reflex plus 4130 preis, ... idea how to crack the activation code for my reflex 3130 from tecnotest . 4d29de3e1b<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/segments-tobias/conex/espnet/bin/mt_train.py b/spaces/segments-tobias/conex/espnet/bin/mt_train.py
deleted file mode 100644
index 7251617e0985565d8026ca8ab1d6c937c974289f..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet/bin/mt_train.py
+++ /dev/null
@@ -1,480 +0,0 @@
-#!/usr/bin/env python3
-# encoding: utf-8
-
-# Copyright 2019 Kyoto University (Hirofumi Inaguma)
-#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
-
-"""Neural machine translation model training script."""
-
-import logging
-import os
-import random
-import subprocess
-import sys
-
-from distutils.version import LooseVersion
-
-import configargparse
-import numpy as np
-import torch
-
-from espnet import __version__
-from espnet.utils.cli_utils import strtobool
-from espnet.utils.training.batchfy import BATCH_COUNT_CHOICES
-
-is_torch_1_2_plus = LooseVersion(torch.__version__) >= LooseVersion("1.2")
-
-
-# NOTE: you need this func to generate our sphinx doc
-def get_parser(parser=None, required=True):
-    """Get default arguments."""
-    if parser is None:
-        parser = configargparse.ArgumentParser(
-            description="Train a neural machine translation (NMT) model on one CPU, "
-            "one or multiple GPUs",
-            config_file_parser_class=configargparse.YAMLConfigFileParser,
-            formatter_class=configargparse.ArgumentDefaultsHelpFormatter,
-        )
-    # general configuration
-    parser.add("--config", is_config_file=True, help="config file path")
-    parser.add(
-        "--config2",
-        is_config_file=True,
-        help="second config file path that overwrites the settings in `--config`.",
-    )
-    parser.add(
-        "--config3",
-        is_config_file=True,
-        help="third config file path that overwrites the settings "
-        "in `--config` and `--config2`.",
-    )
-
-    parser.add_argument(
-        "--ngpu",
-        default=None,
-        type=int,
-        help="Number of GPUs. If not given, use all visible devices",
-    )
-    parser.add_argument(
-        "--train-dtype",
-        default="float32",
-        choices=["float16", "float32", "float64", "O0", "O1", "O2", "O3"],
-        help="Data type for training (only pytorch backend). "
-        "O0,O1,.. flags require apex. "
-        "See https://nvidia.github.io/apex/amp.html#opt-levels",
-    )
-    parser.add_argument(
-        "--backend",
-        default="chainer",
-        type=str,
-        choices=["chainer", "pytorch"],
-        help="Backend library",
-    )
-    parser.add_argument(
-        "--outdir", type=str, required=required, help="Output directory"
-    )
-    parser.add_argument("--debugmode", default=1, type=int, help="Debugmode")
-    parser.add_argument(
-        "--dict", required=required, help="Dictionary for source/target languages"
-    )
-    parser.add_argument("--seed", default=1, type=int, help="Random seed")
-    parser.add_argument("--debugdir", type=str, help="Output directory for debugging")
-    parser.add_argument(
-        "--resume",
-        "-r",
-        default="",
-        nargs="?",
-        help="Resume the training from snapshot",
-    )
-    parser.add_argument(
-        "--minibatches",
-        "-N",
-        type=int,
-        default="-1",
-        help="Process only N minibatches (for debug)",
-    )
-    parser.add_argument("--verbose", "-V", default=0, type=int, help="Verbose option")
-    parser.add_argument(
-        "--tensorboard-dir",
-        default=None,
-        type=str,
-        nargs="?",
-        help="Tensorboard log dir path",
-    )
-    parser.add_argument(
-        "--report-interval-iters",
-        default=100,
-        type=int,
-        help="Report interval iterations",
-    )
-    parser.add_argument(
-        "--save-interval-iters",
-        default=0,
-        type=int,
-        help="Save snapshot interval iterations",
-    )
-    # task related
-    parser.add_argument(
-        "--train-json",
-        type=str,
-        default=None,
-        help="Filename of train label data (json)",
-    )
-    parser.add_argument(
-        "--valid-json",
-        type=str,
-        default=None,
-        help="Filename of validation label data (json)",
-    )
-    # network architecture
-    parser.add_argument(
-        "--model-module",
-        type=str,
-        default=None,
-        help="model defined module (default: espnet.nets.xxx_backend.e2e_mt:E2E)",
-    )
-    # loss related
-    parser.add_argument(
-        "--lsm-weight", default=0.0, type=float, help="Label smoothing weight"
-    )
-    # translations options to compute BLEU
-    parser.add_argument(
-        "--report-bleu",
-        default=True,
-        action="store_true",
-        help="Compute BLEU on development set",
-    )
-    parser.add_argument("--nbest", type=int, default=1, help="Output N-best hypotheses")
-    parser.add_argument("--beam-size", type=int, default=4, help="Beam size")
-    parser.add_argument("--penalty", default=0.0, type=float, help="Incertion penalty")
-    parser.add_argument(
-        "--maxlenratio",
-        default=0.0,
-        type=float,
-        help="""Input length ratio to obtain max output length.
-                        If maxlenratio=0.0 (default), it uses a end-detect function
-                        to automatically find maximum hypothesis lengths""",
-    )
-    parser.add_argument(
-        "--minlenratio",
-        default=0.0,
-        type=float,
-        help="Input length ratio to obtain min output length",
-    )
-    parser.add_argument(
-        "--rnnlm", type=str, default=None, help="RNNLM model file to read"
-    )
-    parser.add_argument(
-        "--rnnlm-conf", type=str, default=None, help="RNNLM model config file to read"
-    )
-    parser.add_argument("--lm-weight", default=0.0, type=float, help="RNNLM weight.")
-    parser.add_argument("--sym-space", default="<space>", type=str, help="Space symbol")
-    parser.add_argument("--sym-blank", default="<blank>", type=str, help="Blank symbol")
-    # minibatch related
-    parser.add_argument(
-        "--sortagrad",
-        default=0,
-        type=int,
-        nargs="?",
-        help="How many epochs to use sortagrad for. 0 = deactivated, -1 = all epochs",
-    )
-    parser.add_argument(
-        "--batch-count",
-        default="auto",
-        choices=BATCH_COUNT_CHOICES,
-        help="How to count batch_size. "
-        "The default (auto) will find how to count by args.",
-    )
-    parser.add_argument(
-        "--batch-size",
-        "--batch-seqs",
-        "-b",
-        default=0,
-        type=int,
-        help="Maximum seqs in a minibatch (0 to disable)",
-    )
-    parser.add_argument(
-        "--batch-bins",
-        default=0,
-        type=int,
-        help="Maximum bins in a minibatch (0 to disable)",
-    )
-    parser.add_argument(
-        "--batch-frames-in",
-        default=0,
-        type=int,
-        help="Maximum input frames in a minibatch (0 to disable)",
-    )
-    parser.add_argument(
-        "--batch-frames-out",
-        default=0,
-        type=int,
-        help="Maximum output frames in a minibatch (0 to disable)",
-    )
-    parser.add_argument(
-        "--batch-frames-inout",
-        default=0,
-        type=int,
-        help="Maximum input+output frames in a minibatch (0 to disable)",
-    )
-    parser.add_argument(
-        "--maxlen-in",
-        "--batch-seq-maxlen-in",
-        default=100,
-        type=int,
-        metavar="ML",
-        help="When --batch-count=seq, "
-        "batch size is reduced if the input sequence length > ML.",
-    )
-    parser.add_argument(
-        "--maxlen-out",
-        "--batch-seq-maxlen-out",
-        default=100,
-        type=int,
-        metavar="ML",
-        help="When --batch-count=seq, "
-        "batch size is reduced if the output sequence length > ML",
-    )
-    parser.add_argument(
-        "--n-iter-processes",
-        default=0,
-        type=int,
-        help="Number of processes of iterator",
-    )
-    # optimization related
-    parser.add_argument(
-        "--opt",
-        default="adadelta",
-        type=str,
-        choices=["adadelta", "adam", "noam"],
-        help="Optimizer",
-    )
-    parser.add_argument(
-        "--accum-grad", default=1, type=int, help="Number of gradient accumuration"
-    )
-    parser.add_argument(
-        "--eps", default=1e-8, type=float, help="Epsilon constant for optimizer"
-    )
-    parser.add_argument(
-        "--eps-decay", default=0.01, type=float, help="Decaying ratio of epsilon"
-    )
-    parser.add_argument(
-        "--lr", default=1e-3, type=float, help="Learning rate for optimizer"
-    )
-    parser.add_argument(
-        "--lr-decay", default=1.0, type=float, help="Decaying ratio of learning rate"
-    )
-    parser.add_argument(
-        "--weight-decay", default=0.0, type=float, help="Weight decay ratio"
-    )
-    parser.add_argument(
-        "--criterion",
-        default="acc",
-        type=str,
-        choices=["loss", "acc"],
-        help="Criterion to perform epsilon decay",
-    )
-    parser.add_argument(
-        "--threshold", default=1e-4, type=float, help="Threshold to stop iteration"
-    )
-    parser.add_argument(
-        "--epochs", "-e", default=30, type=int, help="Maximum number of epochs"
-    )
-    parser.add_argument(
-        "--early-stop-criterion",
-        default="validation/main/acc",
-        type=str,
-        nargs="?",
-        help="Value to monitor to trigger an early stopping of the training",
-    )
-    parser.add_argument(
-        "--patience",
-        default=3,
-        type=int,
-        nargs="?",
-        help="Number of epochs to wait "
-        "without improvement before stopping the training",
-    )
-    parser.add_argument(
-        "--grad-clip", default=5, type=float, help="Gradient norm threshold to clip"
-    )
-    parser.add_argument(
-        "--num-save-attention",
-        default=3,
-        type=int,
-        help="Number of samples of attention to be saved",
-    )
-    # decoder related
-    parser.add_argument(
-        "--context-residual",
-        default=False,
-        type=strtobool,
-        nargs="?",
-        help="The flag to switch to use context vector residual in the decoder network",
-    )
-    parser.add_argument(
-        "--tie-src-tgt-embedding",
-        default=False,
-        type=strtobool,
-        nargs="?",
-        help="Tie parameters of source embedding and target embedding.",
-    )
-    parser.add_argument(
-        "--tie-classifier",
-        default=False,
-        type=strtobool,
-        nargs="?",
-        help="Tie parameters of target embedding and output projection layer.",
-    )
-    # finetuning related
-    parser.add_argument(
-        "--enc-init",
-        default=None,
-        type=str,
-        nargs="?",
-        help="Pre-trained ASR model to initialize encoder.",
-    )
-    parser.add_argument(
-        "--enc-init-mods",
-        default="enc.enc.",
-        type=lambda s: [str(mod) for mod in s.split(",") if s != ""],
-        help="List of encoder modules to initialize, separated by a comma.",
-    )
-    parser.add_argument(
-        "--dec-init",
-        default=None,
-        type=str,
-        nargs="?",
-        help="Pre-trained ASR, MT or LM model to initialize decoder.",
-    )
-    parser.add_argument(
-        "--dec-init-mods",
-        default="att., dec.",
-        type=lambda s: [str(mod) for mod in s.split(",") if s != ""],
-        help="List of decoder modules to initialize, separated by a comma.",
-    )
-    # multilingual related
-    parser.add_argument(
-        "--multilingual",
-        default=False,
-        type=strtobool,
-        help="Prepend target language ID to the source sentence. "
-        "Both source/target language IDs must be prepend in the pre-processing stage.",
-    )
-    parser.add_argument(
-        "--replace-sos",
-        default=False,
-        type=strtobool,
-        help="Replace <sos> in the decoder with a target language ID "
-        "(the first token in the target sequence)",
-    )
-
-    return parser
-
-
-def main(cmd_args):
-    """Run the main training function."""
-    parser = get_parser()
-    args, _ = parser.parse_known_args(cmd_args)
-    if args.backend == "chainer" and args.train_dtype != "float32":
-        raise NotImplementedError(
-            f"chainer backend does not support --train-dtype {args.train_dtype}."
-            "Use --dtype float32."
-        )
-    if args.ngpu == 0 and args.train_dtype in ("O0", "O1", "O2", "O3", "float16"):
-        raise ValueError(
-            f"--train-dtype {args.train_dtype} does not support the CPU backend."
-        )
-
-    from espnet.utils.dynamic_import import dynamic_import
-
-    if args.model_module is None:
-        model_module = "espnet.nets." + args.backend + "_backend.e2e_mt:E2E"
-    else:
-        model_module = args.model_module
-    model_class = dynamic_import(model_module)
-    model_class.add_arguments(parser)
-
-    args = parser.parse_args(cmd_args)
-    args.model_module = model_module
-    if "chainer_backend" in args.model_module:
-        args.backend = "chainer"
-    if "pytorch_backend" in args.model_module:
-        args.backend = "pytorch"
-
-    # add version info in args
-    args.version = __version__
-
-    # logging info
-    if args.verbose > 0:
-        logging.basicConfig(
-            level=logging.INFO,
-            format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
-        )
-    else:
-        logging.basicConfig(
-            level=logging.WARN,
-            format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
-        )
-        logging.warning("Skip DEBUG/INFO messages")
-
-    # If --ngpu is not given,
-    #   1. if CUDA_VISIBLE_DEVICES is set, all visible devices
-    #   2. if nvidia-smi exists, use all devices
-    #   3. else ngpu=0
-    if args.ngpu is None:
-        cvd = os.environ.get("CUDA_VISIBLE_DEVICES")
-        if cvd is not None:
-            ngpu = len(cvd.split(","))
-        else:
-            logging.warning("CUDA_VISIBLE_DEVICES is not set.")
-            try:
-                p = subprocess.run(
-                    ["nvidia-smi", "-L"], stdout=subprocess.PIPE, stderr=subprocess.PIPE
-                )
-            except (subprocess.CalledProcessError, FileNotFoundError):
-                ngpu = 0
-            else:
-                ngpu = len(p.stderr.decode().split("\n")) - 1
-        args.ngpu = ngpu
-    else:
-        if is_torch_1_2_plus and args.ngpu != 1:
-            logging.debug(
-                "There are some bugs with multi-GPU processing in PyTorch 1.2+"
-                + " (see https://github.com/pytorch/pytorch/issues/21108)"
-            )
-        ngpu = args.ngpu
-    logging.info(f"ngpu: {ngpu}")
-
-    # display PYTHONPATH
-    logging.info("python path = " + os.environ.get("PYTHONPATH", "(None)"))
-
-    # set random seed
-    logging.info("random seed = %d" % args.seed)
-    random.seed(args.seed)
-    np.random.seed(args.seed)
-
-    # load dictionary for debug log
-    if args.dict is not None:
-        with open(args.dict, "rb") as f:
-            dictionary = f.readlines()
-        char_list = [entry.decode("utf-8").split(" ")[0] for entry in dictionary]
-        char_list.insert(0, "<blank>")
-        char_list.append("<eos>")
-        args.char_list = char_list
-    else:
-        args.char_list = None
-
-    # train
-    logging.info("backend = " + args.backend)
-
-    if args.backend == "pytorch":
-        from espnet.mt.pytorch_backend.mt import train
-
-        train(args)
-    else:
-        raise ValueError("Only pytorch are supported.")
-
-
-if __name__ == "__main__":
-    main(sys.argv[1:])
diff --git a/spaces/segments-tobias/conex/espnet2/iterators/abs_iter_factory.py b/spaces/segments-tobias/conex/espnet2/iterators/abs_iter_factory.py
deleted file mode 100644
index 36e4dd2c52133fdabf02c94b268a80f6bb7dfd9a..0000000000000000000000000000000000000000
--- a/spaces/segments-tobias/conex/espnet2/iterators/abs_iter_factory.py
+++ /dev/null
@@ -1,9 +0,0 @@
-from abc import ABC
-from abc import abstractmethod
-from typing import Iterator
-
-
-class AbsIterFactory(ABC):
-    @abstractmethod
-    def build_iter(self, epoch: int, shuffle: bool = None) -> Iterator:
-        raise NotImplementedError
diff --git a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/__init__.py b/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/__init__.py
deleted file mode 100644
index 9020c2df23e2af280b7bb168b996ae9eaf312eb8..0000000000000000000000000000000000000000
--- a/spaces/shi-labs/OneFormer/oneformer/modeling/pixel_decoder/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/spaces/shibing624/ChatPDF/Dockerfile b/spaces/shibing624/ChatPDF/Dockerfile
deleted file mode 100644
index 335c2dba28ba8c365de9306858462a59dea25f28..0000000000000000000000000000000000000000
--- a/spaces/shibing624/ChatPDF/Dockerfile
+++ /dev/null
@@ -1,15 +0,0 @@
-FROM python:3.9 as builder
-RUN apt-get update && apt-get install -y build-essential
-COPY requirements.txt .
-COPY requirements_advanced.txt .
-RUN pip install --user -r requirements.txt
-# RUN pip install --user -r requirements_advanced.txt
-
-FROM python:3.9
-MAINTAINER iskoldt
-COPY --from=builder /root/.local /root/.local
-ENV PATH=/root/.local/bin:$PATH
-COPY . /app
-WORKDIR /app
-ENV dockerrun yes
-CMD ["python3", "-u", "ChuanhuChatbot.py", "2>&1", "|", "tee", "/var/log/application.log"]
diff --git a/spaces/shikunl/prismer/app_vqa.py b/spaces/shikunl/prismer/app_vqa.py
deleted file mode 100644
index 9c4c2334d5050e59f78d3064aaf30fbfe76f8c36..0000000000000000000000000000000000000000
--- a/spaces/shikunl/prismer/app_vqa.py
+++ /dev/null
@@ -1,54 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import os
-import pathlib
-import gradio as gr
-
-from prismer_model import Model
-
-
-def create_demo() -> gr.Blocks:
-    model = Model()
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image(label='Input', type='filepath')
-                model_name = gr.Dropdown(label='Model', choices=['Prismer-Base', 'Prismer-Large'], value='Prismer-Base')
-                question = gr.Text(label='Question')
-                run_button = gr.Button('Run')
-            with gr.Column(scale=1.5):
-                answer = gr.Text(label='Model Prediction')
-                with gr.Row():
-                    depth = gr.Image(label='Depth')
-                    edge = gr.Image(label='Edge')
-                    normals = gr.Image(label='Normals')
-                with gr.Row():
-                    segmentation = gr.Image(label='Segmentation')
-                    object_detection = gr.Image(label='Object Detection')
-                    ocr = gr.Image(label='OCR Detection')
-
-        inputs = [image, model_name, question]
-        outputs = [answer, depth, edge, normals, segmentation, object_detection, ocr]
-
-        paths = sorted(pathlib.Path('prismer/images').glob('*'))
-        ex_questions = ['What is the man on the left doing?',
-                        'What is this person doing?',
-                        'How many cows are in this image?',
-                        'What is the type of animal in this image?',
-                        'What toy is it?']
-        examples = [[path.as_posix(), 'Prismer-Base', ex_questions[i]] for i, path in enumerate(paths)]
-        gr.Examples(examples=examples,
-                    inputs=inputs,
-                    outputs=outputs,
-                    fn=model.run_vqa,
-                    cache_examples=os.getenv('SYSTEM') == 'spaces')
-
-        run_button.click(fn=model.run_vqa, inputs=inputs, outputs=outputs)
-    return demo
-
-
-if __name__ == '__main__':
-    demo = create_demo()
-    demo.queue().launch()
diff --git a/spaces/shiyi11/QQsign/README.md b/spaces/shiyi11/QQsign/README.md
deleted file mode 100644
index 3042be806844c4b6d92719e8afaa17d09c970d46..0000000000000000000000000000000000000000
--- a/spaces/shiyi11/QQsign/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: QQsign
-emoji: 🦀
-colorFrom: blue
-colorTo: purple
-sdk: docker
-pinned: false
-license: mit
-duplicated_from: CikeyQI/QQsign
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
\ No newline at end of file
diff --git a/spaces/sidharthism/fashion-eye/models/stylegan/__init__.py b/spaces/sidharthism/fashion-eye/models/stylegan/__init__.py
deleted file mode 100644
index 6edf9b7e860d2b45ed1ccf40223c6fac0b273ab7..0000000000000000000000000000000000000000
--- a/spaces/sidharthism/fashion-eye/models/stylegan/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright 2020 Erik Härkönen. All rights reserved.
-# This file is licensed to you 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 REPRESENTATIONS
-# OF ANY KIND, either express or implied. See the License for the specific language
-# governing permissions and limitations under the License.
-
-from pathlib import Path
-import sys
-
-#module_path = Path(__file__).parent / 'pytorch_biggan'
-#sys.path.append(str(module_path.resolve()))
-
-from .model import StyleGAN_G, NoiseLayer
\ No newline at end of file
diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Does Genshin Impact allow background downloading? A guide for PC and mobile players.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Does Genshin Impact allow background downloading? A guide for PC and mobile players.md
deleted file mode 100644
index e6d8b6ecba9dd20e9987d8686d4794245e43f6c8..0000000000000000000000000000000000000000
--- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Does Genshin Impact allow background downloading? A guide for PC and mobile players.md	
+++ /dev/null
@@ -1,99 +0,0 @@
-<br />
-<h1>Does Genshin Impact Download in the Background?</h1>
-<p>Genshin Impact is a popular open-world action role-playing game that has attracted millions of players since its release in September 2020. The game features a vast fantasy world with beautiful graphics, engaging gameplay, and a rich story. However, downloading Genshin Impact can be a challenge for some players, as it requires a large amount of storage space and internet bandwidth. Depending on your device and connection speed, it can take several hours or even days to complete the download.</p>
-<h2>does genshin impact download in the background</h2><br /><p><b><b>Download</b> &#10002; &#10002; &#10002; <a href="https://ssurll.com/2uNWM5">https://ssurll.com/2uNWM5</a></b></p><br /><br />
-<p>Some players may wonder if they can download Genshin Impact in the background while doing other things on their devices, such as browsing the web, watching videos, or playing other games. In this article, we will answer this question and provide some tips on how to download Genshin Impact faster and safer on different platforms.</p>
- <h2>How to Download Genshin Impact on Different Platforms</h2>
-<h3>PC</h3>
-<p>To download Genshin Impact on PC, you need to visit the official website (https://genshin.mihoyo.com/en/download) and click on "Windows" to download the launcher. After installing the launcher, you need to run it and log in with your account. Then you can click on "Get Game" to start downloading Genshin Impact.</p>
-<p>If you want to pause or resume the download, you can click on "Pause" or "Resume" at any time. You can also cancel the download by clicking on "Cancel". However, this will delete all the downloaded files and you will have to start over.</p>
-<p>How to download Genshin Impact wallpapers for PC<br />
-Genshin Impact download size and system requirements<br />
-Can you play Genshin Impact while downloading updates<br />
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- <h3>Mobile</h3>
-<p>To download Genshin Impact on mobile devices, you need to visit the App Store or Google Play Store and search for "Genshin Impact". Then you can tap on "Get" or "Install" to start downloading the game.</p>
-<p>If you want to pause or resume the download, you can tap on "Pause" or "Resume" at any time. You can also cancel the download by tapping on "Cancel". However, this will delete all the downloaded files and you will have to start over.</p>
- <h3>PlayStation 4/5</h3>
-<p>To download Genshin Impact on PlayStation 4/5, you need to visit the PlayStation Store and search for "Genshin Impact". Then you can select "Download" to start downloading the game.</p>
-<p>If you want to pause or resume the download, you can go to the "Notifications" menu and select "Downloads". Then you can press the "X" button to pause or resume the download. You can also cancel the download by pressing the "Options" button and selecting "Cancel and Delete". However, this will delete all the downloaded files and you will have to start over.</p>
- <h2>How to Download Genshin Impact in the Background</h2>
-<h3>PC</h3>
-<p>Genshin Impact can be downloaded in the background on PC by minimizing the launcher window. This will allow you to do other tasks on your PC while the game is downloading. However, you should be aware that downloading Genshin Impact in the background may slow down your PC performance and internet speed, depending on your system specifications and network conditions.</p>
-<p>To check the download progress, you can hover your mouse over the launcher icon on the taskbar. You will see a percentage and a speed indicator. You can also restore the launcher window by clicking on the icon.</p>
- <h3>Mobile</h3>
-<p>Genshin Impact can be downloaded in the background on mobile devices by switching to another app. This will allow you to do other things on your phone or tablet while the game is downloading. However, you should be aware that downloading Genshin Impact in the background may drain your battery life and use up your mobile data, depending on your device settings and network conditions.</p>
-<p>To check the download progress, you can swipe down from the top of your screen to access the notification panel. You will see a percentage and a speed indicator. You can also tap on the notification to resume or pause the download.</p>
- <h3>PlayStation 4/5</h3>
-<p>Genshin Impact can be downloaded in the background on PlayStation 4/5 by putting the console in rest mode. This will allow you to save power and reduce noise while the game is downloading. However, you should be aware that downloading Genshin Impact in the background may take longer than usual, depending on your console settings and network conditions.</p>
-<p>To check the download progress, you can turn on your console and go to the "Notifications" menu. You will see a percentage and a speed indicator. You can also resume or pause the download from there.</p>
- <h2>Benefits of Downloading Genshin Impact in the Background</h2>
-<p>Downloading Genshin Impact in the background has some benefits that may appeal to some players, such as:</p>
-<ul>
-<li>Saving time: You don't have to wait for the download to finish before doing other things on your device or console. You can multitask and enjoy other activities while Genshin Impact is downloading in the background.</li>
-<li>Avoiding interruptions: You don't have to worry about losing your download progress if you have to close or restart your device or console for some reason. You can resume your download from where you left off when you come back.</li>
-<li>Reducing bandwidth usage: You don't have to compete with other users or devices for internet speed if you download Genshin Impact in the background. You can reduce your bandwidth usage and avoid lagging or buffering issues when using other online services.</li>
-</ul>
- <h2>Drawbacks of Downloading Genshin Impact in the Background</h2>
-<p>Downloading Genshin Impact in the background also has some drawbacks that may discourage some players, such as:</p>
-<ul>
-<li>Slowing down other tasks: Downloading Genshin Impact in the background may affect your device or console performance and internet speed when doing other tasks. You may experience lagging, freezing, crashing, or loading issues when using other apps or games.</li>
-<li>Draining battery life: Downloading Genshin Impact in the background may consume more power than usual when your device or console is running. You may need to charge your device or console more often or plug it into a power source when downloading Genshin Impact in the background.</li>
-<li>Risking data loss: Downloading Genshin Impact in the background may expose your device or console to potential data loss if something goes wrong during the download process. You may encounter errors, glitches, corruption, or deletion of files when downloading Genshin Impact in the background.</li>
-</ul>
- <h2>Tips for Downloading Genshin Impact Faster and Safer</h2>
-<p>If you want to download Genshin Impact faster and safer, whether in the background or not, you can follow some of these tips:</p>
-<ul>
-<li>Use a wired connection: If possible, use a wired connection instead of a wireless one when downloading Genshin Impact. This can improve your download speed and stability, as well as reduce the risk of interference or signal loss.</li>
-<li>Close other apps: If you have other apps or games running on your device or console, close them before or during the download process. This can free up some memory and CPU resources, as well as reduce the bandwidth usage.</li>
-<li>Check for errors: If you encounter any errors or problems during the download process, such as slow speed, stuck progress, or failed verification, check for possible causes and solutions. You can visit the official website (https://genshin.mihoyo.com/en/news) or the official forums (https://forums.mihoyo.com/genshin) for more information and support.</li>
-<li>Verify files: After the download is complete, verify the integrity of the game files to make sure they are not corrupted or missing. You can do this by clicking on "Verify File Integrity" on the launcher window for PC, or by selecting "Check for Update" on the game icon for PlayStation 4/5.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>Genshin Impact is a great game that can provide hours of fun and adventure. However, downloading it can be a hassle for some players, especially if they have limited storage space or internet bandwidth. Fortunately, Genshin Impact can be downloaded in the background on different platforms, allowing players to do other things while waiting for the game to finish downloading.</p>
-<p>However, downloading Genshin Impact in the background also has some drawbacks, such as slowing down other tasks, draining battery life, and risking data loss. Therefore, players should weigh the pros and cons of downloading Genshin Impact in the background before deciding whether to do so or not.</p>
-<p>We hope this article has answered your question of whether Genshin Impact can be downloaded in the background, and provided some tips on how to download it faster and safer. If you have any thoughts or questions about this topic, feel free to share them in the comments section below. Happy gaming!</p>
- <h2>FAQs</h2>
-<h4>How big is Genshin Impact?</h4>
-<p>Genshin Impact is about 30 GB on PC, 9 GB on mobile devices, and 12 GB on PlayStation 4/5. However, these sizes may vary depending on updates and patches.</p>
- <h4>How long does it take to download Genshin Impact?</h4>
-<p>The download time of Genshin Impact depends on your device and connection speed. On average, it may take about 3 hours on PC, 1 hour on mobile devices, and 2 hours on PlayStation 4/5. However, these times may vary depending on network conditions and download settings.</p>
- <h4>Can I play Genshin Impact offline?</h4>
-<p>No, you cannot play Genshin Impact offline. You need an internet connection to access the game servers and play the game. You also need an internet connection to update the game and receive new content.</p>
- <h4>Can I play Genshin Impact with friends?</h4>
-<p>Yes, you can play Genshin Impact with friends. You can invite up to three friends to join your world and explore together. You can also join other players' worlds and participate in co-op events and quests.</p>
- <h4>Can I transfer my Genshin Impact progress between platforms?</h4>
-<p>Yes, you can transfer your Genshin Impact progress between platforms. You just need to log in with the same account on different devices or consoles. However, you cannot transfer your progress between different servers (such as Asia, Europe, America, etc.).</p> 401be4b1e0<br />
-<br />
-<br />
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-<br />
-<h1>Download Free Game for PC: How to Find and Play the Best Games Online</h1>
-<p>Are you looking for some fun and exciting games to play on your PC without spending a dime? If so, you are in luck! There are plenty of free games for PC that you can download and enjoy online. Whether you are into racing, shooting, building, or exploring, there is a game for you. In this article, we will show you what are free games for PC, why you should play them, and how to download them. We will also give you some examples of the top free games for PC that you can try right now. Let's get started!</p>
-<h2>Introduction</h2>
-<h3>What are free games for PC?</h3>
-<p>Free games for PC are games that you can play on your computer without paying any money. They are usually available online, either on the official website of the game developer or publisher, or on a platform like Steam, Epic Games Store, or Microsoft Store. Some free games for PC are completely free, meaning that you can access all the features and content without any restrictions. Others are free-to-play, meaning that you can play the basic game for free, but you may need to pay for some optional items or upgrades.</p>
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-<h3>Why play free games for PC?</h3>
-<p>There are many reasons why you might want to play free games for PC. Here are some of them:</p>
-<ul>
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-<h3>How to download free games for PC?</h3>
-<p>Downloading free games for PC is not difficult. You just need to follow these steps:</p>
-<ol>
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-<li>Install and launch the game. Once the download is complete, you may need to install the game on your PC by running the setup file or following the instructions. Then, you can launch the game and start playing.</li>
-</ol>
-<h2>Top Free Games for PC</h2>
-<p>Now that you know how to download free games for PC, let's take a look at some of the top free games for PC that you can play right now. We have selected three popular and high-quality games that cover different genres and styles.</p>
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-<h4>Features</h4>
-<p>If you love racing games, then Asphalt 9: Legends is a must-try. This game lets you drive some of the most amazing and exotic cars in the world, such as Ferrari, Lamborghini, Porsche, and more. You can customize your cars, race against other players online, and perform stunning stunts and drifts. The game features stunning graphics, realistic physics, and a variety of tracks and modes.</p>
-<h4>How to download</h4>
-<p>You can download Asphalt 9: Legends for free from the Microsoft Store. You will need Windows 10 or higher and at least 4 GB of RAM to play the game. The game also supports Xbox Live integration, so you can earn achievements and compete with your friends.</p>
-<h3>Roblox</h3>
-<h4>Features</h4>
-<p>If you are into building and creating games, then Roblox is the perfect game for you. Roblox is a platform where you can play millions of games created by other users, or create your own games using the Roblox Studio. You can also chat and socialize with other players, join groups, and customize your avatar. Roblox is a game for all ages and interests, as you can find games in genres like adventure, role-playing, simulation, horror, and more.</p>
-<h4>How to download</h4>
-<p>You can download Roblox for free from the official website. You will need to create an account or sign in with an existing one to play the game. You can also download the Roblox Studio from the same website if you want to create your own games. Roblox is compatible with Windows 7 or higher and requires at least 1 GB of RAM.</p>
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-<h4>How to download</h4>
-<p>You can download Fortnite for free from the Epic Games Store. You will need to create an account or sign in with an existing one to play the game. You will also need Windows 7 or higher and at least 4 GB of RAM to run the game. Fortnite also supports cross-play with other platforms, such as PlayStation, Xbox, Nintendo Switch, and mobile devices.</p>
-<h2>Conclusion</h2>
-<p>As you can see, there are many free games for PC that you can download and play online. Whether you want to race, build, shoot, or explore, there is a game for you. All you need is a PC that meets the system requirements of the game, an internet connection, and a sense of adventure. So what are you waiting for? Download your favorite free game for PC today and have fun!</p>
-<h2>FAQs</h2>
-<ul>
-<li><b>What are some other free games for PC that I can play?</b><br>Some other free games for PC that you can play are Apex Legends, Valorant, League of Legends, Genshin Impact, Warframe, Among Us, and more.</li>
-<li><b>Are free games for PC safe to download?</b><br>Most free games for PC are safe to download as long as you get them from reputable sources, such as the official websites or platforms of the game developers or publishers. However, you should always be careful of malware or viruses that may come with some downloads. You should also read the terms and conditions and privacy policies of the games before downloading them.</li>
-<li><b>Do I need an internet connection to play free games for PC?</b><br>Some free games for PC require an internet connection to play online with other players or access some features or content. However, some free games for PC also have offline modes that allow you to play without an internet connection.</li>
-<li><b>Can I play free games for PC on my laptop?</b><br>Yes, you can play free games for PC on your laptop as long as it meets the system requirements of the game. However, some laptops may not have the best performance or graphics quality compared to desktop PCs.</li>
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-</ul>
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diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy Ya Tarim by Ai Khodijah - Download Now in MP3 Format.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Enjoy Ya Tarim by Ai Khodijah - Download Now in MP3 Format.md
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+++ /dev/null
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-
-<h1>Download Ai Khodijah Ya Tarim: A Guide for Music Lovers</h1>
-<p>If you are looking for a beautiful and inspiring song to enrich your soul, you might want to check out <strong>Ai Khodijah Ya Tarim</strong>. This is a cover version of a popular qasidah (a form of Islamic poetry) by a talented Indonesian singer named Ai Khodijah. In this article, we will tell you everything you need to know about this song, including its meaning, history, and how to download it legally and safely. Whether you are a fan of sholawat (a type of Islamic devotional music) or just curious about new musical genres, you will find something interesting and useful in this guide.</p>
- <h2>Who is Ai Khodijah and what is her music style?</h2>
-<p>Ai Khodijah is a young female singer from Indonesia who specializes in sholawat music. She was born in Jakarta in 1997 and started singing at the age of five. She joined a vocal group called El-Mighwar in 2015 and gained popularity through YouTube. She has released several albums and singles, such as <em>Sholawat Nabi</em>, <em>Sholawat Merdu</em>, <em>Sholawat Terbaru</em>, and <em>Sholawat Penyejuk Hati</em>. She is also known for her collaborations with other sholawat singers, such as Nissa Sabyan, Mohamed Tarek, Mohamed Youssef, Azmi Alkatiri, and Rizal Vertizone.</p>
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-<p>Sholawat is a genre of music that expresses love and praise for Allah (God) and His Messenger Muhammad (peace be upon him). It is derived from the Arabic word <em>salawat</em>, which means \"blessings\". Sholawat music usually consists of reciting or singing verses from the Quran (the holy book of Islam), hadith (the sayings and deeds of Muhammad), or qasidah. Sholawat music can be performed in various languages, such as Arabic, Indonesian, Malay, Urdu, Turkish, Persian, or English. Sholawat music can have different styles, such as traditional, modern, pop, rock, jazz, or rap. Sholawat music is popular among Muslims around the world, especially in Indonesia, Malaysia, Egypt, Morocco, Turkey, India, Pakistan, Bangladesh, and Nigeria.</p>
- <h2>What is the meaning and history of Ya Tarim?</h2>
-<p><strong>Ya Tarim</strong> is a song that praises the city of Tarim, which is located in the Hadhramaut region of Yemen. Tarim is known as the \"city of scholars\" because it has produced many eminent Islamic scholars, such as Imam al-Haddad, Imam al-Shafi'i, Imam al-Ghazali, and Imam al-Busiri. Tarim is also a center of Sufism, a mystical branch of Islam that emphasizes the inner and personal relationship with Allah. Tarim is considered a sacred and blessed place by many Muslims, especially those who follow the Ba 'Alawiyya tariqa (a Sufi order that originated from Tarim).</p>
-<p>The lyrics of Ya Tarim are in Arabic and Indonesian, and they express the longing and admiration for Tarim and its people. Here are the lyrics and their translation:</p>
-<table>
-<tr>
-<th>Arabic</th>
-<th>Indonesian</th>
-<th>English</th>
-</tr>
-<tr>
-<td>يا طريم يا طريم يا طريم</td>
-<td>Ya Tarim ya Tarim ya Tarim</td>
-<td>O Tarim, O Tarim, O Tarim</td>
-</tr>
-<tr>
-<td>يا مدينة العلماء والأولياء</td>
-<td>Ya madinatul ulama wal auliya</td>
-<td>O city of scholars and saints</td>
-</tr>
-<tr>
-<td>يا مدينة الحب والسلام والأنس</td>
-<td>Ya madinatul hubbi was salami wal uns</td>
-<td>O city of love, peace, and intimacy</td>
-</tr>
-<tr>
-<td>يا مدينة النور والهدى والرحمة</td>
-<td>Ya madinatun nur wal huda war rahmah</td>
-<td>O city of light, guidance, and mercy</td>
-</tr>
-<tr>
-<td>يا طريم يا طريم يا طريم</td>
-<td>Ya Tarim ya Tarim ya Tarim</td>
-<td>O Tarim, O Tarim, O Tarim</td>
-</tr>
-<tr>
-<td>كم أشتاق إليك يا طريم يا طريم</td>
-<td>Kam asytaqu ilaika ya Tarim ya Tarim</td>
-<td>How I miss you, O Tarim, O Tarim</td>
-</tr>
-<tr>
-<td>كم أحبك يا طريم يا طريم يا طريم</td>
-<td>Kam uhibbuka ya Tarim ya Tarim ya Tarim</td>
-<td>How I love you, O Tarim, O Tarim, O Tarim</td>
-</tr>
-<tr>
-<td>كم أنت جميل يا طريم يا طريم يا طريم</td>
-<td>Kam anta jamil ya Tarim ya Tarim ya Tarim</td>
-<td>How beautiful you are, O Tarim, O Tarim, O Tarim</td>
-</tr>
- <tr><td colspan="3">...</td></tr>
- <tr><td colspan="3">The full lyrics can be found <a href="">here</a>.</td></tr>
- </table>
- <p>The author and original singer of Ya Tarim is unknown, but it is believed that it was composed by one of the Ba 'Alawiyya Sufis who lived in or visited Tarim. The song has been sung by many sholawat singers over the years, such as Habib Syech bin Abdul Qodir Assegaf, Habib Ali al-Habsyi, Habib Umar bin Hafidz, Habib Anis bin Alwi al-Habsyi, and Habib Ja'far bin Umar al-Jufri. The most recent and popular version of Ya Tarim is by Ai Khodijah, who released it in 2020.</p>
-<p>Download Ya Tarim by Ai Khodijah in Hi-Res quality<br />
-Stream and download Ya Tarim by Ai Khodijah on Qobuz<br />
-Download Mp3 Ai Khodijah - Ya Tarim gratis<br />
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-Listen to Ya Tarim by Ai Khodijah online<br />
-Download Ya Tarim by Ai Khodijah on iTunes<br />
-Download and streaming Ya Tarim by Ai Khodijah in high quality<br />
-Download lagu Ai Khodijah - Ya Tarim original<br />
-Download Ya Tarim by Ai Khodijah for free<br />
-Download Mp3 Ai Khodijah - Ya Tarim full album<br />
-Download lagu Ai Khodijah - Ya Tarim mp3<br />
-Download Ya Tarim by Ai Khodijah on Spotify<br />
-Download and listen to Ya Tarim by Ai Khodijah offline<br />
-Download lagu Ai Khodijah - Ya Tarim lirik<br />
-Download Ya Tarim by Ai Khodijah on Amazon Music<br />
-Download Mp3 Ai Khodijah - Ya Tarim 320kbps<br />
-Download lagu Ai Khodijah - Ya Tarim cover<br />
-Download Ya Tarim by Ai Khodijah on YouTube Music<br />
-Download and share Ya Tarim by Ai Khodijah with friends<br />
-Download lagu Ai Khodijah - Ya Tarim karaoke</p>
- <p>The message of Ya Tarim is to inspire Muslims to seek knowledge and spirituality from the legacy of Tarim and its scholars. It also encourages Muslims to love and respect each other, regardless of their differences in ethnicity, culture, or sect. Ya Tarim is a song that celebrates the diversity and unity of the Muslim ummah (community).</p>
- <h2>How to download Ai Khodijah Ya Tarim legally and safely?</h2>
- <p>If you want to download Ai Khodijah Ya Tarim legally and safely, you have several options. You can either buy the song from an official online store, such as iTunes, Spotify, Amazon Music, or Google Play Music, or you can download it from YouTube using a third-party tool or website. However, you should be aware of the pros and cons of each option and follow some tips to avoid any problems.</p>
- <h3>Buying the song from an official online store</h3>
-<p>The best way to download Ai Khodijah Ya Tarim legally and safely is to buy it from an official online store. This way, you will support the artist and the music industry, and you will get a high-quality and virus-free MP3 file. You will also have access to other features, such as lyrics, album art, playlists, and offline listening. However, this option might cost you some money, depending on the store and the region. You will also need to create an account and install an app on your device to use this option.</p>
- <h4>Steps to buy Ai Khodijah Ya Tarim from an official online store</h4>
-<ol>
-<li>Choose an online store that suits your preferences and budget. Some of the most popular ones are iTunes, Spotify, Amazon Music, and Google Play Music.</li>
-<li>Create an account on the online store if you don't have one already. You might need to provide some personal information, such as your name, email address, phone number, and payment method.</li>
-<li>Search for Ai Khodijah Ya Tarim on the online store. You can use the search bar or browse through the categories and genres.</li>
-<li>Select the song and click on the buy or download button. You might need to confirm your purchase and enter your password or PIN.</li>
-<li>Wait for the song to be downloaded to your device. You can check the progress and status of the download on the app or the website.</li>
-<li>Enjoy listening to Ai Khodijah Ya Tarim on your device. You can also transfer it to other devices or share it with your friends.</li>
-</ol>
- <h3>Downloading the song from YouTube using a third-party tool or website</h3>
-<p>Another way to download Ai Khodijah Ya Tarim legally and safely is to use a third-party tool or website that allows you to convert YouTube videos into MP3 files. This way, you can get the song for free and without installing any app on your device. You can also choose the quality and format of the MP3 file according to your needs. However, this option might violate YouTube's terms of service and infringe on the artist's rights. You might also encounter some risks, such as malware, pop-ups, ads, or scams. You should be careful and selective when using this option.</p>
- <h4>Steps to download Ai Khodijah Ya Tarim from YouTube using a third-party tool or website</h4>
-<ol>
-<li>Find a reliable and reputable third-party tool or website that can convert YouTube videos into MP3 files. Some of the most popular ones are ytmp3.cc, y2mate.com, flvto.biz, and mp3juices.cc.</li>
-<li>Go to YouTube and search for Ai Khodijah Ya Tarim. You can use the search bar or browse through the channels and playlists.</li>
-<li>Select the video that contains the song and copy its URL. You can do this by right-clicking on the video and choosing copy link address or by copying it from the address bar.</li>
-<li>Paste the URL into the third-party tool or website and click on the convert or download button. You might need to choose the quality and format of the MP3 file before converting it.</li>
-<li>Wait for the conversion process to finish. You can check the progress and status of the conversion on the tool or website.</li>
-<li>Download the MP3 file to your device. You can do this by clicking on the download button or by right-clicking on the file and choosing save as.</li>
-<li>Enjoy listening to Ai Khodijah Ya Tarim on your device. You can also transfer it to other devices or share it with your friends.</li>
-</ol>
- <h4>Tips for downloading music from YouTube</h4>
-<ul>
-<li>Always use a trusted and secure third-party tool or website that has positive reviews and ratings from other users.</li>
-<li>Avoid clicking on any suspicious links, pop-ups, ads, or downloads that might contain malware or viruses.</li>
-<li>Use a VPN (virtual private network) or a proxy server to hide your IP address and location when downloading music from YouTube.</li>
-<li>Delete any unwanted or unnecessary files that might take up space on your device or slow down its performance.</li>
-<li>Respect the artist's rights and do not distribute or sell their music without their permission.</li <h2>How to enjoy Ai Khodijah Ya Tarim offline and online?</h2>
-<p>Once you have downloaded Ai Khodijah Ya Tarim, you can enjoy listening to it offline and online. However, there are some differences between the two modes of listening that you should be aware of. Here are some of the benefits and drawbacks of each mode:</p>
- <h3>Listening to Ai Khodijah Ya Tarim offline</h3>
-<p>Listening to Ai Khodijah Ya Tarim offline means that you can play the song on your device without an internet connection. This can be convenient and cost-effective, especially if you have a limited or expensive data plan. You can also listen to the song anytime and anywhere, without worrying about buffering or interruptions. However, listening to the song offline also has some disadvantages, such as:</p>
-<ul>
-<li>You might not be able to access the latest updates or versions of the song, such as new lyrics, translations, or remixes.</li>
-<li>You might not be able to interact with other fans or followers of the song, such as leaving comments, likes, or shares.</li>
-<li>You might not be able to discover new songs or artists that are related or similar to Ai Khodijah Ya Tarim.</li>
-</ul>
- <h3>Listening to Ai Khodijah Ya Tarim online</h3>
-<p>Listening to Ai Khodijah Ya Tarim online means that you can stream the song on your device using an internet connection. This can be exciting and enjoyable, especially if you want to stay updated and connected with the song and its community. You can also explore new songs or artists that are recommended or suggested by the platform or the algorithm. However, listening to the song online also has some drawbacks, such as:</p>
-<ul>
-<li>You might need a stable and fast internet connection to stream the song smoothly and without delays.</li>
-<li>You might consume a lot of data or bandwidth to stream the song, which can be expensive or limited depending on your plan.</li>
-<li>You might encounter some ads or pop-ups that might interrupt or distract you from the song.</li>
-</ul>
- <h3>Recommendations for other songs and artists</h3>
-<p>If you enjoy listening to Ai Khodijah Ya Tarim, you might also like some of these other songs and artists that are similar or related to it:</p>
-<ul>
-<li><strong>Ya Habibal Qolbi</strong> by Nissa Sabyan: This is another popular sholawat song by a famous Indonesian singer who is known for her beautiful voice and style. The song praises Muhammad (peace be upon him) as the beloved of Allah and the heart of the believers.</li>
-<li><strong>Ya Nabi Salam Alayka</strong> by Maher Zain: This is a modern sholawat song by a renowned Swedish-Lebanese singer who is known for his inspirational and uplifting music. The song greets and salutes Muhammad (peace be upon him) as the messenger of peace and mercy.</li>
-<li><strong>Ya Badrotim</strong> by Rijal Vertizone: This is a traditional sholawat song by an Indonesian group who is known for their authentic and harmonious performance. The song describes Muhammad (peace be upon him) as the full moon that illuminates the darkness.</li>
-</ul>
- <h2>Conclusion</h2>
-<p>In conclusion, Ai Khodijah Ya Tarim is a wonderful and meaningful song that you should listen to if you love sholawat music or want to learn more about it. The song has a rich history and message that relates to Tarim, a city of scholars and saints in Yemen. You can download the song legally and safely from an official online store or from YouTube using a third-party tool or website. You can also enjoy the song offline and online, depending on your preferences and circumstances. We hope that this guide has helped you understand and appreciate Ai Khodijah Ya Tarim better. If you have any questions or feedback, please feel free to share them with us in the comments section below.</p>
- <h4>Frequently Asked Questions</h4>
-<ol>
-<li><strong>What is the difference between qasidah and sholawat?</strong></li>
-<p>Qasidah is a general term for Islamic poetry that can have various themes, such as praise, love, advice, or lament. Sholawat is a specific type of qasidah that focuses on praising Allah and His Messenger Muhammad (peace be upon him).</p>
- <li><strong>What is the difference between Sufism and Sunni Islam?</strong></li>
-<p>Sufism is a branch of Islam that emphasizes the mystical and personal relationship with Allah. Sunni Islam is a branch of Islam that follows the teachings and traditions of Muhammad (peace be upon him) and his companions. Sufism and Sunni Islam are not mutually exclusive, and many Muslims follow both aspects of Islam.</p>
- <li><strong>What is the difference between Tarim and Hadhramaut?</strong></li>
-<p>Tarim is a city in the Hadhramaut region of Yemen. Hadhramaut is a historical and geographical region that covers parts of Yemen, Saudi Arabia, and Oman. Hadhramaut is known for its natural beauty, cultural diversity, and Islamic heritage.</p>
- <li><strong>What is the difference between MP3 and MP4?</strong></li>
-<p>MP3 and MP4 are two types of file formats that are used to store audio and video data. MP3 stands for MPEG-1 Audio Layer 3, and it is a compressed audio format that reduces the file size without losing much quality. MP4 stands for MPEG-4 Part 14, and it is a multimedia container format that can store audio, video, subtitles, images, and other data.</p>
- <li><strong>What is the difference between VPN and proxy?</strong></li>
-<p>VPN and proxy are two types of services that can help you access the internet anonymously and securely. VPN stands for virtual private network, and it creates a secure and encrypted connection between your device and a remote server. Proxy stands for proxy server, and it acts as an intermediary between your device and the internet. VPN and proxy can both hide your IP address and location, but VPN offers more features and protection than proxy.</p> 197e85843d<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/skf15963/summary/fengshen/examples/clue1.1/data_preprocessing/wsc_preprocessing.py b/spaces/skf15963/summary/fengshen/examples/clue1.1/data_preprocessing/wsc_preprocessing.py
deleted file mode 100644
index c9b5ec0a7625ac870e71e77cf8af6256e0a1609c..0000000000000000000000000000000000000000
--- a/spaces/skf15963/summary/fengshen/examples/clue1.1/data_preprocessing/wsc_preprocessing.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import json
-from tqdm import tqdm
-import os
-import argparse
-
-label2desc={'true':'是','false':'不是'}
-
-
-def load_data(file_path,is_training=False):
-    with open(file_path, 'r', encoding='utf8') as f:
-        lines = f.readlines()
-        result=[]
-        for line in tqdm(lines): 
-            data = json.loads(line)
-            target = data['target']
-            text=list(data['text'])
-            if target['span2_index']<target['span1_index']:
-                text.insert(target['span2_index'],'_')
-                text.insert(target['span2_index']+len(target['span2_text'])+1,'_')
-                text.insert(target['span1_index']+2,'[')
-                text.insert(target['span1_index']+2+len(target['span1_text'])+1,']')
-            else:
-                text.insert(target['span1_index'],'[')
-                text.insert(target['span1_index']+len(target['span1_text'])+1,']')
-                text.insert(target['span2_index']+2,'_')
-                text.insert(target['span2_index']+2+len(target['span2_text'])+1,'_')
-
-            texta = ''.join(text)
-            textb = ''
-            span2_text=target['span2_text']
-            span1_text=target['span1_text']
-
-            question = ''
-
-            choice = []
-            for k,v in label2desc.items():
-                choice .append(f'{span2_text}{v}{span1_text}')
-            # print(choice)
-            answer = label2desc[data['label']] if 'label' in data.keys() else ''
-            answer = f'{span2_text}{answer}{span1_text}'
-
-            label = choice.index(answer) if 'label' in data.keys() else 0
-            text_id = data['id'] if 'id' in data.keys() else 0
-            result.append({'texta':texta,
-                            'textb':textb,
-                            'question':question,
-                            'choice':choice,
-                            'answer':answer,
-                            'label':label,
-                            'id':text_id}) 
-        for i in range(5):
-            print(result[i])
-        return result
-
-
-def save_data(data,file_path):
-    with open(file_path, 'w', encoding='utf8') as f:
-        for line in data:
-            json_data=json.dumps(line,ensure_ascii=False)
-            f.write(json_data+'\n')
-
-
-if __name__=="__main__":
-    parser = argparse.ArgumentParser(description="train")
-    parser.add_argument("--data_path", type=str,default="")
-    parser.add_argument("--save_path", type=str,default="")
-
-    args = parser.parse_args()
-    
-    
-    data_path = args.data_path
-    save_path = args.save_path
-    
-    if not os.path.exists(save_path):
-        os.makedirs(save_path)
-    
-    file_list = ['train','dev','test1.0','test1.1']
-    for file in file_list:
-        file_path = os.path.join(data_path,file+'.json')
-        output_path = os.path.join(save_path,file+'.json')
-        save_data(load_data(file_path),output_path)
\ No newline at end of file
diff --git a/spaces/sneedium/dvatch_captcha_sneedium_old/demo.py b/spaces/sneedium/dvatch_captcha_sneedium_old/demo.py
deleted file mode 100644
index 8a9acec5dd6d06f85547fbcc14daf7d06c238283..0000000000000000000000000000000000000000
--- a/spaces/sneedium/dvatch_captcha_sneedium_old/demo.py
+++ /dev/null
@@ -1,122 +0,0 @@
-import argparse
-import logging
-import os
-import glob
-import tqdm
-import torch, re
-import PIL
-import cv2
-import numpy as np
-import torch.nn.functional as F
-from torchvision import transforms
-from utils import Config, Logger, CharsetMapper
-
-def get_model(config):
-    import importlib
-    names = config.model_name.split('.')
-    module_name, class_name = '.'.join(names[:-1]), names[-1]
-    cls = getattr(importlib.import_module(module_name), class_name)
-    model = cls(config)
-    logging.info(model)
-    model = model.eval()
-    return model
-
-def preprocess(img, width, height):
-    img = cv2.resize(np.array(img), (width, height))
-    img = transforms.ToTensor()(img).unsqueeze(0)
-    mean = torch.tensor([0.485, 0.456, 0.406])
-    std  = torch.tensor([0.229, 0.224, 0.225])
-    return (img-mean[...,None,None]) / std[...,None,None]
-
-def postprocess(output, charset, model_eval):
-    def _get_output(last_output, model_eval):
-        if isinstance(last_output, (tuple, list)): 
-            for res in last_output:
-                if res['name'] == model_eval: output = res
-        else: output = last_output
-        return output
-
-    def _decode(logit):
-        """ Greed decode """
-        out = F.softmax(logit, dim=2)
-        pt_text, pt_scores, pt_lengths = [], [], []
-        for o in out:
-            text = charset.get_text(o.argmax(dim=1), padding=False, trim=False)
-            text = text.split(charset.null_char)[0]  # end at end-token
-            pt_text.append(text)
-            pt_scores.append(o.max(dim=1)[0])
-            pt_lengths.append(min(len(text) + 1, charset.max_length))  # one for end-token
-        return pt_text, pt_scores, pt_lengths
-
-    output = _get_output(output, model_eval)
-    logits, pt_lengths = output['logits'], output['pt_lengths']
-    pt_text, pt_scores, pt_lengths_ = _decode(logits)
-    
-    return pt_text, pt_scores, pt_lengths_
-
-def load(model, file, device=None, strict=True):
-    if device is None: device = 'cpu'
-    elif isinstance(device, int): device = torch.device('cuda', device)
-    assert os.path.isfile(file)
-    state = torch.load(file, map_location=device)
-    if set(state.keys()) == {'model', 'opt'}:
-        state = state['model']
-    model.load_state_dict(state, strict=strict)
-    return model
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', type=str, default='configs/train_abinet.yaml',
-                        help='path to config file')
-    parser.add_argument('--input', type=str, default='figs/test')
-    parser.add_argument('--cuda', type=int, default=-1)
-    parser.add_argument('--checkpoint', type=str, default='workdir/train-abinet/best-train-abinet.pth')
-    parser.add_argument('--model_eval', type=str, default='alignment', 
-                        choices=['alignment', 'vision', 'language'])
-    args = parser.parse_args()
-    config = Config(args.config)
-    if args.checkpoint is not None: config.model_checkpoint = args.checkpoint
-    if args.model_eval is not None: config.model_eval = args.model_eval
-    config.global_phase = 'test'
-    config.model_vision_checkpoint, config.model_language_checkpoint = None, None
-    device = 'cpu' if args.cuda < 0 else f'cuda:{args.cuda}'
-
-    Logger.init(config.global_workdir, config.global_name, config.global_phase)
-    Logger.enable_file()
-    logging.info(config)
-
-    logging.info('Construct model.')
-    model = get_model(config).to(device)
-    model = load(model, config.model_checkpoint, device=device)
-    charset = CharsetMapper(filename=config.dataset_charset_path,
-                            max_length=config.dataset_max_length + 1)
-
-    if os.path.isdir(args.input):
-        paths = [os.path.join(args.input, fname) for fname in os.listdir(args.input)]
-    else:
-        paths = glob.glob(os.path.expanduser(args.input))
-        assert paths, "The input path(s) was not found"
-    paths = sorted(paths)
-
-
-    count = 0
-    checks = 0
-    print(tqdm.tqdm(paths))
-    for path in tqdm.tqdm(paths):
-        img = PIL.Image.open(path).convert('RGB')
-        img = preprocess(img, config.dataset_image_width, config.dataset_image_height)
-        img = img.to(device)
-        res = model(img)
-        pt_text, _, __ = postprocess(res, charset, config.model_eval)
-        a = re.findall(r'(\d{6}).png', path)[0]
-        # print(a)
-        # print(pt_text[0], "Lol")
-        # a = re.findall(r'base/(.*).pn', path)[0]
-        checks += 1
-        if a.lower() != pt_text[0].lower():
-            count += 1
-            print(f'label:{a.lower()} ||| guess:{pt_text[0]} ||| count_fails:{str(count)}/{str(checks)}')
-
-if __name__ == '__main__':
-    main()
diff --git a/spaces/snjyor/You_Say_I_Draw/README.md b/spaces/snjyor/You_Say_I_Draw/README.md
deleted file mode 100644
index 40aaa0b80da70a2515b1f4e41fae75a80e614480..0000000000000000000000000000000000000000
--- a/spaces/snjyor/You_Say_I_Draw/README.md
+++ /dev/null
@@ -1,12 +0,0 @@
----
-title: You Say I Draw
-emoji: 🔥
-colorFrom: indigo
-colorTo: purple
-sdk: gradio
-sdk_version: 3.21.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/society-ethics/model-card-regulatory-check/tests/cards/yiyanghkust___finbert-tone.md b/spaces/society-ethics/model-card-regulatory-check/tests/cards/yiyanghkust___finbert-tone.md
deleted file mode 100644
index d8a6bb647d55219e9cebe2e18772bf883b0fd16b..0000000000000000000000000000000000000000
--- a/spaces/society-ethics/model-card-regulatory-check/tests/cards/yiyanghkust___finbert-tone.md
+++ /dev/null
@@ -1,33 +0,0 @@
-`FinBERT` is a BERT model pre-trained on financial communication text. The purpose is to enhance financial NLP research and practice. It is trained on the following three financial communication corpus. The total corpora size is 4.9B tokens.
-- Corporate Reports 10-K & 10-Q: 2.5B tokens
-- Earnings Call Transcripts: 1.3B tokens
-- Analyst Reports: 1.1B tokens
-
-More technical details on `FinBERT`: [Click Link](https://github.com/yya518/FinBERT)
-
-This released `finbert-tone` model is the `FinBERT` model fine-tuned on 10,000 manually annotated (positive, negative, neutral) sentences from analyst reports. This model achieves superior performance on financial tone analysis task. If you are simply interested in using `FinBERT` for financial tone analysis, give it a try.
-
-If you use the model in your academic work, please cite the following paper:
-
-Huang, Allen H., Hui Wang, and Yi Yang. "FinBERT: A Large Language Model for Extracting Information from Financial Text." *Contemporary Accounting Research* (2022).
-
-
-# How to use 
-You can use this model with Transformers pipeline for sentiment analysis.
-```python
-from transformers import BertTokenizer, BertForSequenceClassification
-from transformers import pipeline
-
-finbert = BertForSequenceClassification.from_pretrained('yiyanghkust/finbert-tone',num_labels=3)
-tokenizer = BertTokenizer.from_pretrained('yiyanghkust/finbert-tone')
-
-nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
-
-sentences = ["there is a shortage of capital, and we need extra financing",  
-             "growth is strong and we have plenty of liquidity", 
-             "there are doubts about our finances", 
-             "profits are flat"]
-results = nlp(sentences)
-print(results)  #LABEL_0: neutral; LABEL_1: positive; LABEL_2: negative
-
-```
\ No newline at end of file
diff --git a/spaces/spock74/whisper-webui/src/__init__.py b/spaces/spock74/whisper-webui/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/sravya-abburi/ResumeParserLLM/app.py b/spaces/sravya-abburi/ResumeParserLLM/app.py
deleted file mode 100644
index 77c7c1be44e93e73b8bab34351ebadbfd9b38939..0000000000000000000000000000000000000000
--- a/spaces/sravya-abburi/ResumeParserLLM/app.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import subprocess
-subprocess.call(['pip', 'install', 'python-dotenv'])
-subprocess.call(['pip', 'install', 'transformers'])
-subprocess.call(['pip', 'install', 'torch'])
-subprocess.call(['pip', 'install', 'numpy'])
-from transformers import BertForTokenClassification, BertTokenizer
-import gradio as gr
-import os
-import torch
-
-import numpy as np
-
-from dotenv import load_dotenv
-import sys
-
-# Install python-dotenv
-
-
-# Load environment variables from .env
-load_dotenv()
-
-# Read environment variables
-github_username =  os.environ.get('GIT_USER')
-repo_name = os.environ.get('GIT_REPO')
-pat = os.environ.get('GIT_TOKEN')
-# Define a directory to clone the repository
-clone_dir = "temp_clone_dir"
-label_map = {
-    0: "O",         # Other
-    1: "NAME",
-    2: "CLG",
-    3: "DEG",
-    4: "GRADYEAR",
-    5: "YOE",
-    6: "COMPANY",
-    7: "DESIG",
-    8: "SKILLS",
-    9: "LOC",
-    10: "EMAIL"
-}
-
-# Function to clone the private repository
-def clone_private_repo():
-    try:
-        # Construct the GitHub repository URL with the PAT
-        repo_url = f"https://{github_username}:{pat}@github.com/{github_username}/{repo_name}.git"
-        
-        # Clone the repository to the specified directory
-        if os.path.exists(clone_dir):
-            subprocess.call(["git", "pull"], cwd=clone_dir)  # Update if it already exists
-        else:
-            subprocess.call(["git", "clone", repo_url, clone_dir])
-        return True
-    except Exception as e:
-        print(f"Error cloning repository: {str(e)}")
-        return False
-idx2tag={0: 'U-EMAIL',
- 1: 'B-GRADYEAR',
- 2: 'I-YOE',
- 3: 'I-COMPANY',
- 4: '[SEP]',
- 5: 'I-DESIG',
- 6: 'L-LOC',
- 7: 'U-DEG',
- 8: 'L-GRADYEAR',
- 9: 'I-SKILLS',
- 10: 'L-DEG',
- 11: 'B-COMPANY',
- 12: 'I-LOC',
- 13: 'I-CLG',
- 14: 'L-EMAIL',
- 15: 'I-EMAIL',
- 16: 'L-CLG',
- 17: 'U-CLG',
- 18: 'U-SKILLS',
- 19: 'U-COMPANY',
- 20: 'B-DEG',
- 21: '[CLS]',
- 22: 'I-NAME',
- 23: 'B-LOC',
- 24: 'L-SKILLS',
- 25: 'U-GRADYEAR',
- 26: 'U-YOE',
- 27: 'L-NAME',
- 28: 'B-DESIG',
- 29: 'U-DESIG',
- 30: 'X',
- 31: 'O',
- 32: 'B-EMAIL',
- 33: 'I-DEG',
- 34: 'B-YOE',
- 35: 'L-COMPANY',
- 36: 'L-YOE',
- 37: 'B-CLG',
- 38: 'B-NAME',
- 39: 'I-GRADYEAR',
- 40: 'U-LOC',
- 41: 'L-DESIG',
- 42: 'B-SKILLS'}
-# Clone the private repository when the Gradio interface starts
-if clone_private_repo():
-    sys.path.append(clone_dir)
-    model_path = "sravya-abburi/ResumeParserBERT"
-   
-    tokenizer = BertTokenizer.from_pretrained(model_path)
-    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-    model = BertForTokenClassification.from_pretrained(model_path)
-    model.resize_token_embeddings(len(tokenizer))
-
-    # Import your script or module from the cloned repository
-    from DataandModel.resumeparser import clean_and_predict_resume  # Adjust the path as needed
-
-    # Define the Gradio interface and function
-    def resume_parser(input_file):
-    # Execute your function with input_text
-        result = clean_and_predict_resume(input_file.name)
-        print("Processed Result:", result)
-        
-        inputs = tokenizer(result, return_tensors="pt", truncation=True, padding=True)
-        with torch.no_grad():
-            logits = model(**inputs).logits
-        logits = logits.detach().cpu().numpy()
-        predicted_token_class_ids = [list(p) for p in np.argmax(logits, axis=2)][0]  # Assuming batch size of 1
-
-        tokens = tokenizer.tokenize(result)
-        predicted_labels = [idx2tag[id] for id in predicted_token_class_ids]
-
-        # Grouping tokens by their predicted entity type
-        entity_tokens = {}
-        for label, token in zip(predicted_labels, tokens):
-            # Extract base entity type by removing any prefix
-            base_label = label.split("-")[-1]
-            
-            if base_label not in ["O", "X", "[CLS]", "[SEP]"]:  # Exclude irrelevant labels
-                if base_label not in entity_tokens:
-                    entity_tokens[base_label] = []
-                # Remove the '##' prefix from the tokens
-                token = token.replace('##', '')
-                entity_tokens[base_label].append(token)
-
-        # Convert the lists of tokens for each entity type back to text
-        entity_text = {key: " ".join(value) for key, value in entity_tokens.items()}
-
-        return {"entities": entity_text}
-
-    example_dir = "examples"
-
-    iface = gr.Interface(
-        fn=resume_parser,
-        inputs="file",
-        outputs="text",
-        title="Resume Entity Recognition",
-        description="Upload a PDF or DOCX resume (max 2MB), and this tool will extract and display entities.",
-        examples=[
-            [os.path.join(example_dir, "Accountant-Entry-Level.pdf")],
-            [os.path.join(example_dir, "Creative sales resume.docx")]
-        ]
-    )
-    
-    iface.launch()
-
-else:
-
-    print("Repository cloning failed. Check your credentials and repository details.")
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py
deleted file mode 100644
index 5f4faa99218b0b30c980cad167c52b2297cd92c3..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/copy_aligned_text.py
+++ /dev/null
@@ -1,4 +0,0 @@
-import sys
-
-for idx, line in enumerate(sys.stdin):
-    print(f"utt{idx:010d} {line}", end='')
\ No newline at end of file
diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/test_dataset.py b/spaces/sriramelango/Social_Classification_Public/fairseq/tests/test_dataset.py
deleted file mode 100644
index a3e3970028bc4b0259153e403951e1735bb0cd3e..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/fairseq/tests/test_dataset.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-#
-# This source code is licensed under the MIT license found in the
-# LICENSE file in the root directory of this source tree.
-
-import logging
-import unittest
-from typing import Sequence
-
-from fairseq.data import LanguagePairDataset, ListDataset, RoundRobinZipDatasets
-from tests.test_train import mock_dict
-
-
-def lang_pair_dataset(lengths: Sequence[int]) -> LanguagePairDataset:
-    tokens = [[i] * l for i, l in enumerate(lengths)]
-    return LanguagePairDataset(ListDataset(tokens), lengths, mock_dict())
-
-
-def sample(id: int, length: int):
-    return {"id": id, "source": [id] * length, "target": None}
-
-
-class TestDataset(unittest.TestCase):
-    def setUp(self):
-        logging.disable(logging.CRITICAL)
-
-    def tearDown(self):
-        logging.disable(logging.NOTSET)
-
-    def test_round_robin_zip_datasets(self):
-        long_dataset = lang_pair_dataset([10, 9, 8, 11])
-        short_dataset = lang_pair_dataset([11, 9])
-
-        dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset})
-        # Dataset is now sorted by sentence length
-        dataset.ordered_indices()
-        assert dataset.longest_dataset is long_dataset
-        self.assertEqual(dict(dataset[0]), {"a": sample(2, 8), "b": sample(1, 9)})
-        # The item 2 of dataset 'a' is with item (2 % 2 = 0) of dataset 'b'
-        self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 9)})
-
-    def test_round_robin_zip_datasets_filtered(self):
-        long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12])
-        short_dataset = lang_pair_dataset([11, 20, 9, 1000])
-
-        dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset})
-        # Dataset is now sorted by sentence length
-        idx = dataset.ordered_indices()
-        idx, _ = dataset.filter_indices_by_size(idx, {"a": 19, "b": 900})
-        self.assertEqual(list(idx), [0, 1, 2, 3, 4])
-        self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)})
-        self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(1, 20)})
-        self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(0, 11)})
-
-    def test_round_robin_zip_datasets_filtered_with_tuple(self):
-        long_dataset = lang_pair_dataset([10, 20, 8, 11, 1000, 7, 12])
-        short_dataset = lang_pair_dataset([11, 20, 9, 1000])
-
-        dataset = RoundRobinZipDatasets({"a": long_dataset, "b": short_dataset})
-        # Dataset is now sorted by sentence length
-        idx = dataset.ordered_indices()
-        idx, _ = dataset.filter_indices_by_size(idx, 19)
-        self.assertEqual(list(idx), [0, 1, 2, 3, 4])
-        self.assertEqual(dict(dataset[0]), {"a": sample(5, 7), "b": sample(2, 9)})
-        self.assertEqual(dict(dataset[2]), {"a": sample(0, 10), "b": sample(2, 9)})
-        self.assertEqual(dict(dataset[4]), {"a": sample(6, 12), "b": sample(2, 9)})
diff --git a/spaces/sriramelango/Social_Classification_Public/models/__init__.py b/spaces/sriramelango/Social_Classification_Public/models/__init__.py
deleted file mode 100644
index 5ca74d790a95a2b14d3fbb0cf9f0a9959416d305..0000000000000000000000000000000000000000
--- a/spaces/sriramelango/Social_Classification_Public/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .ofa import OFAModel, ofa_base_architecture, ofa_large_architecture, ofa_huge_architecture
\ No newline at end of file
diff --git a/spaces/stevechong/cny-goodluck-detector/app.py b/spaces/stevechong/cny-goodluck-detector/app.py
deleted file mode 100644
index a3e5af88a440cbc38da181c0624d91521719b503..0000000000000000000000000000000000000000
--- a/spaces/stevechong/cny-goodluck-detector/app.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import cv2
-import numpy as np
-from PIL import Image
-import streamlit as st
-import tensorflow as tf
-from tensorflow.keras.models import load_model
-
-# most of this code has been obtained from Datature's prediction script
-# https://github.com/datature/resources/blob/main/scripts/bounding_box/prediction.py
-
-st.set_option('deprecation.showfileUploaderEncoding', False)
-
-@st.cache(allow_output_mutation=True)
-def load_model():
-	return tf.saved_model.load('./saved_model')
-
-def load_label_map(label_map_path):
-    """
-    Reads label map in the format of .pbtxt and parse into dictionary
-    Args:
-      label_map_path: the file path to the label_map
-    Returns:
-      dictionary with the format of {label_index: {'id': label_index, 'name': label_name}}
-    """
-    label_map = {}
-
-    with open(label_map_path, "r") as label_file:
-        for line in label_file:
-            if "id" in line:
-                label_index = int(line.split(":")[-1])
-                label_name = next(label_file).split(":")[-1].strip().strip('"')
-                label_map[label_index] = {"id": label_index, "name": label_name}
-    return label_map
-	
-def predict_class(image, model):
-	image = tf.cast(image, tf.float32)
-	image = tf.image.resize(image, [150, 150])
-	image = np.expand_dims(image, axis = 0)
-	return model.predict(image)
-
-def plot_boxes_on_img(color_map, classes, bboxes, image_origi, origi_shape):
-	for idx, each_bbox in enumerate(bboxes):
-		color = color_map[classes[idx]]
-
-		## Draw bounding box
-		cv2.rectangle(
-			image_origi,
-			(int(each_bbox[1] * origi_shape[1]),
-			 int(each_bbox[0] * origi_shape[0]),),
-			(int(each_bbox[3] * origi_shape[1]),
-			 int(each_bbox[2] * origi_shape[0]),),
-			color,
-			2,
-		)
-		## Draw label background
-		cv2.rectangle(
-			image_origi,
-			(int(each_bbox[1] * origi_shape[1]),
-			 int(each_bbox[2] * origi_shape[0]),),
-			(int(each_bbox[3] * origi_shape[1]),
-			 int(each_bbox[2] * origi_shape[0] + 15),),
-			color,
-			-1,
-		)
-		## Insert label class & score
-		cv2.putText(
-			image_origi,
-			"Class: {}, Score: {}".format(
-				str(category_index[classes[idx]]["name"]),
-				str(round(scores[idx], 2)),
-			),
-			(int(each_bbox[1] * origi_shape[1]),
-			 int(each_bbox[2] * origi_shape[0] + 10),),
-			cv2.FONT_HERSHEY_SIMPLEX,
-			0.3,
-			(0, 0, 0),
-			1,
-			cv2.LINE_AA,
-		)
-	return image_origi
-
-
-# Webpage code starts here
-
-#TODO change this
-st.title('CNY Good Luck 福 Detector')
-st.text('Made by Steve Chong')
-st.markdown('## Upload an image with one or more 福 characters, count the number of good luck on the image!')
-st.text('Credits to tutorial by @anebzt')
-
-with st.spinner('Model is being loaded...'):
-	model = load_model()
-
-# ask user to upload an image
-file = st.file_uploader("Upload image", type=["jpg", "png"])
-
-if file is None:
-	st.text('Waiting for upload...')
-else:
-	st.text('Running inference...')
-	# open image
-	test_image = Image.open(file).convert("RGB")
-	origi_shape = np.asarray(test_image).shape
-	# resize image to default shape
-	default_shape = 320
-	image_resized = np.array(test_image.resize((default_shape, default_shape)))
-
-	## Load color map
-	category_index = load_label_map("./label_map.pbtxt")
-
-	# TODO Add more colors if there are more classes
-  # color of each label. check label_map.pbtxt to check the index for each class
-	color_map = {
-		1: [0, 255, 0] # fu-goodluck -> green
-	}
-
-	## The model input needs to be a tensor
-	input_tensor = tf.convert_to_tensor(image_resized)
-	## The model expects a batch of images, so add an axis with `tf.newaxis`.
-	input_tensor = input_tensor[tf.newaxis, ...]
-
-	## Feed image into model and obtain output
-	detections_output = model(input_tensor)
-	num_detections = int(detections_output.pop("num_detections"))
-	detections = {key: value[0, :num_detections].numpy() for key, value in detections_output.items()}
-	detections["num_detections"] = num_detections
-
-	## Filter out predictions below threshold
-	# if threshold is higher, there will be fewer predictions
-	# TODO change this number to see how the predictions change
-	confidence_threshold = 0.8
-	indexes = np.where(detections["detection_scores"] > confidence_threshold)
-
-	## Extract predicted bounding boxes
-	bboxes = detections["detection_boxes"][indexes]
-	# there are no predicted boxes
-	if len(bboxes) == 0:
-		st.error('No boxes predicted')
-	# there are predicted boxes
-	else:
-		st.success('Boxes predicted')
-		classes = detections["detection_classes"][indexes].astype(int)
-		scores = detections["detection_scores"][indexes]
-
-		# plot boxes and labels on image
-		image_origi = np.array(Image.fromarray(image_resized).resize((origi_shape[1], origi_shape[0])))
-		image_origi = plot_boxes_on_img(color_map, classes, bboxes, image_origi, origi_shape)
-
-		# show image in web page
-		st.image(Image.fromarray(image_origi), caption="Image with predictions", width=400)
-		st.markdown("### Predicted boxes")
-		for idx in range(len((bboxes))):
-			st.markdown(f"* Class: {str(category_index[classes[idx]]['name'])}, confidence score: {str(round(scores[idx], 2))}")
\ No newline at end of file
diff --git "a/spaces/suchun/chatGPT_acdemic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py" "b/spaces/suchun/chatGPT_acdemic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
deleted file mode 100644
index 72ffe6b1a8f2a59a3c5c364e30dfb4949bd6a929..0000000000000000000000000000000000000000
--- "a/spaces/suchun/chatGPT_acdemic/crazy_functions/\350\257\273\346\226\207\347\253\240\345\206\231\346\221\230\350\246\201.py"
+++ /dev/null
@@ -1,67 +0,0 @@
-from toolbox import update_ui
-from toolbox import CatchException, report_execption, write_results_to_file
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
-
-
-def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import time, glob, os
-    print('begin analysis on:', file_manifest)
-    for index, fp in enumerate(file_manifest):
-        with open(fp, 'r', encoding='utf-8', errors='replace') as f:
-            file_content = f.read()
-
-        prefix = "接下来请你逐文件分析下面的论文文件，概括其内容" if index==0 else ""
-        i_say = prefix + f'请对下面的文章片段用中文做一个概述，文件名是{os.path.relpath(fp, project_folder)}，文章内容是 ```{file_content}```'
-        i_say_show_user = prefix + f'[{index}/{len(file_manifest)}] 请对下面的文章片段做一个概述: {os.path.abspath(fp)}'
-        chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-        if not fast_debug: 
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
-
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    all_file = ', '.join([os.path.relpath(fp, project_folder) for index, fp in enumerate(file_manifest)])
-    i_say = f'根据以上你自己的分析，对全文进行概括，用学术性语言写一段中文摘要，然后再写一段英文摘要（包括{all_file}）。'
-    chatbot.append((i_say, "[Local Message] waiting gpt response."))
-    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-
-    if not fast_debug: 
-        msg = '正常'
-        # ** gpt request **
-        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say, llm_kwargs, chatbot, history=history, sys_prompt=system_prompt)   # 带超时倒计时
-
-        chatbot[-1] = (i_say, gpt_say)
-        history.append(i_say); history.append(gpt_say)
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-        res = write_results_to_file(history)
-        chatbot.append(("完成了吗？", res))
-        yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-
-
-
-@CatchException
-def 读文章写摘要(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
-    history = []    # 清空历史，以免输入溢出
-    import glob, os
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "": txt = '空空如也的输入栏'
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.tex', recursive=True)] # + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.cpp', recursive=True)] + \
-                    # [f for f in glob.glob(f'{project_folder}/**/*.c', recursive=True)]
-    if len(file_manifest) == 0:
-        report_execption(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex文件: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-    yield from 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
diff --git a/spaces/sunmaiyyyy/combined-GI-RVC-model/app.py b/spaces/sunmaiyyyy/combined-GI-RVC-model/app.py
deleted file mode 100644
index c92d11ac9dd05deac644d5372dd0f5f7f247475a..0000000000000000000000000000000000000000
--- a/spaces/sunmaiyyyy/combined-GI-RVC-model/app.py
+++ /dev/null
@@ -1,178 +0,0 @@
-import os
-import glob
-import json
-import argparse
-import traceback
-import logging
-import gradio as gr
-import numpy as np
-import librosa
-import torch
-import asyncio
-import edge_tts
-from datetime import datetime
-from fairseq import checkpoint_utils
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
-from vc_infer_pipeline import VC
-from config import Config
-config = Config()
-logging.getLogger("numba").setLevel(logging.WARNING)
-limitation = os.getenv("SYSTEM") == "spaces"  # limit audio length in huggingface spaces
-
-def create_vc_fn(tgt_sr, net_g, vc, if_f0, file_index):
-    def vc_fn(
-        input_audio,
-        f0_up_key,
-        f0_method,
-        index_rate,
-        tts_mode,
-        tts_text,
-        tts_voice
-    ):
-        try:
-            if tts_mode:
-                if len(tts_text) > 100 and limitation:
-                    return "Text is too long", None
-                if tts_text is None or tts_voice is None:
-                    return "You need to enter text and select a voice", None
-                asyncio.run(edge_tts.Communicate(tts_text, "-".join(tts_voice.split('-')[:-1])).save("tts.mp3"))
-                audio, sr = librosa.load("tts.mp3", sr=16000, mono=True)
-            else:
-                if input_audio is None:
-                    return "You need to upload an audio", None
-                sampling_rate, audio = input_audio
-                duration = audio.shape[0] / sampling_rate
-                if duration > 20 and limitation:
-                    return "Please upload an audio file that is less than 20 seconds. If you need to generate a longer audio file, please use Colab.", None
-                audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-                if len(audio.shape) > 1:
-                    audio = librosa.to_mono(audio.transpose(1, 0))
-                if sampling_rate != 16000:
-                    audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
-            times = [0, 0, 0]
-            f0_up_key = int(f0_up_key)
-            audio_opt = vc.pipeline(
-                hubert_model,
-                net_g,
-                0,
-                audio,
-                times,
-                f0_up_key,
-                f0_method,
-                file_index,
-                index_rate,
-                if_f0,
-                f0_file=None,
-            )
-            print(
-                f"[{datetime.now().strftime('%Y-%m-%d %H:%M')}]: npy: {times[0]}, f0: {times[1]}s, infer: {times[2]}s"
-            )
-            return (tgt_sr, audio_opt)
-        except:
-            info = traceback.format_exc()
-            print(info)
-            return info, (None, None)
-    return vc_fn
-
-def load_hubert():
-    global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    hubert_model.eval()
-
-def change_to_tts_mode(tts_mode):
-    if tts_mode:
-        return gr.Audio.update(visible=False), gr.Textbox.update(visible=True), gr.Dropdown.update(visible=True)
-    else:
-        return gr.Audio.update(visible=True), gr.Textbox.update(visible=False), gr.Dropdown.update(visible=False)
-
-if __name__ == '__main__':
-    load_hubert()
-    models = []
-    tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-    voices = [f"{v['ShortName']}-{v['Gender']}" for v in tts_voice_list]
-    with open("weights/model_info.json", "r", encoding="utf-8") as f:
-        models_info = json.load(f)
-    for model_name, info in models_info.items():
-        if not info['enable']:
-            continue
-        model_title = info['title']
-        model_author = info.get("author", None)
-        model_cover = f"weights/{model_name}/{info['cover']}"
-        model_index = f"weights/{model_name}/{info['feature_retrieval_library']}"
-        cpt = torch.load(f"weights/{model_name}/{model_name}.pth", map_location="cpu")
-        tgt_sr = cpt["config"][-1]
-        cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-        if_f0 = cpt.get("f0", 1)
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-        del net_g.enc_q
-        print(net_g.load_state_dict(cpt["weight"], strict=False))
-        net_g.eval().to(config.device)
-        if config.is_half:
-            net_g = net_g.half()
-        else:
-            net_g = net_g.float()
-        vc = VC(tgt_sr, config)
-        print(f"Model loaded: {model_name}")
-        models.append((model_name, model_title, model_author, model_cover, create_vc_fn(tgt_sr, net_g, vc, if_f0, model_index)))
-    with gr.Blocks() as app:
-        gr.Markdown(
-            "# <center> Combined Genshin Impact RVC Models\n"
-            "## <center> The input audio should be clean and pure voice without background music.\n"
-            "### <center> It is recommended to use google colab for more features. \n"
-            "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Tgr6q9kKiB5P37rUitrB3CsNl8JP9iQZ?usp=sharing)\n\n"
-            "[![Original Repo](https://badgen.net/badge/icon/github?icon=github&label=Original%20Repo)](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI)"
-        )
-        with gr.Tabs():
-            for (name, title, author, cover, vc_fn) in models:
-                with gr.TabItem(name):
-                    with gr.Row():
-                        gr.Markdown(
-                            '<div align="center">'
-                            f'<div>{title}</div>\n'+
-                            (f'<div>Model author: {author}</div>' if author else "")+
-                            (f'<img style="width:auto;height:300px;" src="file/{cover}">' if cover else "")+
-                            '</div>'
-                        )
-                    with gr.Row():
-                        with gr.Column():
-                            vc_input = gr.Audio(label="Input audio"+' (less than 20 seconds)' if limitation else '')
-                            vc_transpose = gr.Number(label="Transpose", value=0, info='Type "12" to change from male to female voice. Type "-12" to change female to male voice')
-                            vc_f0method = gr.Radio(
-                                label="Pitch extraction algorithm",
-                                choices=["pm", "harvest"],
-                                value="pm",
-                                interactive=True,
-                                info="PM is fast but Harvest is better for low frequencies. (Default: PM)"
-                            )
-                            vc_index_ratio = gr.Slider(
-                                minimum=0,
-                                maximum=1,
-                                label="Retrieval feature ratio",
-                                value=0.6,
-                                interactive=True,
-                                info="(Default: 0.6)"
-                            )
-                            tts_mode = gr.Checkbox(label="tts (use edge-tts as input)", value=False)
-                            tts_text = gr.Textbox(visible=False,label="TTS text (100 words limitation)" if limitation else "TTS text")
-                            tts_voice = gr.Dropdown(label="Edge-tts speaker", choices=voices, visible=False, allow_custom_value=False, value="en-US-AnaNeural-Female")
-                            vc_submit = gr.Button("Generate", variant="primary")
-                        with gr.Column():
-                            vc_output = gr.Audio(label="Output Audio")
-                vc_submit.click(vc_fn, [vc_input, vc_transpose, vc_f0method, vc_index_ratio, tts_mode, tts_text, tts_voice], [vc_output])
-                tts_mode.change(change_to_tts_mode, [tts_mode], [vc_input, tts_text, tts_voice])
-        gr.Markdown('# <center>Changelog 2023.05.30')
-        gr.Markdown('- Added kirara-jp, yelan-jp, zhongli-jp, alhaitham-jp, and barbara-jp')
-        gr.Markdown('- Minor fix and adjustment')
-        app.queue(concurrency_count=1, max_size=20, api_open=config.api).launch(share=config.colab)
\ No newline at end of file
diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Minecraft Comes Alive Installer.md b/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Minecraft Comes Alive Installer.md
deleted file mode 100644
index c82d0b76c9ccc86f03f59311493cad9974ec8c7a..0000000000000000000000000000000000000000
--- a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Minecraft Comes Alive Installer.md	
+++ /dev/null
@@ -1,110 +0,0 @@
-
-<h1>Minecraft Comes Alive Installer: How to Download and Install the Mod</h1>
-
-<p>If you are bored of the default villagers in Minecraft, you might want to try out the Minecraft Comes Alive mod. This mod replaces the villagers with human characters that have different personalities, skins, and abilities. You can interact with them, build relationships, get married, and even have children. In this article, we will show you how to download and install the Minecraft Comes Alive installer for your Minecraft version.</p>
-<h2>Minecraft Comes Alive Installer</h2><br /><p><b><b>Download File</b> &#9733; <a href="https://cinurl.com/2uEXJi">https://cinurl.com/2uEXJi</a></b></p><br /><br />
-
-<h2>What is Minecraft Comes Alive?</h2>
-
-<p>Minecraft Comes Alive (MCA) is a popular mod that adds a new dimension to the game. It makes the villagers more realistic and immersive, giving them names, genders, professions, and traits. You can talk to them, give them gifts, hire them, trade with them, and more. You can also find your soulmate among the villagers, propose to them, and start a family. Your children will grow up over time and help you with various chores. You can even marry other players and have children in multiplayer mode.</p>
-
-<p>The mod also adds some new features to the game, such as divorce papers, matchmaker's rings, baby boys and girls, tombstones, and more. You can customize the mod's settings to your liking, such as changing the villager skins, disabling aging, enabling infection, and so on. The mod is compatible with most other mods that do not overwrite the villagers.</p>
-
-<h2>How to Download Minecraft Comes Alive Installer</h2>
-
-<p>The first step to install the Minecraft Comes Alive mod is to download the installer from a reliable source. One of the best places to download the mod is CurseForge, a website that hosts thousands of mods for various games. You can find the Minecraft Comes Alive installer on this page: <a href="https://www.curseforge.com/minecraft/mc-mods/minecraft-comes-alive-mca">https://www.curseforge.com/minecraft/mc-mods/minecraft-comes-alive-mca</a>.</p>
-
-<p>On this page, you will see a list of files for different versions of Minecraft. Choose the file that matches your Minecraft version and click on it. Then click on the download button to start downloading the installer. The file name should look something like this: minecraft-comes-alive-6.0.0-beta+1.12.2-universal.jar.</p>
-
-<h2>How to Install Minecraft Comes Alive Installer</h2>
-
-<p>Once you have downloaded the Minecraft Comes Alive installer, you need to run it to install the mod. Before you do that, make sure you have installed Minecraft Forge on your computer. Minecraft Forge is a tool that allows you to run mods on Minecraft. You can download it from here: <a href="https://files.minecraftforge.net/">https://files.minecraftforge.net/</a>.</p>
-
-<p>After installing Minecraft Forge, follow these steps to install the Minecraft Comes Alive installer:</p>
-<p></p>
-
-<ol>
-<li>Open the folder where you downloaded the Minecraft Comes Alive installer and double-click on it.</li>
-<li>A window will pop up asking you to select an install client or server. Choose install client and click OK.</li>
-<li>The installer will automatically detect your Minecraft folder and install the mod there.</li>
-<li>When the installation is complete, click OK.</li>
-<li>Launch Minecraft and select Forge as your profile.</li>
-<li>Click on Mods and make sure Minecraft Comes Alive is enabled.</li>
-<li>Enjoy the mod!</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Minecraft Comes Alive is a mod that adds more life and fun to your Minecraft world. It lets you interact with human villagers, get married, have children, and more. To install the mod, you need to download and run the Minecraft Comes Alive installer from CurseForge and have Minecraft Forge installed on your computer. We hope this article helped you learn how to download and install the Minecraft Comes Alive installer for your Minecraft version.</p>
-<h2>How to Use Minecraft Comes Alive Installer</h2>
-
-<p>After installing the Minecraft Comes Alive mod, you can start enjoying its features. To interact with a villager, you need to right-click on them. A menu will appear with different options, such as chat, follow, trade, gift, and more. You can also use special items to interact with villagers, such as the whistle, the divorce papers, the matchmaker's ring, and the baby boy or girl.</p>
-
-<p>As you interact with villagers, you will build relationships with them. You can see your relationship level with a villager by hovering over their name in the menu. The higher your relationship level, the more options you will have with them. For example, you can ask them to marry you if your relationship level is high enough. You can also have children with your spouse by using a baby boy or girl item.</p>
-
-<p>Your children will grow up over time and do different chores for you. You can assign them chores by right-clicking on them and choosing the chore option. You can also give them gifts, teach them skills, and tell them stories. Your children will eventually grow up into adults, who can get married and have children of their own.</p>
-
-<h2>How to Uninstall Minecraft Comes Alive Installer</h2>
-
-<p>If you want to uninstall the Minecraft Comes Alive mod, you need to follow these steps:</p>
-
-<ol>
-<li>Close Minecraft and any other programs that use it.</li>
-<li>Open the folder where you installed the Minecraft Comes Alive installer and delete it.</li>
-<li>Open the folder where your Minecraft saves are located and delete the MCA folder.</li>
-<li>Launch Minecraft and select Forge as your profile.</li>
-<li>Click on Mods and disable or remove Minecraft Comes Alive.</li>
-<li>Enjoy vanilla Minecraft again!</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Minecraft Comes Alive is a mod that adds more life and fun to your Minecraft world. It lets you interact with human villagers, get married, have children, and more. To install the mod, you need to download and run the Minecraft Comes Alive installer from CurseForge and have Minecraft Forge installed on your computer. We hope this article helped you learn how to download, install, use, and uninstall the Minecraft Comes Alive installer for your Minecraft version.</p>
-<h2>How to Play Minecraft Comes Alive</h2>
-
-<p>Once you have installed the Minecraft Comes Alive mod, you can start exploring its features. You will notice that the villagers are now human characters with different skins, genders, and personalities. You can interact with them by right-clicking on them and choosing an option from the menu. You can chat with them, compliment them, joke with them, flirt with them, and more. You can also give them gifts, such as flowers, diamonds, or cakes, to increase your relationship level with them.</p>
-
-<p>As you interact with villagers, you will see their hearts change color depending on their mood and attitude towards you. The hearts can be green, yellow, orange, red, or black. Green means they are friendly, yellow means they are neutral, orange means they are unfriendly, red means they are hostile, and black means they are dead. You can also see their name, profession, trait, and relationship level by hovering over their name in the menu.</p>
-
-<p>If you want to get married to a villager, you need to have a high relationship level with them and a matchmaker's ring. You can craft a matchmaker's ring with a diamond and a gold ingot. To propose to a villager, you need to right-click on them with the ring and choose the marry option. If they accept, you will see a message saying that you are now married. You can also marry other players in multiplayer mode by using the same method.</p>
-
-<h2>How to Customize Minecraft Comes Alive</h2>
-
-<p>The Minecraft Comes Alive mod has many settings that you can customize to your liking. You can access the settings by pressing the MCA key on your keyboard (default is V). A menu will appear with different tabs, such as General Settings, Villager Settings, Family Settings, World Settings, and Server Settings. You can change various options in each tab, such as enabling or disabling aging, infection, story progression, villager skins, and more.</p>
-
-<p>You can also customize your own character in the mod by using the editor screen. You can access the editor screen by pressing the MCA key on your keyboard (default is V) and choosing the editor option. A screen will appear with different options to change your appearance, such as your skin color, hair color, eye color, facial features, clothing style, and more. You can also change your name and gender in this screen.</p>
-
-<p>The mod also has some commands that you can use to customize your experience. You can use these commands by typing them in the chat window (default is T). Some of the commands are /mca help (to see a list of all commands), /mca reset (to reset all MCA data), /mca revive (to revive a dead villager), /mca divorce (to divorce your spouse), /mca sethome (to set your home location), and more.</p>
-<h2>How to Troubleshoot Minecraft Comes Alive Installer</h2>
-
-<p>Sometimes, you may encounter some issues when installing or using the Minecraft Comes Alive mod. Here are some common problems and solutions that may help you troubleshoot the mod:</p>
-
-<ul>
-<li>If you get a message saying that the mod is missing dependencies, such as RadixCore or Minecraft Forge, you need to download and install them from their respective websites.</li>
-<li>If you get a message saying that the mod is incompatible with your Minecraft version, you need to download and install the correct version of the mod from CurseForge or our website.</li>
-<li>If you get a message saying that the mod has crashed or caused an error, you need to check the crash report and see what caused the problem. You can also send us the crash report via our website or Discord server for further assistance.</li>
-<li>If you have any other issues with the mod, such as glitches, bugs, or conflicts with other mods, you can report them on our website or Discord server. Please provide as much information as possible, such as screenshots, logs, and steps to reproduce the issue.</li>
-</ul>
-
-<h2>How to Update Minecraft Comes Alive Installer</h2>
-
-<p>The Minecraft Comes Alive mod is constantly being updated with new features, improvements, and bug fixes. To enjoy the latest version of the mod, you need to update it regularly. You can update the mod by following these steps:</p>
-
-<ol>
-<li>Close Minecraft and any other programs that use it.</li>
-<li>Open the folder where you downloaded the Minecraft Comes Alive installer and delete it.</li>
-<li>Open CurseForge or our website and download the latest version of the installer.</li>
-<li>Run the installer and follow the same steps as before to install the mod.</li>
-<li>Launch Minecraft and select Forge as your profile.</li>
-<li>Click on Mods and make sure Minecraft Comes Alive is enabled.</li>
-<li>Enjoy the updated mod!</li>
-</ol>
-
-<h2>Conclusion</h2>
-
-<p>Minecraft Comes Alive is a mod that adds more life and fun to your Minecraft world. It lets you interact with human villagers, get married, have children, and more. To install the mod, you need to download and run the Minecraft Comes Alive installer from CurseForge and have Minecraft Forge installed on your computer. We hope this article helped you learn how to download, install, use, update, and troubleshoot the Minecraft Comes Alive installer for your Minecraft version.</p>
-<h2>Conclusion</h2>
-
-<p>Minecraft Comes Alive is a mod that adds more life and fun to your Minecraft world. It lets you interact with human villagers, get married, have children, and more. To install the mod, you need to download and run the Minecraft Comes Alive installer from CurseForge and have Minecraft Forge installed on your computer. We hope this article helped you learn how to download, install, use, update, and troubleshoot the Minecraft Comes Alive installer for your Minecraft version.</p> 3cee63e6c2<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/szk1ck/similarity_by_fasttext/README.md b/spaces/szk1ck/similarity_by_fasttext/README.md
deleted file mode 100644
index c6a8be55092a4dbbbfd71f4366b1459bd7f67f48..0000000000000000000000000000000000000000
--- a/spaces/szk1ck/similarity_by_fasttext/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Similarity By Fasttext
-emoji: 🏆
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 3.36.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/szukevin/VISOR-GPT/train/finetune/run_classifier.py b/spaces/szukevin/VISOR-GPT/train/finetune/run_classifier.py
deleted file mode 100644
index 97504020581de7100ff1640cc56844b612149361..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/finetune/run_classifier.py
+++ /dev/null
@@ -1,366 +0,0 @@
-"""
-This script provides an example to wrap TencentPretrain for classification.
-"""
-import sys
-import os
-import random
-import argparse
-import torch
-import torch.nn as nn
-
-tencentpretrain_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
-sys.path.append(tencentpretrain_dir)
-
-from tencentpretrain.embeddings import *
-from tencentpretrain.encoders import *
-from tencentpretrain.utils.vocab import Vocab
-from tencentpretrain.utils.constants import *
-from tencentpretrain.utils import *
-from tencentpretrain.utils.optimizers import *
-from tencentpretrain.utils.config import load_hyperparam
-from tencentpretrain.utils.seed import set_seed
-from tencentpretrain.utils.logging import init_logger
-from tencentpretrain.utils.misc import pooling
-from tencentpretrain.model_saver import save_model
-from tencentpretrain.opts import finetune_opts, tokenizer_opts, adv_opts
-
-
-class Classifier(nn.Module):
-    def __init__(self, args):
-        super(Classifier, self).__init__()
-        self.embedding = Embedding(args)
-        for embedding_name in args.embedding:
-            tmp_emb = str2embedding[embedding_name](args, len(args.tokenizer.vocab))
-            self.embedding.update(tmp_emb, embedding_name)
-        self.encoder = str2encoder[args.encoder](args)
-        self.labels_num = args.labels_num
-        self.pooling_type = args.pooling
-        self.soft_targets = args.soft_targets
-        self.soft_alpha = args.soft_alpha
-        self.output_layer_1 = nn.Linear(args.hidden_size, args.hidden_size)
-        self.output_layer_2 = nn.Linear(args.hidden_size, self.labels_num)
-
-    def forward(self, src, tgt, seg, soft_tgt=None):
-        """
-        Args:
-            src: [batch_size x seq_length]
-            tgt: [batch_size]
-            seg: [batch_size x seq_length]
-        """
-        # Embedding.
-        emb = self.embedding(src, seg)
-        # Encoder.
-        output = self.encoder(emb, seg)
-        # Target.
-        output = pooling(output, seg, self.pooling_type)
-        output = torch.tanh(self.output_layer_1(output))
-        logits = self.output_layer_2(output)
-        if tgt is not None:
-            if self.soft_targets and soft_tgt is not None:
-                loss = self.soft_alpha * nn.MSELoss()(logits, soft_tgt) + \
-                       (1 - self.soft_alpha) * nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1))
-            else:
-                loss = nn.NLLLoss()(nn.LogSoftmax(dim=-1)(logits), tgt.view(-1))
-            return loss, logits
-        else:
-            return None, logits
-
-
-def count_labels_num(path):
-    labels_set, columns = set(), {}
-    with open(path, mode="r", encoding="utf-8") as f:
-        for line_id, line in enumerate(f):
-            if line_id == 0:
-                for i, column_name in enumerate(line.rstrip("\r\n").split("\t")):
-                    columns[column_name] = i
-                continue
-            line = line.rstrip("\r\n").split("\t")
-            label = int(line[columns["label"]])
-            labels_set.add(label)
-    return len(labels_set)
-
-
-def load_or_initialize_parameters(args, model):
-    if args.pretrained_model_path is not None:
-        # Initialize with pretrained model.
-        model.load_state_dict(torch.load(args.pretrained_model_path, map_location="cpu"), strict=False)
-    else:
-        # Initialize with normal distribution.
-        for n, p in list(model.named_parameters()):
-            if "gamma" not in n and "beta" not in n:
-                p.data.normal_(0, 0.02)
-
-
-def build_optimizer(args, model):
-    param_optimizer = list(model.named_parameters())
-    no_decay = ["bias", "gamma", "beta"]
-    optimizer_grouped_parameters = [
-        {"params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], "weight_decay": 0.01},
-        {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0},
-    ]
-    if args.optimizer in ["adamw"]:
-        optimizer = str2optimizer[args.optimizer](optimizer_grouped_parameters, lr=args.learning_rate, correct_bias=False)
-    else:
-        optimizer = str2optimizer[args.optimizer](optimizer_grouped_parameters, lr=args.learning_rate,
-                                                  scale_parameter=False, relative_step=False)
-    if args.scheduler in ["constant"]:
-        scheduler = str2scheduler[args.scheduler](optimizer)
-    elif args.scheduler in ["constant_with_warmup"]:
-        scheduler = str2scheduler[args.scheduler](optimizer, args.train_steps*args.warmup)
-    else:
-        scheduler = str2scheduler[args.scheduler](optimizer, args.train_steps*args.warmup, args.train_steps)
-    return optimizer, scheduler
-
-
-def batch_loader(batch_size, src, tgt, seg, soft_tgt=None):
-    instances_num = src.size()[0]
-    for i in range(instances_num // batch_size):
-        src_batch = src[i * batch_size : (i + 1) * batch_size, :]
-        tgt_batch = tgt[i * batch_size : (i + 1) * batch_size]
-        seg_batch = seg[i * batch_size : (i + 1) * batch_size, :]
-        if soft_tgt is not None:
-            soft_tgt_batch = soft_tgt[i * batch_size : (i + 1) * batch_size, :]
-            yield src_batch, tgt_batch, seg_batch, soft_tgt_batch
-        else:
-            yield src_batch, tgt_batch, seg_batch, None
-    if instances_num > instances_num // batch_size * batch_size:
-        src_batch = src[instances_num // batch_size * batch_size :, :]
-        tgt_batch = tgt[instances_num // batch_size * batch_size :]
-        seg_batch = seg[instances_num // batch_size * batch_size :, :]
-        if soft_tgt is not None:
-            soft_tgt_batch = soft_tgt[instances_num // batch_size * batch_size :, :]
-            yield src_batch, tgt_batch, seg_batch, soft_tgt_batch
-        else:
-            yield src_batch, tgt_batch, seg_batch, None
-
-
-def read_dataset(args, path):
-    dataset, columns = [], {}
-    with open(path, mode="r", encoding="utf-8") as f:
-        for line_id, line in enumerate(f):
-            if line_id == 0:
-                for i, column_name in enumerate(line.rstrip("\r\n").split("\t")):
-                    columns[column_name] = i
-                continue
-            line = line.rstrip("\r\n").split("\t")
-            tgt = int(line[columns["label"]])
-            if args.soft_targets and "logits" in columns.keys():
-                soft_tgt = [float(value) for value in line[columns["logits"]].split(" ")]
-            if "text_b" not in columns:  # Sentence classification.
-                text_a = line[columns["text_a"]]
-                src = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
-                seg = [1] * len(src)
-            else:  # Sentence-pair classification.
-                text_a, text_b = line[columns["text_a"]], line[columns["text_b"]]
-                src_a = args.tokenizer.convert_tokens_to_ids([CLS_TOKEN] + args.tokenizer.tokenize(text_a) + [SEP_TOKEN])
-                src_b = args.tokenizer.convert_tokens_to_ids(args.tokenizer.tokenize(text_b) + [SEP_TOKEN])
-                src = src_a + src_b
-                seg = [1] * len(src_a) + [2] * len(src_b)
-
-            if len(src) > args.seq_length:
-                src = src[: args.seq_length]
-                seg = seg[: args.seq_length]
-            PAD_ID = args.tokenizer.convert_tokens_to_ids([PAD_TOKEN])[0]
-            while len(src) < args.seq_length:
-                src.append(PAD_ID)
-                seg.append(0)
-            if args.soft_targets and "logits" in columns.keys():
-                dataset.append((src, tgt, seg, soft_tgt))
-            else:
-                dataset.append((src, tgt, seg))
-
-    return dataset
-
-
-def train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch=None):
-    model.zero_grad()
-
-    src_batch = src_batch.to(args.device)
-    tgt_batch = tgt_batch.to(args.device)
-    seg_batch = seg_batch.to(args.device)
-    if soft_tgt_batch is not None:
-        soft_tgt_batch = soft_tgt_batch.to(args.device)
-
-    loss, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch)
-    if torch.cuda.device_count() > 1:
-        loss = torch.mean(loss)
-
-    if args.fp16:
-        with args.amp.scale_loss(loss, optimizer) as scaled_loss:
-            scaled_loss.backward()
-    else:
-        loss.backward()
-
-    if args.use_adv and args.adv_type == "fgm":
-        args.adv_method.attack(epsilon=args.fgm_epsilon)
-        loss_adv, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch)
-        if torch.cuda.device_count() > 1:
-            loss_adv = torch.mean(loss_adv)
-        loss_adv.backward()
-        args.adv_method.restore()
-
-    if args.use_adv and args.adv_type == "pgd":
-        K = args.pgd_k
-        args.adv_method.backup_grad()
-        for t in range(K):
-            # apply the perturbation to embedding
-            args.adv_method.attack(epsilon=args.pgd_epsilon, alpha=args.pgd_alpha,
-                                   is_first_attack=(t == 0))
-            if t != K - 1:
-                model.zero_grad()
-            else:
-                args.adv_method.restore_grad()
-            loss_adv, _ = model(src_batch, tgt_batch, seg_batch, soft_tgt_batch)
-            if torch.cuda.device_count() > 1:
-                loss_adv = torch.mean(loss_adv)
-            loss_adv.backward()
-        args.adv_method.restore()
-
-    optimizer.step()
-    scheduler.step()
-
-    return loss
-
-
-def evaluate(args, dataset):
-    src = torch.LongTensor([sample[0] for sample in dataset])
-    tgt = torch.LongTensor([sample[1] for sample in dataset])
-    seg = torch.LongTensor([sample[2] for sample in dataset])
-
-    batch_size = args.batch_size
-
-    correct = 0
-    # Confusion matrix.
-    confusion = torch.zeros(args.labels_num, args.labels_num, dtype=torch.long)
-
-    args.model.eval()
-
-    for i, (src_batch, tgt_batch, seg_batch, _) in enumerate(batch_loader(batch_size, src, tgt, seg)):
-        src_batch = src_batch.to(args.device)
-        tgt_batch = tgt_batch.to(args.device)
-        seg_batch = seg_batch.to(args.device)
-        with torch.no_grad():
-            _, logits = args.model(src_batch, tgt_batch, seg_batch)
-        pred = torch.argmax(nn.Softmax(dim=1)(logits), dim=1)
-        gold = tgt_batch
-        for j in range(pred.size()[0]):
-            confusion[pred[j], gold[j]] += 1
-        correct += torch.sum(pred == gold).item()
-
-    args.logger.info("Confusion matrix:")
-    args.logger.info(confusion)
-    args.logger.info("Report precision, recall, and f1:")
-
-    eps = 1e-9
-    for i in range(confusion.size()[0]):
-        p = confusion[i, i].item() / (confusion[i, :].sum().item() + eps)
-        r = confusion[i, i].item() / (confusion[:, i].sum().item() + eps)
-        f1 = 2 * p * r / (p + r + eps)
-        args.logger.info("Label {}: {:.3f}, {:.3f}, {:.3f}".format(i, p, r, f1))
-
-    args.logger.info("Acc. (Correct/Total): {:.4f} ({}/{}) ".format(correct / len(dataset), correct, len(dataset)))
-    return correct / len(dataset), confusion
-
-
-def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    finetune_opts(parser)
-
-    tokenizer_opts(parser)
-
-    parser.add_argument("--soft_targets", action='store_true',
-                        help="Train model with logits.")
-    parser.add_argument("--soft_alpha", type=float, default=0.5,
-                        help="Weight of the soft targets loss.")
-
-    adv_opts(parser)
-
-    args = parser.parse_args()
-
-    # Load the hyperparameters from the config file.
-    args = load_hyperparam(args)
-    # Count the number of labels.
-    args.labels_num = count_labels_num(args.train_path)
-
-    # Build tokenizer.
-    args.tokenizer = str2tokenizer[args.tokenizer](args)
-    set_seed(args.seed)
-
-    # Build classification model.
-    model = Classifier(args)
-
-    # Load or initialize parameters.
-    load_or_initialize_parameters(args, model)
-
-    # Get logger.
-    args.logger = init_logger(args)
-
-    args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    model = model.to(args.device)
-
-    # Training phase.
-    trainset = read_dataset(args, args.train_path)
-    instances_num = len(trainset)
-    batch_size = args.batch_size
-
-    args.train_steps = int(instances_num * args.epochs_num / batch_size) + 1
-
-    args.logger.info("Batch size: {}".format(batch_size))
-    args.logger.info("The number of training instances: {}".format(instances_num))
-    optimizer, scheduler = build_optimizer(args, model)
-
-    if args.fp16:
-        try:
-            from apex import amp
-        except ImportError:
-            raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
-        model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
-        args.amp = amp
-
-    if torch.cuda.device_count() > 1:
-        args.logger.info("{} GPUs are available. Let's use them.".format(torch.cuda.device_count()))
-        model = torch.nn.DataParallel(model)
-    args.model = model
-
-    if args.use_adv:
-        args.adv_method = str2adv[args.adv_type](model)
-
-    total_loss, result, best_result = 0.0, 0.0, 0.0
-
-    args.logger.info("Start training.")
-    for epoch in range(1, args.epochs_num + 1):
-        random.shuffle(trainset)
-        src = torch.LongTensor([example[0] for example in trainset])
-        tgt = torch.LongTensor([example[1] for example in trainset])
-        seg = torch.LongTensor([example[2] for example in trainset])
-        if args.soft_targets:
-            soft_tgt = torch.FloatTensor([example[3] for example in trainset])
-        else:
-            soft_tgt = None
-
-        model.train()
-        for i, (src_batch, tgt_batch, seg_batch, soft_tgt_batch) in enumerate(batch_loader(batch_size, src, tgt, seg, soft_tgt)):
-            loss = train_model(args, model, optimizer, scheduler, src_batch, tgt_batch, seg_batch, soft_tgt_batch)
-            total_loss += loss.item()
-            if (i + 1) % args.report_steps == 0:
-                args.logger.info("Epoch id: {}, Training steps: {}, Avg loss: {:.3f}".format(epoch, i + 1, total_loss / args.report_steps))
-                total_loss = 0.0
-
-        result = evaluate(args, read_dataset(args, args.dev_path))
-        if result[0] > best_result:
-            best_result = result[0]
-            save_model(model, args.output_model_path)
-
-    # Evaluation phase.
-    if args.test_path is not None:
-        args.logger.info("Test set evaluation.")
-        if torch.cuda.device_count() > 1:
-            args.model.module.load_state_dict(torch.load(args.output_model_path))
-        else:
-            args.model.load_state_dict(torch.load(args.output_model_path))
-        evaluate(args, read_dataset(args, args.test_path))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/spaces/szukevin/VISOR-GPT/train/scripts/preprocess_coord.py b/spaces/szukevin/VISOR-GPT/train/scripts/preprocess_coord.py
deleted file mode 100644
index afe8c239a7fc6015cd26860ab592e6c4d5a1dd55..0000000000000000000000000000000000000000
--- a/spaces/szukevin/VISOR-GPT/train/scripts/preprocess_coord.py
+++ /dev/null
@@ -1,249 +0,0 @@
-import argparse
-import collections
-import torch
-import json
-import numpy as np
-import random
-from interval import Interval
-from tqdm import tqdm
-
-# [Annotation type] [Object centric or Multiple instances] [Number of instances] [Number of keypoints] [Class A, Class B, ...] [Box A, Box B, ...]
-
-# ----- kinhane
-def get_size(coordinate, type, small=Interval(0, 32**2), medium=Interval(32**2, 96**2, lower_closed=False)):
-
-    if type == 'box':
-        coordinate = np.array(coordinate)
-        mean_area = np.mean((coordinate[:, 2] - coordinate[:, 0]) * (coordinate[:, 3] - coordinate[:, 1]))
-        # import ipdb
-        # ipdb.set_trace()
-    elif type == 'keypoint' or type == 'mask':
-        area_list = []
-        for coord in coordinate:
-            if type == 'mask':
-                coord = np.array(coord).squeeze(1)
-            else:
-                # delete unannotated key points
-                tmp = []
-                for kpt in coord:
-                    _, _, v = kpt
-                    if v != 0:
-                        tmp.append(kpt)
-                coord = np.array(tmp)
-            # import ipdb
-            # ipdb.set_trace()
-            area = (np.max(coord[:, 0]) - np.min(coord[:, 0])) * (np.max(coord[:, 1]) - np.min(coord[:, 1]))
-            area_list.append(area)
-        mean_area = np.mean(area_list)
-    else:
-        raise NotImplementedError
-
-    if mean_area in small:
-        return 'small'
-    elif mean_area in medium:
-        return 'medium'
-    else:
-        return 'large'
-
-# ----- kinhane
-
-def filter_keypoint(keypoints):
-    output = []
-    for kp_list in keypoints:
-        output_single = []
-        for kp in kp_list:
-            for name, point in kp.items():
-                if np.array(point).sum() > 0:
-                    output_single.append({name: point})
-        if len(output_single) > 0:
-            output.append(output_single)
-    return output
-
-
-def keypoint_to_formular_data(keypoints):
-    output = []
-    for kp_list in tqdm(keypoints):
-        random.shuffle(kp_list)
-        output_single = {"anno_type": "key point",
-                         "prefix": "Multiple instances",
-                         "flag": None,
-                         "instances_num": 0,
-                         "keypoints_num": None,
-                         "categories": [],
-                         "coordinate": []
-                         }
-        for kp in kp_list:
-            for name, point in kp.items():
-                # ----- kinhane omit instances with less 3 key points
-                if np.where(np.array(point)[:, -1] != 0)[0].shape[0] < 3:
-                    continue
-                # ----- kinhane
-                output_single["instances_num"] += 1
-                output_single["categories"].append(name)
-                output_single["coordinate"].append(point)
-                output_single["keypoints_num"] = len(point)
-
-                if output_single["instances_num"] > 7:
-                    break
-
-        # ----- kinhane omit idle list
-        if len(output_single["coordinate"]) == 0:
-            continue
-        # ----- kinhane
-
-        if random.random() < 0.5:
-            flag = get_size(output_single["coordinate"], type='keypoint')  # add by kinhane
-        else:
-            flag = "random"
-        output_single["flag"] = flag  # add by kinhane
-        output.append(output_single)
-
-    return output
-
-
-def mask_to_formular_data(keypoints):
-    output = []
-    for mask_list in tqdm(keypoints):
-        point_counter = 0
-
-        random.shuffle(keypoints)
-        output_single = {"anno_type": "mask",
-                         "prefix": "Multiple instances",
-                         "flag": None,
-                         "instances_num": 0,
-                         "keypoints_num": 0,
-                         "categories": [],
-                         "coordinate": []
-                         }
-        for mask in mask_list:
-            for name, point in mask.items():
-                # ----- kinhane omit very small masks
-                if len(point) < 5:
-                    continue
-                if point_counter + len(point) >= 150:
-                    break
-                else:
-                    point_counter += len(point)
-
-                # ----- kinhane
-                output_single["categories"].append(name)
-                output_single["coordinate"].append(point)
-                output_single["instances_num"] += 1
-
-
-
-        # ----- kinhane omit idle list
-        if len(output_single["coordinate"]) == 0:
-            continue
-        # ----- kinhane
-
-        flag = get_size(output_single["coordinate"], type='mask')  # add by kinhane
-        output_single["flag"] = flag  # add by kinhane
-        output.append(output_single)
-
-    return output
-
-
-def box_to_formular_data(keypoints, centric=0):
-    output = []
-    for mask_list in tqdm(keypoints):
-        random.shuffle(mask_list)
-        output_single = {"anno_type": "box",
-                         "prefix": "multiple instances",
-                         "flag": None,
-                         "instances_num": 0,
-                         "keypoints_num": 0,
-                         "categories": [],
-                         "coordinate": []
-                         }
-        if centric == 1:
-            output_single["prefix"] = "object centric"
-        for mask in mask_list[:20]:
-            for name, point in mask.items():
-                output_single["categories"].append(name)
-                output_single["coordinate"].append(point)
-                output_single["instances_num"] += 1
-
-        flag = get_size(output_single["coordinate"], type='box')  # add by kinhane
-        output_single["flag"] = flag  # add by kinhane
-        output.append(output_single)
-
-    return output
-
-num2char = {0: 'a', 1: 'b', 2: 'c', 3: 'd', 4: 'e', 5: 'f', 6: 'g', 7: 'h', 8: 'i', 9: 'j',
-            10: 'k', 11: 'l', 12: 'm', 13: 'n', 14: 'o', 15: 'p', 16: 'q', 17: 'r', 18: 's', 19: 't',
-            20: 'u', 21: 'v', 22: 'w'}
-
-def formular_data_to_str(data_list, type):
-
-    def keyporint_coord_to_str(keypoints):
-        output = ""
-        for points_list in keypoints:
-            output = output + '['
-            for i, point in enumerate(points_list):
-                output = output + ' ' + num2char[i] + ' $' + str(point[0]) + ' $'+ str(point[1])
-            output = output + '] '
-        return output
-
-    def mask_coord_to_str(keypoints):
-        output = ""
-        for points_list in keypoints:
-            output = output + '['
-            for i, point in enumerate(points_list):
-                output = output + ' ' + 'm'+str(i) + ' $' + str(point[0][0]) + ' $'+ str(point[0][1])
-            output = output + '] '
-        return output
-
-    def box_coord_to_str(boxes):
-        output = ""
-        for box in boxes:
-            output = output + '[ xmin $' + str(box[0]) + ' ymin $'+ str(box[1]) + \
-                     ' xmax $'+ str(box[2]) + ' ymax $'+ str(box[3]) +'] '
-        return output
-
-    output = []
-    for data in tqdm(data_list):
-        output_single = '; '.join([data["anno_type"], data["prefix"], str(data["instances_num"]), str(data["keypoints_num"]), data['flag']])
-        output_single = output_single + '; ' + ', '.join(data["categories"]) +'; '
-        if type == "keypoint":
-            output_single = output_single + keyporint_coord_to_str(data["coordinate"])
-        elif type == "box":
-            output_single = output_single + box_coord_to_str(data["coordinate"])
-        else:
-            output_single = output_single + mask_coord_to_str(data["coordinate"])
-        output.append(output_single)
-
-    return output
-
-def main():
-    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-    parser.add_argument("--input_path", type=str, default='data/xjh_coco_val.json', help="data/xjh_coco_val.json")
-    parser.add_argument("--output_path", type=str, default='test.txt', help="test.txt")
-    parser.add_argument("--data_type", type=str, default='mask', help="box")
-    parser.add_argument("--centric", type=int, default=0, help="box")
-
-    args = parser.parse_args()
-
-    with open(args.input_path) as f:
-        data = json.load(f)
-
-    print("data_type ", args.data_type)
-
-
-    if args.data_type == "keypoint":
-        keypoints = filter_keypoint(data['keypoints'] )
-        data_json = keypoint_to_formular_data(keypoints)
-    elif args.data_type == "box":
-        data_json = box_to_formular_data(data['bboxes'], args.centric)
-    else:
-        data_json = mask_to_formular_data(data['masks'])
-
-    data_str = formular_data_to_str(data_json, args.data_type)
-
-    with open(args.output_path, 'w') as f:
-        for l in data_str:
-            f.write(l + '\n')
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/spaces/taesiri/DeticChatGPT/detic/modeling/meta_arch/custom_rcnn.py b/spaces/taesiri/DeticChatGPT/detic/modeling/meta_arch/custom_rcnn.py
deleted file mode 100644
index 9a5ac721d42e40a8b4f28508b10a932cef827fcf..0000000000000000000000000000000000000000
--- a/spaces/taesiri/DeticChatGPT/detic/modeling/meta_arch/custom_rcnn.py
+++ /dev/null
@@ -1,232 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import copy
-import logging
-import numpy as np
-from typing import Dict, List, Optional, Tuple
-import torch
-from torch import nn
-import json
-from detectron2.utils.events import get_event_storage
-from detectron2.config import configurable
-from detectron2.structures import ImageList, Instances, Boxes
-import detectron2.utils.comm as comm
-
-from detectron2.modeling.meta_arch.build import META_ARCH_REGISTRY
-from detectron2.modeling.meta_arch.rcnn import GeneralizedRCNN
-from detectron2.modeling.postprocessing import detector_postprocess
-from detectron2.utils.visualizer import Visualizer, _create_text_labels
-from detectron2.data.detection_utils import convert_image_to_rgb
-
-from torch.cuda.amp import autocast
-from ..text.text_encoder import build_text_encoder
-from ..utils import load_class_freq, get_fed_loss_inds
-
-@META_ARCH_REGISTRY.register()
-class CustomRCNN(GeneralizedRCNN):
-    '''
-    Add image labels
-    '''
-    @configurable
-    def __init__(
-        self, 
-        with_image_labels = False,
-        dataset_loss_weight = [],
-        fp16 = False,
-        sync_caption_batch = False,
-        roi_head_name = '',
-        cap_batch_ratio = 4,
-        with_caption = False,
-        dynamic_classifier = False,
-        **kwargs):
-        """
-        """
-        self.with_image_labels = with_image_labels
-        self.dataset_loss_weight = dataset_loss_weight
-        self.fp16 = fp16
-        self.with_caption = with_caption
-        self.sync_caption_batch = sync_caption_batch
-        self.roi_head_name = roi_head_name
-        self.cap_batch_ratio = cap_batch_ratio
-        self.dynamic_classifier = dynamic_classifier
-        self.return_proposal = False
-        if self.dynamic_classifier:
-            self.freq_weight = kwargs.pop('freq_weight')
-            self.num_classes = kwargs.pop('num_classes')
-            self.num_sample_cats = kwargs.pop('num_sample_cats')
-        super().__init__(**kwargs)
-        assert self.proposal_generator is not None
-        if self.with_caption:
-            assert not self.dynamic_classifier
-            self.text_encoder = build_text_encoder(pretrain=True)
-            for v in self.text_encoder.parameters():
-                v.requires_grad = False
-
-
-    @classmethod
-    def from_config(cls, cfg):
-        ret = super().from_config(cfg)
-        ret.update({
-            'with_image_labels': cfg.WITH_IMAGE_LABELS,
-            'dataset_loss_weight': cfg.MODEL.DATASET_LOSS_WEIGHT,
-            'fp16': cfg.FP16,
-            'with_caption': cfg.MODEL.WITH_CAPTION,
-            'sync_caption_batch': cfg.MODEL.SYNC_CAPTION_BATCH,
-            'dynamic_classifier': cfg.MODEL.DYNAMIC_CLASSIFIER,
-            'roi_head_name': cfg.MODEL.ROI_HEADS.NAME,
-            'cap_batch_ratio': cfg.MODEL.CAP_BATCH_RATIO,
-        })
-        if ret['dynamic_classifier']:
-            ret['freq_weight'] = load_class_freq(
-                cfg.MODEL.ROI_BOX_HEAD.CAT_FREQ_PATH,
-                cfg.MODEL.ROI_BOX_HEAD.FED_LOSS_FREQ_WEIGHT)
-            ret['num_classes'] = cfg.MODEL.ROI_HEADS.NUM_CLASSES
-            ret['num_sample_cats'] = cfg.MODEL.NUM_SAMPLE_CATS
-        return ret
-
-
-    def inference(
-        self,
-        batched_inputs: Tuple[Dict[str, torch.Tensor]],
-        detected_instances: Optional[List[Instances]] = None,
-        do_postprocess: bool = True,
-    ):
-        assert not self.training
-        assert detected_instances is None
-
-        images = self.preprocess_image(batched_inputs)
-        features = self.backbone(images.tensor)
-        proposals, _ = self.proposal_generator(images, features, None)
-        results, _ = self.roi_heads(images, features, proposals)
-        if do_postprocess:
-            assert not torch.jit.is_scripting(), \
-                "Scripting is not supported for postprocess."
-            return CustomRCNN._postprocess(
-                results, batched_inputs, images.image_sizes)
-        else:
-            return results
-
-
-    def forward(self, batched_inputs: List[Dict[str, torch.Tensor]]):
-        """
-        Add ann_type
-        Ignore proposal loss when training with image labels
-        """
-        if not self.training:
-            return self.inference(batched_inputs)
-
-        images = self.preprocess_image(batched_inputs)
-
-        ann_type = 'box'
-        gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
-        if self.with_image_labels:
-            for inst, x in zip(gt_instances, batched_inputs):
-                inst._ann_type = x['ann_type']
-                inst._pos_category_ids = x['pos_category_ids']
-            ann_types = [x['ann_type'] for x in batched_inputs]
-            assert len(set(ann_types)) == 1
-            ann_type = ann_types[0]
-            if ann_type in ['prop', 'proptag']:
-                for t in gt_instances:
-                    t.gt_classes *= 0
-        
-        if self.fp16: # TODO (zhouxy): improve
-            with autocast():
-                features = self.backbone(images.tensor.half())
-            features = {k: v.float() for k, v in features.items()}
-        else:
-            features = self.backbone(images.tensor)
-
-        cls_features, cls_inds, caption_features = None, None, None
-
-        if self.with_caption and 'caption' in ann_type:
-            inds = [torch.randint(len(x['captions']), (1,))[0].item() \
-                for x in batched_inputs]
-            caps = [x['captions'][ind] for ind, x in zip(inds, batched_inputs)]
-            caption_features = self.text_encoder(caps).float()
-        if self.sync_caption_batch:
-            caption_features = self._sync_caption_features(
-                caption_features, ann_type, len(batched_inputs))
-        
-        if self.dynamic_classifier and ann_type != 'caption':
-            cls_inds = self._sample_cls_inds(gt_instances, ann_type) # inds, inv_inds
-            ind_with_bg = cls_inds[0].tolist() + [-1]
-            cls_features = self.roi_heads.box_predictor[
-                0].cls_score.zs_weight[:, ind_with_bg].permute(1, 0).contiguous()
-
-        classifier_info = cls_features, cls_inds, caption_features
-        proposals, proposal_losses = self.proposal_generator(
-            images, features, gt_instances)
-
-        if self.roi_head_name in ['StandardROIHeads', 'CascadeROIHeads']:
-            proposals, detector_losses = self.roi_heads(
-                images, features, proposals, gt_instances)
-        else:
-            proposals, detector_losses = self.roi_heads(
-                images, features, proposals, gt_instances,
-                ann_type=ann_type, classifier_info=classifier_info)
-        
-        if self.vis_period > 0:
-            storage = get_event_storage()
-            if storage.iter % self.vis_period == 0:
-                self.visualize_training(batched_inputs, proposals)
-
-        losses = {}
-        losses.update(detector_losses)
-        if self.with_image_labels:
-            if ann_type in ['box', 'prop', 'proptag']:
-                losses.update(proposal_losses)
-            else: # ignore proposal loss for non-bbox data
-                losses.update({k: v * 0 for k, v in proposal_losses.items()})
-        else:
-            losses.update(proposal_losses)
-        if len(self.dataset_loss_weight) > 0:
-            dataset_sources = [x['dataset_source'] for x in batched_inputs]
-            assert len(set(dataset_sources)) == 1
-            dataset_source = dataset_sources[0]
-            for k in losses:
-                losses[k] *= self.dataset_loss_weight[dataset_source]
-        
-        if self.return_proposal:
-            return proposals, losses
-        else:
-            return losses
-
-
-    def _sync_caption_features(self, caption_features, ann_type, BS):
-        has_caption_feature = (caption_features is not None)
-        BS = (BS * self.cap_batch_ratio) if (ann_type == 'box') else BS
-        rank = torch.full(
-            (BS, 1), comm.get_rank(), dtype=torch.float32, 
-            device=self.device)
-        if not has_caption_feature:
-            caption_features = rank.new_zeros((BS, 512))
-        caption_features = torch.cat([caption_features, rank], dim=1)
-        global_caption_features = comm.all_gather(caption_features)
-        caption_features = torch.cat(
-            [x.to(self.device) for x in global_caption_features], dim=0) \
-                if has_caption_feature else None # (NB) x (D + 1)
-        return caption_features
-
-
-    def _sample_cls_inds(self, gt_instances, ann_type='box'):
-        if ann_type == 'box':
-            gt_classes = torch.cat(
-                [x.gt_classes for x in gt_instances])
-            C = len(self.freq_weight)
-            freq_weight = self.freq_weight
-        else:
-            gt_classes = torch.cat(
-                [torch.tensor(
-                    x._pos_category_ids, 
-                    dtype=torch.long, device=x.gt_classes.device) \
-                    for x in gt_instances])
-            C = self.num_classes
-            freq_weight = None
-        assert gt_classes.max() < C, '{} {}'.format(gt_classes.max(), C)
-        inds = get_fed_loss_inds(
-            gt_classes, self.num_sample_cats, C, 
-            weight=freq_weight)
-        cls_id_map = gt_classes.new_full(
-            (self.num_classes + 1,), len(inds))
-        cls_id_map[inds] = torch.arange(len(inds), device=cls_id_map.device)
-        return inds, cls_id_map
\ No newline at end of file
diff --git a/spaces/tang155/bingo/src/components/tailwind-indicator.tsx b/spaces/tang155/bingo/src/components/tailwind-indicator.tsx
deleted file mode 100644
index f2a1291213dd67055fcebe67fab574c8441338df..0000000000000000000000000000000000000000
--- a/spaces/tang155/bingo/src/components/tailwind-indicator.tsx
+++ /dev/null
@@ -1,14 +0,0 @@
-export function TailwindIndicator() {
-  if (process.env.NODE_ENV === 'production') return null
-
-  return (
-    <div className="fixed bottom-1 left-1 z-50 flex h-6 w-6 items-center justify-center rounded-full bg-gray-800 p-3 font-mono text-xs text-white">
-      <div className="block sm:hidden">xs</div>
-      <div className="hidden sm:block md:hidden">sm</div>
-      <div className="hidden md:block lg:hidden">md</div>
-      <div className="hidden lg:block xl:hidden">lg</div>
-      <div className="hidden xl:block 2xl:hidden">xl</div>
-      <div className="hidden 2xl:block">2xl</div>
-    </div>
-  )
-}
diff --git a/spaces/tbvl/Fake_Face_Detection/face_cropper.py b/spaces/tbvl/Fake_Face_Detection/face_cropper.py
deleted file mode 100644
index 7538c0ede73eda6218e629f6930d52494b243517..0000000000000000000000000000000000000000
--- a/spaces/tbvl/Fake_Face_Detection/face_cropper.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import cv2
-import mediapipe as mp
-import os
-from gradio_client import Client
-# from test_image_fusion import Test
-# from test_image_fusion import Test
-from test_image import Test
-import numpy as np
-
-
-
-from PIL import Image
-import numpy as np
-import cv2
-
-# client = Client("https://tbvl-real-and-fake-face-detection.hf.space/--replicas/40d41jxhhx/")
-
-data = 'faceswap'
-dct = 'fft'
-
-
-# testet = Test(model_paths = [f"weights/{data}-hh-best_model.pth",
-#                             f"weights/{data}-fft-best_model.pth"],
-#                             multi_modal = ['hh', 'fft'])
-
-testet = Test(model_path =f"weights/{data}-hh-best_model.pth",
-                            multi_modal ='hh')
-
-# Initialize MediaPipe Face Detection
-mp_face_detection = mp.solutions.face_detection
-mp_drawing = mp.solutions.drawing_utils
-face_detection = mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.35)
-
-# Create a directory to save the cropped face images if it does not exist
-save_dir = "cropped_faces"
-os.makedirs(save_dir, exist_ok=True)
-
-# def detect_and_label_faces(image_path):
-
-
-# Function to crop faces from a video and save them as images
-# def crop_faces_from_video(video_path):
-#     # Read the video
-#     cap = cv2.VideoCapture(video_path)
-#     frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-#     frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-#     fps = int(cap.get(cv2.CAP_PROP_FPS))
-#     total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
-    
-#     # Define the codec and create VideoWriter object
-#     out = cv2.VideoWriter(f'output_{real}_{data}_fusion.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, (frame_width, frame_height))
-
-#     if not cap.isOpened():
-#         print("Error: Could not open video.")
-#         return
-# Convert PIL Image to NumPy array for OpenCV
-def pil_to_opencv(pil_image):
-    open_cv_image = np.array(pil_image)
-    # Convert RGB to BGR for OpenCV
-    open_cv_image = open_cv_image[:, :, ::-1].copy()
-    return open_cv_image
-
-# Convert OpenCV NumPy array to PIL Image
-def opencv_to_pil(opencv_image):
-    # Convert BGR to RGB
-    pil_image = Image.fromarray(opencv_image[:, :, ::-1])
-    return pil_image
-
-
-
-
-def detect_and_label_faces(frame):
-    frame = pil_to_opencv(frame)
-
-
-    print(type(frame))
-    # Convert the frame to RGB
-    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    # Perform face detection
-    results = face_detection.process(frame_rgb)
-
-    # If faces are detected, crop and save each face as an image
-    if results.detections:
-        for face_count,detection in enumerate(results.detections):
-            bboxC = detection.location_data.relative_bounding_box
-            ih, iw, _ = frame.shape
-            x, y, w, h = int(bboxC.xmin * iw), int(bboxC.ymin * ih), int(bboxC.width * iw), int(bboxC.height * ih)
-            # Crop the face region and make sure the bounding box is within the frame dimensions
-            crop_img = frame[max(0, y):min(ih, y+h), max(0, x):min(iw, x+w)]
-            if crop_img.size > 0:
-                face_filename = os.path.join(save_dir, f'face_{face_count}.jpg')
-                cv2.imwrite(face_filename, crop_img)
-
-                label = testet.testimage(face_filename)
-
-                if os.path.exists(face_filename):
-                    os.remove(face_filename)
-
-                color = (0, 0, 255) if label == 'fake' else (0, 255, 0)
-                cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
-                cv2.putText(frame, label, (x, y + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2)
-    return opencv_to_pil(frame)
-
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deleted file mode 100644
index 923199327a7acc3ec316aacf09f958f40a337246..0000000000000000000000000000000000000000
--- a/spaces/terfces0erbo/CollegeProjectV2/Connectify Hotspot Max Lifetime Crack.md	
+++ /dev/null
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deleted file mode 100644
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--- a/spaces/terfces0erbo/CollegeProjectV2/FSXPMDG747QOTSIICrackCrack.md
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deleted file mode 100644
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/Forex Gump EA Cracked How to Trade Like a Pro with the Most Powerful Forex Robot.md b/spaces/tialenAdioni/chat-gpt-api/logs/Forex Gump EA Cracked How to Trade Like a Pro with the Most Powerful Forex Robot.md
deleted file mode 100644
index 43872b765e0260eb5c0f046defa1014a0eeb74a2..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/Forex Gump EA Cracked How to Trade Like a Pro with the Most Powerful Forex Robot.md	
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/GC Agarwal Physics Book Free Download A Comprehensive Guide for JEE Aspirants.md b/spaces/tialenAdioni/chat-gpt-api/logs/GC Agarwal Physics Book Free Download A Comprehensive Guide for JEE Aspirants.md
deleted file mode 100644
index 44b68426ad4ef810180c568bc377a6ee7e8a4484..0000000000000000000000000000000000000000
--- a/spaces/tialenAdioni/chat-gpt-api/logs/GC Agarwal Physics Book Free Download A Comprehensive Guide for JEE Aspirants.md	
+++ /dev/null
@@ -1,156 +0,0 @@
-
-<h1>GC Agarwal Physics Book Free Download: A Complete Guide</h1>
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- <p>The other option is to use online platforms that provide access to GC Agarwal Physics Book eBooks. These platforms are more reliable and secure than the websites that offer free downloads. They also have features like bookmarking, highlighting, annotating, etc. that can enhance your reading experience. Some of the online platforms that provide access to GC Agarwal Physics Book eBooks are:</p>
- <table>
-<tr>
-<th>Name</th>
-<th>Link</th>
-<th>Price</th>
-</tr>
-<tr>
-<td>Vedantu</td>
-<td><a href="https://www.vedantu.com/books/gc-agarwal-physics-book">https://www.vedantu.com/books/gc-agarwal-physics-book</a></td>
-<td>Free</td>
-</tr>
-<tr>
-<td>EduRev</td>
-<td><a href="https://edurev.in/studytube/GC-Agarwal-Physics-Book--Class-11--Class-12--IIT-JE/4f6c0f7b-4d9d-4b0e-bf8e-5b0f7a8f9c5a_t">https://edurev.in/studytube/GC-Agarwal-Physics-Book--Class-11--Class-12--IIT-JE/4f6c0f7b-4d9d-4b0e-bf8e-5b0f7a8f9c5a_t</a></td>
-<td>Free</td>
-</tr>
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-<td>Scribd</td>
-<td><a href="https://www.scribd.com/search?query=gc+agarwal+physics+book">https://www.scribd.com/search?query=gc+agarwal+physics+book</a></td>
-<td>$9.99 per month (after 30-day free trial)</td>
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-<td>Amazon Kindle</td>
-<td><a href="https://www.amazon.in/s?k=gc+agarwal+physics+book&rh=n%3A1634753031&ref=nb_sb_noss">https://www.amazon.in/s?k=gc+agarwal+physics+book&rh=n%3A1634753031&ref=nb_sb_noss</a></td>
-<td>$2.99-$9.99 per book (depending on volume)</td>
-</tr>
-</table>
- <h2>Benefits of GC Agarwal Physics Book</h2>
- <h3>Comprehensive coverage of topics</h3>
- <p>One of the main benefits of GC Agarwal Physics Book is that it covers all the topics that are relevant for competitive exams like JEE Main, JEE Advanced, NEET, etc. The books follow the latest syllabus and exam pattern prescribed by the National Testing Agency (NTA) and other exam conducting bodies. The books also cover some additional topics that are not included in the syllabus but are important for enhancing your knowledge and skills.</p>
- <h3>Clear and concise explanations</h3>
- <p>Another benefit of GC Agarwal Physics Book is that it provides clear and concise explanations of the concepts and theories of physics. The books use simple and lucid language that can be easily understood by students of any level. The books also use diagrams, graphs, tables, etc. to illustrate the concepts and make them more visual and appealing. The books avoid unnecessary details and jargon that can confuse or bore the readers.</p>
- <h3>Solved examples and exercises</h3>
- <p>A third benefit of GC Agarwal Physics Book is that it includes a large number of solved examples and exercises that can help you practice and master the topics. The books provide step-by-step solutions to the examples that can help you learn the methods and techniques of solving problems. The books also provide exercises at the end of each chapter that contain various types of questions such as objective type, subjective type, multiple choice type, assertion-reason type, etc. The exercises are graded according to their level of difficulty and complexity.</p>
- <h3>Tips and tricks <h3>Tips and tricks for solving problems</h3>
- <p>A fourth benefit of GC Agarwal Physics Book is that it gives tips and tricks for solving problems quickly and accurately. The books provide shortcuts, formulas, rules, mnemonics, etc. that can help you save time and effort while solving problems. The books also provide hints and clues to some difficult or tricky questions that can help you overcome your doubts or difficulties.</p>
- <h2>Drawbacks of GC Agarwal Physics Book</h2>
- <h3>Outdated syllabus and content</h3>
- <p>One of the main drawbacks of GC Agarwal Physics Book is that it has an outdated syllabus and content that does not match with the current trends and requirements of competitive exams like JEE Main, JEE Advanced, NEET, etc. The books were written several years ago when the syllabus and exam pattern were different from what they are now. The books do not include some new topics or concepts that have been introduced or modified in recent years.</p>
- <h3>Lack of online support and resources</h3>
- <p>Another drawback of GC Agarwal Physics Book is that it lacks online support and resources that can enhance your learning experience. The books do not have any official website or app where you can access additional features or services such as video lectures, interactive quizzes, doubt clearing sessions, mock tests, etc. The books also do not provide any online solutions or answer keys to the exercises or questions. You have to rely on other sources or platforms to get these online support and resources.</p>
- <h3>Errors and mistakes in some solutions</h3>
- <p>A third drawback of GC Agarwal Physics Book is that it contains some errors and mistakes in some solutions or answers. The books have not been revised or updated for a long time, so some of the solutions or answers may be incorrect or incomplete. This can create confusion or frustration among the students who use these books. You have to be careful while using these books and cross-check the solutions or answers with other sources or platforms.</p>
- <h2>Alternatives to GC Agarwal Physics Book</h2>
- <h3>NCERT Physics Books</h3>
- <p>One of the best alternatives to GC Agarwal Physics Book is NCERT Physics Books. These are the official textbooks prescribed by the CBSE board for class 11 and 12 physics. These books are also recommended by the NTA for competitive exams like JEE Main, JEE Advanced, NEET, etc. These books have several advantages over GC Agarwal Physics Book such as:</p>
- <ul>
-<li>They are updated and revised regularly according to the latest syllabus and exam pattern.</li>
-<li>They are available online for free on the official website of NCERT.</li>
-<li>They provide online solutions and answer keys to the exercises and questions on various platforms such as Vedantu, EduRev, etc.</li>
-<li>They are simple and easy to understand, with minimal errors and mistakes.</li>
-</ul>
- <h3>HC Verma Concepts of Physics</h3>
- <p>Another alternative to GC Agarwal Physics Book is HC Verma Concepts of Physics. This is a series of two physics books written by Dr. H.C. Verma, a famous physicist and author. These books cover the topics of mechanics, waves, thermodynamics, electricity, magnetism, optics, modern physics, etc. These books are also widely used by students for competitive exams like JEE Main, JEE Advanced, NEET, etc. These books have several advantages over GC Agarwal Physics Book such as:</p>
- <ul>
-<li>They are based on the concept-building approach rather than the rote-learning approach.</li>
-<li>They provide a variety of questions such as objective type, subjective type, multiple choice type, fill in the blanks type, true-false type, matching type, etc. that test your understanding and application skills.</li>
-<li>They provide detailed solutions and explanations to all the questions in a separate book called Solutions to Concepts of Physics by HC Verma.</li>
-<li>They are engaging and interesting, with real-life examples and applications.</li>
-</ul>
- <h3>DC Pandey Understanding Physics</h3>
- <p>A third alternative to GC Agarwal Physics Book is DC Pandey Understanding Physics. This is a series of five physics books written by D.C. Pandey, a renowned physics teacher and author. These books cover the topics of mechanics, electricity and magnetism, waves and thermodynamics, optics and modern physics, etc. These books are also popular among students for competitive exams like JEE Main, JEE Advanced, NEET, etc. These books have several advantages over GC Agarwal Physics Book such as:</p>
- <ul>
-<li>They are aligned with the latest syllabus and exam pattern of competitive exams.</li>
-<li>They provide online access to video lectures, interactive quizzes, doubt clearing sessions, mock tests, etc. on various platforms such as Arihant Online Test Series (AOTS), Arihant Live (ALIVE), etc.</li>
-<li>They provide a large number of questions such as objective type, subjective type, multiple choice type, assertion-reason type, comprehension type, match the column type, integer answer type, etc. that cover all the aspects and dimensions of physics.</li>
-<li>They provide tips and tricks <li>They provide tips and tricks for solving problems efficiently and effectively.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In conclusion, GC Agarwal Physics Book is a series of physics books that can help you prepare for competitive exams like JEE Main, JEE Advanced, NEET, etc. The books have some benefits such as comprehensive coverage of topics, clear and concise explanations, solved examples and exercises, tips and tricks for solving problems, etc. However, the books also have some drawbacks such as outdated syllabus and content, lack of online support and resources, errors and mistakes in some solutions, etc. Therefore, you should consider some alternatives to GC Agarwal Physics Book such as NCERT Physics Books, HC Verma Concepts of Physics, DC Pandey Understanding Physics, etc. These alternatives can provide you with more updated and relevant content, online support and resources, variety and quality of questions, etc. You should choose the best physics book for yourself according to your needs and preferences.</p>
- <h2>FAQs</h2>
- <p>Here are some frequently asked questions about GC Agarwal Physics Book and its alternatives.</p>
- <ol>
-<li>Is GC Agarwal Physics Book enough for JEE Main and JEE Advanced?</li>
-<p>No, GC Agarwal Physics Book is not enough for JEE Main and JEE Advanced. The book has an outdated syllabus and content that does not match with the current trends and requirements of these exams. You need to supplement GC Agarwal Physics Book with other books or sources that cover the new topics or concepts that are asked in these exams.</p>
-<li>Which is better: HC Verma Concepts of Physics or DC Pandey Understanding Physics?</li>
-<p>Both HC Verma Concepts of Physics and DC Pandey Understanding Physics are good physics books for competitive exams like JEE Main and JEE Advanced. However, they have different strengths and weaknesses. HC Verma Concepts of Physics is better for building your concepts and understanding of physics. DC Pandey Understanding Physics is better for practicing your problem-solving skills and applying your concepts to different types of questions. You should use both books to get the best results.</p>
-<li>How can I get the solutions to GC Agarwal Physics Book?</li>
-<p>You can get the solutions to GC Agarwal Physics Book from various sources or platforms such as Vedantu, EduRev, etc. These sources or platforms provide online solutions or answer keys to the exercises or questions in GC Agarwal Physics Book. However, you should be careful while using these sources or platforms as they may contain some errors or mistakes in some solutions or answers. You should cross-check the solutions or answers with other sources or platforms.</p>
-<li>Which is the best online platform to access GC Agarwal Physics Book eBooks?</li>
-<p>The best online platform to access GC Agarwal Physics Book eBooks depends on your preferences and budget. Some of the online platforms that provide access to GC Agarwal Physics Book eBooks are Vedantu, EduRev, Scribd, Amazon Kindle, etc. These platforms have different features and prices that can suit your needs. You should compare these platforms and choose the one that offers you the best value for money.</p>
-<li>Which is the best alternative to GC Agarwal Physics Book?</li>
-<p>The best alternative to GC Agarwal Physics Book depends on your level and goal. Some of the alternatives to GC Agarwal Physics Book are NCERT Physics Books, HC Verma Concepts of Physics, DC Pandey Understanding Physics, etc. These alternatives have different advantages and disadvantages that can suit your needs. You should evaluate these alternatives and choose the one that matches your level and goal.</p>
-</ol>
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diff --git a/spaces/tialenAdioni/chat-gpt-api/logs/How to Get 3ds Max 2022 xforce Free A Step-by-Step Guide.md b/spaces/tialenAdioni/chat-gpt-api/logs/How to Get 3ds Max 2022 xforce Free A Step-by-Step Guide.md
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diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Angry Birds Classic Hack Download the Best Mod for Your Android Device.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Angry Birds Classic Hack Download the Best Mod for Your Android Device.md
deleted file mode 100644
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--- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Angry Birds Classic Hack Download the Best Mod for Your Android Device.md	
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-<h1>Angry Birds Classic Hack Download: A Guide for Gamers</h1>
- <p>Angry Birds Classic is one of the most popular and addictive mobile games ever created. It was released in 2009 by Rovio Entertainment and has since spawned several sequels, spin-offs, movies, and merchandise. The game involves using a slingshot to launch different types of birds at structures made of various materials, such as wood, glass, stone, and metal, where green pigs are hiding. The goal is to destroy all the pigs and collect stars, eggs, and other items along the way.</p>
- <p>The game features challenging physics-based gameplay and hundreds of levels across different episodes and themes. Each bird has its own unique ability that can be activated by tapping the screen while it is in flight. For example, the yellow bird can speed up, the black bird can explode, and the white bird can drop egg bombs. The game also offers power-ups, such as extra birds, mighty eagle, sling scope, king sling, and super seeds, that can enhance the birds' destructive power.</p>
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- <p>However, not everyone can master the game easily. Some levels are very difficult to complete with three stars, some pigs are very hard to reach or kill, and some power-ups are very expensive to buy or use. That's why some players resort to using hacks for Angry Birds Classic. A hack is a modification or alteration of the game's code or data that gives the player an unfair advantage or changes the game's behavior in some way. For example, a hack can give unlimited birds, coins, power-ups, or stars; unlock all levels and episodes; remove ads; or disable obstacles.</p>
- <p>Using hacks can make the game more fun and easy for some players who want to enjoy the game without spending too much time or money on it. However, using hacks can also have some drawbacks and risks, such as ruining the game's balance and challenge; violating the game's terms of service and privacy policy; exposing the device to malware or viruses; or getting banned or detected by Rovio's anti-cheat system.</p>
- <p>If you still want to try using hacks for Angry Birds Classic, this article will guide you on how to download them for different devices: Android, iOS, and PC. Please note that this article is for educational purposes only and does not endorse or encourage hacking in any way. Use hacks at your own risk and discretion.</p>
- <h2>How to Download Angry Birds Classic Hack for Android Devices</h2>
- <p>If you have an Android device, such as a smartphone or tablet, you can follow these steps to download a hack for Angry Birds Classic:</p>
- <ol>
-<li>Go to  on your device's browser. This is a website that offers various mods for Android games, including Angry Birds Classic.</li>
-<li>Scroll down until you see Angry Birds Classic (MOD, Unlimited Money) 8.0.3. This is a modded version of the game that gives you unlimited coins and power-ups.</li>
-<li>Tap on Download (99.1Mb) to start downloading the modded APK file.</li>
-<li>Once the download is complete, go to your device's settings and enable installation from unknown sources. This will allow you to install apps that are not from the Google Play Store.</li>
-<li>Go to your device's file Birds Classic.</li>
-<li>Scroll down until you see Angry Birds Classic v8.0.3 +4 Trainer. This is a trainer that gives you four options to modify the game, such as unlimited birds, coins, power-ups, and stars.</li>
-<li>Click on Download to start downloading the trainer file.</li>
-<li>Once the download is complete, extract the trainer file to a folder of your choice.</li>
-<li>Launch the trainer file and click on Run as administrator. You may need to disable your antivirus or firewall to run the trainer.</li>
-<li>Launch the game from your PC or from the trainer. You will see a small window with the trainer options and hotkeys.</li>
-<li>Press the hotkeys to activate or deactivate the trainer options as you play the game.</li>
-<li>Enjoy playing Angry Birds Classic with the trainer!</li>
-</ol>
- <p>Some of the pros of using this hack are:</p>
- <ul>
-<li>You can use any bird, coin, power-up, or star you want without worrying about running out of them or spending real money.</li>
-<li>You can easily complete any level with three stars and unlock all episodes and themes.</li>
-<li>You can have more fun and variety in the game with different trainer options and hotkeys.</li>
-</ul>
- <p>Some of the cons of using this hack are:</p>
- <ul>
-<li>You may lose the original challenge and satisfaction of playing the game as intended.</li>
-<li>You may encounter some bugs or glitches in the game due to the trainer code or data.</li>
-<li>You may risk getting banned or detected by Rovio if they find out that you are using a hack.</li>
-</ul>
- <h2>Conclusion</h2>
- <p>In this article, we have shown you how to download Angry Birds Classic hack for different devices: Android, iOS, and PC. We have also discussed some of the pros and cons of using hacks for this game. Using hacks can make the game more fun and easy for some players who want to enjoy the game without spending too much time or money on it. However, using hacks can also have some drawbacks and risks, such as ruining the game's balance and challenge; violating the game's terms of service and privacy policy; exposing the device to malware or viruses; or getting banned or detected by Rovio's anti-cheat system.</p>
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- <ul>
-<li>Use hacks sparingly and moderately. Don't use them all the time or on every level. Try to play some levels without hacks to test your skills and enjoy the game's original design.</li>
-<li>Use hacks wisely and strategically. Don't use them randomly or recklessly. Try to use them only when you need them or when they can help you achieve your goals.</li>
-<li>Use hacks responsibly and ethically. Don't use them to cheat or harm other players or Rovio. Try to respect the game's rules and community.</li>
-</ul>
- <p>We hope that this article has been helpful and informative for you. If you have any questions or comments about Angry Birds Classic hack download, feel free to leave them below. Happy gaming!</p>
- <h2>FAQs</h2>
- <h3>What are some of the best hacks for Angry Birds Classic?</h3>
- <p>Some of the best hacks for Angry Birds Classic are:</p>
- <ul>
-<li>Unlimited birds, coins, power-ups, and stars. These hacks give you unlimited resources to use in the game, making it easier and more fun to play.</li>
-<li>Unlock all levels and episodes. These hacks let you access all the content in the game, giving you more variety and options to play.</li>
-<li>Mod menu. This hack gives you a menu with various options to customize the game, such as changing the bird's size, speed, gravity, angle, etc., giving you more control and creativity in the game.</li>
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- <h3>Is it legal to use hacks for Angry Birds Classic?</h3>
- <p>No, it is not legal to use hacks for Angry Birds Classic. Using hacks violates the game's terms of service and privacy policy, which prohibit modifying, altering, or interfering with the game's code or data. Using hacks can also be considered as cheating, which is illegal and unethical in most games and competitions. Using hacks can result in legal actions or penalties from Rovio, such as banning, suspending, or deleting your account or progress.</p>
- <h3>Will using hacks affect my game progress or achievements?</h3>
- <p>Yes, using hacks will affect your game progress or achievements. Using hacks can make your game progress faster or easier, but it can also make it less meaningful or rewarding. Using hacks can also affect your game achievements, such as stars, eggs, trophies, or leaderboards. Using hacks can make your achievements easier to obtain, but it can also make them less valuable or credible. Using hacks can also affect your game sync, backup, or restore, as the game may not recognize or accept your hacked data.</p>
- <h3>How can I avoid getting banned or detected by Rovio for using hacks?</h3>
- <p>There is no sure way to avoid getting banned or detected by Rovio for using hacks. Rovio has a sophisticated anti-cheat system that can monitor and analyze your game activity and data. If Rovio finds any evidence or suspicion of hacking, they can ban or detect your account or device at any time. However, some possible ways to reduce the risk of getting banned or detected are:</p>
- <ul>
-<li>Use hacks only from trusted and reputable sources. Avoid downloading hacks from unknown or shady websites that may contain malware or viruses.</li>
-<li>Use hacks only on offline mode. Avoid using hacks on online mode or connecting to the internet while playing the game.</li>
-<li>Use hacks only occasionally and moderately. Avoid using hacks all the time or on every level.</li>
-<li>Use hacks only for personal use. Avoid sharing or bragging about your hacks with other players or on social media.</li>
-</ul>
- <h3>Where can I find more information and resources about Angry Birds Classic hacks?</h3>
- <p>If you want to find more information and resources about Angry Birds Classic hacks, you can visit some of these websites:</p>
- <ul>
-<li>. This is a website that offers various mods for Android games, including Angry Birds Classic.</li>
-<li>. This is a website that offers various tweaks and hacks for iOS games and apps, including Angry Birds Classic.</li>
-<li>. This is a website that offers various cheats and trainers for PC games, including Angry Birds Classic.</li>
-<li>. This is a website that offers various tips and guides for Angry Birds Classic, including how to use power-ups, how to get three stars, how to unlock episodes, etc.</li>
-</ul></p> 401be4b1e0<br />
-<br />
-<br />
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diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Driver San Francisco Ubisoft Game Launcher TOP Download.md b/spaces/tioseFevbu/cartoon-converter/scripts/Driver San Francisco Ubisoft Game Launcher TOP Download.md
deleted file mode 100644
index 4bc925343a192c95283458b2b1b374e2d2651dea..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Driver San Francisco Ubisoft Game Launcher TOP Download.md	
+++ /dev/null
@@ -1,20 +0,0 @@
-<br />
-<h1>How to Download and Play Driver San Francisco on PC with Ubisoft Game Launcher</h1>
-<p>Driver San Francisco is a 2011 action-adventure racing game developed by Ubisoft Reflections and published by Ubisoft. The game features an innovative gameplay feature that allows players to seamlessly shift between over 130 licensed cars and explore a realistic recreation of San Francisco. The game also supports online multiplayer modes and a story mode that follows the protagonist John Tanner, a detective who can use his ability to shift into other drivers' bodies to stop a crime lord.</p>
-<h2>Driver San Francisco Ubisoft Game Launcher Download</h2><br /><p><b><b>Download File</b> &raquo; <a href="https://urlcod.com/2uHx4t">https://urlcod.com/2uHx4t</a></b></p><br /><br />
-<p>If you want to download and play Driver San Francisco on your PC, you will need to use the Ubisoft Game Launcher, a free service that connects you with other Ubisoft players and games across all platforms. Here are the steps to follow:</p>
-<ol>
-<li>Create a Ubisoft account or log in with your existing one.</li>
-<li>Download the Ubisoft Game Launcher from <a href="https://ubisoftconnect.com/">https://ubisoftconnect.com/</a> and install it on your PC.</li>
-<li>Launch the Ubisoft Game Launcher and go to the Store tab. Search for Driver San Francisco and purchase it. You can also redeem a code if you have one.</li>
-<li>Go to the Library tab and find Driver San Francisco in your games list. Click on the Download button and wait for the game to be installed on your PC.</li>
-<li>Once the installation is complete, click on the Play button and enjoy Driver San Francisco on your PC.</li>
-</ol>
-<p>Note: You may need to adjust some compatibility settings for the game to run properly on your PC. For example, you may need to disable fullscreen optimization or run the game as an administrator. You can find more details on how to fix common issues on <a href="https://kosgames.com/driver-san-francisco-how-to-fix-game-not-launch-in-2021-play-online-1074/">https://kosgames.com/driver-san-francisco-how-to-fix-game-not-launch-in-2021-play-online-1074/</a>.</p>
-
-<p>Driver San Francisco is a game that offers a unique and thrilling experience for racing fans and action lovers. The game has received positive reviews from critics and players alike, praising its innovative gameplay, diverse car selection, realistic graphics, and engaging story. The game also features a variety of challenges and activities to keep you entertained, such as races, stunts, dares, and missions. You can also customize your cars and unlock new ones as you progress through the game.</p>
-<p>If you want to play Driver San Francisco online with other players, you can join the Ubisoft Connect network and access various multiplayer modes. You can compete with up to seven other players in different modes, such as tag, trailblazer, takedown, and capture the flag. You can also team up with a friend in split-screen or online co-op mode and complete missions together. You can also use your shifting ability to help or hinder other players in online mode.</p>
-<p></p>
-<p>Driver San Francisco is a game that will keep you on the edge of your seat and make you feel like a real driver. If you are looking for a fun and exciting game to play on your PC, you should definitely give Driver San Francisco a try. You can download it from the Ubisoft Game Launcher and start your adventure in the city by the bay.</p> cec2833e83<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Flexisign Pro 10 Keygen 35.md b/spaces/tioseFevbu/cartoon-converter/scripts/Flexisign Pro 10 Keygen 35.md
deleted file mode 100644
index a12b1732946057346eaa7545e1f39ff29f634757..0000000000000000000000000000000000000000
--- a/spaces/tioseFevbu/cartoon-converter/scripts/Flexisign Pro 10 Keygen 35.md	
+++ /dev/null
@@ -1,66 +0,0 @@
-
-<h1>Flexisign Pro 10 Keygen 35: How to Download and Activate the Best Graphic Design Software for Signs</h1>
- <p>If you are looking for a powerful graphic design software that can help you create professional-looking logos and vector graphics, design direct-to-vinyl signs with multiple colors, text, and vector graphics, and offer a complete set of design, cutting, RIPing, and printing tools for maximum productivity, then you might want to check out Flexisign Pro 10.</p>
- <p>Flexisign Pro 10 is one of the best and the most useful Photoshop alternatives, which is a reliable and free image editing and vector graphic software. It combines the power of genuine Adobe® PostScript® 3 RIP engine, ICC profile support, and built-in direct drivers offering a complete set of design, cutting, RIPing, and printing tools for maximum productivity. It also includes vector graphics scheme enabling you to generate very advanced logos together with powerful web design. It offers full graphic design, text serialization, and color tracing features. It allows you to easily modify and design the vectors using a wide range of available resources to generate excellent vector graphics and logos.</p>
-<h2>flexisign pro 10 keygen 35</h2><br /><p><b><b>DOWNLOAD</b> ->>->>->> <a href="https://urlcod.com/2uHw1L">https://urlcod.com/2uHw1L</a></b></p><br /><br />
- <p>However, Flexisign Pro 10 is not a free software. You need to purchase a license to use it legally. But what if you don't have enough money to buy it? Or what if you just want to try it out before buying it? Is there a way to get Flexisign Pro 10 for free?</p>
- <p>The answer is yes. There is a way to get Flexisign Pro 10 for free by using a keygen. A keygen is a software that can generate a serial number and an activation code for a software that you want to use. By using a keygen, you can bypass the license verification process and activate the software without paying anything.</p>
- <p>But is it legal to use a keygen? And where can you find a reliable and safe keygen for Flexisign Pro 10? How can you download and activate Flexisign Pro 10 with a keygen? And how can you use Flexisign Pro 10 for graphic design? In this article, we will answer all these questions and more. We will show you how to download and activate Flexisign Pro 10 with keygen 35, which is one of the most popular and working keygens for Flexisign Pro 10. We will also show you how to use Flexisign Pro 10 for graphic design and create stunning logos and vector graphics with ease.</p>
-<p></p>
- <h2>What is Flexisign Pro 10?</h2>
- <p>Flexisign Pro 10 is a graphic design software that is specially designed for signs, banners, decals, stickers, vehicle wraps, and other types of graphics. It is developed by SA International (SAi), which is a leading provider of software solutions for the signmaking, digital printing, textile, and CNC machining industries. Flexisign Pro 10 is part of the SAi Flexi family of products, which also includes FlexiSIGN, FlexiPRINT, FlexiDESIGNER, and FlexiEXPERT.</p>
- <h3>Features and benefits of Flexisign Pro 10</h3>
- <p>Flexisign Pro 10 has many features and benefits that make it one of the best graphic design software for signs. Some of them are:</p>
- <ul>
-<li>It has a user-friendly interface that allows you to easily access all the tools and features you need.</li>
-<li>It supports a wide range of file formats, such as EPS, PDF, AI, PSD, JPG, PNG, TIFF, BMP, SVG, DXF, PLT, and more.</li>
-<li>It has a powerful vector graphics engine that lets you create and edit vector graphics with precision and quality.</li>
-<li>It has a comprehensive text tool that lets you add and edit text with various fonts, styles, effects, and alignment options.</li>
-<li>It has a color management system that ensures accurate color reproduction across different devices and media.</li>
-<li>It has a genuine Adobe® PostScript® 3 RIP engine that enables fast and reliable printing of complex graphics.</li>
-<li>It has built-in direct drivers that support over 1,000 cutters, printers, hybrids, flatbeds, and print-and-cut devices.</li>
-<li>It has a cutting tool that lets you cut your designs directly from the software or export them to a cutter.</li>
-<li>It has a contour cutting tool that lets you create contour cut lines around your graphics for easy cutting.</li>
-<li>It has a print-and-cut tool that lets you print your designs on a printer and then cut them on a cutter with automatic registration marks.</li>
-<li>It has a nesting tool that lets you optimize the use of media by arranging multiple graphics on one sheet.</li>
-<li>It has a tiling tool that lets you split large graphics into smaller tiles for printing on multiple sheets.</li>
-<li>It has an ICC profile support that lets you use custom profiles for different media types and print modes.</li>
-<li>It has an advanced color tracing tool that lets you convert bitmap images into vector graphics with accurate colors.</li>
-<li>It has an image editing tool that lets you enhance your images with filters, adjustments, effects, and masks.</li>
-</ul>
- <h3>System requirements for Flexisign Pro 10</h3>
- <p>To run Flexisign Pro 10 smoothly on your computer, you need to meet the following system requirements:</p>
- <table>
-<tr><th>Operating system</th><th>Windows 7/8/10 (32-bit or 64-bit)</th></tr>
-<tr><td>CPU</td><td>Pentium IV or higher</td></tr>
-<tr><td>RAM</td><td>1 GB or higher</td></tr>
-<tr><td>HDD</td><td>4 GB or higher</td></tr>
-<tr><td>Graphics card</td><td>Dedicated OpenGL 2.0 compatible with at least 256 MB memory</td></tr>
-<tr><td>Monitor resolution</td><td>1024 x 768 or higher</td></tr>
-<tr><td>DVD drive</td><td>Required for installation</td></tr>
-<tr><td>Internet connection</td><td>Required for activation and updates</td></tr>
-</table>
- <h 2: What is a keygen and why do you need it?</h2>
- <p>A keygen is a software that can generate a serial number and an activation code for another software that you want to use. A serial number is a unique identifier that is required to install and register a software. An activation code is a code that is required to activate a software and unlock its full features. A keygen can create these codes by using a specific algorithm that matches the one used by the original software developer.</p>
- <p>You need a keygen if you want to use a software without paying for it or without having a valid license. By using a keygen, you can bypass the license verification process and activate the software for free. This way, you can enjoy the full functionality of the software without any limitations or restrictions.</p>
- <h3>The difference between a keygen and a crack</h3>
- <p>A keygen is not the same as a crack. A crack is a software that modifies or patches another software to remove or disable its protection mechanisms, such as license verification, trial expiration, or online activation. A crack does not generate any codes, but rather alters the original software files to make it think that it is already activated or registered.</p>
- <p>The main difference between a keygen and a crack is that a keygen does not modify or damage the original software files, but rather creates new codes that can be used to activate the software. A keygen is usually safer and easier to use than a crack, as it does not require any changes to the system files or registry entries. However, both keygens and cracks are illegal and unethical, as they violate the intellectual property rights of the software developers and distributors.</p>
- <h3>The advantages and disadvantages of using a keygen</h3>
- <p>Using a keygen has some advantages and disadvantages that you should be aware of before deciding to use one. Some of the advantages are:</p>
- <ul>
-<li>You can use a software for free without paying anything.</li>
-<li>You can access all the features and functions of the software without any limitations or restrictions.</li>
-<li>You can use the software offline without needing an internet connection for activation.</li>
-<li>You can use the software on multiple devices without needing multiple licenses.</li>
-</ul>
- <p>Some of the disadvantages are:</p>
- <ul>
-<li>You are breaking the law and risking legal consequences for piracy and theft.</li>
-<li>You are violating the terms and conditions of the software license agreement and losing your right to technical support and updates.</li>
-<li>You are exposing your computer and data to potential viruses and malware that may be hidden in the keygen or downloaded from untrusted sources.</li>
-<li>You are compromising the quality and performance of the software, as it may not work properly or may crash frequently due to compatibility issues or bugs.</li>
-<li>You are disrespecting the hard work and creativity of the software developers and distributors who deserve to be paid for their products and services.</li>
-</ul></p> b2dd77e56b<br />
-<br />
-<br />
\ No newline at end of file
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/eucjpprober.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/eucjpprober.py
deleted file mode 100644
index abf2e66e283eb45c404e2d566c3933ae369324e8..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/pip/_vendor/chardet/eucjpprober.py
+++ /dev/null
@@ -1,95 +0,0 @@
-######################## BEGIN LICENSE BLOCK ########################
-# The Original Code is mozilla.org code.
-#
-# The Initial Developer of the Original Code is
-# Netscape Communications Corporation.
-# Portions created by the Initial Developer are Copyright (C) 1998
-# the Initial Developer. All Rights Reserved.
-#
-# Contributor(s):
-#   Mark Pilgrim - port to Python
-#
-# 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 .chardistribution import EUCJPDistributionAnalysis
-from .codingstatemachine import CodingStateMachine
-from .enums import MachineState, ProbingState
-from .jpcntx import EUCJPContextAnalysis
-from .mbcharsetprober import MultiByteCharSetProber
-from .mbcssm import EUCJP_SM_MODEL
-
-
-class EUCJPProber(MultiByteCharSetProber):
-    def __init__(self):
-        super().__init__()
-        self.coding_sm = CodingStateMachine(EUCJP_SM_MODEL)
-        self.distribution_analyzer = EUCJPDistributionAnalysis()
-        self.context_analyzer = EUCJPContextAnalysis()
-        self.reset()
-
-    def reset(self):
-        super().reset()
-        self.context_analyzer.reset()
-
-    @property
-    def charset_name(self):
-        return "EUC-JP"
-
-    @property
-    def language(self):
-        return "Japanese"
-
-    def feed(self, byte_str):
-        for i, byte in enumerate(byte_str):
-            # PY3K: byte_str is a byte array, so byte is an int, not a byte
-            coding_state = self.coding_sm.next_state(byte)
-            if coding_state == MachineState.ERROR:
-                self.logger.debug(
-                    "%s %s prober hit error at byte %s",
-                    self.charset_name,
-                    self.language,
-                    i,
-                )
-                self._state = ProbingState.NOT_ME
-                break
-            if coding_state == MachineState.ITS_ME:
-                self._state = ProbingState.FOUND_IT
-                break
-            if coding_state == MachineState.START:
-                char_len = self.coding_sm.get_current_charlen()
-                if i == 0:
-                    self._last_char[1] = byte
-                    self.context_analyzer.feed(self._last_char, char_len)
-                    self.distribution_analyzer.feed(self._last_char, char_len)
-                else:
-                    self.context_analyzer.feed(byte_str[i - 1 : i + 1], char_len)
-                    self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len)
-
-        self._last_char[0] = byte_str[-1]
-
-        if self.state == ProbingState.DETECTING:
-            if self.context_analyzer.got_enough_data() and (
-                self.get_confidence() > self.SHORTCUT_THRESHOLD
-            ):
-                self._state = ProbingState.FOUND_IT
-
-        return self.state
-
-    def get_confidence(self):
-        context_conf = self.context_analyzer.get_confidence()
-        distrib_conf = self.distribution_analyzer.get_confidence()
-        return max(context_conf, distrib_conf)
diff --git a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/__init__.py b/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/__init__.py
deleted file mode 100644
index b3ac0146cb3f4cb1894f55fc09775875bc4e1177..0000000000000000000000000000000000000000
--- a/spaces/tjburns/ask_marcus_aurelius/.venv/lib/python3.10/site-packages/setuptools/_distutils/__init__.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""distutils
-
-The main package for the Python Module Distribution Utilities.  Normally
-used from a setup script as
-
-   from distutils.core import setup
-
-   setup (...)
-"""
-
-import sys
-import importlib
-
-__version__ = sys.version[: sys.version.index(' ')]
-
-
-try:
-    # Allow Debian and pkgsrc (only) to customize system
-    # behavior. Ref pypa/distutils#2 and pypa/distutils#16.
-    # This hook is deprecated and no other environments
-    # should use it.
-    importlib.import_module('_distutils_system_mod')
-except ImportError:
-    pass
diff --git a/spaces/tomar79/webcam/README.md b/spaces/tomar79/webcam/README.md
deleted file mode 100644
index bae6348b2bc47317380411833a6aeed4e49334de..0000000000000000000000000000000000000000
--- a/spaces/tomar79/webcam/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Webcam
-emoji: 🏢
-colorFrom: gray
-colorTo: blue
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: openrail
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/data/datasets/coco.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/data/datasets/coco.py
deleted file mode 100644
index 030238d1a6f3b8b78f1dd21474ed5c0eaa7003d7..0000000000000000000000000000000000000000
--- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/data/datasets/coco.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
-import torch
-import torchvision
-
-from maskrcnn_benchmark.structures.bounding_box import BoxList
-from maskrcnn_benchmark.structures.segmentation_mask import SegmentationMask
-
-
-class COCODataset(torchvision.datasets.coco.CocoDetection):
-    def __init__(
-        self, ann_file, root, remove_images_without_annotations, transforms=None
-    ):
-        super(COCODataset, self).__init__(root, ann_file)
-
-        # sort indices for reproducible results
-        self.ids = sorted(self.ids)
-
-        # filter images without detection annotations
-        if remove_images_without_annotations:
-            self.ids = [
-                img_id
-                for img_id in self.ids
-                if len(self.coco.getAnnIds(imgIds=img_id, iscrowd=None)) > 0
-            ]
-
-        self.json_category_id_to_contiguous_id = {
-            v: i + 1 for i, v in enumerate(self.coco.getCatIds())
-        }
-        self.contiguous_category_id_to_json_id = {
-            v: k for k, v in self.json_category_id_to_contiguous_id.items()
-        }
-        self.id_to_img_map = {k: v for k, v in enumerate(self.ids)}
-        self.transforms = transforms
-
-    def __getitem__(self, idx):
-        img, anno = super(COCODataset, self).__getitem__(idx)
-
-        # filter crowd annotations
-        # TODO might be better to add an extra field
-        anno = [obj for obj in anno if obj["iscrowd"] == 0]
-
-        boxes = [obj["bbox"] for obj in anno]
-        boxes = torch.as_tensor(boxes).reshape(-1, 4)  # guard against no boxes
-        target = BoxList(boxes, img.size, mode="xywh",use_char_ann=False).convert("xyxy")
-
-        classes = [obj["category_id"] for obj in anno]
-        classes = [self.json_category_id_to_contiguous_id[c] for c in classes]
-        classes = torch.tensor(classes)
-        target.add_field("labels", classes)
-
-        masks = [obj["segmentation"] for obj in anno]
-        masks = SegmentationMask(masks, img.size)
-        target.add_field("masks", masks)
-
-        target = target.clip_to_image(remove_empty=True)
-
-        if self.transforms is not None:
-            img, target = self.transforms(img, target)
-
-        return img, target, idx
-
-    def get_img_info(self, index):
-        img_id = self.id_to_img_map[index]
-        img_data = self.coco.imgs[img_id]
-        return img_data
diff --git a/spaces/tomzhang1019/ChatGPT/modules/models/StableLM.py b/spaces/tomzhang1019/ChatGPT/modules/models/StableLM.py
deleted file mode 100644
index f4affc3699e335f1e42bf5fc8c93e92a41d027fe..0000000000000000000000000000000000000000
--- a/spaces/tomzhang1019/ChatGPT/modules/models/StableLM.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
-import time
-import numpy as np
-from torch.nn import functional as F
-import os
-from .base_model import BaseLLMModel
-from threading import Thread
-
-STABLELM_MODEL = None
-STABLELM_TOKENIZER = None
-
-
-class StopOnTokens(StoppingCriteria):
-    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
-        stop_ids = [50278, 50279, 50277, 1, 0]
-        for stop_id in stop_ids:
-            if input_ids[0][-1] == stop_id:
-                return True
-        return False
-
-
-class StableLM_Client(BaseLLMModel):
-    def __init__(self, model_name, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_name)
-        global STABLELM_MODEL, STABLELM_TOKENIZER
-        print(f"Starting to load StableLM to memory")
-        if model_name == "StableLM":
-            model_name = "stabilityai/stablelm-tuned-alpha-7b"
-        else:
-            model_name = f"models/{model_name}"
-        if STABLELM_MODEL is None:
-            STABLELM_MODEL = AutoModelForCausalLM.from_pretrained(
-                model_name, torch_dtype=torch.float16).cuda()
-        if STABLELM_TOKENIZER is None:
-            STABLELM_TOKENIZER = AutoTokenizer.from_pretrained(model_name)
-        self.generator = pipeline(
-            'text-generation', model=STABLELM_MODEL, tokenizer=STABLELM_TOKENIZER, device=0)
-        print(f"Sucessfully loaded StableLM to the memory")
-        self.system_prompt = """StableAssistant
-- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
-- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
-- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
-- StableAssistant will refuse to participate in anything that could harm a human."""
-        self.max_generation_token = 1024
-        self.top_p = 0.95
-        self.temperature = 1.0
-
-    def _get_stablelm_style_input(self):
-        history = self.history + [{"role": "assistant", "content": ""}]
-        print(history)
-        messages = self.system_prompt + \
-            "".join(["".join(["<|USER|>"+history[i]["content"], "<|ASSISTANT|>"+history[i + 1]["content"]])
-                    for i in range(0, len(history), 2)])
-        return messages
-
-    def _generate(self, text, bad_text=None):
-        stop = StopOnTokens()
-        result = self.generator(text, max_new_tokens=self.max_generation_token, num_return_sequences=1, num_beams=1, do_sample=True,
-                                temperature=self.temperature, top_p=self.top_p, top_k=1000, stopping_criteria=StoppingCriteriaList([stop]))
-        return result[0]["generated_text"].replace(text, "")
-
-    def get_answer_at_once(self):
-        messages = self._get_stablelm_style_input()
-        return self._generate(messages), len(messages)
-
-    def get_answer_stream_iter(self):
-        stop = StopOnTokens()
-        messages = self._get_stablelm_style_input()
-
-        # model_inputs = tok([messages], return_tensors="pt")['input_ids'].cuda()[:, :4096-1024]
-        model_inputs = STABLELM_TOKENIZER(
-            [messages], return_tensors="pt").to("cuda")
-        streamer = TextIteratorStreamer(
-            STABLELM_TOKENIZER, timeout=10., skip_prompt=True, skip_special_tokens=True)
-        generate_kwargs = dict(
-            model_inputs,
-            streamer=streamer,
-            max_new_tokens=self.max_generation_token,
-            do_sample=True,
-            top_p=self.top_p,
-            top_k=1000,
-            temperature=self.temperature,
-            num_beams=1,
-            stopping_criteria=StoppingCriteriaList([stop])
-        )
-        t = Thread(target=STABLELM_MODEL.generate, kwargs=generate_kwargs)
-        t.start()
-
-        partial_text = ""
-        for new_text in streamer:
-            partial_text += new_text
-            yield partial_text
diff --git a/spaces/touchscale/White-box-Cartoonization/README.md b/spaces/touchscale/White-box-Cartoonization/README.md
deleted file mode 100644
index 9860239cf42c94e385faaaa75a85311e010d64f7..0000000000000000000000000000000000000000
--- a/spaces/touchscale/White-box-Cartoonization/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
----
-python_version: 3.7
-title: White Box Cartoonization
-emoji: 📚
-colorFrom: purple
-colorTo: green
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: hylee/White-box-Cartoonization
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
diff --git a/spaces/trttung1610/musicgen/audiocraft/grids/diffusion/4_bands_base_32khz.py b/spaces/trttung1610/musicgen/audiocraft/grids/diffusion/4_bands_base_32khz.py
deleted file mode 100644
index f7e67bcc89dd0c8e50d770e600b55f179fe19588..0000000000000000000000000000000000000000
--- a/spaces/trttung1610/musicgen/audiocraft/grids/diffusion/4_bands_base_32khz.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-"""
-Training of the 4 diffusion models described in
-"From Discrete Tokens to High-Fidelity Audio Using Multi-Band Diffusion"
-(paper link).
-"""
-
-from ._explorers import DiffusionExplorer
-
-
-@DiffusionExplorer
-def explorer(launcher):
-    launcher.slurm_(gpus=4, partition='learnfair')
-
-    launcher.bind_({'solver': 'diffusion/default',
-                    'dset': 'internal/music_10k_32khz'})
-
-    with launcher.job_array():
-        launcher({'filter.use': True, 'filter.idx_band': 0, "processor.use": False, 'processor.power_std': 0.4})
-        launcher({'filter.use': True, 'filter.idx_band': 1, "processor.use": False, 'processor.power_std': 0.4})
-        launcher({'filter.use': True, 'filter.idx_band': 2, "processor.use": True, 'processor.power_std': 0.4})
-        launcher({'filter.use': True, 'filter.idx_band': 3, "processor.use": True, 'processor.power_std': 0.75})
diff --git a/spaces/tuan2010/DocumentGPT/utils.py b/spaces/tuan2010/DocumentGPT/utils.py
deleted file mode 100644
index 0cdd0dbad68e08fc98178e25890585f64ba44ebd..0000000000000000000000000000000000000000
--- a/spaces/tuan2010/DocumentGPT/utils.py
+++ /dev/null
@@ -1,27 +0,0 @@
-
-def split_text(text: str, chunk_size: int = 150, chunk_overlap: int = 25) -> list:
-    """
-    Splits a long string into chunks with a specified size and overlap.
-    
-    Args:
-    text (str): The long string to be split.
-    chunk_size (int): The desired size of each chunk.
-    chunk_overlap (int): The desired overlap between adjacent chunks.
-    
-    Returns:
-    A list of string chunks.
-    """
-    words = text.split(" ")
-    chunks = []
-    start = 0
-    end = chunk_size
-    
-    while end <= len(words):
-        chunks.append(" ".join(words[start:end]))
-        start += chunk_size - chunk_overlap
-        end += chunk_size - chunk_overlap
-    
-    if end > len(words) and start < len(words):
-        chunks.append(" ".join(words[start:]))
-    
-    return chunks
\ No newline at end of file
diff --git a/spaces/unity/ML-Agents-SoccerTwos/index.html b/spaces/unity/ML-Agents-SoccerTwos/index.html
deleted file mode 100644
index 8f0c125792fd357eb0bc4a72524d8eef6c361153..0000000000000000000000000000000000000000
--- a/spaces/unity/ML-Agents-SoccerTwos/index.html
+++ /dev/null
@@ -1,92 +0,0 @@
-<!DOCTYPE html>
-<html lang="en-us">
-  <head>
-    <meta charset="utf-8">
-    <meta http-equiv="Content-Type" content="text/html; charset=utf-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
-    <title>SoccerTwos</title>
-    <link rel="shortcut icon" href="TemplateData/favicon.ico">
-    <link rel="stylesheet" href="TemplateData/style.css">
-  </head>
-  <body class="dark">
-    <div id="unity-container" class="unity-desktop">
-      <canvas id="unity-canvas"></canvas>
-    </div>
-    <div id="loading-cover" style="display:none;">
-      <div id="unity-loading-bar">
-        <div id="unity-logo"><img src="logo.png"></div>
-        <div id="unity-progress-bar-empty" style="display: none;">
-          <div id="unity-progress-bar-full"></div>
-        </div>
-        <div class="spinner"></div>
-      </div>
-    </div>
-    <div id="unity-fullscreen-button" style="display: none;"></div>
-    <script>
-      const hideFullScreenButton = "";
-      const buildUrl = "Build";
-      const loaderUrl = buildUrl + "/SOCCERTWOSAG.loader.js";
-      const config = {
-        dataUrl: buildUrl + "/SOCCERTWOSAG.data.unityweb",
-        frameworkUrl: buildUrl + "/SOCCERTWOSAG.framework.js.unityweb",
-        codeUrl: buildUrl + "/SOCCERTWOSAG.wasm.unityweb",
-        streamingAssetsUrl: "StreamingAssets",
-        companyName: "Unity Technologies",
-        productName: "SoccerTwos",
-        productVersion: "1.0",
-      };
-
-      const container = document.querySelector("#unity-container");
-      const canvas = document.querySelector("#unity-canvas");
-      const loadingCover = document.querySelector("#loading-cover");
-      const progressBarEmpty = document.querySelector("#unity-progress-bar-empty");
-      const progressBarFull = document.querySelector("#unity-progress-bar-full");
-      const fullscreenButton = document.querySelector("#unity-fullscreen-button");
-      const spinner = document.querySelector('.spinner');
-
-      const canFullscreen = (function() {
-        for (const key of [
-            'exitFullscreen',
-            'webkitExitFullscreen',
-            'webkitCancelFullScreen',
-            'mozCancelFullScreen',
-            'msExitFullscreen',
-          ]) {
-          if (key in document) {
-            return true;
-          }
-        }
-        return false;
-      }());
-
-      if (/iPhone|iPad|iPod|Android/i.test(navigator.userAgent)) {
-        container.className = "unity-mobile";
-        config.devicePixelRatio = 1;
-      }
-      loadingCover.style.display = "";
-
-      const script = document.createElement("script");
-      script.src = loaderUrl;
-      script.onload = () => {
-        createUnityInstance(canvas, config, (progress) => {
-          spinner.style.display = "none";
-          progressBarEmpty.style.display = "";
-          progressBarFull.style.width = `${100 * progress}%`;
-        }).then((unityInstance) => {
-          loadingCover.style.display = "none";
-          if (canFullscreen) {
-            if (!hideFullScreenButton) {
-              fullscreenButton.style.display = "";
-            }
-            fullscreenButton.onclick = () => {
-              unityInstance.SetFullscreen(1);
-            };
-          }
-        }).catch((message) => {
-          alert(message);
-        });
-      };
-      document.body.appendChild(script);
-    </script>
-  </body>
-</html>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/11 Year Old Girls Pussy Pics.rar.md b/spaces/usbethFlerru/sovits-modelsV2/example/11 Year Old Girls Pussy Pics.rar.md
deleted file mode 100644
index 0c237931b2e7ba0e8dd77f4aa3631ad5757be25c..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/11 Year Old Girls Pussy Pics.rar.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>11 Year Old Girls Pussy Pics.rar</h2><br /><p><b><b>Download</b> > <a href="https://urlcod.com/2uyVTx">https://urlcod.com/2uyVTx</a></b></p><br /><br />
-<br />
- aaccfb2cb3<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/usbethFlerru/sovits-modelsV2/example/Candydoll Tv Fotos.md b/spaces/usbethFlerru/sovits-modelsV2/example/Candydoll Tv Fotos.md
deleted file mode 100644
index 3566edcedaee8ebef6aeabf90f0a33c4589b80a7..0000000000000000000000000000000000000000
--- a/spaces/usbethFlerru/sovits-modelsV2/example/Candydoll Tv Fotos.md	
+++ /dev/null
@@ -1,6 +0,0 @@
-<h2>Candydoll Tv Fotos</h2><br /><p><b><b>Download File</b> &mdash;&mdash;&mdash;&mdash;&mdash; <a href="https://urlcod.com/2uyXaF">https://urlcod.com/2uyXaF</a></b></p><br /><br />
-<br />
-Encontre as fotografias de stock e imagens de notícias editoriais de Natasha ... Signed photos, posters & more for actors, musicians, singers, TV stars and other ... Comments Off on CANDYDOLL CANDYDOLLTV - NATASHA C - SET 19 - 75P. 1fdad05405<br />
-<br />
-<br />
-<p></p>
diff --git a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/javascript/deforum.js b/spaces/user238921933/stable-diffusion-webui/extensions/deforum/javascript/deforum.js
deleted file mode 100644
index 889e76f39c937d9eb3b602aa5510d8ac637f5e3c..0000000000000000000000000000000000000000
--- a/spaces/user238921933/stable-diffusion-webui/extensions/deforum/javascript/deforum.js
+++ /dev/null
@@ -1,21 +0,0 @@
-function submit_deforum(){
-	// alert('Hello, Deforum!')
-    rememberGallerySelection('deforum_gallery')
-    showSubmitButtons('deforum', false)
-
-    var id = randomId()
-    requestProgress(id, gradioApp().getElementById('deforum_gallery_container'), gradioApp().getElementById('deforum_gallery'), function(){
-        showSubmitButtons('deforum', true)
-    })
-
-    var res = create_submit_args(arguments)
-
-    res[0] = id
-	// res[1] = get_tab_index('deforum')
-
-    return res
-}
-
-onUiUpdate(function(){
-    check_gallery('deforum_gallery')
-})
diff --git a/spaces/utensil/model-memory-usage/README.md b/spaces/utensil/model-memory-usage/README.md
deleted file mode 100644
index c11d74e1bad6720036ae3b9661c834cbdd7f226d..0000000000000000000000000000000000000000
--- a/spaces/utensil/model-memory-usage/README.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-title: Model Memory Utility
-emoji: 🚀
-colorFrom: pink
-colorTo: blue
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: apache-2.0
-duplicated_from: hf-accelerate/model-memory-usage
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/overrides/partials/comments.html b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/overrides/partials/comments.html
deleted file mode 100644
index ff1455b25eac59b84f260561ce177cf33dde8d33..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/overrides/partials/comments.html
+++ /dev/null
@@ -1,50 +0,0 @@
-{% if page.meta.comments %}
-<h2 id="__comments">{{ lang.t("meta.comments") }}</h2>
-
-<!-- Insert Giscus code snippet from https://giscus.app/ here -->
-<script src="https://giscus.app/client.js"
-        data-repo="ultralytics/ultralytics"
-        data-repo-id="R_kgDOH-jzvQ"
-        data-category="Docs"
-        data-category-id="DIC_kwDOH-jzvc4CWLkL"
-        data-mapping="title"
-        data-strict="0"
-        data-reactions-enabled="1"
-        data-emit-metadata="0"
-        data-input-position="bottom"
-        data-theme="preferred_color_scheme"
-        data-lang="en"
-        crossorigin="anonymous"
-        async>
-</script>
-
-<!-- Synchronize Giscus theme with palette -->
-<script>
-    var giscus = document.querySelector("script[src*=giscus]")
-
-    /* Set palette on initial load */
-    var palette = __md_get("__palette")
-    if (palette && typeof palette.color === "object") {
-      var theme = palette.color.scheme === "slate" ? "dark" : "light"
-      giscus.setAttribute("data-theme", theme)
-    }
-
-    /* Register event handlers after documented loaded */
-    document.addEventListener("DOMContentLoaded", function() {
-      var ref = document.querySelector("[data-md-component=palette]")
-      ref.addEventListener("change", function() {
-        var palette = __md_get("__palette")
-        if (palette && typeof palette.color === "object") {
-          var theme = palette.color.scheme === "slate" ? "dark" : "light"
-
-          /* Instruct Giscus to change theme */
-          var frame = document.querySelector(".giscus-frame")
-          frame.contentWindow.postMessage(
-            { giscus: { setConfig: { theme } } },
-            "https://giscus.app"
-          )
-        }
-      })
-    })
-</script>
-{% endif %}
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/v8/pose/val.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/v8/pose/val.md
deleted file mode 100644
index c26c255e3010b579ee0734431fdf22897b458bdb..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/reference/yolo/v8/pose/val.md
+++ /dev/null
@@ -1,14 +0,0 @@
----
-description: Ensure proper human poses in images with YOLOv8 Pose Validation, part of the Ultralytics YOLO v8 suite.
-keywords: PoseValidator, Ultralytics YOLO, object detection, pose analysis, validation
----
-
-## PoseValidator
----
-### ::: ultralytics.yolo.v8.pose.val.PoseValidator
-<br><br>
-
-## val
----
-### ::: ultralytics.yolo.v8.pose.val.val
-<br><br>
diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/yolov5/tutorials/train_custom_data.md b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/yolov5/tutorials/train_custom_data.md
deleted file mode 100644
index ce11a95f6973306a743bff294545c55b696f2b3a..0000000000000000000000000000000000000000
--- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/docs/yolov5/tutorials/train_custom_data.md
+++ /dev/null
@@ -1,237 +0,0 @@
----
-comments: true
-description: Train your custom dataset with YOLOv5. Learn to collect, label and annotate images, and train and deploy models. Get started now.
-keywords: YOLOv5, train custom dataset, object detection, artificial intelligence, deep learning, computer vision
----
-
-📚 This guide explains how to train your own **custom dataset** with [YOLOv5](https://github.com/ultralytics/yolov5) 🚀.  
-UPDATED 7 June 2023.
-
-## Before You Start
-
-Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
-
-```bash
-git clone https://github.com/ultralytics/yolov5  # clone
-cd yolov5
-pip install -r requirements.txt  # install
-```
-
-## Train On Custom Data
-
-<a href="https://bit.ly/ultralytics_hub" target="_blank">
-<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a>
-<br>
-<br>
-
-Creating a custom model to detect your objects is an iterative process of collecting and organizing images, labeling your objects of interest, training a model, deploying it into the wild to make predictions, and then using that deployed model to collect examples of edge cases to repeat and improve.
-
-### 1. Create Dataset
-
-YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data. There are two options for creating your dataset before you start training:
-
-<details markdown>
-<summary>Use <a href="https://roboflow.com/?ref=ultralytics">Roboflow</a> to create your dataset in YOLO format</summary>
-
-!!! warning
-
-    Roboflow users can use Ultralytics under the [AGPL license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) or procure an [Enterprise license](https://ultralytics.com/license) directly from Ultralytics. Be aware that Roboflow does **not** provide Ultralytics licenses, and it is the responsibility of the user to ensure appropriate licensing.
-
-### 1.1 Collect Images
-
-Your model will learn by example. Training on images similar to the ones it will see in the wild is of the utmost importance. Ideally, you will collect a wide variety of images from the same configuration (camera, angle, lighting, etc.) as you will ultimately deploy your project.
-
-If this is not possible, you can start from [a public dataset](https://universe.roboflow.com/?ref=ultralytics) to train your initial model and then [sample images from the wild during inference](https://blog.roboflow.com/computer-vision-active-learning-tips/?ref=ultralytics) to improve your dataset and model iteratively.
-
-### 1.2 Create Labels
-
-Once you have collected images, you will need to annotate the objects of interest to create a ground truth for your model to learn from.
-
-<p align="center"><a href="https://app.roboflow.com/?model=yolov5&ref=ultralytics" title="Create a Free Roboflow Account"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif" /></a></p>
-
-[Roboflow Annotate](https://roboflow.com/annotate?ref=ultralytics) is a simple
-web-based tool for managing and labeling your images with your team and exporting
-them in [YOLOv5's annotation format](https://roboflow.com/formats/yolov5-pytorch-txt?ref=ultralytics).
-
-### 1.3 Prepare Dataset for YOLOv5
-
-Whether you [label your images with Roboflow](https://roboflow.com/annotate?ref=ultralytics) or not, you can use it to convert your dataset into YOLO format, create a YOLOv5 YAML configuration file, and host it for importing into your training script.
-
-[Create a free Roboflow account](https://app.roboflow.com/?model=yolov5&ref=ultralytics)
-and upload your dataset to a `Public` workspace, label any unannotated images,
-then generate and export a version of your dataset in `YOLOv5 Pytorch` format.
-
-Note: YOLOv5 does online augmentation during training, so we do not recommend
-applying any augmentation steps in Roboflow for training with YOLOv5. But we
-recommend applying the following preprocessing steps:
-
-<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273477fccf42a0fd3d6_roboflow-preprocessing.png" title="Recommended Preprocessing Steps" /></p>
-
-* **Auto-Orient** - to strip EXIF orientation from your images.
-* **Resize (Stretch)** - to the square input size of your model (640x640 is the YOLOv5 default).
-
-Generating a version will give you a point in time snapshot of your dataset so
-you can always go back and compare your future model training runs against it,
-even if you add more images or change its configuration later.
-
-<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a2733fd1da943619934e_roboflow-export.png" title="Export in YOLOv5 Format" /></p>
-
-Export in `YOLOv5 Pytorch` format, then copy the snippet into your training
-script or notebook to download your dataset.
-
-<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273a92e4f5cb72594df_roboflow-snippet.png" title="Roboflow dataset download snippet" /></p>
-
-Now continue with `2. Select a Model`.
-</details>
-
-<details open markdown>
-<summary>Or manually prepare your dataset</summary>
-
-### 1.1 Create dataset.yaml
-
-[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](http://cocodataset.org/#home) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of overfitting. [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or *.txt files with image paths) and 2) a class `names` dictionary:
-
-```yaml
-# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
-val: images/train2017  # val images (relative to 'path') 128 images
-test:  # test images (optional)
-
-# Classes (80 COCO classes)
-names:
-  0: person
-  1: bicycle
-  2: car
-  ...
-  77: teddy bear
-  78: hair drier
-  79: toothbrush
-```
-
-### 1.2 Create Labels
-
-After using an annotation tool to label your images, export your labels to **YOLO format**, with one `*.txt` file per image (if no objects in image, no `*.txt` file is required). The `*.txt` file specifications are:
-
-- One row per object
-- Each row is `class x_center y_center width height` format.
-- Box coordinates must be in **normalized xywh** format (from 0 - 1). If your boxes are in pixels, divide `x_center` and `width` by image width, and `y_center` and `height` by image height.
-- Class numbers are zero-indexed (start from 0).
-
-<p align="center"><img width="750" src="https://user-images.githubusercontent.com/26833433/91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg"></p>
-
-The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):
-
-<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png"></p>
-
-### 1.3 Organize Directories
-
-Organize your train and val images and labels according to the example below. YOLOv5 assumes  `/coco128` is inside a `/datasets` directory **next to** the `/yolov5` directory. **YOLOv5 locates labels automatically for each image** by replacing the last instance of `/images/` in each image path with `/labels/`. For example:
-
-```bash
-../datasets/coco128/images/im0.jpg  # image
-../datasets/coco128/labels/im0.txt  # label
-```
-
-<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/134436012-65111ad1-9541-4853-81a6-f19a3468b75f.png"></p>
-</details>
-
-### 2. Select a Model
-
-Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the second-smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
-
-<p align="center"><img width="800" alt="YOLOv5 Models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
-
-### 3. Train
-
-Train a YOLOv5s model on COCO128 by specifying dataset, batch-size, image size and either pretrained `--weights yolov5s.pt` (recommended), or randomly initialized `--weights '' --cfg yolov5s.yaml` (not recommended). Pretrained weights are auto-downloaded from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
-
-```bash
-python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt
-```
-
-!!! tip "Tip"
-
-    💡 Add `--cache ram` or `--cache disk` to speed up training (requires significant RAM/disk resources).  
-
-!!! tip "Tip"
-
-    💡 Always train from a local dataset. Mounted or network drives like Google Drive will be very slow. 
-
-All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc. For more details see the Training section of our tutorial notebook. <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-
-### 4. Visualize
-
-#### Comet Logging and Visualization 🌟 NEW
-
-[Comet](https://bit.ly/yolov5-readme-comet) is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
-
-Getting started is easy:
-
-```shell
-pip install comet_ml  # 1. install
-export COMET_API_KEY=<Your API Key>  # 2. paste API key
-python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt  # 3. train
-```
-
-To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook:
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
-
-<img width="1920" alt="yolo-ui" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
-
-#### ClearML Logging and Automation 🌟 NEW
-
-[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML:
-
-- `pip install clearml`
-- run `clearml-init` to connect to a ClearML server (**deploy your own open-source server [here](https://github.com/allegroai/clearml-server)**, or use our free hosted server [here](https://cutt.ly/yolov5-notebook-clearml))
-
-You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).
-
-You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!
-
-<a href="https://cutt.ly/yolov5-notebook-clearml">
-<img alt="ClearML Experiment Management UI" src="https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg" width="1280"/></a>
-
-#### Local Logging
-
-Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.
-
-This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
-
-<img alt="Local logging results" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg" width="1280"/>
-
-Results file `results.csv` is updated after each epoch, and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:
-
-```python
-from utils.plots import plot_results
-
-plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'
-```
-
-<p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/results.png"></p>
-
-## Next Steps
-
-Once your model is trained you can use your best checkpoint `best.pt` to:
-
-* Run [CLI](https://github.com/ultralytics/yolov5#quick-start-examples) or [Python](https://docs.ultralytics.com/yolov5/tutorials/pytorch_hub_model_loading) inference on new images and videos
-* [Validate](https://github.com/ultralytics/yolov5/blob/master/val.py) accuracy on train, val and test splits
-* [Export](https://docs.ultralytics.com/yolov5/tutorials/model_export) to TensorFlow, Keras, ONNX, TFlite, TF.js, CoreML and TensorRT formats
-* [Evolve](https://docs.ultralytics.com/yolov5/tutorials/hyperparameter_evolution) hyperparameters to improve performance
-* [Improve](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics) your model by sampling real-world images and adding them to your dataset
-
-## Environments
-
-YOLOv5 is designed to be run in the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
-- **Notebooks** with free GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)
-- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-
-## Status
-
-<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
-
-If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
\ No newline at end of file
diff --git a/spaces/valhalla/glide-text2im/glide_text2im/clip/attention.py b/spaces/valhalla/glide-text2im/glide_text2im/clip/attention.py
deleted file mode 100644
index 33775913e5cd604faea084190b1c218f34d908ac..0000000000000000000000000000000000000000
--- a/spaces/valhalla/glide-text2im/glide_text2im/clip/attention.py
+++ /dev/null
@@ -1,179 +0,0 @@
-import math
-from abc import ABC, abstractmethod
-from itertools import product
-from typing import Any, Optional
-
-import attr
-import numpy as np
-import torch
-
-
-@attr.s
-class AttentionMask(ABC):
-    query_context_size: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
-    key_context_size: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
-    block_size: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
-    n_head: int = attr.ib(validator=lambda i, a, x: x >= 1)  # type: ignore
-    is_head_specific: bool = attr.ib(default=False)
-    n_query_pad: int = attr.ib(default=0)
-    n_key_pad: int = attr.ib(default=0)
-
-    def __attrs_post_init__(self) -> None:
-        if self.query_context_size % self.block_size != 0:
-            raise ValueError()
-        if self.key_context_size % self.block_size != 0:
-            raise ValueError()
-        if self.n_query_pad >= self.query_context_size:
-            raise ValueError()
-        if self.n_key_pad >= self.key_context_size:
-            raise ValueError()
-
-        self.n_query_block = self.query_context_size // self.block_size
-        self.n_key_block = self.key_context_size // self.block_size
-        self.first_pad_query_block_idx = self.n_query_block - int(
-            math.ceil(self.n_query_pad / self.block_size)
-        )
-        self.first_pad_key_block_idx = self.n_key_block - int(
-            math.ceil(self.n_key_pad / self.block_size)
-        )
-
-    def _make_global_layout(self) -> None:
-        if not self.is_head_specific:
-            m = np.ones([self.n_query_block, self.n_key_block], dtype=np.bool)
-            r = product(*[range(n) for n in m.shape])
-
-            for qb, kb in r:
-                m[qb, kb] = np.any(self.block_layout(None, 0, qb, kb, 0))
-        else:
-            m = np.ones([self.n_head, self.n_query_block, self.n_key_block], dtype=np.bool)
-            r = product(*[range(n) for n in m.shape])
-
-            for h, qb, kb in r:
-                m[h, qb, kb] = np.any(self.block_layout(None, h, qb, kb, 0))
-
-        self.global_layout = m
-
-    @abstractmethod
-    def _block_layout(
-        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
-    ) -> np.ndarray:
-        raise NotImplementedError()
-
-    def block_layout(
-        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
-    ) -> np.ndarray:
-        """
-        `query_idx`, `key_idx` are block-level, zero-based indices.
-        """
-
-        m = np.ones([self.block_size, self.block_size], dtype=np.bool)
-
-        if query_idx >= self.first_pad_query_block_idx:
-            n_pad = min(
-                self.block_size,
-                (query_idx + 1) * self.block_size - (self.query_context_size - self.n_query_pad),
-            )
-            assert n_pad > 0
-            m[self.block_size - n_pad :] = False
-        if key_idx >= self.first_pad_key_block_idx:
-            n_pad = min(
-                self.block_size,
-                (key_idx + 1) * self.block_size - (self.key_context_size - self.n_key_pad),
-            )
-            assert n_pad > 0
-            m[:, self.block_size - n_pad :] = False
-
-        return m & self._block_layout(blk_shape, head_idx, query_idx, key_idx, blk_idx)
-
-
-@attr.s
-class DenseAttentionMask(AttentionMask):
-    def __attrs_post_init__(self) -> None:
-        super().__attrs_post_init__()
-
-        self.global_layout = np.ones([self.n_query_block, self.n_key_block], dtype=np.bool)
-        n_zero_query_blocks = self.n_query_pad // self.block_size
-        n_zero_key_blocks = self.n_key_pad // self.block_size
-        self.global_layout[self.n_query_block - n_zero_query_blocks :] = False
-        self.global_layout[:, self.n_key_block - n_zero_key_blocks :] = False
-
-    def _block_layout(
-        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
-    ) -> np.ndarray:
-        return np.ones([self.block_size, self.block_size], dtype=np.bool)
-
-
-@attr.s
-class DenseCausalAttentionMask(AttentionMask):
-    def __attrs_post_init__(self) -> None:
-        super().__attrs_post_init__()
-
-        self.global_layout = np.tril(np.ones([self.n_query_block, self.n_key_block], dtype=np.bool))
-        n_zero_query_blocks = self.n_query_pad // self.block_size
-        n_zero_key_blocks = self.n_key_pad // self.block_size
-        self.global_layout[self.n_query_block - n_zero_query_blocks :] = False
-        self.global_layout[:, self.n_key_block - n_zero_key_blocks :] = False
-
-    def _block_layout(
-        self, blk_shape: Any, head_idx: int, query_idx: int, key_idx: int, blk_idx: int
-    ) -> np.ndarray:
-        if query_idx > key_idx:
-            return np.ones(2 * [self.block_size], dtype=np.bool)
-        elif query_idx < key_idx:
-            return np.zeros(2 * [self.block_size], dtype=np.bool)
-        else:
-            return np.tril(np.ones(2 * [self.block_size], dtype=np.bool))
-
-
-@attr.s(eq=False, repr=False)
-class AttentionInfo:
-    n_heads: int = attr.ib()
-    ctx_blks_q: int = attr.ib()
-    ctx_blks_k: int = attr.ib()
-    block_size: int = attr.ib()
-    pytorch_attn_bias: Optional[torch.Tensor] = attr.ib()
-
-
-def to_attention_info(d: AttentionMask) -> AttentionInfo:
-    return AttentionInfo(
-        n_heads=d.n_head,
-        ctx_blks_q=d.n_query_block,
-        ctx_blks_k=d.n_key_block,
-        block_size=d.block_size,
-        pytorch_attn_bias=None,
-    )
-
-
-def make_full_layout(d: AttentionMask) -> np.ndarray:
-    """
-    Returns the `context_size x context_size` layout matrix described by `d`. If the layout is dependent on the index of
-    the attention head, a `attention_head x context_size x context_size` layout matrix is returned instead.
-    """
-
-    if not d.is_head_specific:
-        u = np.reshape(d.global_layout, [d.n_query_block, d.n_key_block, 1, 1])
-        r = product(range(d.n_query_block), range(d.n_key_block))
-        v = np.array([d.block_layout(None, 0, i, j, 0) for i, j in r])
-        v = np.reshape(v, [d.n_query_block, d.n_key_block, d.block_size, d.block_size])
-
-        w = u * v
-        w = np.transpose(w, [0, 2, 1, 3])
-        w = np.reshape(w, [d.query_context_size, d.key_context_size])
-        return w
-    else:
-        if len(d.global_layout.shape) == 2:
-            u = np.reshape(d.global_layout, [1, d.n_query_block, d.n_key_block, 1, 1])
-            u = np.tile(u, [d.n_head, 1, 1, 1, 1])
-        elif len(d.global_layout.shape) == 3:
-            u = np.reshape(d.global_layout, [d.n_head, d.n_query_block, d.n_key_block, 1, 1])
-        else:
-            raise RuntimeError()
-
-        s = product(range(d.n_head), range(d.n_query_block), range(d.n_key_block))
-        v = np.array([d.block_layout(None, i, j, k, 0) for i, j, k in s])
-        v = np.reshape(v, [d.n_head, d.n_query_block, d.n_key_block, d.block_size, d.block_size])
-
-        w = u * v
-        w = np.transpose(w, [0, 1, 3, 2, 4])
-        w = np.reshape(w, [d.n_head, d.query_context_size, d.key_context_size])
-        return w
diff --git a/spaces/viait/multi-fusion-sd-dalle/README.md b/spaces/viait/multi-fusion-sd-dalle/README.md
deleted file mode 100644
index a6adf57f1bc816dbca595aebaed74486924f3e06..0000000000000000000000000000000000000000
--- a/spaces/viait/multi-fusion-sd-dalle/README.md
+++ /dev/null
@@ -1,9 +0,0 @@
----
-title: MultiFusion — Stable Diffusion & Dall•E-2
-metaTitle: DALL·E mini by craiyon.com on Hugging Face
-emoji: 💧MF
-colorFrom: yellow
-colorTo: green
-sdk: static
-pinned: true
----
diff --git a/spaces/video-p2p-library/Video-P2P-Demo/app_upload.py b/spaces/video-p2p-library/Video-P2P-Demo/app_upload.py
deleted file mode 100644
index f839c0c33c1ab8a43bc269ede0af920e61ef76cc..0000000000000000000000000000000000000000
--- a/spaces/video-p2p-library/Video-P2P-Demo/app_upload.py
+++ /dev/null
@@ -1,106 +0,0 @@
-#!/usr/bin/env python
-
-from __future__ import annotations
-
-import pathlib
-
-import gradio as gr
-import slugify
-
-from constants import MODEL_LIBRARY_ORG_NAME, UploadTarget
-from uploader import Uploader
-from utils import find_exp_dirs
-
-
-class ModelUploader(Uploader):
-    def upload_model(
-        self,
-        folder_path: str,
-        repo_name: str,
-        upload_to: str,
-        private: bool,
-        delete_existing_repo: bool,
-        input_token: str | None = None,
-    ) -> str:
-        if not folder_path:
-            raise ValueError
-        if not repo_name:
-            repo_name = pathlib.Path(folder_path).name
-        repo_name = slugify.slugify(repo_name)
-
-        if upload_to == UploadTarget.PERSONAL_PROFILE.value:
-            organization = ''
-        elif upload_to == UploadTarget.MODEL_LIBRARY.value:
-            organization = MODEL_LIBRARY_ORG_NAME
-        else:
-            raise ValueError
-
-        return self.upload(folder_path,
-                           repo_name,
-                           organization=organization,
-                           private=private,
-                           delete_existing_repo=delete_existing_repo,
-                           input_token=input_token)
-
-
-def load_local_model_list() -> dict:
-    choices = find_exp_dirs()
-    return gr.update(choices=choices, value=choices[0] if choices else None)
-
-
-def create_upload_demo(hf_token: str | None) -> gr.Blocks:
-    uploader = ModelUploader(hf_token)
-    model_dirs = find_exp_dirs()
-
-    with gr.Blocks() as demo:
-        with gr.Box():
-            gr.Markdown('Local Models')
-            reload_button = gr.Button('Reload Model List')
-            model_dir = gr.Dropdown(
-                label='Model names',
-                choices=model_dirs,
-                value=model_dirs[0] if model_dirs else None)
-        with gr.Box():
-            gr.Markdown('Upload Settings')
-            with gr.Row():
-                use_private_repo = gr.Checkbox(label='Private', value=True)
-                delete_existing_repo = gr.Checkbox(
-                    label='Delete existing repo of the same name', value=False)
-            upload_to = gr.Radio(label='Upload to',
-                                 choices=[_.value for _ in UploadTarget],
-                                 value=UploadTarget.MODEL_LIBRARY.value)
-            model_name = gr.Textbox(label='Model Name')
-            input_token = gr.Text(label='Hugging Face Write Token',
-                                  placeholder='',
-                                  visible=False if hf_token else True)
-        upload_button = gr.Button('Upload')
-        gr.Markdown(f'''
-            - You can upload your trained model to your personal profile (i.e. https://huggingface.co/{{your_username}}/{{model_name}}) or to the public [Video-P2P Library](https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}) (i.e. https://huggingface.co/{MODEL_LIBRARY_ORG_NAME}/{{model_name}}).
-            ''')
-        with gr.Box():
-            gr.Markdown('Output message')
-            output_message = gr.Markdown()
-
-        reload_button.click(fn=load_local_model_list,
-                            inputs=None,
-                            outputs=model_dir)
-        upload_button.click(fn=uploader.upload_model,
-                            inputs=[
-                                model_dir,
-                                model_name,
-                                upload_to,
-                                use_private_repo,
-                                delete_existing_repo,
-                                input_token,
-                            ],
-                            outputs=output_message)
-
-    return demo
-
-
-if __name__ == '__main__':
-    import os
-
-    hf_token = os.getenv('HF_TOKEN')
-    demo = create_upload_demo(hf_token)
-    demo.queue(max_size=1).launch(share=False)
diff --git a/spaces/vumichien/Whisper_speaker_diarization/app.py b/spaces/vumichien/Whisper_speaker_diarization/app.py
deleted file mode 100644
index ef334ec7fa10419f6f382a944f305f8f732f3cf8..0000000000000000000000000000000000000000
--- a/spaces/vumichien/Whisper_speaker_diarization/app.py
+++ /dev/null
@@ -1,473 +0,0 @@
-# import whisper
-from faster_whisper import WhisperModel
-import datetime
-import subprocess
-import gradio as gr
-from pathlib import Path
-import pandas as pd
-import re
-import time
-import os 
-import numpy as np
-from sklearn.cluster import AgglomerativeClustering
-from sklearn.metrics import silhouette_score
-
-from pytube import YouTube
-import yt_dlp
-import torch
-import pyannote.audio
-from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
-from pyannote.audio import Audio
-from pyannote.core import Segment
-
-from gpuinfo import GPUInfo
-
-import wave
-import contextlib
-from transformers import pipeline
-import psutil
-
-whisper_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2"]
-source_languages = {
-    "en": "English",
-    "zh": "Chinese",
-    "de": "German",
-    "es": "Spanish",
-    "ru": "Russian",
-    "ko": "Korean",
-    "fr": "French",
-    "ja": "Japanese",
-    "pt": "Portuguese",
-    "tr": "Turkish",
-    "pl": "Polish",
-    "ca": "Catalan",
-    "nl": "Dutch",
-    "ar": "Arabic",
-    "sv": "Swedish",
-    "it": "Italian",
-    "id": "Indonesian",
-    "hi": "Hindi",
-    "fi": "Finnish",
-    "vi": "Vietnamese",
-    "he": "Hebrew",
-    "uk": "Ukrainian",
-    "el": "Greek",
-    "ms": "Malay",
-    "cs": "Czech",
-    "ro": "Romanian",
-    "da": "Danish",
-    "hu": "Hungarian",
-    "ta": "Tamil",
-    "no": "Norwegian",
-    "th": "Thai",
-    "ur": "Urdu",
-    "hr": "Croatian",
-    "bg": "Bulgarian",
-    "lt": "Lithuanian",
-    "la": "Latin",
-    "mi": "Maori",
-    "ml": "Malayalam",
-    "cy": "Welsh",
-    "sk": "Slovak",
-    "te": "Telugu",
-    "fa": "Persian",
-    "lv": "Latvian",
-    "bn": "Bengali",
-    "sr": "Serbian",
-    "az": "Azerbaijani",
-    "sl": "Slovenian",
-    "kn": "Kannada",
-    "et": "Estonian",
-    "mk": "Macedonian",
-    "br": "Breton",
-    "eu": "Basque",
-    "is": "Icelandic",
-    "hy": "Armenian",
-    "ne": "Nepali",
-    "mn": "Mongolian",
-    "bs": "Bosnian",
-    "kk": "Kazakh",
-    "sq": "Albanian",
-    "sw": "Swahili",
-    "gl": "Galician",
-    "mr": "Marathi",
-    "pa": "Punjabi",
-    "si": "Sinhala",
-    "km": "Khmer",
-    "sn": "Shona",
-    "yo": "Yoruba",
-    "so": "Somali",
-    "af": "Afrikaans",
-    "oc": "Occitan",
-    "ka": "Georgian",
-    "be": "Belarusian",
-    "tg": "Tajik",
-    "sd": "Sindhi",
-    "gu": "Gujarati",
-    "am": "Amharic",
-    "yi": "Yiddish",
-    "lo": "Lao",
-    "uz": "Uzbek",
-    "fo": "Faroese",
-    "ht": "Haitian creole",
-    "ps": "Pashto",
-    "tk": "Turkmen",
-    "nn": "Nynorsk",
-    "mt": "Maltese",
-    "sa": "Sanskrit",
-    "lb": "Luxembourgish",
-    "my": "Myanmar",
-    "bo": "Tibetan",
-    "tl": "Tagalog",
-    "mg": "Malagasy",
-    "as": "Assamese",
-    "tt": "Tatar",
-    "haw": "Hawaiian",
-    "ln": "Lingala",
-    "ha": "Hausa",
-    "ba": "Bashkir",
-    "jw": "Javanese",
-    "su": "Sundanese",
-}
-
-source_language_list = [key[0] for key in source_languages.items()]
-
-MODEL_NAME = "vumichien/whisper-medium-jp"
-lang = "ja"
-
-device = 0 if torch.cuda.is_available() else "cpu"
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-os.makedirs('output', exist_ok=True)
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
-
-embedding_model = PretrainedSpeakerEmbedding( 
-    "speechbrain/spkrec-ecapa-voxceleb",
-    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
-
-def transcribe(microphone, file_upload):
-    warn_output = ""
-    if (microphone is not None) and (file_upload is not None):
-        warn_output = (
-            "WARNING: You've uploaded an audio file and used the microphone. "
-            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-        )
-
-    elif (microphone is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
-
-    file = microphone if microphone is not None else file_upload
-
-    text = pipe(file)["text"]
-
-    return warn_output + text
-
-def _return_yt_html_embed(yt_url):
-    video_id = yt_url.split("?v=")[-1]
-    HTML_str = (
-        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-        " </center>"
-    )
-    return HTML_str
-
-def yt_transcribe(yt_url):
-    # yt = YouTube(yt_url)
-    # html_embed_str = _return_yt_html_embed(yt_url)
-    # stream = yt.streams.filter(only_audio=True)[0]
-    # stream.download(filename="audio.mp3")
-
-    ydl_opts = {
-        'format': 'bestvideo*+bestaudio/best',
-        'postprocessors': [{
-            'key': 'FFmpegExtractAudio',
-            'preferredcodec': 'mp3',
-            'preferredquality': '192',
-        }],
-        'outtmpl':'audio.%(ext)s',
-    }
-    
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([yt_url])
-        
-    text = pipe("audio.mp3")["text"]
-    return html_embed_str, text
-
-def convert_time(secs):
-    return datetime.timedelta(seconds=round(secs))
-
-def get_youtube(video_url):
-    # yt = YouTube(video_url)
-    # abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download()
-    
-    ydl_opts = {
-      'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
-    }
-    
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        info = ydl.extract_info(video_url, download=False)
-        abs_video_path = ydl.prepare_filename(info)
-        ydl.process_info(info) 
-        
-    print("Success download video")
-    print(abs_video_path)
-    return abs_video_path
-
-def speech_to_text(video_file_path, selected_source_lang, whisper_model, num_speakers):
-    """
-    # Transcribe youtube link using OpenAI Whisper
-    1. Using Open AI's Whisper model to seperate audio into segments and generate transcripts.
-    2. Generating speaker embeddings for each segments.
-    3. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
-    
-    Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper
-    Speaker diarization model and pipeline from by https://github.com/pyannote/pyannote-audio
-    """
-    
-    # model = whisper.load_model(whisper_model)
-    # model = WhisperModel(whisper_model, device="cuda", compute_type="int8_float16")
-    model = WhisperModel(whisper_model, compute_type="int8")
-    time_start = time.time()
-    if(video_file_path == None):
-        raise ValueError("Error no video input")
-    print(video_file_path)
-
-    try:
-        # Read and convert youtube video
-        _,file_ending = os.path.splitext(f'{video_file_path}')
-        print(f'file enging is {file_ending}')
-        audio_file = video_file_path.replace(file_ending, ".wav")
-        print("starting conversion to wav")
-        os.system(f'ffmpeg -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file}"')
-        
-        # Get duration
-        with contextlib.closing(wave.open(audio_file,'r')) as f:
-            frames = f.getnframes()
-            rate = f.getframerate()
-            duration = frames / float(rate)
-        print(f"conversion to wav ready, duration of audio file: {duration}")
-
-        # Transcribe audio
-        options = dict(language=selected_source_lang, beam_size=5, best_of=5)
-        transcribe_options = dict(task="transcribe", **options)
-        segments_raw, info = model.transcribe(audio_file, **transcribe_options)
-
-        # Convert back to original openai format
-        segments = []
-        i = 0
-        for segment_chunk in segments_raw:
-            chunk = {}
-            chunk["start"] = segment_chunk.start
-            chunk["end"] = segment_chunk.end
-            chunk["text"] = segment_chunk.text
-            segments.append(chunk)
-            i += 1
-        print("transcribe audio done with fast whisper")
-    except Exception as e:
-        raise RuntimeError("Error converting video to audio")
-
-    try:
-        # Create embedding
-        def segment_embedding(segment):
-            audio = Audio()
-            start = segment["start"]
-            # Whisper overshoots the end timestamp in the last segment
-            end = min(duration, segment["end"])
-            clip = Segment(start, end)
-            waveform, sample_rate = audio.crop(audio_file, clip)
-            return embedding_model(waveform[None])
-
-        embeddings = np.zeros(shape=(len(segments), 192))
-        for i, segment in enumerate(segments):
-            embeddings[i] = segment_embedding(segment)
-        embeddings = np.nan_to_num(embeddings)
-        print(f'Embedding shape: {embeddings.shape}')
-
-        if num_speakers == 0:
-        # Find the best number of speakers
-            score_num_speakers = {}
-    
-            for num_speakers in range(2, 10+1):
-                clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
-                score = silhouette_score(embeddings, clustering.labels_, metric='euclidean')
-                score_num_speakers[num_speakers] = score
-            best_num_speaker = max(score_num_speakers, key=lambda x:score_num_speakers[x])
-            print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score")
-        else:
-            best_num_speaker = num_speakers
-            
-        # Assign speaker label   
-        clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings)
-        labels = clustering.labels_
-        for i in range(len(segments)):
-            segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
-
-        # Make output
-        objects = {
-            'Start' : [],
-            'End': [],
-            'Speaker': [],
-            'Text': []
-        }
-        text = ''
-        for (i, segment) in enumerate(segments):
-            if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
-                objects['Start'].append(str(convert_time(segment["start"])))
-                objects['Speaker'].append(segment["speaker"])
-                if i != 0:
-                    objects['End'].append(str(convert_time(segments[i - 1]["end"])))
-                    objects['Text'].append(text)
-                    text = ''
-            text += segment["text"] + ' '
-        objects['End'].append(str(convert_time(segments[i - 1]["end"])))
-        objects['Text'].append(text)
-        
-        time_end = time.time()
-        time_diff = time_end - time_start
-        memory = psutil.virtual_memory()
-        gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
-        gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
-        gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
-        system_info = f"""
-        *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.* 
-        *Processing time: {time_diff:.5} seconds.*
-        *GPU Utilization: {gpu_utilization}%, GPU Memory: {gpu_memory}MiB.*
-        """
-        save_path = "output/transcript_result.csv"
-        df_results = pd.DataFrame(objects)
-        df_results.to_csv(save_path)
-        return df_results, system_info, save_path
-    
-    except Exception as e:
-        raise RuntimeError("Error Running inference with local model", e)
-
-
-# ---- Gradio Layout -----
-# Inspiration from https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles
-video_in = gr.Video(label="Video file", mirror_webcam=False)
-youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True)
-df_init = pd.DataFrame(columns=['Start', 'End', 'Speaker', 'Text'])
-memory = psutil.virtual_memory()
-selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="en", label="Spoken language in video", interactive=True)
-selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model", interactive=True)
-number_speakers = gr.Number(precision=0, value=0, label="Input number of speakers for better results. If value=0, model will automatic find the best number of speakers", interactive=True)
-system_info = gr.Markdown(f"*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB*")
-download_transcript = gr.File(label="Download transcript")
-transcription_df = gr.DataFrame(value=df_init,label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
-title = "Whisper speaker diarization"
-demo = gr.Blocks(title=title)
-demo.encrypt = False
-
-
-with demo:
-    with gr.Tab("Whisper speaker diarization"):
-        gr.Markdown('''
-            <div>
-            <h1 style='text-align: center'>Whisper speaker diarization</h1>
-            This space uses Whisper models from <a href='https://github.com/openai/whisper' target='_blank'><b>OpenAI</b></a> with <a href='https://github.com/guillaumekln/faster-whisper' target='_blank'><b>CTranslate2</b></a> which is a fast inference engine for Transformer models to recognize the speech (4 times faster than original openai model with same accuracy)
-            and ECAPA-TDNN model from <a href='https://github.com/speechbrain/speechbrain' target='_blank'><b>SpeechBrain</b></a> to encode and clasify speakers
-            </div>
-        ''')
-
-        with gr.Row():
-            gr.Markdown('''
-            ### Transcribe youtube link using OpenAI Whisper
-            ##### 1. Using Open AI's Whisper model to seperate audio into segments and generate transcripts.
-            ##### 2. Generating speaker embeddings for each segments.
-            ##### 3. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
-            ''')
-            
-        with gr.Row():         
-            gr.Markdown('''
-                ### You can test by following examples:
-                ''')
-        examples = gr.Examples(examples=
-                [ "https://www.youtube.com/watch?v=j7BfEzAFuYc&t=32s", 
-                  "https://www.youtube.com/watch?v=-UX0X45sYe4", 
-                  "https://www.youtube.com/watch?v=7minSgqi-Gw"],
-              label="Examples", inputs=[youtube_url_in])
-              
-
-        with gr.Row():
-            with gr.Column():
-                youtube_url_in.render()
-                download_youtube_btn = gr.Button("Download Youtube video")
-                download_youtube_btn.click(get_youtube, [youtube_url_in], [
-                    video_in])
-                print(video_in)
-                
-
-        with gr.Row():
-            with gr.Column():
-                video_in.render()
-                with gr.Column():
-                    gr.Markdown('''
-                    ##### Here you can start the transcription process.
-                    ##### Please select the source language for transcription.
-                    ##### You can select a range of assumed numbers of speakers.
-                    ''')
-                selected_source_lang.render()
-                selected_whisper_model.render()
-                number_speakers.render()
-                transcribe_btn = gr.Button("Transcribe audio and diarization")
-                transcribe_btn.click(speech_to_text, 
-                                     [video_in, selected_source_lang, selected_whisper_model, number_speakers], 
-                                     [transcription_df, system_info, download_transcript]
-                                    )
-                
-        with gr.Row():
-            gr.Markdown('''
-            ##### Here you will get transcription  output
-            ##### ''')
-            
-
-        with gr.Row():
-            with gr.Column():
-                download_transcript.render()
-                transcription_df.render()
-                system_info.render()
-                gr.Markdown('''<center><img src='https://visitor-badge.glitch.me/badge?page_id=WhisperDiarizationSpeakers' alt='visitor badge'><a href="https://opensource.org/licenses/Apache-2.0"><img src='https://img.shields.io/badge/License-Apache_2.0-blue.svg' alt='License: Apache 2.0'></center>''')
-
-                
-    
-    with gr.Tab("Whisper Transcribe Japanese Audio"):
-        gr.Markdown(f'''
-              <div>
-              <h1 style='text-align: center'>Whisper Transcribe Japanese Audio</h1>
-              </div>
-              Transcribe long-form microphone or audio inputs with the click of a button! The fine-tuned
-              checkpoint <a href='https://huggingface.co/{MODEL_NAME}' target='_blank'><b>{MODEL_NAME}</b></a> to transcribe audio files of arbitrary length.
-          ''')
-        microphone = gr.inputs.Audio(source="microphone", type="filepath", optional=True)
-        upload = gr.inputs.Audio(source="upload", type="filepath", optional=True)
-        transcribe_btn = gr.Button("Transcribe Audio")
-        text_output = gr.Textbox()
-        with gr.Row():         
-            gr.Markdown('''
-                ### You can test by following examples:
-                ''')
-        examples = gr.Examples(examples=
-              [ "sample1.wav", 
-                "sample2.wav", 
-                ],
-              label="Examples", inputs=[upload])
-        transcribe_btn.click(transcribe, [microphone, upload], outputs=text_output)
-    
-    with gr.Tab("Whisper Transcribe Japanese YouTube"):
-        gr.Markdown(f'''
-              <div>
-              <h1 style='text-align: center'>Whisper Transcribe Japanese YouTube</h1>
-              </div>
-                Transcribe long-form YouTube videos with the click of a button! The fine-tuned checkpoint:
-                <a href='https://huggingface.co/{MODEL_NAME}' target='_blank'><b>{MODEL_NAME}</b></a> to transcribe audio files of arbitrary length.
-            ''')
-        youtube_link = gr.Textbox(label="Youtube url", lines=1, interactive=True)
-        yt_transcribe_btn = gr.Button("Transcribe YouTube")
-        text_output2 = gr.Textbox()
-        html_output = gr.Markdown()
-        yt_transcribe_btn.click(yt_transcribe, [youtube_link], outputs=[html_output, text_output2])
-
-demo.launch(debug=True)
\ No newline at end of file
diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/dataset_wrappers.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/dataset_wrappers.py
deleted file mode 100644
index d6a5e957ec3b44465432617cf6e8f0b86a8a5efa..0000000000000000000000000000000000000000
--- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/dataset_wrappers.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
-
-from .builder import DATASETS
-
-
-@DATASETS.register_module()
-class ConcatDataset(_ConcatDataset):
-    """A wrapper of concatenated dataset.
-
-    Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
-    concat the group flag for image aspect ratio.
-
-    Args:
-        datasets (list[:obj:`Dataset`]): A list of datasets.
-    """
-
-    def __init__(self, datasets):
-        super(ConcatDataset, self).__init__(datasets)
-        self.CLASSES = datasets[0].CLASSES
-        self.PALETTE = datasets[0].PALETTE
-
-
-@DATASETS.register_module()
-class RepeatDataset(object):
-    """A wrapper of repeated dataset.
-
-    The length of repeated dataset will be `times` larger than the original
-    dataset. This is useful when the data loading time is long but the dataset
-    is small. Using RepeatDataset can reduce the data loading time between
-    epochs.
-
-    Args:
-        dataset (:obj:`Dataset`): The dataset to be repeated.
-        times (int): Repeat times.
-    """
-
-    def __init__(self, dataset, times):
-        self.dataset = dataset
-        self.times = times
-        self.CLASSES = dataset.CLASSES
-        self.PALETTE = dataset.PALETTE
-        self._ori_len = len(self.dataset)
-
-    def __getitem__(self, idx):
-        """Get item from original dataset."""
-        return self.dataset[idx % self._ori_len]
-
-    def __len__(self):
-        """The length is multiplied by ``times``"""
-        return self.times * self._ori_len
diff --git a/spaces/wilson1/bingo/src/components/ui/alert-dialog.tsx b/spaces/wilson1/bingo/src/components/ui/alert-dialog.tsx
deleted file mode 100644
index 17fec4d16510328deacc1416569173c97761ef72..0000000000000000000000000000000000000000
--- a/spaces/wilson1/bingo/src/components/ui/alert-dialog.tsx
+++ /dev/null
@@ -1,150 +0,0 @@
-'use client'
-
-import * as React from 'react'
-import * as AlertDialogPrimitive from '@radix-ui/react-alert-dialog'
-
-import { cn } from '@/lib/utils'
-import { buttonVariants } from '@/components/ui/button'
-
-const AlertDialog = AlertDialogPrimitive.Root
-
-const AlertDialogTrigger = AlertDialogPrimitive.Trigger
-
-const AlertDialogPortal = ({
-  className,
-  children,
-  ...props
-}: AlertDialogPrimitive.AlertDialogPortalProps) => (
-  <AlertDialogPrimitive.Portal className={cn(className)} {...props}>
-    <div className="fixed inset-0 z-50 flex items-end justify-center sm:items-center">
-      {children}
-    </div>
-  </AlertDialogPrimitive.Portal>
-)
-AlertDialogPortal.displayName = AlertDialogPrimitive.Portal.displayName
-
-const AlertDialogOverlay = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Overlay>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Overlay>
->(({ className, children, ...props }, ref) => (
-  <AlertDialogPrimitive.Overlay
-    className={cn(
-      'fixed inset-0 z-50 bg-background/80 backdrop-blur-sm transition-opacity animate-in fade-in',
-      className
-    )}
-    {...props}
-    ref={ref}
-  />
-))
-AlertDialogOverlay.displayName = AlertDialogPrimitive.Overlay.displayName
-
-const AlertDialogContent = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Content>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Content>
->(({ className, ...props }, ref) => (
-  <AlertDialogPortal>
-    <AlertDialogOverlay />
-    <AlertDialogPrimitive.Content
-      ref={ref}
-      className={cn(
-        'fixed z-50 grid w-full max-w-lg scale-100 gap-4 border bg-background p-6 opacity-100 shadow-lg animate-in fade-in-90 slide-in-from-bottom-10 sm:rounded-lg sm:zoom-in-90 sm:slide-in-from-bottom-0 md:w-full',
-        className
-      )}
-      {...props}
-    />
-  </AlertDialogPortal>
-))
-AlertDialogContent.displayName = AlertDialogPrimitive.Content.displayName
-
-const AlertDialogHeader = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col space-y-2 text-center sm:text-left',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogHeader.displayName = 'AlertDialogHeader'
-
-const AlertDialogFooter = ({
-  className,
-  ...props
-}: React.HTMLAttributes<HTMLDivElement>) => (
-  <div
-    className={cn(
-      'flex flex-col-reverse sm:flex-row sm:justify-end sm:space-x-2',
-      className
-    )}
-    {...props}
-  />
-)
-AlertDialogFooter.displayName = 'AlertDialogFooter'
-
-const AlertDialogTitle = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Title>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Title>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Title
-    ref={ref}
-    className={cn('text-lg font-semibold', className)}
-    {...props}
-  />
-))
-AlertDialogTitle.displayName = AlertDialogPrimitive.Title.displayName
-
-const AlertDialogDescription = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Description>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Description>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Description
-    ref={ref}
-    className={cn('text-sm text-muted-foreground', className)}
-    {...props}
-  />
-))
-AlertDialogDescription.displayName =
-  AlertDialogPrimitive.Description.displayName
-
-const AlertDialogAction = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Action>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Action>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Action
-    ref={ref}
-    className={cn(buttonVariants(), className)}
-    {...props}
-  />
-))
-AlertDialogAction.displayName = AlertDialogPrimitive.Action.displayName
-
-const AlertDialogCancel = React.forwardRef<
-  React.ElementRef<typeof AlertDialogPrimitive.Cancel>,
-  React.ComponentPropsWithoutRef<typeof AlertDialogPrimitive.Cancel>
->(({ className, ...props }, ref) => (
-  <AlertDialogPrimitive.Cancel
-    ref={ref}
-    className={cn(
-      buttonVariants({ variant: 'outline' }),
-      'mt-2 sm:mt-0',
-      className
-    )}
-    {...props}
-  />
-))
-AlertDialogCancel.displayName = AlertDialogPrimitive.Cancel.displayName
-
-export {
-  AlertDialog,
-  AlertDialogTrigger,
-  AlertDialogContent,
-  AlertDialogHeader,
-  AlertDialogFooter,
-  AlertDialogTitle,
-  AlertDialogDescription,
-  AlertDialogAction,
-  AlertDialogCancel
-}
diff --git a/spaces/xSaXx/llama2-70b-nochat/README.md b/spaces/xSaXx/llama2-70b-nochat/README.md
deleted file mode 100644
index 4519d2afc6eeaacb9c2936c7050ee560ab8ef822..0000000000000000000000000000000000000000
--- a/spaces/xSaXx/llama2-70b-nochat/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: Llama2 70b Nochat
-emoji: 🏢
-colorFrom: yellow
-colorTo: red
-sdk: gradio
-sdk_version: 3.40.1
-app_file: app.py
-pinned: false
-license: llama2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/inceptionv4.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/inceptionv4.py
deleted file mode 100644
index b14916f140712298866c943ebdb4ebad67d72fc4..0000000000000000000000000000000000000000
--- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/inceptionv4.py
+++ /dev/null
@@ -1,381 +0,0 @@
-from __future__ import division, absolute_import
-import torch
-import torch.nn as nn
-import torch.utils.model_zoo as model_zoo
-
-__all__ = ['inceptionv4']
-"""
-Code imported from https://github.com/Cadene/pretrained-models.pytorch
-"""
-
-pretrained_settings = {
-    'inceptionv4': {
-        'imagenet': {
-            'url':
-            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
-            'input_space': 'RGB',
-            'input_size': [3, 299, 299],
-            'input_range': [0, 1],
-            'mean': [0.5, 0.5, 0.5],
-            'std': [0.5, 0.5, 0.5],
-            'num_classes': 1000
-        },
-        'imagenet+background': {
-            'url':
-            'http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth',
-            'input_space': 'RGB',
-            'input_size': [3, 299, 299],
-            'input_range': [0, 1],
-            'mean': [0.5, 0.5, 0.5],
-            'std': [0.5, 0.5, 0.5],
-            'num_classes': 1001
-        }
-    }
-}
-
-
-class BasicConv2d(nn.Module):
-
-    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
-        super(BasicConv2d, self).__init__()
-        self.conv = nn.Conv2d(
-            in_planes,
-            out_planes,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=padding,
-            bias=False
-        ) # verify bias false
-        self.bn = nn.BatchNorm2d(
-            out_planes,
-            eps=0.001, # value found in tensorflow
-            momentum=0.1, # default pytorch value
-            affine=True
-        )
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        x = self.bn(x)
-        x = self.relu(x)
-        return x
-
-
-class Mixed_3a(nn.Module):
-
-    def __init__(self):
-        super(Mixed_3a, self).__init__()
-        self.maxpool = nn.MaxPool2d(3, stride=2)
-        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
-
-    def forward(self, x):
-        x0 = self.maxpool(x)
-        x1 = self.conv(x)
-        out = torch.cat((x0, x1), 1)
-        return out
-
-
-class Mixed_4a(nn.Module):
-
-    def __init__(self):
-        super(Mixed_4a, self).__init__()
-
-        self.branch0 = nn.Sequential(
-            BasicConv2d(160, 64, kernel_size=1, stride=1),
-            BasicConv2d(64, 96, kernel_size=3, stride=1)
-        )
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(160, 64, kernel_size=1, stride=1),
-            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
-            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
-            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1)
-        )
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        out = torch.cat((x0, x1), 1)
-        return out
-
-
-class Mixed_5a(nn.Module):
-
-    def __init__(self):
-        super(Mixed_5a, self).__init__()
-        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
-        self.maxpool = nn.MaxPool2d(3, stride=2)
-
-    def forward(self, x):
-        x0 = self.conv(x)
-        x1 = self.maxpool(x)
-        out = torch.cat((x0, x1), 1)
-        return out
-
-
-class Inception_A(nn.Module):
-
-    def __init__(self):
-        super(Inception_A, self).__init__()
-        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(384, 64, kernel_size=1, stride=1),
-            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1)
-        )
-
-        self.branch2 = nn.Sequential(
-            BasicConv2d(384, 64, kernel_size=1, stride=1),
-            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
-            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
-        )
-
-        self.branch3 = nn.Sequential(
-            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
-            BasicConv2d(384, 96, kernel_size=1, stride=1)
-        )
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        x3 = self.branch3(x)
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
-
-
-class Reduction_A(nn.Module):
-
-    def __init__(self):
-        super(Reduction_A, self).__init__()
-        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(384, 192, kernel_size=1, stride=1),
-            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
-            BasicConv2d(224, 256, kernel_size=3, stride=2)
-        )
-
-        self.branch2 = nn.MaxPool2d(3, stride=2)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        out = torch.cat((x0, x1, x2), 1)
-        return out
-
-
-class Inception_B(nn.Module):
-
-    def __init__(self):
-        super(Inception_B, self).__init__()
-        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(1024, 192, kernel_size=1, stride=1),
-            BasicConv2d(
-                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
-            ),
-            BasicConv2d(
-                224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)
-            )
-        )
-
-        self.branch2 = nn.Sequential(
-            BasicConv2d(1024, 192, kernel_size=1, stride=1),
-            BasicConv2d(
-                192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)
-            ),
-            BasicConv2d(
-                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)
-            ),
-            BasicConv2d(
-                224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)
-            ),
-            BasicConv2d(
-                224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
-            )
-        )
-
-        self.branch3 = nn.Sequential(
-            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
-            BasicConv2d(1024, 128, kernel_size=1, stride=1)
-        )
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        x3 = self.branch3(x)
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
-
-
-class Reduction_B(nn.Module):
-
-    def __init__(self):
-        super(Reduction_B, self).__init__()
-
-        self.branch0 = nn.Sequential(
-            BasicConv2d(1024, 192, kernel_size=1, stride=1),
-            BasicConv2d(192, 192, kernel_size=3, stride=2)
-        )
-
-        self.branch1 = nn.Sequential(
-            BasicConv2d(1024, 256, kernel_size=1, stride=1),
-            BasicConv2d(
-                256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)
-            ),
-            BasicConv2d(
-                256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)
-            ), BasicConv2d(320, 320, kernel_size=3, stride=2)
-        )
-
-        self.branch2 = nn.MaxPool2d(3, stride=2)
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-        x1 = self.branch1(x)
-        x2 = self.branch2(x)
-        out = torch.cat((x0, x1, x2), 1)
-        return out
-
-
-class Inception_C(nn.Module):
-
-    def __init__(self):
-        super(Inception_C, self).__init__()
-
-        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
-
-        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
-        self.branch1_1a = BasicConv2d(
-            384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
-        )
-        self.branch1_1b = BasicConv2d(
-            384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
-        )
-
-        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
-        self.branch2_1 = BasicConv2d(
-            384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0)
-        )
-        self.branch2_2 = BasicConv2d(
-            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1)
-        )
-        self.branch2_3a = BasicConv2d(
-            512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1)
-        )
-        self.branch2_3b = BasicConv2d(
-            512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0)
-        )
-
-        self.branch3 = nn.Sequential(
-            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
-            BasicConv2d(1536, 256, kernel_size=1, stride=1)
-        )
-
-    def forward(self, x):
-        x0 = self.branch0(x)
-
-        x1_0 = self.branch1_0(x)
-        x1_1a = self.branch1_1a(x1_0)
-        x1_1b = self.branch1_1b(x1_0)
-        x1 = torch.cat((x1_1a, x1_1b), 1)
-
-        x2_0 = self.branch2_0(x)
-        x2_1 = self.branch2_1(x2_0)
-        x2_2 = self.branch2_2(x2_1)
-        x2_3a = self.branch2_3a(x2_2)
-        x2_3b = self.branch2_3b(x2_2)
-        x2 = torch.cat((x2_3a, x2_3b), 1)
-
-        x3 = self.branch3(x)
-
-        out = torch.cat((x0, x1, x2, x3), 1)
-        return out
-
-
-class InceptionV4(nn.Module):
-    """Inception-v4.
-
-    Reference:
-        Szegedy et al. Inception-v4, Inception-ResNet and the Impact of Residual
-        Connections on Learning. AAAI 2017.
-
-    Public keys:
-        - ``inceptionv4``: InceptionV4.
-    """
-
-    def __init__(self, num_classes, loss, **kwargs):
-        super(InceptionV4, self).__init__()
-        self.loss = loss
-
-        self.features = nn.Sequential(
-            BasicConv2d(3, 32, kernel_size=3, stride=2),
-            BasicConv2d(32, 32, kernel_size=3, stride=1),
-            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
-            Mixed_3a(),
-            Mixed_4a(),
-            Mixed_5a(),
-            Inception_A(),
-            Inception_A(),
-            Inception_A(),
-            Inception_A(),
-            Reduction_A(), # Mixed_6a
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Inception_B(),
-            Reduction_B(), # Mixed_7a
-            Inception_C(),
-            Inception_C(),
-            Inception_C()
-        )
-        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
-        self.classifier = nn.Linear(1536, num_classes)
-
-    def forward(self, x):
-        f = self.features(x)
-        v = self.global_avgpool(f)
-        v = v.view(v.size(0), -1)
-
-        if not self.training:
-            return v
-
-        y = self.classifier(v)
-
-        if self.loss == 'softmax':
-            return y
-        elif self.loss == 'triplet':
-            return y, v
-        else:
-            raise KeyError('Unsupported loss: {}'.format(self.loss))
-
-
-def init_pretrained_weights(model, model_url):
-    """Initializes model with pretrained weights.
-    
-    Layers that don't match with pretrained layers in name or size are kept unchanged.
-    """
-    pretrain_dict = model_zoo.load_url(model_url)
-    model_dict = model.state_dict()
-    pretrain_dict = {
-        k: v
-        for k, v in pretrain_dict.items()
-        if k in model_dict and model_dict[k].size() == v.size()
-    }
-    model_dict.update(pretrain_dict)
-    model.load_state_dict(model_dict)
-
-
-def inceptionv4(num_classes, loss='softmax', pretrained=True, **kwargs):
-    model = InceptionV4(num_classes, loss, **kwargs)
-    if pretrained:
-        model_url = pretrained_settings['inceptionv4']['imagenet']['url']
-        init_pretrained_weights(model, model_url)
-    return model
diff --git a/spaces/xiang-wuu/yolov5/utils/loggers/wandb/README.md b/spaces/xiang-wuu/yolov5/utils/loggers/wandb/README.md
deleted file mode 100644
index d78324b4c8e9405f388091310227d51d1ead5712..0000000000000000000000000000000000000000
--- a/spaces/xiang-wuu/yolov5/utils/loggers/wandb/README.md
+++ /dev/null
@@ -1,162 +0,0 @@
-📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021.
-
-- [About Weights & Biases](#about-weights-&-biases)
-- [First-Time Setup](#first-time-setup)
-- [Viewing runs](#viewing-runs)
-- [Disabling wandb](#disabling-wandb)
-- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
-- [Reports: Share your work with the world!](#reports)
-
-## About Weights & Biases
-
-Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
-
-Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
-
-- [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
-- [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
-- [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
-- [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
-- [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
-- [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
-
-## First-Time Setup
-
-<details open>
- <summary> Toggle Details </summary>
-When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.
-
-W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as:
-
-```shell
-$ python train.py --project ... --name ...
-```
-
-YOLOv5 notebook example: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-<img width="960" alt="Screen Shot 2021-09-29 at 10 23 13 PM" src="https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png">
-
-</details>
-
-## Viewing Runs
-
-<details open>
-  <summary> Toggle Details </summary>
-Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in <b>realtime</b> . All important information is logged:
-
-- Training & Validation losses
-- Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95
-- Learning Rate over time
-- A bounding box debugging panel, showing the training progress over time
-- GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**
-- System: Disk I/0, CPU utilization, RAM memory usage
-- Your trained model as W&B Artifact
-- Environment: OS and Python types, Git repository and state, **training command**
-
-<p align="center"><img width="900" alt="Weights & Biases dashboard" src="https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png"></p>
-</details>
-
-## Disabling wandb
-
-- training after running `wandb disabled` inside that directory creates no wandb run
-  ![Screenshot (84)](https://user-images.githubusercontent.com/15766192/143441777-c780bdd7-7cb4-4404-9559-b4316030a985.png)
-
-- To enable wandb again, run `wandb online`
-  ![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png)
-
-## Advanced Usage
-
-You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
-
-<details open>
- <h3> 1: Train and Log Evaluation simultaneousy </h3>
-   This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
-   Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
-   so no images will be uploaded from your system more than once.
- <details open>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --upload_data val</code>
-
-![Screenshot from 2021-11-21 17-40-06](https://user-images.githubusercontent.com/15766192/142761183-c1696d8c-3f38-45ab-991a-bb0dfd98ae7d.png)
-
-</details>
-
-<h3>2. Visualize and Version Datasets</h3>
- Log, visualize, dynamically query, and understand your data with <a href='https://docs.wandb.ai/guides/data-vis/tables'>W&B Tables</a>. You can use the following command to log your dataset as a W&B Table. This will generate a <code>{dataset}_wandb.yaml</code> file which can be used to train from dataset artifact.
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
-
-![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png)
-
-</details>
-
-<h3> 3: Train using dataset artifact </h3>
-   When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
-   can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
- <details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --data {data}_wandb.yaml </code>
-
-![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png)
-
-</details>
-
-<h3> 4: Save model checkpoints as artifacts </h3>
-  To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
-  You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
-
-<details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --save_period 1 </code>
-
-![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png)
-
-</details>
-
-</details>
-
-<h3> 5: Resume runs from checkpoint artifacts. </h3>
-Any run can be resumed using artifacts if the <code>--resume</code> argument starts with <code>wandb-artifact://</code> prefix followed by the run path, i.e, <code>wandb-artifact://username/project/runid </code>. This doesn't require the model checkpoint to be present on the local system.
-
-<details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-
-![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
-
-</details>
-
-<h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
- <b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
- The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
- train from <code>_wandb.yaml</code> file and set <code>--save_period</code>
-
-<details>
-  <summary> <b>Usage</b> </summary>
-   <b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
-
-![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png)
-
-</details>
-
-</details>
-
-<h3> Reports </h3>
-W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).
-
-<img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">
-
-## Environments
-
-YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
-- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
-- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-
-## Status
-
-![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
-
-If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/spaces/xikacat/xikacatbing/Dockerfile b/spaces/xikacat/xikacatbing/Dockerfile
deleted file mode 100644
index 3698c7cb7938e025afc53b18a571ae2961fbdffe..0000000000000000000000000000000000000000
--- a/spaces/xikacat/xikacatbing/Dockerfile
+++ /dev/null
@@ -1,34 +0,0 @@
-# Build Stage
-# 使用 golang:alpine 作为构建阶段的基础镜像
-FROM golang:alpine AS builder
-
-# 添加 git，以便之后能从GitHub克隆项目
-RUN apk --no-cache add git
-
-# 从 GitHub 克隆 go-proxy-bingai 项目到 /workspace/app 目录下
-RUN git clone https://github.com/Harry-zklcdc/go-proxy-bingai.git /workspace/app
-
-# 设置工作目录为之前克隆的项目目录
-WORKDIR /workspace/app
-
-# 编译 go 项目。-ldflags="-s -w" 是为了减少编译后的二进制大小
-RUN go build -ldflags="-s -w" -tags netgo -trimpath -o go-proxy-bingai main.go
-
-# Runtime Stage
-# 使用轻量级的 alpine 镜像作为运行时的基础镜像
-FROM alpine
-
-# 设置工作目录
-WORKDIR /workspace/app
-
-# 从构建阶段复制编译后的二进制文件到运行时镜像中
-COPY --from=builder /workspace/app/go-proxy-bingai .
-
-# 设置环境变量，此处为随机字符
-ENV Go_Proxy_BingAI_USER_TOKEN_1="kJs8hD92ncMzLaoQWYtX5rG6bE3fZ4iO"
-
-# 暴露8080端口
-EXPOSE 8080
-
-# 容器启动时运行的命令
-CMD ["/workspace/app/go-proxy-bingai"]
\ No newline at end of file
diff --git a/spaces/xswu/HPSv2/src/__init__.py b/spaces/xswu/HPSv2/src/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/spaces/xuwenhao83/simple_chatbot/app.py b/spaces/xuwenhao83/simple_chatbot/app.py
deleted file mode 100644
index 57dd8c74bd7c1e5441f3893bb63fcb04dcef4407..0000000000000000000000000000000000000000
--- a/spaces/xuwenhao83/simple_chatbot/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import openai
-import os
-import gradio as gr
-
-openai.api_key = os.environ.get("OPENAI_API_KEY")
-
-class Conversation:
-    def __init__(self, prompt, num_of_round):
-        self.prompt = prompt
-        self.num_of_round = num_of_round
-        self.messages = []
-        self.messages.append({"role": "system", "content": self.prompt})
-
-    def ask(self, question):
-        try:
-            self.messages.append( {"role": "user", "content": question})
-            response = openai.ChatCompletion.create(
-                model="gpt-3.5-turbo",
-                messages=self.messages,
-                temperature=0.5,
-                max_tokens=2048,
-                top_p=1,
-            )
-        except Exception as e:
-            print(e)
-            return e
-
-        message = response["choices"][0]["message"]["content"]
-        self.messages.append({"role": "assistant", "content": message})
-        
-        if len(self.messages) > self.num_of_round*2 + 1:
-            del self.messages[1:3]
-        return message
-    
-
-prompt = """你是一个中国厨师，用中文回答做菜的问题。你的回答需要满足以下要求:
-1. 你的回答必须是中文
-2. 回答限制在100个字以内"""
-
-conv = Conversation(prompt, 5)
-
-def predict(input, history=[]):
-    history.append(input)
-    response = conv.ask(input)
-    history.append(response)
-    responses = [(u,b) for u,b in zip(history[::2], history[1::2])]
-    return responses, history
-
-with gr.Blocks(css="#chatbot{height:350px} .overflow-y-auto{height:500px}") as demo:
-    chatbot = gr.Chatbot(elem_id="chatbot")
-    state = gr.State([])
-
-    with gr.Row():
-        txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter").style(container=False)
-
-    txt.submit(predict, [txt, state], [chatbot, state])
-
-demo.launch()
\ No newline at end of file
diff --git a/spaces/xxccc/gpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h b/spaces/xxccc/gpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h
deleted file mode 100644
index c9004bb8043a12e32814436baa6262a00c8ef68e..0000000000000000000000000000000000000000
--- a/spaces/xxccc/gpt-academic/crazy_functions/test_project/cpp/cppipc/prod_cons.h
+++ /dev/null
@@ -1,433 +0,0 @@
-#pragma once
-
-#include <atomic>
-#include <utility>
-#include <cstring>
-#include <type_traits>
-#include <cstdint>
-
-#include "libipc/def.h"
-
-#include "libipc/platform/detail.h"
-#include "libipc/circ/elem_def.h"
-#include "libipc/utility/log.h"
-#include "libipc/utility/utility.h"
-
-namespace ipc {
-
-////////////////////////////////////////////////////////////////
-/// producer-consumer implementation
-////////////////////////////////////////////////////////////////
-
-template <typename Flag>
-struct prod_cons_impl;
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> rd_; // read index
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_; // write index
-
-    constexpr circ::u2_t cursor() const noexcept {
-        return 0;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        auto cur_wt = circ::index_of(wt_.load(std::memory_order_relaxed));
-        if (cur_wt == circ::index_of(rd_.load(std::memory_order_acquire) - 1)) {
-            return false; // full
-        }
-        std::forward<F>(f)(&(elems[cur_wt].data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    /**
-     * In single-single-unicast, 'force_push' means 'no reader' or 'the only one reader is dead'.
-     * So we could just disconnect all connections of receiver, and return false.
-    */
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(~static_cast<circ::cc_t>(0u));
-        return false;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E* elems) {
-        auto cur_rd = circ::index_of(rd_.load(std::memory_order_relaxed));
-        if (cur_rd == circ::index_of(wt_.load(std::memory_order_acquire))) {
-            return false; // empty
-        }
-        std::forward<F>(f)(&(elems[cur_rd].data_));
-        std::forward<R>(out)(true);
-        rd_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi , trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::single, trans::unicast>> {
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            if (circ::index_of(cur_rd) ==
-                circ::index_of(wt_.load(std::memory_order_acquire))) {
-                return false; // empty
-            }
-            std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-            if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                std::forward<F>(f)(buff);
-                std::forward<R>(out)(true);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi , relat::multi, trans::unicast>>
-     : prod_cons_impl<wr<relat::single, relat::multi, trans::unicast>> {
-
-    using flag_t = std::uint64_t;
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_; // commit index
-
-    template <typename W, typename F, typename E>
-    bool push(W* /*wrapper*/, F&& f, E* elems) {
-        circ::u2_t cur_ct, nxt_ct;
-        for (unsigned k = 0;;) {
-            cur_ct = ct_.load(std::memory_order_relaxed);
-            if (circ::index_of(nxt_ct = cur_ct + 1) ==
-                circ::index_of(rd_.load(std::memory_order_acquire))) {
-                return false; // full
-            }
-            if (ct_.compare_exchange_weak(cur_ct, nxt_ct, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        auto* el = elems + circ::index_of(cur_ct);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        while (1) {
-            auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-            if (cur_ct != wt_.load(std::memory_order_relaxed)) {
-                return true;
-            }
-            if ((~cac_ct) != cur_ct) {
-                return true;
-            }
-            if (!el->f_ct_.compare_exchange_strong(cac_ct, 0, std::memory_order_relaxed)) {
-                return true;
-            }
-            wt_.store(nxt_ct, std::memory_order_release);
-            cur_ct = nxt_ct;
-            nxt_ct = cur_ct + 1;
-            el = elems + circ::index_of(cur_ct);
-        }
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&&, E*) {
-        wrapper->elems()->disconnect_receiver(1);
-        return false;
-    }
-
-    template <typename W, typename F, typename R, 
-              template <std::size_t, std::size_t> class E, std::size_t DS, std::size_t AS>
-    bool pop(W* /*wrapper*/, circ::u2_t& /*cur*/, F&& f, R&& out, E<DS, AS>* elems) {
-        byte_t buff[DS];
-        for (unsigned k = 0;;) {
-            auto cur_rd = rd_.load(std::memory_order_relaxed);
-            auto cur_wt = wt_.load(std::memory_order_acquire);
-            auto id_rd  = circ::index_of(cur_rd);
-            auto id_wt  = circ::index_of(cur_wt);
-            if (id_rd == id_wt) {
-                auto* el = elems + id_wt;
-                auto cac_ct = el->f_ct_.load(std::memory_order_acquire);
-                if ((~cac_ct) != cur_wt) {
-                    return false; // empty
-                }
-                if (el->f_ct_.compare_exchange_weak(cac_ct, 0, std::memory_order_relaxed)) {
-                    wt_.store(cur_wt + 1, std::memory_order_release);
-                }
-                k = 0;
-            }
-            else {
-                std::memcpy(buff, &(elems[circ::index_of(cur_rd)].data_), sizeof(buff));
-                if (rd_.compare_exchange_weak(cur_rd, cur_rd + 1, std::memory_order_release)) {
-                    std::forward<F>(f)(buff);
-                    std::forward<R>(out)(true);
-                    return true;
-                }
-                ipc::yield(k);
-            }
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::single, relat::multi, trans::broadcast>> {
-
-    using rc_t = std::uint64_t;
-
-    enum : rc_t {
-        ep_mask = 0x00000000ffffffffull,
-        ep_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t> rc_ { 0 }; // read-counter
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> wt_;   // write index
-    alignas(cache_line_size) rc_t epoch_ { 0 };             // only one writer
-
-    circ::u2_t cursor() const noexcept {
-        return wt_.load(std::memory_order_acquire);
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch_)) {
-                return false; // has not finished yet
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        epoch_ += ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(wt_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & ep_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, epoch_ | static_cast<rc_t>(cc), std::memory_order_release)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        std::forward<F>(f)(&(el->data_));
-        wt_.fetch_add(1, std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E* elems) {
-        if (cur == cursor()) return false; // acquire
-        auto* el = elems + circ::index_of(cur++);
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & ep_mask) == 0) {
-                std::forward<R>(out)(true);
-                return true;
-            }
-            auto nxt_rc = cur_rc & ~static_cast<rc_t>(wrapper->connected_id());
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)((nxt_rc & ep_mask) == 0);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-template <>
-struct prod_cons_impl<wr<relat::multi, relat::multi, trans::broadcast>> {
-
-    using rc_t   = std::uint64_t;
-    using flag_t = std::uint64_t;
-
-    enum : rc_t {
-        rc_mask = 0x00000000ffffffffull,
-        ep_mask = 0x00ffffffffffffffull,
-        ep_incr = 0x0100000000000000ull,
-        ic_mask = 0xff000000ffffffffull,
-        ic_incr = 0x0000000100000000ull
-    };
-
-    template <std::size_t DataSize, std::size_t AlignSize>
-    struct elem_t {
-        std::aligned_storage_t<DataSize, AlignSize> data_ {};
-        std::atomic<rc_t  > rc_   { 0 }; // read-counter
-        std::atomic<flag_t> f_ct_ { 0 }; // commit flag
-    };
-
-    alignas(cache_line_size) std::atomic<circ::u2_t> ct_;   // commit index
-    alignas(cache_line_size) std::atomic<rc_t> epoch_ { 0 };
-
-    circ::u2_t cursor() const noexcept {
-        return ct_.load(std::memory_order_acquire);
-    }
-
-    constexpr static rc_t inc_rc(rc_t rc) noexcept {
-        return (rc & ic_mask) | ((rc + ic_incr) & ~ic_mask);
-    }
-
-    constexpr static rc_t inc_mask(rc_t rc) noexcept {
-        return inc_rc(rc) & ~rc_mask;
-    }
-
-    template <typename W, typename F, typename E>
-    bool push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.load(std::memory_order_acquire);
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_relaxed);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if ((cc & rem_cc) && ((cur_rc & ~ep_mask) == epoch)) {
-                return false; // has not finished yet
-            }
-            else if (!rem_cc) {
-                auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-                if ((cur_fl != cur_ct) && cur_fl) {
-                    return false; // full
-                }
-            }
-            // consider rem_cc to be 0 here
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed) &&
-                epoch_.compare_exchange_weak(epoch, epoch, std::memory_order_acq_rel)) {
-                break;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename E>
-    bool force_push(W* wrapper, F&& f, E* elems) {
-        E* el;
-        circ::u2_t cur_ct;
-        rc_t epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-        for (unsigned k = 0;;) {
-            circ::cc_t cc = wrapper->elems()->connections(std::memory_order_relaxed);
-            if (cc == 0) return false; // no reader
-            el = elems + circ::index_of(cur_ct = ct_.load(std::memory_order_relaxed));
-            // check all consumers have finished reading this element
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            circ::cc_t rem_cc = cur_rc & rc_mask;
-            if (cc & rem_cc) {
-                ipc::log("force_push: k = %u, cc = %u, rem_cc = %u\n", k, cc, rem_cc);
-                cc = wrapper->elems()->disconnect_receiver(rem_cc); // disconnect all invalid readers
-                if (cc == 0) return false; // no reader
-            }
-            // just compare & exchange
-            if (el->rc_.compare_exchange_weak(
-                        cur_rc, inc_mask(epoch | (cur_rc & ep_mask)) | static_cast<rc_t>(cc), std::memory_order_relaxed)) {
-                if (epoch == epoch_.load(std::memory_order_acquire)) {
-                    break;
-                }
-                else if (push(wrapper, std::forward<F>(f), elems)) {
-                    return true;
-                }
-                epoch = epoch_.fetch_add(ep_incr, std::memory_order_release) + ep_incr;
-            }
-            ipc::yield(k);
-        }
-        // only one thread/process would touch here at one time
-        ct_.store(cur_ct + 1, std::memory_order_release);
-        std::forward<F>(f)(&(el->data_));
-        // set flag & try update wt
-        el->f_ct_.store(~static_cast<flag_t>(cur_ct), std::memory_order_release);
-        return true;
-    }
-
-    template <typename W, typename F, typename R, typename E, std::size_t N>
-    bool pop(W* wrapper, circ::u2_t& cur, F&& f, R&& out, E(& elems)[N]) {
-        auto* el = elems + circ::index_of(cur);
-        auto cur_fl = el->f_ct_.load(std::memory_order_acquire);
-        if (cur_fl != ~static_cast<flag_t>(cur)) {
-            return false; // empty
-        }
-        ++cur;
-        std::forward<F>(f)(&(el->data_));
-        for (unsigned k = 0;;) {
-            auto cur_rc = el->rc_.load(std::memory_order_acquire);
-            if ((cur_rc & rc_mask) == 0) {
-                std::forward<R>(out)(true);
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-                return true;
-            }
-            auto nxt_rc = inc_rc(cur_rc) & ~static_cast<rc_t>(wrapper->connected_id());
-            bool last_one = false;
-            if ((last_one = (nxt_rc & rc_mask) == 0)) {
-                el->f_ct_.store(cur + N - 1, std::memory_order_release);
-            }
-            if (el->rc_.compare_exchange_weak(cur_rc, nxt_rc, std::memory_order_release)) {
-                std::forward<R>(out)(last_one);
-                return true;
-            }
-            ipc::yield(k);
-        }
-    }
-};
-
-} // namespace ipc
diff --git a/spaces/xxccc/gpt-academic/docs/README.md.Italian.md b/spaces/xxccc/gpt-academic/docs/README.md.Italian.md
deleted file mode 100644
index 04c5ed6bec16d90d6ce53852b4902e8b7d8c55a8..0000000000000000000000000000000000000000
--- a/spaces/xxccc/gpt-academic/docs/README.md.Italian.md
+++ /dev/null
@@ -1,310 +0,0 @@
-> **Nota**
->
-> Durante l'installazione delle dipendenze, selezionare rigorosamente le **versioni specificate** nel file requirements.txt.
->
-> ` pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/`
-
-# <img src="docs/logo.png" width="40" > GPT Ottimizzazione Accademica (GPT Academic)
-
-**Se ti piace questo progetto, ti preghiamo di dargli una stella. Se hai sviluppato scorciatoie accademiche o plugin funzionali più utili, non esitare ad aprire una issue o pull request. Abbiamo anche una README in [Inglese|](docs/README_EN.md)[Giapponese|](docs/README_JP.md)[Coreano|](https://github.com/mldljyh/ko_gpt_academic)[Russo|](docs/README_RS.md)[Francese](docs/README_FR.md) tradotta da questo stesso progetto.
-Per tradurre questo progetto in qualsiasi lingua con GPT, leggere e eseguire [`multi_language.py`](multi_language.py) (sperimentale).
-
-> **Nota**
->
-> 1. Si prega di notare che solo i plugin (pulsanti) contrassegnati in **rosso** supportano la lettura di file, alcuni plugin sono posizionati nel **menu a discesa** nella zona dei plugin. Accettiamo e gestiamo PR per qualsiasi nuovo plugin con **massima priorità**!
->
-> 2. Le funzionalità di ogni file di questo progetto sono descritte dettagliatamente nella propria analisi di autotraduzione [`self_analysis.md`](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A). Con l'iterazione delle versioni, è possibile fare clic sui plugin funzionali correlati in qualsiasi momento per richiamare GPT e generare nuovamente il rapporto di analisi automatica del progetto. Le domande frequenti sono riassunte nella [`wiki`](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98). [Metodo di installazione] (#installazione).
->
-> 3. Questo progetto è compatibile e incoraggia l'utilizzo di grandi modelli di linguaggio di produzione nazionale come chatglm, RWKV, Pangu ecc. Supporta la coesistenza di più api-key e può essere compilato nel file di configurazione come `API_KEY="openai-key1,openai-key2,api2d-key3"`. Per sostituire temporaneamente `API_KEY`, inserire `API_KEY` temporaneo nell'area di input e premere Invio per renderlo effettivo. 
-
-<div align="center">Funzione | Descrizione
---- | ---
-Correzione immediata | Supporta correzione immediata e ricerca degli errori di grammatica del documento con un solo clic
-Traduzione cinese-inglese immediata | Traduzione cinese-inglese immediata con un solo clic
-Spiegazione del codice immediata | Visualizzazione del codice, spiegazione del codice, generazione del codice, annotazione del codice con un solo clic
-[Scorciatoie personalizzate](https://www.bilibili.com/video/BV14s4y1E7jN) | Supporta scorciatoie personalizzate
-Design modularizzato | Supporta potenti [plugin di funzioni](https://github.com/binary-husky/chatgpt_academic/tree/master/crazy_functions) personalizzati, i plugin supportano l'[aggiornamento in tempo reale](https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97)
-[Auto-profiling del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] [Comprensione immediata](https://github.com/binary-husky/chatgpt_academic/wiki/chatgpt-academic%E9%A1%B9%E7%9B%AE%E8%87%AA%E8%AF%91%E8%A7%A3%E6%8A%A5%E5%91%8A) del codice sorgente di questo progetto
-[Analisi del programma](https://www.bilibili.com/video/BV1cj411A7VW) | [Plugin di funzioni] Un clic può analizzare l'albero di altri progetti Python/C/C++/Java/Lua/...
-Lettura del documento, [traduzione](https://www.bilibili.com/video/BV1KT411x7Wn) del documento | [Plugin di funzioni] La lettura immediata dell'intero documento latex/pdf di un documento e la generazione di un riassunto
-Traduzione completa di un documento Latex, [correzione immediata](https://www.bilibili.com/video/BV1FT411H7c5/) | [Plugin di funzioni] Una traduzione o correzione immediata di un documento Latex
-Generazione di annotazioni in batch | [Plugin di funzioni] Generazione automatica delle annotazioni di funzione con un solo clic
-[Traduzione cinese-inglese di Markdown](https://www.bilibili.com/video/BV1yo4y157jV/) | [Plugin di funzioni] Hai letto il [README](https://github.com/binary-husky/chatgpt_academic/blob/master/docs/README_EN.md) delle cinque lingue sopra?
-Generazione di report di analisi di chat | [Plugin di funzioni] Generazione automatica di un rapporto di sintesi dopo l'esecuzione
-[Funzione di traduzione di tutto il documento PDF](https://www.bilibili.com/video/BV1KT411x7Wn) | [Plugin di funzioni] Estrarre il titolo e il sommario dell'articolo PDF + tradurre l'intero testo (multithreading)
-[Assistente di Arxiv](https://www.bilibili.com/video/BV1LM4y1279X) | [Plugin di funzioni] Inserire l'URL dell'articolo di Arxiv e tradurre il sommario con un clic + scaricare il PDF
-[Assistente integrato di Google Scholar](https://www.bilibili.com/video/BV19L411U7ia) | [Plugin di funzioni] Con qualsiasi URL di pagina di ricerca di Google Scholar, lascia che GPT ti aiuti a scrivere il tuo [relatedworks](https://www.bilibili.com/video/BV1GP411U7Az/)
-Aggregazione delle informazioni su Internet + GPT | [Plugin di funzioni] Fai in modo che GPT rilevi le informazioni su Internet prima di rispondere alle domande, senza mai diventare obsolete
-Visualizzazione di formule/img/tabelle | È possibile visualizzare un'equazione in forma [tex e render](https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png) contemporaneamente, supporta equazioni e evidenziazione del codice
-Supporto per plugin di funzioni multithreading | Supporto per chiamata multithreaded di chatgpt, elaborazione con un clic di grandi quantità di testo o di un programma
-Avvia il tema di gradio [scuro](https://github.com/binary-husky/chatgpt_academic/issues/173) | Aggiungere ```/?__theme=dark``` dopo l'URL del browser per passare a un tema scuro
-Supporto per maggiori modelli LLM, supporto API2D | Sentirsi serviti simultaneamente da GPT3.5, GPT4, [Tsinghua ChatGLM](https://github.com/THUDM/ChatGLM-6B), [Fudan MOSS](https://github.com/OpenLMLab/MOSS) deve essere una grande sensazione, giusto?
-Ulteriori modelli LLM supportat,i supporto per l'implementazione di Huggingface | Aggiunta di un'interfaccia Newbing (Nuovo Bing), introdotta la compatibilità con Tsinghua [Jittorllms](https://github.com/Jittor/JittorLLMs), [LLaMA](https://github.com/facebookresearch/llama), [RWKV](https://github.com/BlinkDL/ChatRWKV) e [PanGu-α](https://openi.org.cn/pangu/)
-Ulteriori dimostrazioni di nuove funzionalità (generazione di immagini, ecc.)... | Vedere la fine di questo documento...
- 
-- Nuova interfaccia (modificare l'opzione LAYOUT in `config.py` per passare dal layout a sinistra e a destra al layout superiore e inferiore)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230361456-61078362-a966-4eb5-b49e-3c62ef18b860.gif" width="700" >
-</div>Sei un traduttore professionista di paper accademici.
-
-- Tutti i pulsanti vengono generati dinamicamente leggendo il file functional.py, e aggiungerci nuove funzionalità è facile, liberando la clipboard.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231975334-b4788e91-4887-412f-8b43-2b9c5f41d248.gif" width="700" >
-</div>
-
-- Revisione/Correzione
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/231980294-f374bdcb-3309-4560-b424-38ef39f04ebd.gif" width="700" >
-</div>
-
-- Se l'output contiene una formula, viene visualizzata sia come testo che come formula renderizzata, per facilitare la copia e la visualizzazione.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/230598842-1d7fcddd-815d-40ee-af60-baf488a199df.png" width="700" >
-</div>
-
-- Non hai tempo di leggere il codice del progetto? Passa direttamente a chatgpt e chiedi informazioni.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="700" >
-</div>
-
-- Chiamata mista di vari modelli di lingua di grandi dimensioni (ChatGLM + OpenAI-GPT3.5 + [API2D](https://api2d.com/)-GPT4)
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/232537274-deca0563-7aa6-4b5d-94a2-b7c453c47794.png" width="700" >
-</div>
-
----
-# Installazione
-## Installazione - Metodo 1: Esecuzione diretta (Windows, Linux o MacOS)
-
-1. Scarica il progetto
-```sh
-git clone https://github.com/binary-husky/chatgpt_academic.git
-cd chatgpt_academic
-```
-
-2. Configura API_KEY
-
-In `config.py`, configura la tua API KEY e altre impostazioni, [configs for special network environments](https://github.com/binary-husky/gpt_academic/issues/1).
-
-(N.B. Quando il programma viene eseguito, verifica prima se esiste un file di configurazione privato chiamato `config_private.py` e sovrascrive le stesse configurazioni in `config.py`.  Pertanto, se capisci come funziona la nostra logica di lettura della configurazione, ti consigliamo vivamente di creare un nuovo file di configurazione chiamato `config_private.py` accanto a `config.py`, e spostare (copiare) le configurazioni di `config.py` in `config_private.py`. 'config_private.py' non è sotto la gestione di git e può proteggere ulteriormente le tue informazioni personali. NB Il progetto supporta anche la configurazione della maggior parte delle opzioni tramite "variabili d'ambiente". La sintassi della variabile d'ambiente è descritta nel file `docker-compose`. Priorità di lettura: "variabili d'ambiente" > "config_private.py" > "config.py")
-
-
-3. Installa le dipendenze
-```sh
-# (Scelta I: se sei familiare con python) (python 3.9 o superiore, più nuovo è meglio), N.B.: utilizza il repository ufficiale pip o l'aliyun pip repository, metodo temporaneo per cambiare il repository: python -m pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/
-python -m pip install -r requirements.txt
-
-# (Scelta II: se non conosci Python) utilizza anaconda, il processo è simile (https://www.bilibili.com/video/BV1rc411W7Dr):
-conda create -n gptac_venv python=3.11    # crea l'ambiente anaconda
-conda activate gptac_venv                 # attiva l'ambiente anaconda
-python -m pip install -r requirements.txt # questo passaggio funziona allo stesso modo dell'installazione con pip
-```
-
-<details><summary>Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, fare clic qui per espandere</summary>
-<p>
-
-【Passaggio facoltativo】 Se si desidera supportare ChatGLM di Tsinghua/MOSS di Fudan come backend, è necessario installare ulteriori dipendenze (prerequisiti: conoscenza di Python, esperienza con Pytorch e computer sufficientemente potente):
-```sh
-# 【Passaggio facoltativo I】 Supporto a ChatGLM di Tsinghua. Note su ChatGLM di Tsinghua: in caso di errore "Call ChatGLM fail 不能正常加载ChatGLM的参数" , fare quanto segue: 1. Per impostazione predefinita, viene installata la versione di torch + cpu; per usare CUDA, è necessario disinstallare torch e installare nuovamente torch + cuda; 2. Se non è possibile caricare il modello a causa di una configurazione insufficiente del computer, è possibile modificare la precisione del modello in request_llm/bridge_chatglm.py, cambiando AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) in AutoTokenizer.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True)
-python -m pip install -r request_llm/requirements_chatglm.txt  
-
-# 【Passaggio facoltativo II】 Supporto a MOSS di Fudan
-python -m pip install -r request_llm/requirements_moss.txt
-git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss  # Si prega di notare che quando si esegue questa riga di codice, si deve essere nella directory radice del progetto
-
-# 【Passaggio facoltativo III】 Assicurati che il file di configurazione config.py includa tutti i modelli desiderati, al momento tutti i modelli supportati sono i seguenti (i modelli della serie jittorllms attualmente supportano solo la soluzione docker):
-AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "chatglm", "newbing", "moss"] # + ["jittorllms_rwkv", "jittorllms_pangualpha", "jittorllms_llama"]
-```
-
-</p>
-</details>
-
-
-
-4. Esegui
-```sh
-python main.py
-```5. Plugin di test delle funzioni
-```
-- Funzione plugin di test (richiede una risposta gpt su cosa è successo oggi in passato), puoi utilizzare questa funzione come template per implementare funzionalità più complesse
-    Clicca su "[Demo del plugin di funzione] Oggi nella storia"
-```
-
-## Installazione - Metodo 2: Utilizzo di Docker
-
-1. Solo ChatGPT (consigliato per la maggior parte delle persone)
-
-``` sh
-git clone https://github.com/binary-husky/chatgpt_academic.git  # scarica il progetto
-cd chatgpt_academic                                 # entra nel percorso
-nano config.py                                      # con un qualsiasi editor di testo, modifica config.py configurando "Proxy", "API_KEY" e "WEB_PORT" (ad esempio 50923)
-docker build -t gpt-academic .                      # installa
-
-#(ultimo passaggio - selezione 1) In un ambiente Linux, utilizzare '--net=host' è più conveniente e veloce
-docker run --rm -it --net=host gpt-academic
-#(ultimo passaggio - selezione 2) In un ambiente MacOS/Windows, l'opzione -p può essere utilizzata per esporre la porta del contenitore (ad es. 50923) alla porta della macchina
-docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
-```
-
-2. ChatGPT + ChatGLM + MOSS (richiede familiarità con Docker)
-
-``` sh
-# Modifica docker-compose.yml, elimina i piani 1 e 3, mantieni il piano 2. Modifica la configurazione del piano 2 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
-docker-compose up
-```
-
-3. ChatGPT + LLAMA + Pangu + RWKV (richiede familiarità con Docker)
-
-``` sh
-# Modifica docker-compose.yml, elimina i piani 1 e 2, mantieni il piano 3. Modifica la configurazione del piano 3 in docker-compose.yml, si prega di fare riferimento alle relative annotazioni
-docker-compose up
-```
-
-
-## Installazione - Metodo 3: Altre modalità di distribuzione
-
-1. Come utilizzare un URL di reindirizzamento / AzureAPI Cloud Microsoft
-Configura API_URL_REDIRECT seguendo le istruzioni nel file `config.py`.
-
-2. Distribuzione su un server cloud remoto (richiede conoscenze ed esperienza di server cloud)
-Si prega di visitare [wiki di distribuzione-1] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BA%91%E6%9C%8D%E5%8A%A1%E5%99%A8%E8%BF%9C%E7%A8%8B%E9%83%A8%E7%BD%B2%E6%8C%87%E5%8D%97)
-
-3. Utilizzo di WSL2 (Windows Subsystem for Linux)
-Si prega di visitare [wiki di distribuzione-2] (https://github.com/binary-husky/chatgpt_academic/wiki/%E4%BD%BF%E7%94%A8WSL2%EF%BC%88Windows-Subsystem-for-Linux-%E5%AD%90%E7%B3%BB%E7%BB%9F%EF%BC%89%E9%83%A8%E7%BD%B2)
-
-4. Come far funzionare ChatGPT all'interno di un sottodominio (ad es. `http://localhost/subpath`)
-Si prega di visitare [Istruzioni per l'esecuzione con FastAPI] (docs/WithFastapi.md)
-
-5. Utilizzo di docker-compose per l'esecuzione
-Si prega di leggere il file docker-compose.yml e seguire le istruzioni fornite.
-
----
-# Uso avanzato
-## Personalizzazione dei pulsanti / Plugin di funzione personalizzati
-
-1. Personalizzazione dei pulsanti (scorciatoie accademiche)
-Apri `core_functional.py` con qualsiasi editor di testo e aggiungi la voce seguente, quindi riavvia il programma (se il pulsante è già stato aggiunto con successo e visibile, il prefisso e il suffisso supportano la modifica in tempo reale, senza bisogno di riavviare il programma).
-
-ad esempio
-```
-"超级英译中": {
-    # Prefisso, verrà aggiunto prima del tuo input. Ad esempio, descrivi la tua richiesta, come tradurre, spiegare il codice, correggere errori, ecc.
-    "Prefix": "Per favore traduci questo testo in Cinese, e poi spiega tutti i termini tecnici nel testo con una tabella markdown:\n\n", 
-    
-    # Suffisso, verrà aggiunto dopo il tuo input. Ad esempio, con il prefisso puoi circondare il tuo input con le virgolette.
-    "Suffix": "",
-},
-```
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226899272-477c2134-ed71-4326-810c-29891fe4a508.png" width="500" >
-</div>
-
-2. Plugin di funzione personalizzati
-
-Scrivi plugin di funzione personalizzati e esegui tutte le attività che desideri o non hai mai pensato di fare.
-La difficoltà di scrittura e debug dei plugin del nostro progetto è molto bassa. Se si dispone di una certa conoscenza di base di Python, è possibile realizzare la propria funzione del plugin seguendo il nostro modello. Per maggiori dettagli, consultare la [guida al plugin per funzioni] (https://github.com/binary-husky/chatgpt_academic/wiki/%E5%87%BD%E6%95%B0%E6%8F%92%E4%BB%B6%E6%8C%87%E5%8D%97).
-
----
-# Ultimo aggiornamento
-## Nuove funzionalità dinamiche1. Funzionalità di salvataggio della conversazione. Nell'area dei plugin della funzione, fare clic su "Salva la conversazione corrente" per salvare la conversazione corrente come file html leggibile e ripristinabile, inoltre, nell'area dei plugin della funzione (menu a discesa), fare clic su "Carica la cronologia della conversazione archiviata" per ripristinare la conversazione precedente. Suggerimento: fare clic su "Carica la cronologia della conversazione archiviata" senza specificare il file consente di visualizzare la cache degli archivi html di cronologia, fare clic su "Elimina tutti i record di cronologia delle conversazioni locali" per eliminare tutte le cache degli archivi html.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/235222390-24a9acc0-680f-49f5-bc81-2f3161f1e049.png" width="500" >
-</div>
-
-2. Generazione di rapporti. La maggior parte dei plugin genera un rapporto di lavoro dopo l'esecuzione.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/227503770-fe29ce2c-53fd-47b0-b0ff-93805f0c2ff4.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504617-7a497bb3-0a2a-4b50-9a8a-95ae60ea7afd.png" height="300" >
-<img src="https://user-images.githubusercontent.com/96192199/227504005-efeaefe0-b687-49d0-bf95-2d7b7e66c348.png" height="300" >
-</div>
-
-3. Progettazione modulare delle funzioni, semplici interfacce ma in grado di supportare potenti funzionalità.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/229288270-093643c1-0018-487a-81e6-1d7809b6e90f.png" height="400" >
-<img src="https://user-images.githubusercontent.com/96192199/227504931-19955f78-45cd-4d1c-adac-e71e50957915.png" height="400" >
-</div>
-
-4. Questo è un progetto open source che può "tradursi da solo".
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226936850-c77d7183-0749-4c1c-9875-fd4891842d0c.png" width="500" >
-</div>
-
-5. Tradurre altri progetti open source è semplice.
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226935232-6b6a73ce-8900-4aee-93f9-733c7e6fef53.png" width="500" >
-</div>
-
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/226969067-968a27c1-1b9c-486b-8b81-ab2de8d3f88a.png" width="500" >
-</div>
-
-6. Piccola funzione decorativa per [live2d](https://github.com/fghrsh/live2d_demo) (disattivata per impostazione predefinita, è necessario modificare `config.py`).
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236432361-67739153-73e8-43fe-8111-b61296edabd9.png" width="500" >
-</div>
-
-7. Supporto del grande modello linguistico MOSS
-<div align="center">
-<img src="https://user-images.githubusercontent.com/96192199/236639178-92836f37-13af-4fdd-984d-b4450fe30336.png" width="500" >
-</div>
-
-8. Generazione di immagini OpenAI
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/bc7ab234-ad90-48a0-8d62-f703d9e74665" width="500" >
-</div>
-
-9. Analisi e sintesi audio OpenAI
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/709ccf95-3aee-498a-934a-e1c22d3d5d5b" width="500" >
-</div>
-
-10. Verifica completa dei testi in LaTeX
-<div align="center">
-<img src="https://github.com/binary-husky/gpt_academic/assets/96192199/651ccd98-02c9-4464-91e1-77a6b7d1b033" width="500" >
-</div>
-
-
-## Versione:
-- versione 3.5(Todo): utilizzo del linguaggio naturale per chiamare tutti i plugin di funzioni del progetto (alta priorità)
-- versione 3.4(Todo): supporto multi-threading per il grande modello linguistico locale Chatglm
-- versione 3.3: +funzionalità di sintesi delle informazioni su Internet
-- versione 3.2: i plugin di funzioni supportano più interfacce dei parametri (funzionalità di salvataggio della conversazione, lettura del codice in qualsiasi lingua + richiesta simultanea di qualsiasi combinazione di LLM)
-- versione 3.1: supporto per interrogare contemporaneamente più modelli gpt! Supporto api2d, bilanciamento del carico per più apikey
-- versione 3.0: supporto per Chatglm e altri piccoli LLM
-- versione 2.6: ristrutturazione della struttura del plugin, miglioramento dell'interattività, aggiunta di più plugin
-- versione 2.5: auto-aggiornamento, risoluzione del problema di testo troppo lungo e overflow del token durante la sintesi di grandi progetti di ingegneria
-- versione 2.4: (1) funzionalità di traduzione dell'intero documento in formato PDF aggiunta; (2) funzionalità di scambio dell'area di input aggiunta; (3) opzione di layout verticale aggiunta; (4) ottimizzazione della funzione di plugin multi-threading.
-- versione 2.3: miglioramento dell'interattività multi-threading
-- versione 2.2: i plugin di funzioni supportano l'hot-reload
-- versione 2.1: layout ripiegabile
-- versione 2.0: introduzione di plugin di funzioni modulari
-- versione 1.0: funzione di basegpt_academic sviluppatori gruppo QQ-2: 610599535
-
-- Problemi noti
-    - Alcuni plugin di traduzione del browser interferiscono con l'esecuzione del frontend di questo software
-    - La versione di gradio troppo alta o troppo bassa può causare diversi malfunzionamenti
-
-## Riferimenti e apprendimento
-
-```
-Il codice fa riferimento a molte altre eccellenti progettazioni di progetti, principalmente:
-
-# Progetto 1: ChatGLM-6B di Tsinghua:
-https://github.com/THUDM/ChatGLM-6B
-
-# Progetto 2: JittorLLMs di Tsinghua:
-https://github.com/Jittor/JittorLLMs
-
-# Progetto 3: Edge-GPT:
-https://github.com/acheong08/EdgeGPT
-
-# Progetto 4: ChuanhuChatGPT:
-https://github.com/GaiZhenbiao/ChuanhuChatGPT
-
-# Progetto 5: ChatPaper:
-https://github.com/kaixindelele/ChatPaper
-
-# Altro:
-https://github.com/gradio-app/gradio
-https://github.com/fghrsh/live2d_demo
-```
\ No newline at end of file
diff --git a/spaces/yderre-aubay/midi-player-demo/src/main/stores/reactions.ts b/spaces/yderre-aubay/midi-player-demo/src/main/stores/reactions.ts
deleted file mode 100644
index 761697765af2e19b24372db5dbe21070c288d9ad..0000000000000000000000000000000000000000
--- a/spaces/yderre-aubay/midi-player-demo/src/main/stores/reactions.ts
+++ /dev/null
@@ -1,74 +0,0 @@
-import { autorun, observe } from "mobx"
-import { resetSelection } from "../actions"
-import MIDIOutput from "../services/MIDIOutput"
-import RootStore from "./RootStore"
-
-export const registerReactions = (rootStore: RootStore) => {
-  // reset selection when tool changed
-  observe(rootStore.pianoRollStore, "mouseMode", resetSelection(rootStore))
-
-  observe(
-    rootStore.midiDeviceStore,
-    "enabledOutputs",
-    updateOutputDevices(rootStore),
-  )
-
-  autorun(updateInputDevices(rootStore))
-  autorun(updateOutputDevices(rootStore))
-
-  observe(
-    rootStore.midiRecorder,
-    "isRecording",
-    disableSeekWhileRecording(rootStore),
-  )
-
-  observe(rootStore.player, "isPlaying", stopRecordingWhenStopPlayer(rootStore))
-}
-
-type Reaction = (rootStore: RootStore) => () => void
-
-// sync synthGroup.output to enabledOutputIds/isFactorySoundEnabled
-const updateOutputDevices: Reaction =
-  ({ midiDeviceStore, player, synth, synthGroup }) =>
-  () => {
-    const { outputs, enabledOutputs, isFactorySoundEnabled } = midiDeviceStore
-
-    player.allSoundsOff()
-
-    const midiDeviceEntries = outputs.map((device) => ({
-      synth: new MIDIOutput(device),
-      isEnabled: enabledOutputs[device.id],
-    }))
-
-    synthGroup.outputs = [
-      {
-        synth: synth,
-        isEnabled: isFactorySoundEnabled,
-      },
-      ...midiDeviceEntries,
-    ]
-  }
-
-const updateInputDevices: Reaction =
-  ({ midiDeviceStore, midiInput }) =>
-  () => {
-    const { inputs, enabledInputs } = midiDeviceStore
-
-    const devices = inputs.filter((d) => enabledInputs[d.id])
-
-    midiInput.removeAllDevices()
-    devices.forEach(midiInput.addDevice)
-  }
-
-const disableSeekWhileRecording: Reaction =
-  ({ player, midiRecorder }) =>
-  () =>
-    (player.disableSeek = midiRecorder.isRecording)
-
-const stopRecordingWhenStopPlayer: Reaction =
-  ({ player, midiRecorder }) =>
-  () => {
-    if (!player.isPlaying) {
-      midiRecorder.isRecording = false
-    }
-  }
diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/big_bird/__init__.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/big_bird/__init__.py
deleted file mode 100644
index ef8ad80aa6b5e6c516aaae9a3d229ce1f4a3e261..0000000000000000000000000000000000000000
--- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/big_bird/__init__.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# Copyright 2021 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import TYPE_CHECKING
-
-from ...utils import (
-    OptionalDependencyNotAvailable,
-    _LazyModule,
-    is_flax_available,
-    is_sentencepiece_available,
-    is_tf_available,
-    is_tokenizers_available,
-    is_torch_available,
-)
-
-
-_import_structure = {
-    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
-}
-
-try:
-    if not is_sentencepiece_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_big_bird"] = ["BigBirdTokenizer"]
-
-try:
-    if not is_tokenizers_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["tokenization_big_bird_fast"] = ["BigBirdTokenizerFast"]
-
-try:
-    if not is_torch_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_big_bird"] = [
-        "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
-        "BigBirdForCausalLM",
-        "BigBirdForMaskedLM",
-        "BigBirdForMultipleChoice",
-        "BigBirdForPreTraining",
-        "BigBirdForQuestionAnswering",
-        "BigBirdForSequenceClassification",
-        "BigBirdForTokenClassification",
-        "BigBirdLayer",
-        "BigBirdModel",
-        "BigBirdPreTrainedModel",
-        "load_tf_weights_in_big_bird",
-    ]
-
-try:
-    if not is_flax_available():
-        raise OptionalDependencyNotAvailable()
-except OptionalDependencyNotAvailable:
-    pass
-else:
-    _import_structure["modeling_flax_big_bird"] = [
-        "FlaxBigBirdForCausalLM",
-        "FlaxBigBirdForMaskedLM",
-        "FlaxBigBirdForMultipleChoice",
-        "FlaxBigBirdForPreTraining",
-        "FlaxBigBirdForQuestionAnswering",
-        "FlaxBigBirdForSequenceClassification",
-        "FlaxBigBirdForTokenClassification",
-        "FlaxBigBirdModel",
-        "FlaxBigBirdPreTrainedModel",
-    ]
-
-if TYPE_CHECKING:
-    from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig
-
-    try:
-        if not is_sentencepiece_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_big_bird import BigBirdTokenizer
-
-    try:
-        if not is_tokenizers_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .tokenization_big_bird_fast import BigBirdTokenizerFast
-
-    try:
-        if not is_torch_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_big_bird import (
-            BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
-            BigBirdForCausalLM,
-            BigBirdForMaskedLM,
-            BigBirdForMultipleChoice,
-            BigBirdForPreTraining,
-            BigBirdForQuestionAnswering,
-            BigBirdForSequenceClassification,
-            BigBirdForTokenClassification,
-            BigBirdLayer,
-            BigBirdModel,
-            BigBirdPreTrainedModel,
-            load_tf_weights_in_big_bird,
-        )
-
-    try:
-        if not is_flax_available():
-            raise OptionalDependencyNotAvailable()
-    except OptionalDependencyNotAvailable:
-        pass
-    else:
-        from .modeling_flax_big_bird import (
-            FlaxBigBirdForCausalLM,
-            FlaxBigBirdForMaskedLM,
-            FlaxBigBirdForMultipleChoice,
-            FlaxBigBirdForPreTraining,
-            FlaxBigBirdForQuestionAnswering,
-            FlaxBigBirdForSequenceClassification,
-            FlaxBigBirdForTokenClassification,
-            FlaxBigBirdModel,
-            FlaxBigBirdPreTrainedModel,
-        )
-
-else:
-    import sys
-
-    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/webUI.py b/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/webUI.py
deleted file mode 100644
index 17e39b21fa24d7ec9867b693723b7b087840a9b4..0000000000000000000000000000000000000000
--- a/spaces/yl12053/so-vits-4.1-Matikanefukukitaru/webUI.py
+++ /dev/null
@@ -1,379 +0,0 @@
-import io
-import os
-
-# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
-import gradio as gr
-import gradio.processing_utils as gr_pu
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import re
-import json
-
-import subprocess
-import edge_tts
-import asyncio
-from scipy.io import wavfile
-import librosa
-import torch
-import time
-import traceback
-from itertools import chain
-from utils import mix_model
-from compress_model import removeOptimizer
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('multipart').setLevel(logging.WARNING)
-
-model = None
-spk = None
-debug = False
-
-cuda = {}
-if torch.cuda.is_available():
-    for i in range(torch.cuda.device_count()):
-        device_name = torch.cuda.get_device_properties(i).name
-        cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}"
-
-def upload_mix_append_file(files,sfiles):
-    try:
-        if(sfiles == None):
-            file_paths = [file.name for file in files]
-        else:
-            file_paths = [file.name for file in chain(files,sfiles)]
-        p = {file:100 for file in file_paths}
-        return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2))
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def mix_submit_click(js,mode):
-    try:
-        assert js.lstrip()!=""
-        modes = {"凸组合":0, "线性组合":1}
-        mode = modes[mode]
-        data = json.loads(js)
-        data = list(data.items())
-        model_path,mix_rate = zip(*data)
-        path = mix_model(model_path,mix_rate,mode)
-        return f"成功，文件被保存在了{path}"
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def updata_mix_info(files):
-    try:
-        if files == None : return mix_model_output1.update(value="")
-        p = {file.name:100 for file in files}
-        return mix_model_output1.update(value=json.dumps(p,indent=2))
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix):
-    global model
-    try:
-        device = cuda[device] if "CUDA" in device else device
-        cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster"
-        fr = ".pkl" in cluster_filepath[1]
-        #model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance)
-        model = Svc(model_path.name,
-                config_path.name,
-                device=device if device != "Auto" else None,
-                cluster_model_path = cluster_model_path.name if cluster_model_path is not None else "",
-                nsf_hifigan_enhance=enhance,
-                diffusion_model_path = diff_model_path.name if diff_model_path is not None else "",
-                diffusion_config_path = diff_config_path.name if diff_config_path is not None else "",
-                shallow_diffusion = True if diff_model_path is not None else False,
-                only_diffusion = only_diffusion,
-                spk_mix_enable = use_spk_mix,
-                feature_retrieval = fr
-                )
-        spks = list(model.spk2id.keys())
-        device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
-        msg = f"成功加载模型到设备{device_name}上\n"
-        if cluster_model_path is None:
-            msg += "未加载聚类模型或特征检索模型\n"
-        elif fr:
-            msg += f"特征检索模型{cluster_filepath[1]}加载成功\n"
-        else:
-            msg += f"聚类模型{cluster_filepath[1]}加载成功\n"
-        if diff_model_path is None:
-            msg += "未加载扩散模型\n"
-        else:
-            msg += f"扩散模型{diff_model_path.name}加载成功\n"
-        msg += "当前模型的可用音色：\n"
-        for i in spks:
-            msg += i + " "
-        return sid.update(choices = spks,value=spks[0]), msg
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-    
-def modelUnload():
-    global model
-    if model is None:
-        return sid.update(choices = [],value=""),"没有模型需要卸载!"
-    else:
-        model.unload_model()
-        model = None
-        torch.cuda.empty_cache()
-        return sid.update(choices = [],value=""),"模型卸载完毕!"
-
-def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment):
-    global model
-    try:
-        if input_audio is None:
-            return "You need to upload an audio", None
-        if model is None:
-            return "You need to upload an model", None
-        print(input_audio)    
-        sampling_rate, audio = input_audio
-        print(audio.shape,sampling_rate)
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        print(audio.dtype)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        temp_path = "temp.wav"
-        soundfile.write(temp_path, audio, sampling_rate, format="wav")
-        _audio = model.slice_inference(
-            temp_path,
-            sid,
-            vc_transform,
-            slice_db,
-            cluster_ratio,
-            auto_f0,
-            noise_scale,
-            pad_seconds,
-            cl_num,
-            lg_num,
-            lgr_num,
-            f0_predictor,
-            enhancer_adaptive_key,
-            cr_threshold,
-            k_step,
-            use_spk_mix,
-            second_encoding,
-            loudness_envelope_adjustment
-        )
-        model.clear_empty()
-        os.remove(temp_path)
-        #构建保存文件的路径，并保存到results文件夹内
-        timestamp = str(int(time.time()))
-        if not os.path.exists("results"):
-            os.makedirs("results")
-        output_file = os.path.join("results", sid + "_" + timestamp + ".wav")
-        soundfile.write(output_file, _audio, model.target_sample, format="wav")
-        return "Success", output_file
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def tts_func(_text,_rate,_voice):
-    #使用edge-tts把文字转成音频
-    # voice = "zh-CN-XiaoyiNeural"#女性，较高音
-    # voice = "zh-CN-YunxiNeural"#男性
-    voice = "zh-CN-YunxiNeural"#男性
-    if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural"
-    output_file = _text[0:10]+".wav"
-    # communicate = edge_tts.Communicate(_text, voice)
-    # await communicate.save(output_file)
-    if _rate>=0:
-        ratestr="+{:.0%}".format(_rate)
-    elif _rate<0:
-        ratestr="{:.0%}".format(_rate)#减号自带
-
-    p=subprocess.Popen("edge-tts "+
-                        " --text "+_text+
-                        " --write-media "+output_file+
-                        " --voice "+voice+
-                        " --rate="+ratestr
-                        ,shell=True,
-                        stdout=subprocess.PIPE,
-                        stdin=subprocess.PIPE)
-    p.wait()
-    return output_file
-
-def text_clear(text):
-    return re.sub(r"[\n\,\(\) ]", "", text)
-
-def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold):
-    #使用edge-tts把文字转成音频
-    text2tts=text_clear(text2tts)
-    output_file=tts_func(text2tts,tts_rate,tts_voice)
-
-    #调整采样率
-    sr2=44100
-    wav, sr = librosa.load(output_file)
-    wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2)
-    save_path2= text2tts[0:10]+"_44k"+".wav"
-    wavfile.write(save_path2,sr2,
-                (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-                )
-
-    #读取音频
-    sample_rate, data=gr_pu.audio_from_file(save_path2)
-    vc_input=(sample_rate, data)
-
-    a,b=vc_fn(sid, vc_input, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold)
-    os.remove(output_file)
-    os.remove(save_path2)
-    return a,b
-
-def model_compression(_model):
-    if _model == "":
-        return "请先选择要压缩的模型"
-    else:
-        model_path = os.path.split(_model.name)
-        filename, extension = os.path.splitext(model_path[1])
-        output_model_name = f"{filename}_compressed{extension}"
-        output_path = os.path.join(os.getcwd(), output_model_name)
-        removeOptimizer(_model.name, output_path)
-        return f"模型已成功被保存在了{output_path}"
-
-def debug_change():
-    global debug
-    debug = debug_button.value
-
-with gr.Blocks(
-    theme=gr.themes.Base(
-        primary_hue = gr.themes.colors.green,
-        font=["Source Sans Pro", "Arial", "sans-serif"],
-        font_mono=['JetBrains mono', "Consolas", 'Courier New']
-    ),
-) as app:
-    with gr.Tabs():
-        with gr.TabItem("推理"):
-            gr.Markdown(value="""
-                So-vits-svc 4.0 推理 webui
-                """)
-            with gr.Row(variant="panel"):
-                with gr.Column():
-                    gr.Markdown(value="""
-                        <font size=2> 模型设置</font>
-                        """)
-                    with gr.Row():
-                        model_path = gr.File(label="选择模型文件")
-                        config_path = gr.File(label="选择配置文件")
-                    with gr.Row():
-                        diff_model_path = gr.File(label="选择扩散模型文件")
-                        diff_config_path = gr.File(label="选择扩散模型配置文件")
-                    cluster_model_path = gr.File(label="选择聚类模型或特征检索文件（没有可以不选）")
-                    device = gr.Dropdown(label="推理设备，默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"cpu"], value="Auto")
-                    enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False)
-                    only_diffusion = gr.Checkbox(label="是否使用全扩散推理，开启后将不使用So-VITS模型，仅使用扩散模型进行完整扩散推理，默认关闭", value=False)
-                with gr.Column():
-                    gr.Markdown(value="""
-                        <font size=3>左侧文件全部选择完毕后(全部文件模块显示download)，点击“加载模型”进行解析：</font>
-                        """)
-                    model_load_button = gr.Button(value="加载模型", variant="primary")
-                    model_unload_button = gr.Button(value="卸载模型", variant="primary")
-                    sid = gr.Dropdown(label="音色（说话人）")
-                    sid_output = gr.Textbox(label="Output Message")
-
-
-            with gr.Row(variant="panel"):
-                with gr.Column():
-                    gr.Markdown(value="""
-                        <font size=2> 推理设置</font>
-                        """)
-                    auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）", value=False)
-                    f0_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意：crepe为原F0使用均值滤波器)", choices=["pm","dio","harvest","crepe"], value="pm")
-                    vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
-                    cluster_ratio = gr.Number(label="聚类模型/特征检索混合比例，0-1之间，0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
-                    slice_db = gr.Number(label="切片阈值", value=-40)
-                    noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
-                    k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000)
-                with gr.Column():
-                    pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
-                    cl_num = gr.Number(label="音频自动切片，0为不切片，单位为秒(s)", value=0)
-                    lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0)
-                    lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75)
-                    enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0)
-                    cr_threshold = gr.Number(label="F0过滤阈值，只有启动crepe时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05)
-                    loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络", value = 0)
-                    second_encoding = gr.Checkbox(label = "二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，效果时好时差，默认关闭", value=False)
-                    use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False)
-            with gr.Tabs():
-                with gr.TabItem("音频转音频"):
-                    vc_input3 = gr.Audio(label="选择音频")
-                    vc_submit = gr.Button("音频转换", variant="primary")
-                with gr.TabItem("文字转音频"):
-                    text2tts=gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪")
-                    tts_rate = gr.Number(label="tts语速", value=0)
-                    tts_voice = gr.Radio(label="性别",choices=["男","女"], value="男")
-                    vc_submit2 = gr.Button("文字转换", variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    vc_output1 = gr.Textbox(label="Output Message")
-                with gr.Column():
-                    vc_output2 = gr.Audio(label="Output Audio", interactive=False)
-
-        with gr.TabItem("小工具/实验室特性"):
-            gr.Markdown(value="""
-                        <font size=2> So-vits-svc 4.0 小工具/实验室特性</font>
-                        """)
-            with gr.Tabs():
-                with gr.TabItem("静态声线融合"):
-                    gr.Markdown(value="""
-                        <font size=2> 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线 
-                                          注意：
-                                          1.该功能仅支持单说话人的模型
-                                          2.如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音
-                                          3.保证所有待混合模型的config.json中的model字段是相同的
-                                          4.输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用
-                                          5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传
-                                          6.混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果
-                                          7.混合完毕后，文件将会保存在项目根目录中，文件名为output.pth
-                                          8.凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会
-                        </font>
-                        """)
-                    mix_model_path = gr.Files(label="选择需要混合模型文件")
-                    mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple")
-                    mix_model_output1 = gr.Textbox(
-                                            label="混合比例调整，单位/%",
-                                            interactive = True
-                                         )
-                    mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式",value="凸组合",interactive = True)
-                    mix_submit = gr.Button("声线融合启动", variant="primary")
-                    mix_model_output2 = gr.Textbox(
-                                            label="Output Message"
-                                         )
-                    mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1])
-                    mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1])
-                    mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2])
-                
-                with gr.TabItem("模型压缩工具"):
-                    gr.Markdown(value="""
-                        该工具可以实现对模型的体积压缩，在**不影响模型推理功能**的情况下，将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。
-                        **注意：压缩后的模型将无法继续训练，请在确认封炉后再压缩。**
-                    """)
-                    model_to_compress = gr.File(label="模型上传")
-                    compress_model_btn = gr.Button("压缩模型", variant="primary")
-                    compress_model_output = gr.Textbox(label="输出信息", value="")
-
-                    compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output])
-                    
-                    
-    with gr.Tabs():
-        with gr.Row(variant="panel"):
-            with gr.Column():
-                gr.Markdown(value="""
-                    <font size=2> WebUI设置</font>
-                    """)
-                debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug)
-        vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
-        vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,f0_predictor,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2])
-        debug_button.change(debug_change,[],[])
-        model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix],[sid,sid_output])
-        model_unload_button.click(modelUnload,[],[sid,sid_output])
-    app.launch()
-
-
- 
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/data/build.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/data/build.py
deleted file mode 100644
index a31369d1693f86154a7a9249fc043d49f3e9f390..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/data/build.py
+++ /dev/null
@@ -1,542 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import itertools
-import logging
-import numpy as np
-import operator
-import pickle
-from typing import Any, Callable, Dict, List, Optional, Union
-import torch
-import torch.utils.data as torchdata
-from tabulate import tabulate
-from termcolor import colored
-
-from detectron2.config import configurable
-from detectron2.structures import BoxMode
-from detectron2.utils.comm import get_world_size
-from detectron2.utils.env import seed_all_rng
-from detectron2.utils.file_io import PathManager
-from detectron2.utils.logger import _log_api_usage, log_first_n
-
-from .catalog import DatasetCatalog, MetadataCatalog
-from .common import AspectRatioGroupedDataset, DatasetFromList, MapDataset, ToIterableDataset
-from .dataset_mapper import DatasetMapper
-from .detection_utils import check_metadata_consistency
-from .samplers import (
-    InferenceSampler,
-    RandomSubsetTrainingSampler,
-    RepeatFactorTrainingSampler,
-    TrainingSampler,
-)
-
-"""
-This file contains the default logic to build a dataloader for training or testing.
-"""
-
-__all__ = [
-    "build_batch_data_loader",
-    "build_detection_train_loader",
-    "build_detection_test_loader",
-    "get_detection_dataset_dicts",
-    "load_proposals_into_dataset",
-    "print_instances_class_histogram",
-]
-
-
-def filter_images_with_only_crowd_annotations(dataset_dicts):
-    """
-    Filter out images with none annotations or only crowd annotations
-    (i.e., images without non-crowd annotations).
-    A common training-time preprocessing on COCO dataset.
-
-    Args:
-        dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
-
-    Returns:
-        list[dict]: the same format, but filtered.
-    """
-    num_before = len(dataset_dicts)
-
-    def valid(anns):
-        for ann in anns:
-            if ann.get("iscrowd", 0) == 0:
-                return True
-        return False
-
-    dataset_dicts = [x for x in dataset_dicts if valid(x["annotations"])]
-    num_after = len(dataset_dicts)
-    logger = logging.getLogger(__name__)
-    logger.info(
-        "Removed {} images with no usable annotations. {} images left.".format(
-            num_before - num_after, num_after
-        )
-    )
-    return dataset_dicts
-
-
-def filter_images_with_few_keypoints(dataset_dicts, min_keypoints_per_image):
-    """
-    Filter out images with too few number of keypoints.
-
-    Args:
-        dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
-
-    Returns:
-        list[dict]: the same format as dataset_dicts, but filtered.
-    """
-    num_before = len(dataset_dicts)
-
-    def visible_keypoints_in_image(dic):
-        # Each keypoints field has the format [x1, y1, v1, ...], where v is visibility
-        annotations = dic["annotations"]
-        return sum(
-            (np.array(ann["keypoints"][2::3]) > 0).sum()
-            for ann in annotations
-            if "keypoints" in ann
-        )
-
-    dataset_dicts = [
-        x for x in dataset_dicts if visible_keypoints_in_image(x) >= min_keypoints_per_image
-    ]
-    num_after = len(dataset_dicts)
-    logger = logging.getLogger(__name__)
-    logger.info(
-        "Removed {} images with fewer than {} keypoints.".format(
-            num_before - num_after, min_keypoints_per_image
-        )
-    )
-    return dataset_dicts
-
-
-def load_proposals_into_dataset(dataset_dicts, proposal_file):
-    """
-    Load precomputed object proposals into the dataset.
-
-    The proposal file should be a pickled dict with the following keys:
-
-    - "ids": list[int] or list[str], the image ids
-    - "boxes": list[np.ndarray], each is an Nx4 array of boxes corresponding to the image id
-    - "objectness_logits": list[np.ndarray], each is an N sized array of objectness scores
-      corresponding to the boxes.
-    - "bbox_mode": the BoxMode of the boxes array. Defaults to ``BoxMode.XYXY_ABS``.
-
-    Args:
-        dataset_dicts (list[dict]): annotations in Detectron2 Dataset format.
-        proposal_file (str): file path of pre-computed proposals, in pkl format.
-
-    Returns:
-        list[dict]: the same format as dataset_dicts, but added proposal field.
-    """
-    logger = logging.getLogger(__name__)
-    logger.info("Loading proposals from: {}".format(proposal_file))
-
-    with PathManager.open(proposal_file, "rb") as f:
-        proposals = pickle.load(f, encoding="latin1")
-
-    # Rename the key names in D1 proposal files
-    rename_keys = {"indexes": "ids", "scores": "objectness_logits"}
-    for key in rename_keys:
-        if key in proposals:
-            proposals[rename_keys[key]] = proposals.pop(key)
-
-    # Fetch the indexes of all proposals that are in the dataset
-    # Convert image_id to str since they could be int.
-    img_ids = set({str(record["image_id"]) for record in dataset_dicts})
-    id_to_index = {str(id): i for i, id in enumerate(proposals["ids"]) if str(id) in img_ids}
-
-    # Assuming default bbox_mode of precomputed proposals are 'XYXY_ABS'
-    bbox_mode = BoxMode(proposals["bbox_mode"]) if "bbox_mode" in proposals else BoxMode.XYXY_ABS
-
-    for record in dataset_dicts:
-        # Get the index of the proposal
-        i = id_to_index[str(record["image_id"])]
-
-        boxes = proposals["boxes"][i]
-        objectness_logits = proposals["objectness_logits"][i]
-        # Sort the proposals in descending order of the scores
-        inds = objectness_logits.argsort()[::-1]
-        record["proposal_boxes"] = boxes[inds]
-        record["proposal_objectness_logits"] = objectness_logits[inds]
-        record["proposal_bbox_mode"] = bbox_mode
-
-    return dataset_dicts
-
-
-def print_instances_class_histogram(dataset_dicts, class_names):
-    """
-    Args:
-        dataset_dicts (list[dict]): list of dataset dicts.
-        class_names (list[str]): list of class names (zero-indexed).
-    """
-    num_classes = len(class_names)
-    hist_bins = np.arange(num_classes + 1)
-    histogram = np.zeros((num_classes,), dtype=np.int)
-    for entry in dataset_dicts:
-        annos = entry["annotations"]
-        classes = np.asarray(
-            [x["category_id"] for x in annos if not x.get("iscrowd", 0)], dtype=np.int
-        )
-        if len(classes):
-            assert classes.min() >= 0, f"Got an invalid category_id={classes.min()}"
-            assert (
-                classes.max() < num_classes
-            ), f"Got an invalid category_id={classes.max()} for a dataset of {num_classes} classes"
-        histogram += np.histogram(classes, bins=hist_bins)[0]
-
-    N_COLS = min(6, len(class_names) * 2)
-
-    def short_name(x):
-        # make long class names shorter. useful for lvis
-        if len(x) > 13:
-            return x[:11] + ".."
-        return x
-
-    data = list(
-        itertools.chain(*[[short_name(class_names[i]), int(v)] for i, v in enumerate(histogram)])
-    )
-    total_num_instances = sum(data[1::2])
-    data.extend([None] * (N_COLS - (len(data) % N_COLS)))
-    if num_classes > 1:
-        data.extend(["total", total_num_instances])
-    data = itertools.zip_longest(*[data[i::N_COLS] for i in range(N_COLS)])
-    table = tabulate(
-        data,
-        headers=["category", "#instances"] * (N_COLS // 2),
-        tablefmt="pipe",
-        numalign="left",
-        stralign="center",
-    )
-    log_first_n(
-        logging.INFO,
-        "Distribution of instances among all {} categories:\n".format(num_classes)
-        + colored(table, "cyan"),
-        key="message",
-    )
-
-
-def get_detection_dataset_dicts(
-    names,
-    filter_empty=True,
-    min_keypoints=0,
-    proposal_files=None,
-    check_consistency=True,
-):
-    """
-    Load and prepare dataset dicts for instance detection/segmentation and semantic segmentation.
-
-    Args:
-        names (str or list[str]): a dataset name or a list of dataset names
-        filter_empty (bool): whether to filter out images without instance annotations
-        min_keypoints (int): filter out images with fewer keypoints than
-            `min_keypoints`. Set to 0 to do nothing.
-        proposal_files (list[str]): if given, a list of object proposal files
-            that match each dataset in `names`.
-        check_consistency (bool): whether to check if datasets have consistent metadata.
-
-    Returns:
-        list[dict]: a list of dicts following the standard dataset dict format.
-    """
-    if isinstance(names, str):
-        names = [names]
-    assert len(names), names
-    dataset_dicts = [DatasetCatalog.get(dataset_name) for dataset_name in names]
-    for dataset_name, dicts in zip(names, dataset_dicts):
-        assert len(dicts), "Dataset '{}' is empty!".format(dataset_name)
-
-    if proposal_files is not None:
-        assert len(names) == len(proposal_files)
-        # load precomputed proposals from proposal files
-        dataset_dicts = [
-            load_proposals_into_dataset(dataset_i_dicts, proposal_file)
-            for dataset_i_dicts, proposal_file in zip(dataset_dicts, proposal_files)
-        ]
-
-    if isinstance(dataset_dicts[0], torchdata.Dataset):
-        return torchdata.ConcatDataset(dataset_dicts)
-
-    dataset_dicts = list(itertools.chain.from_iterable(dataset_dicts))
-
-    has_instances = "annotations" in dataset_dicts[0]
-    if filter_empty and has_instances:
-        dataset_dicts = filter_images_with_only_crowd_annotations(dataset_dicts)
-    if min_keypoints > 0 and has_instances:
-        dataset_dicts = filter_images_with_few_keypoints(dataset_dicts, min_keypoints)
-
-    if check_consistency and has_instances:
-        try:
-            class_names = MetadataCatalog.get(names[0]).thing_classes
-            check_metadata_consistency("thing_classes", names)
-            print_instances_class_histogram(dataset_dicts, class_names)
-        except AttributeError:  # class names are not available for this dataset
-            pass
-
-    assert len(dataset_dicts), "No valid data found in {}.".format(",".join(names))
-    return dataset_dicts
-
-
-def build_batch_data_loader(
-    dataset,
-    sampler,
-    total_batch_size,
-    *,
-    aspect_ratio_grouping=False,
-    num_workers=0,
-    collate_fn=None,
-):
-    """
-    Build a batched dataloader. The main differences from `torch.utils.data.DataLoader` are:
-    1. support aspect ratio grouping options
-    2. use no "batch collation", because this is common for detection training
-
-    Args:
-        dataset (torch.utils.data.Dataset): a pytorch map-style or iterable dataset.
-        sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces indices.
-            Must be provided iff. ``dataset`` is a map-style dataset.
-        total_batch_size, aspect_ratio_grouping, num_workers, collate_fn: see
-            :func:`build_detection_train_loader`.
-
-    Returns:
-        iterable[list]. Length of each list is the batch size of the current
-            GPU. Each element in the list comes from the dataset.
-    """
-    world_size = get_world_size()
-    assert (
-        total_batch_size > 0 and total_batch_size % world_size == 0
-    ), "Total batch size ({}) must be divisible by the number of gpus ({}).".format(
-        total_batch_size, world_size
-    )
-    batch_size = total_batch_size // world_size
-
-    if isinstance(dataset, torchdata.IterableDataset):
-        assert sampler is None, "sampler must be None if dataset is IterableDataset"
-    else:
-        dataset = ToIterableDataset(dataset, sampler)
-
-    if aspect_ratio_grouping:
-        data_loader = torchdata.DataLoader(
-            dataset,
-            num_workers=num_workers,
-            collate_fn=operator.itemgetter(0),  # don't batch, but yield individual elements
-            worker_init_fn=worker_init_reset_seed,
-        )  # yield individual mapped dict
-        data_loader = AspectRatioGroupedDataset(data_loader, batch_size)
-        if collate_fn is None:
-            return data_loader
-        return MapDataset(data_loader, collate_fn)
-    else:
-        return torchdata.DataLoader(
-            dataset,
-            batch_size=batch_size,
-            drop_last=True,
-            num_workers=num_workers,
-            collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
-            worker_init_fn=worker_init_reset_seed,
-        )
-
-
-def _train_loader_from_config(cfg, mapper=None, *, dataset=None, sampler=None):
-    if dataset is None:
-        dataset = get_detection_dataset_dicts(
-            cfg.DATASETS.TRAIN,
-            filter_empty=cfg.DATALOADER.FILTER_EMPTY_ANNOTATIONS,
-            min_keypoints=cfg.MODEL.ROI_KEYPOINT_HEAD.MIN_KEYPOINTS_PER_IMAGE
-            if cfg.MODEL.KEYPOINT_ON
-            else 0,
-            proposal_files=cfg.DATASETS.PROPOSAL_FILES_TRAIN if cfg.MODEL.LOAD_PROPOSALS else None,
-        )
-        _log_api_usage("dataset." + cfg.DATASETS.TRAIN[0])
-
-    if mapper is None:
-        mapper = DatasetMapper(cfg, True)
-
-    if sampler is None:
-        sampler_name = cfg.DATALOADER.SAMPLER_TRAIN
-        logger = logging.getLogger(__name__)
-        logger.info("Using training sampler {}".format(sampler_name))
-        if sampler_name == "TrainingSampler":
-            sampler = TrainingSampler(len(dataset))
-        elif sampler_name == "RepeatFactorTrainingSampler":
-            repeat_factors = RepeatFactorTrainingSampler.repeat_factors_from_category_frequency(
-                dataset, cfg.DATALOADER.REPEAT_THRESHOLD
-            )
-            sampler = RepeatFactorTrainingSampler(repeat_factors)
-        elif sampler_name == "RandomSubsetTrainingSampler":
-            sampler = RandomSubsetTrainingSampler(len(dataset), cfg.DATALOADER.RANDOM_SUBSET_RATIO)
-        else:
-            raise ValueError("Unknown training sampler: {}".format(sampler_name))
-
-    return {
-        "dataset": dataset,
-        "sampler": sampler,
-        "mapper": mapper,
-        "total_batch_size": cfg.SOLVER.IMS_PER_BATCH,
-        "aspect_ratio_grouping": cfg.DATALOADER.ASPECT_RATIO_GROUPING,
-        "num_workers": cfg.DATALOADER.NUM_WORKERS,
-    }
-
-
-@configurable(from_config=_train_loader_from_config)
-def build_detection_train_loader(
-    dataset,
-    *,
-    mapper,
-    sampler=None,
-    total_batch_size,
-    aspect_ratio_grouping=True,
-    num_workers=0,
-    collate_fn=None,
-):
-    """
-    Build a dataloader for object detection with some default features.
-
-    Args:
-        dataset (list or torch.utils.data.Dataset): a list of dataset dicts,
-            or a pytorch dataset (either map-style or iterable). It can be obtained
-            by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
-        mapper (callable): a callable which takes a sample (dict) from dataset and
-            returns the format to be consumed by the model.
-            When using cfg, the default choice is ``DatasetMapper(cfg, is_train=True)``.
-        sampler (torch.utils.data.sampler.Sampler or None): a sampler that produces
-            indices to be applied on ``dataset``.
-            If ``dataset`` is map-style, the default sampler is a :class:`TrainingSampler`,
-            which coordinates an infinite random shuffle sequence across all workers.
-            Sampler must be None if ``dataset`` is iterable.
-        total_batch_size (int): total batch size across all workers.
-        aspect_ratio_grouping (bool): whether to group images with similar
-            aspect ratio for efficiency. When enabled, it requires each
-            element in dataset be a dict with keys "width" and "height".
-        num_workers (int): number of parallel data loading workers
-        collate_fn: a function that determines how to do batching, same as the argument of
-            `torch.utils.data.DataLoader`. Defaults to do no collation and return a list of
-            data. No collation is OK for small batch size and simple data structures.
-            If your batch size is large and each sample contains too many small tensors,
-            it's more efficient to collate them in data loader.
-
-    Returns:
-        torch.utils.data.DataLoader:
-            a dataloader. Each output from it is a ``list[mapped_element]`` of length
-            ``total_batch_size / num_workers``, where ``mapped_element`` is produced
-            by the ``mapper``.
-    """
-    if isinstance(dataset, list):
-        dataset = DatasetFromList(dataset, copy=False)
-    if mapper is not None:
-        dataset = MapDataset(dataset, mapper)
-
-    if isinstance(dataset, torchdata.IterableDataset):
-        assert sampler is None, "sampler must be None if dataset is IterableDataset"
-    else:
-        if sampler is None:
-            sampler = TrainingSampler(len(dataset))
-        assert isinstance(sampler, torchdata.Sampler), f"Expect a Sampler but got {type(sampler)}"
-    return build_batch_data_loader(
-        dataset,
-        sampler,
-        total_batch_size,
-        aspect_ratio_grouping=aspect_ratio_grouping,
-        num_workers=num_workers,
-        collate_fn=collate_fn,
-    )
-
-
-def _test_loader_from_config(cfg, dataset_name, mapper=None):
-    """
-    Uses the given `dataset_name` argument (instead of the names in cfg), because the
-    standard practice is to evaluate each test set individually (not combining them).
-    """
-    if isinstance(dataset_name, str):
-        dataset_name = [dataset_name]
-
-    dataset = get_detection_dataset_dicts(
-        dataset_name,
-        filter_empty=False,
-        proposal_files=[
-            cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
-        ]
-        if cfg.MODEL.LOAD_PROPOSALS
-        else None,
-    )
-    if mapper is None:
-        mapper = DatasetMapper(cfg, False)
-    return {
-        "dataset": dataset,
-        "mapper": mapper,
-        "num_workers": cfg.DATALOADER.NUM_WORKERS,
-        "sampler": InferenceSampler(len(dataset)),
-    }
-
-
-@configurable(from_config=_test_loader_from_config)
-def build_detection_test_loader(
-    dataset: Union[List[Any], torchdata.Dataset],
-    *,
-    mapper: Callable[[Dict[str, Any]], Any],
-    sampler: Optional[torchdata.Sampler] = None,
-    batch_size: int = 1,
-    num_workers: int = 0,
-    collate_fn: Optional[Callable[[List[Any]], Any]] = None,
-) -> torchdata.DataLoader:
-    """
-    Similar to `build_detection_train_loader`, with default batch size = 1,
-    and sampler = :class:`InferenceSampler`. This sampler coordinates all workers
-    to produce the exact set of all samples.
-
-    Args:
-        dataset: a list of dataset dicts,
-            or a pytorch dataset (either map-style or iterable). They can be obtained
-            by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
-        mapper: a callable which takes a sample (dict) from dataset
-           and returns the format to be consumed by the model.
-           When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
-        sampler: a sampler that produces
-            indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
-            which splits the dataset across all workers. Sampler must be None
-            if `dataset` is iterable.
-        batch_size: the batch size of the data loader to be created.
-            Default to 1 image per worker since this is the standard when reporting
-            inference time in papers.
-        num_workers: number of parallel data loading workers
-        collate_fn: same as the argument of `torch.utils.data.DataLoader`.
-            Defaults to do no collation and return a list of data.
-
-    Returns:
-        DataLoader: a torch DataLoader, that loads the given detection
-        dataset, with test-time transformation and batching.
-
-    Examples:
-    ::
-        data_loader = build_detection_test_loader(
-            DatasetRegistry.get("my_test"),
-            mapper=DatasetMapper(...))
-
-        # or, instantiate with a CfgNode:
-        data_loader = build_detection_test_loader(cfg, "my_test")
-    """
-    if isinstance(dataset, list):
-        dataset = DatasetFromList(dataset, copy=False)
-    if mapper is not None:
-        dataset = MapDataset(dataset, mapper)
-    if isinstance(dataset, torchdata.IterableDataset):
-        assert sampler is None, "sampler must be None if dataset is IterableDataset"
-    else:
-        if sampler is None:
-            sampler = InferenceSampler(len(dataset))
-    return torchdata.DataLoader(
-        dataset,
-        batch_size=batch_size,
-        sampler=sampler,
-        drop_last=False,
-        num_workers=num_workers,
-        collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
-    )
-
-
-def trivial_batch_collator(batch):
-    """
-    A batch collator that does nothing.
-    """
-    return batch
-
-
-def worker_init_reset_seed(worker_id):
-    initial_seed = torch.initial_seed() % 2 ** 31
-    seed_all_rng(initial_seed + worker_id)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py
deleted file mode 100644
index deb886c0417285ed1d5ad85eb941fa1ac757cdab..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/export/caffe2_inference.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-
-import logging
-import numpy as np
-from itertools import count
-import torch
-from caffe2.proto import caffe2_pb2
-from caffe2.python import core
-
-from .caffe2_modeling import META_ARCH_CAFFE2_EXPORT_TYPE_MAP, convert_batched_inputs_to_c2_format
-from .shared import ScopedWS, get_pb_arg_vali, get_pb_arg_vals, infer_device_type
-
-logger = logging.getLogger(__name__)
-
-
-# ===== ref: mobile-vision predictor's 'Caffe2Wrapper' class ======
-class ProtobufModel(torch.nn.Module):
-    """
-    Wrapper of a caffe2's protobuf model.
-    It works just like nn.Module, but running caffe2 under the hood.
-    Input/Output are tuple[tensor] that match the caffe2 net's external_input/output.
-    """
-
-    _ids = count(0)
-
-    def __init__(self, predict_net, init_net):
-        logger.info(f"Initializing ProtobufModel for: {predict_net.name} ...")
-        super().__init__()
-        assert isinstance(predict_net, caffe2_pb2.NetDef)
-        assert isinstance(init_net, caffe2_pb2.NetDef)
-        # create unique temporary workspace for each instance
-        self.ws_name = "__tmp_ProtobufModel_{}__".format(next(self._ids))
-        self.net = core.Net(predict_net)
-
-        logger.info("Running init_net once to fill the parameters ...")
-        with ScopedWS(self.ws_name, is_reset=True, is_cleanup=False) as ws:
-            ws.RunNetOnce(init_net)
-            uninitialized_external_input = []
-            for blob in self.net.Proto().external_input:
-                if blob not in ws.Blobs():
-                    uninitialized_external_input.append(blob)
-                    ws.CreateBlob(blob)
-            ws.CreateNet(self.net)
-
-        self._error_msgs = set()
-        self._input_blobs = uninitialized_external_input
-
-    def _infer_output_devices(self, inputs):
-        """
-        Returns:
-            list[str]: list of device for each external output
-        """
-
-        def _get_device_type(torch_tensor):
-            assert torch_tensor.device.type in ["cpu", "cuda"]
-            assert torch_tensor.device.index == 0
-            return torch_tensor.device.type
-
-        predict_net = self.net.Proto()
-        input_device_types = {
-            (name, 0): _get_device_type(tensor) for name, tensor in zip(self._input_blobs, inputs)
-        }
-        device_type_map = infer_device_type(
-            predict_net, known_status=input_device_types, device_name_style="pytorch"
-        )
-        ssa, versions = core.get_ssa(predict_net)
-        versioned_outputs = [(name, versions[name]) for name in predict_net.external_output]
-        output_devices = [device_type_map[outp] for outp in versioned_outputs]
-        return output_devices
-
-    def forward(self, inputs):
-        """
-        Args:
-            inputs (tuple[torch.Tensor])
-
-        Returns:
-            tuple[torch.Tensor]
-        """
-        assert len(inputs) == len(self._input_blobs), (
-            f"Length of inputs ({len(inputs)}) "
-            f"doesn't match the required input blobs: {self._input_blobs}"
-        )
-
-        with ScopedWS(self.ws_name, is_reset=False, is_cleanup=False) as ws:
-            for b, tensor in zip(self._input_blobs, inputs):
-                ws.FeedBlob(b, tensor)
-
-            try:
-                ws.RunNet(self.net.Proto().name)
-            except RuntimeError as e:
-                if not str(e) in self._error_msgs:
-                    self._error_msgs.add(str(e))
-                    logger.warning("Encountered new RuntimeError: \n{}".format(str(e)))
-                logger.warning("Catch the error and use partial results.")
-
-            c2_outputs = [ws.FetchBlob(b) for b in self.net.Proto().external_output]
-            # Remove outputs of current run, this is necessary in order to
-            # prevent fetching the result from previous run if the model fails
-            # in the middle.
-            for b in self.net.Proto().external_output:
-                # Needs to create uninitialized blob to make the net runable.
-                # This is "equivalent" to: ws.RemoveBlob(b) then ws.CreateBlob(b),
-                # but there'no such API.
-                ws.FeedBlob(b, f"{b}, a C++ native class of type nullptr (uninitialized).")
-
-        # Cast output to torch.Tensor on the desired device
-        output_devices = (
-            self._infer_output_devices(inputs)
-            if any(t.device.type != "cpu" for t in inputs)
-            else ["cpu" for _ in self.net.Proto().external_output]
-        )
-
-        outputs = []
-        for name, c2_output, device in zip(
-            self.net.Proto().external_output, c2_outputs, output_devices
-        ):
-            if not isinstance(c2_output, np.ndarray):
-                raise RuntimeError(
-                    "Invalid output for blob {}, received: {}".format(name, c2_output)
-                )
-            outputs.append(torch.tensor(c2_output).to(device=device))
-        return tuple(outputs)
-
-
-class ProtobufDetectionModel(torch.nn.Module):
-    """
-    A class works just like a pytorch meta arch in terms of inference, but running
-    caffe2 model under the hood.
-    """
-
-    def __init__(self, predict_net, init_net, *, convert_outputs=None):
-        """
-        Args:
-            predict_net, init_net (core.Net): caffe2 nets
-            convert_outptus (callable): a function that converts caffe2
-                outputs to the same format of the original pytorch model.
-                By default, use the one defined in the caffe2 meta_arch.
-        """
-        super().__init__()
-        self.protobuf_model = ProtobufModel(predict_net, init_net)
-        self.size_divisibility = get_pb_arg_vali(predict_net, "size_divisibility", 0)
-        self.device = get_pb_arg_vals(predict_net, "device", b"cpu").decode("ascii")
-
-        if convert_outputs is None:
-            meta_arch = get_pb_arg_vals(predict_net, "meta_architecture", b"GeneralizedRCNN")
-            meta_arch = META_ARCH_CAFFE2_EXPORT_TYPE_MAP[meta_arch.decode("ascii")]
-            self._convert_outputs = meta_arch.get_outputs_converter(predict_net, init_net)
-        else:
-            self._convert_outputs = convert_outputs
-
-    def _convert_inputs(self, batched_inputs):
-        # currently all models convert inputs in the same way
-        return convert_batched_inputs_to_c2_format(
-            batched_inputs, self.size_divisibility, self.device
-        )
-
-    def forward(self, batched_inputs):
-        c2_inputs = self._convert_inputs(batched_inputs)
-        c2_results = self.protobuf_model(c2_inputs)
-        c2_results = dict(zip(self.protobuf_model.net.Proto().external_output, c2_results))
-        return self._convert_outputs(batched_inputs, c2_inputs, c2_results)
diff --git a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py b/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py
deleted file mode 100644
index e7a9f3a323ddbe75845b668ee6b40c5385d206c3..0000000000000000000000000000000000000000
--- a/spaces/ynhe/AskAnything/models/grit_src/third_party/CenterNet2/detectron2/layers/mask_ops.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-import numpy as np
-from typing import Tuple
-import torch
-from PIL import Image
-from torch.nn import functional as F
-
-__all__ = ["paste_masks_in_image"]
-
-
-BYTES_PER_FLOAT = 4
-# TODO: This memory limit may be too much or too little. It would be better to
-# determine it based on available resources.
-GPU_MEM_LIMIT = 1024 ** 3  # 1 GB memory limit
-
-
-def _do_paste_mask(masks, boxes, img_h: int, img_w: int, skip_empty: bool = True):
-    """
-    Args:
-        masks: N, 1, H, W
-        boxes: N, 4
-        img_h, img_w (int):
-        skip_empty (bool): only paste masks within the region that
-            tightly bound all boxes, and returns the results this region only.
-            An important optimization for CPU.
-
-    Returns:
-        if skip_empty == False, a mask of shape (N, img_h, img_w)
-        if skip_empty == True, a mask of shape (N, h', w'), and the slice
-            object for the corresponding region.
-    """
-    # On GPU, paste all masks together (up to chunk size)
-    # by using the entire image to sample the masks
-    # Compared to pasting them one by one,
-    # this has more operations but is faster on COCO-scale dataset.
-    device = masks.device
-
-    if skip_empty and not torch.jit.is_scripting():
-        x0_int, y0_int = torch.clamp(boxes.min(dim=0).values.floor()[:2] - 1, min=0).to(
-            dtype=torch.int32
-        )
-        x1_int = torch.clamp(boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
-        y1_int = torch.clamp(boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
-    else:
-        x0_int, y0_int = 0, 0
-        x1_int, y1_int = img_w, img_h
-    x0, y0, x1, y1 = torch.split(boxes, 1, dim=1)  # each is Nx1
-
-    N = masks.shape[0]
-
-    img_y = torch.arange(y0_int, y1_int, device=device, dtype=torch.float32) + 0.5
-    img_x = torch.arange(x0_int, x1_int, device=device, dtype=torch.float32) + 0.5
-    img_y = (img_y - y0) / (y1 - y0) * 2 - 1
-    img_x = (img_x - x0) / (x1 - x0) * 2 - 1
-    # img_x, img_y have shapes (N, w), (N, h)
-
-    gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
-    gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
-    grid = torch.stack([gx, gy], dim=3)
-
-    if not torch.jit.is_scripting():
-        if not masks.dtype.is_floating_point:
-            masks = masks.float()
-    img_masks = F.grid_sample(masks, grid.to(masks.dtype), align_corners=False)
-
-    if skip_empty and not torch.jit.is_scripting():
-        return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
-    else:
-        return img_masks[:, 0], ()
-
-
-# Annotate boxes as Tensor (but not Boxes) in order to use scripting
-@torch.jit.script_if_tracing
-def paste_masks_in_image(
-    masks: torch.Tensor, boxes: torch.Tensor, image_shape: Tuple[int, int], threshold: float = 0.5
-):
-    """
-    Paste a set of masks that are of a fixed resolution (e.g., 28 x 28) into an image.
-    The location, height, and width for pasting each mask is determined by their
-    corresponding bounding boxes in boxes.
-
-    Note:
-        This is a complicated but more accurate implementation. In actual deployment, it is
-        often enough to use a faster but less accurate implementation.
-        See :func:`paste_mask_in_image_old` in this file for an alternative implementation.
-
-    Args:
-        masks (tensor): Tensor of shape (Bimg, Hmask, Wmask), where Bimg is the number of
-            detected object instances in the image and Hmask, Wmask are the mask width and mask
-            height of the predicted mask (e.g., Hmask = Wmask = 28). Values are in [0, 1].
-        boxes (Boxes or Tensor): A Boxes of length Bimg or Tensor of shape (Bimg, 4).
-            boxes[i] and masks[i] correspond to the same object instance.
-        image_shape (tuple): height, width
-        threshold (float): A threshold in [0, 1] for converting the (soft) masks to
-            binary masks.
-
-    Returns:
-        img_masks (Tensor): A tensor of shape (Bimg, Himage, Wimage), where Bimg is the
-        number of detected object instances and Himage, Wimage are the image width
-        and height. img_masks[i] is a binary mask for object instance i.
-    """
-
-    assert masks.shape[-1] == masks.shape[-2], "Only square mask predictions are supported"
-    N = len(masks)
-    if N == 0:
-        return masks.new_empty((0,) + image_shape, dtype=torch.uint8)
-    if not isinstance(boxes, torch.Tensor):
-        boxes = boxes.tensor
-    device = boxes.device
-    assert len(boxes) == N, boxes.shape
-
-    img_h, img_w = image_shape
-
-    # The actual implementation split the input into chunks,
-    # and paste them chunk by chunk.
-    if device.type == "cpu" or torch.jit.is_scripting():
-        # CPU is most efficient when they are pasted one by one with skip_empty=True
-        # so that it performs minimal number of operations.
-        num_chunks = N
-    else:
-        # GPU benefits from parallelism for larger chunks, but may have memory issue
-        # int(img_h) because shape may be tensors in tracing
-        num_chunks = int(np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / GPU_MEM_LIMIT))
-        assert (
-            num_chunks <= N
-        ), "Default GPU_MEM_LIMIT in mask_ops.py is too small; try increasing it"
-    chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
-
-    img_masks = torch.zeros(
-        N, img_h, img_w, device=device, dtype=torch.bool if threshold >= 0 else torch.uint8
-    )
-    for inds in chunks:
-        masks_chunk, spatial_inds = _do_paste_mask(
-            masks[inds, None, :, :], boxes[inds], img_h, img_w, skip_empty=device.type == "cpu"
-        )
-
-        if threshold >= 0:
-            masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
-        else:
-            # for visualization and debugging
-            masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
-
-        if torch.jit.is_scripting():  # Scripting does not use the optimized codepath
-            img_masks[inds] = masks_chunk
-        else:
-            img_masks[(inds,) + spatial_inds] = masks_chunk
-    return img_masks
-
-
-# The below are the original paste function (from Detectron1) which has
-# larger quantization error.
-# It is faster on CPU, while the aligned one is faster on GPU thanks to grid_sample.
-
-
-def paste_mask_in_image_old(mask, box, img_h, img_w, threshold):
-    """
-    Paste a single mask in an image.
-    This is a per-box implementation of :func:`paste_masks_in_image`.
-    This function has larger quantization error due to incorrect pixel
-    modeling and is not used any more.
-
-    Args:
-        mask (Tensor): A tensor of shape (Hmask, Wmask) storing the mask of a single
-            object instance. Values are in [0, 1].
-        box (Tensor): A tensor of shape (4, ) storing the x0, y0, x1, y1 box corners
-            of the object instance.
-        img_h, img_w (int): Image height and width.
-        threshold (float): Mask binarization threshold in [0, 1].
-
-    Returns:
-        im_mask (Tensor):
-            The resized and binarized object mask pasted into the original
-            image plane (a tensor of shape (img_h, img_w)).
-    """
-    # Conversion from continuous box coordinates to discrete pixel coordinates
-    # via truncation (cast to int32). This determines which pixels to paste the
-    # mask onto.
-    box = box.to(dtype=torch.int32)  # Continuous to discrete coordinate conversion
-    # An example (1D) box with continuous coordinates (x0=0.7, x1=4.3) will map to
-    # a discrete coordinates (x0=0, x1=4). Note that box is mapped to 5 = x1 - x0 + 1
-    # pixels (not x1 - x0 pixels).
-    samples_w = box[2] - box[0] + 1  # Number of pixel samples, *not* geometric width
-    samples_h = box[3] - box[1] + 1  # Number of pixel samples, *not* geometric height
-
-    # Resample the mask from it's original grid to the new samples_w x samples_h grid
-    mask = Image.fromarray(mask.cpu().numpy())
-    mask = mask.resize((samples_w, samples_h), resample=Image.BILINEAR)
-    mask = np.array(mask, copy=False)
-
-    if threshold >= 0:
-        mask = np.array(mask > threshold, dtype=np.uint8)
-        mask = torch.from_numpy(mask)
-    else:
-        # for visualization and debugging, we also
-        # allow it to return an unmodified mask
-        mask = torch.from_numpy(mask * 255).to(torch.uint8)
-
-    im_mask = torch.zeros((img_h, img_w), dtype=torch.uint8)
-    x_0 = max(box[0], 0)
-    x_1 = min(box[2] + 1, img_w)
-    y_0 = max(box[1], 0)
-    y_1 = min(box[3] + 1, img_h)
-
-    im_mask[y_0:y_1, x_0:x_1] = mask[
-        (y_0 - box[1]) : (y_1 - box[1]), (x_0 - box[0]) : (x_1 - box[0])
-    ]
-    return im_mask
-
-
-# Our pixel modeling requires extrapolation for any continuous
-# coordinate < 0.5 or > length - 0.5. When sampling pixels on the masks,
-# we would like this extrapolation to be an interpolation between boundary values and zero,
-# instead of using absolute zero or boundary values.
-# Therefore `paste_mask_in_image_old` is often used with zero padding around the masks like this:
-# masks, scale = pad_masks(masks[:, 0, :, :], 1)
-# boxes = scale_boxes(boxes.tensor, scale)
-
-
-def pad_masks(masks, padding):
-    """
-    Args:
-        masks (tensor): A tensor of shape (B, M, M) representing B masks.
-        padding (int): Number of cells to pad on all sides.
-
-    Returns:
-        The padded masks and the scale factor of the padding size / original size.
-    """
-    B = masks.shape[0]
-    M = masks.shape[-1]
-    pad2 = 2 * padding
-    scale = float(M + pad2) / M
-    padded_masks = masks.new_zeros((B, M + pad2, M + pad2))
-    padded_masks[:, padding:-padding, padding:-padding] = masks
-    return padded_masks, scale
-
-
-def scale_boxes(boxes, scale):
-    """
-    Args:
-        boxes (tensor): A tensor of shape (B, 4) representing B boxes with 4
-            coords representing the corners x0, y0, x1, y1,
-        scale (float): The box scaling factor.
-
-    Returns:
-        Scaled boxes.
-    """
-    w_half = (boxes[:, 2] - boxes[:, 0]) * 0.5
-    h_half = (boxes[:, 3] - boxes[:, 1]) * 0.5
-    x_c = (boxes[:, 2] + boxes[:, 0]) * 0.5
-    y_c = (boxes[:, 3] + boxes[:, 1]) * 0.5
-
-    w_half *= scale
-    h_half *= scale
-
-    scaled_boxes = torch.zeros_like(boxes)
-    scaled_boxes[:, 0] = x_c - w_half
-    scaled_boxes[:, 2] = x_c + w_half
-    scaled_boxes[:, 1] = y_c - h_half
-    scaled_boxes[:, 3] = y_c + h_half
-    return scaled_boxes
-
-
-@torch.jit.script_if_tracing
-def _paste_masks_tensor_shape(
-    masks: torch.Tensor,
-    boxes: torch.Tensor,
-    image_shape: Tuple[torch.Tensor, torch.Tensor],
-    threshold: float = 0.5,
-):
-    """
-    A wrapper of paste_masks_in_image where image_shape is Tensor.
-    During tracing, shapes might be tensors instead of ints. The Tensor->int
-    conversion should be scripted rather than traced.
-    """
-    return paste_masks_in_image(masks, boxes, (int(image_shape[0]), int(image_shape[1])), threshold)
diff --git a/spaces/ysharma/OSChatbots_ChatGPT_ToeToToe/README.md b/spaces/ysharma/OSChatbots_ChatGPT_ToeToToe/README.md
deleted file mode 100644
index 063daee6fdabcc9c2ba69522bf68ccc2c3c8f9d4..0000000000000000000000000000000000000000
--- a/spaces/ysharma/OSChatbots_ChatGPT_ToeToToe/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
----
-title: OSChatbots ChatGPT ToeToToe
-emoji: 🏢
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.20.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/spaces/yuan1615/EmpathyTTS/app.py b/spaces/yuan1615/EmpathyTTS/app.py
deleted file mode 100644
index 9c9be92a9086ee1d786130c7f1923ef3f00dfbd0..0000000000000000000000000000000000000000
--- a/spaces/yuan1615/EmpathyTTS/app.py
+++ /dev/null
@@ -1,80 +0,0 @@
-import argparse
-from models import SynthesizerTrn
-from text.symbols import symbols
-import utils
-import gradio as gr
-import tempfile
-from scipy.io import wavfile
-import numpy as np
-import re
-from synthesize_fastapi import *
-config = "./configs/baker_base.json"
-
-import subprocess
-def install(name):
-    subprocess.call(['pip', 'install', 'packages/ttsfrd-0.0.4-cp38-cp38-linux_x86_64.whl'])
-install('ttsfrd')
-
-import ttsfrd
-
-print("---------- Loading VITS Model ----------")
-hps = utils.get_hparams_from_file(config)
-net_g = SynthesizerTrn(
-    len(symbols),
-    hps.data.filter_length // 2 + 1,
-    hps.train.segment_size // hps.data.hop_length,
-    **hps.model)
-_ = net_g.eval()
-
-_ = utils.load_checkpoint("./ckpt/pretrained_baker.pth", net_g, None)
-
-lexicon_mandarin = read_lexicon("./lexicon/pinyin-lexicon-r.txt")
-
-# 解压文件
-import zipfile
-f = zipfile.ZipFile("./resource.zip", 'r')  # 压缩文件位置
-for file in f.namelist():
-    f.extract(file, "./")  # 解压位置
-f.close()
-
-frontend = ttsfrd.TtsFrontendEngine()
-model_dir = './resource'
-frontend.initialize(model_dir)
-frontend.set_lang_type('zhcn')
-
-
-def tts(text):
-    audio_all = np.zeros(1, dtype=np.int16)  # 设置初始音频
-    # 删除英文
-    text = re.sub('[a-zA-Z]', '', text)
-    text = text.replace(',[object Object]', '')
-    pinyin_list, prosody_list = g2p_mandarin(frontend, text)
-    for texts, phone_prosody in zip(pinyin_list, prosody_list):
-        print(texts)
-        print(phone_prosody)
-        stn_tst = get_text(texts, hps)
-        prosody = get_prosody(phone_prosody, hps)
-        with torch.no_grad():
-            x_tst = stn_tst.unsqueeze(0)
-            prosody = prosody.unsqueeze(0)
-            x_tst_lengths = torch.LongTensor([stn_tst.size(0)])
-            audio = net_g.infer(x_tst, x_tst_lengths, prosody, noise_scale=.667, noise_scale_w=0.0, length_scale=1)[0][
-                        0, 0].data.cpu().float().numpy() * 32767.0
-        i = np.random.uniform(0.12, 0.35, 1)[0]
-        space_time = np.zeros(int(i * 22050), dtype=np.int16)
-        audio_all = np.concatenate((audio_all, audio, space_time))
-    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
-        wavfile.write(
-            fp.name,
-            22050,
-            audio_all.astype(np.int16),
-        )
-    return fp.name
-
-
-inputs = [gr.inputs.Textbox(label="Input Text", default='国务院银行业监督管理机构会按照国家法律法规规定的程序处理', lines=6)]
-outputs = gr.Audio(label="Output")
-interface = gr.Interface(fn=tts, inputs=inputs, outputs=outputs,
-                         title='Empathy-TTS',
-                         description='Note: This space is running on CPU, inference times will be higher.')
-interface.launch(server_name='0.0.0.0')
diff --git a/spaces/yuezih/BLIP-SMILE/SMILE/BLIP/train_caption.py b/spaces/yuezih/BLIP-SMILE/SMILE/BLIP/train_caption.py
deleted file mode 100644
index efdf7bb052c43af2c8fab5e8b72f974534fbdfbc..0000000000000000000000000000000000000000
--- a/spaces/yuezih/BLIP-SMILE/SMILE/BLIP/train_caption.py
+++ /dev/null
@@ -1,221 +0,0 @@
-'''
- * Copyright (c) 2022, salesforce.com, inc.
- * All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
- * By Junnan Li
-
- * Modified by Zihao Yue
-'''
-
-import argparse
-import os
-try:
-    import ruamel_yaml as yaml
-except:
-    import ruamel.yaml as yaml
-import numpy as np
-import random
-import time
-import datetime
-import json
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-from torch.utils.data import DataLoader
-
-from models.model import caption_model
-import utils
-from utils import warmup_lr_schedule, step_lr_schedule, cosine_lr_schedule
-from data import create_dataset, create_sampler, create_loader
-from data.utils import save_result, coco_caption_eval
-
-def train(model, data_loader, optimizer, epoch, device):
-    # train
-    model.train()  
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
-    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
-    header = 'Train Caption Epoch: [{}]'.format(epoch)
-    print_freq = 50
-
-    for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
-        image = image.to(device)       
-        
-        loss = model(image, caption)      
-        
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()    
-        
-        metric_logger.update(loss=loss.item())
-        metric_logger.update(lr=optimizer.param_groups[0]["lr"])
-
-    # gather the stats from all processes
-    metric_logger.synchronize_between_processes()
-    print("Averaged stats:", metric_logger.global_avg())     
-    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}  
-
-
-@torch.no_grad()
-def evaluate(model, data_loader, device, config):
-    # evaluate
-    model.eval() 
-    
-    metric_logger = utils.MetricLogger(delimiter="  ")
-    header = 'Caption generation:'
-    print_freq = 10
-
-    result = []
-    for image, image_id in metric_logger.log_every(data_loader, print_freq, header): 
-        
-        image = image.to(device)       
-        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'], min_length=config['min_length'])
-        
-        for caption, img_id in zip(captions, image_id):
-            result.append({"image_id": img_id.item(), "caption": caption})
-  
-    return result
-
-
-def main(args, config):
-    utils.init_distributed_mode(args)
-    
-    device = torch.device(args.device)
-
-    # fix the seed for reproducibility
-    seed = args.seed + utils.get_rank()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
-    random.seed(seed)
-    cudnn.benchmark = True
-
-    #### Dataset #### 
-    print("Creating captioning dataset")
-    train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', config)  
-
-    if args.distributed:
-        num_tasks = utils.get_world_size()
-        global_rank = utils.get_rank()            
-        samplers = create_sampler([train_dataset,val_dataset,test_dataset], [True,False,False], num_tasks, global_rank)         
-    else:
-        samplers = [None, None, None]
-
-    train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset],samplers,
-                                                          batch_size=[config['batch_size']]*3,num_workers=[4,4,4],
-                                                          is_trains=[True, False, False], collate_fns=[None,None,None])         
-
-    #### Model #### 
-    print("Creating model")
-    model = caption_model(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'], 
-                           vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'], 
-                           prompt=config['prompt'])
-
-    model = model.to(device)
-    
-    model_without_ddp = model
-    if args.distributed:
-        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
-        model_without_ddp = model.module    
-    
-    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])
-
-    best = 0
-    best_epoch = 0
-
-    print("Start training")
-    start_time = time.time()    
-    for epoch in range(0, config['max_epoch']):
-        if not args.evaluate:        
-            if args.distributed:
-                train_loader.sampler.set_epoch(epoch)
-                
-            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])
-                
-            train_stats = train(model, train_loader, optimizer, epoch, device) 
-        
-        if args.eval_split == 'val' or not args.evaluate:
-            val_result = evaluate(model_without_ddp, val_loader, device, config)  
-            val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d'%epoch, remove_duplicate='image_id')
-        else:
-            test_result = evaluate(model_without_ddp, test_loader, device, config)  
-            test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d'%epoch, remove_duplicate='image_id')
-
-        if utils.is_main_process():
-            
-            if args.eval_split == 'val' or not args.evaluate:
-                coco_val = coco_caption_eval(config['coco_gt_root'],val_result_file,'val')
-            else:
-                coco_test = coco_caption_eval(config['coco_gt_root'],test_result_file,'test')
-            
-            if args.evaluate:            
-                if args.eval_split == 'val':
-                    log_stats = {
-                        **{f'val_{k}': v for k, v in coco_val.eval.items()},                     
-                                }
-                else:
-                    log_stats = {
-                        **{f'test_{k}': v for k, v in coco_test.eval.items()},                       
-                                }
-                with open(os.path.join(args.output_dir, "evaluate.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")                   
-            else:             
-                save_obj = {
-                    'model': model_without_ddp.state_dict(),
-                    'optimizer': optimizer.state_dict(),
-                    'config': config,
-                    'epoch': epoch,
-                }
-
-                if coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best:
-                    best = coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4']
-                    best_epoch = epoch                
-                    # torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))
-                # save each epoch
-                torch.save(save_obj, os.path.join(args.output_dir, 'epoch%d.pth'%epoch))
-                    
-                log_stats = {**{f'train_{k}': float(v) for k, v in train_stats.items()},
-                             **{f'val_{k}': v for k, v in coco_val.eval.items()},                     
-                             'epoch': epoch,
-                             'best_epoch': best_epoch,
-                            }
-                with open(os.path.join(args.output_dir, "log.txt"),"a") as f:
-                    f.write(json.dumps(log_stats) + "\n")     
-                    
-        if args.evaluate: 
-            break
-        dist.barrier()     
-
-    total_time = time.time() - start_time
-    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
-    print('Training time {}'.format(total_time_str)) 
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--config', default='./configs/caption_coco.yaml')
-    parser.add_argument('--output_dir', default='output/caption_coco')        
-    parser.add_argument('--evaluate', action='store_true')    
-    parser.add_argument('--device', default='cuda')
-    parser.add_argument('--seed', default=42, type=int)
-    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')    
-    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
-    parser.add_argument('--distributed', default=True, type=bool)
-    parser.add_argument('--eval_split', default='val', type=str)
-    args = parser.parse_args()
-
-    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)
-
-    args.result_dir = os.path.join(args.output_dir, 'result')
-
-    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
-    Path(args.result_dir).mkdir(parents=True, exist_ok=True)
-        
-    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))    
-    
-    main(args, config)
\ No newline at end of file
diff --git a/spaces/z-uo/streamlit_music_demo/app.py b/spaces/z-uo/streamlit_music_demo/app.py
deleted file mode 100644
index 64beee86a5c60aec8301814f73f542e6ae1098a9..0000000000000000000000000000000000000000
--- a/spaces/z-uo/streamlit_music_demo/app.py
+++ /dev/null
@@ -1,58 +0,0 @@
-import os
-import io
-
-import streamlit as st
-from PIL import Image
-
-import pretty_midi
-import music21
-import note_seq
-from scipy.io import wavfile
-
-import numpy as np
-
-st.title('Music Demo')
-st.markdown('This example need a .xml music file as input and show partiture, piano roll and a simply wav. You can generate it from MuseScore or similar software.')
-
-example_1 = st.button('Test Example')
-
-uploaded_file = st.file_uploader("Choose a .xml music file", type='xml')
-
-if uploaded_file is not None or example_1:
-    if uploaded_file is not None:
-        # save file
-        with open(os.path.join(os.getcwd(), uploaded_file.name), "wb") as f:
-            f.write(uploaded_file.getbuffer())
-        file_path = os.path.join(os.getcwd(), uploaded_file.name)
-    else:
-        file_path = 'examples/test.xml'
-
-    # Show partiture with music21
-    music = music21.converter.parse(file_path)
-    streaming_partiture = str(music.write('lily.png'))
-    image = Image.open(streaming_partiture)
-    st.text('Partiture')
-    st.image(image)
-
-    # save to mid
-    midi_path = file_path + '.mid'
-    music.write('midi', fp=midi_path)
-
-    # plot piano rol
-    unconditional_ns = note_seq.midi_file_to_note_sequence(midi_path)
-    piano_roll = note_seq.plot_sequence(unconditional_ns, show_figure=False)
-    st.text('Piano Roll')
-    st.bokeh_chart(piano_roll, use_container_width=True)
-
-    # generate wav
-    with st.spinner(f"Sinthezizing to wav"):
-        midi_data = pretty_midi.PrettyMIDI(midi_path)
-        audio_data = midi_data.synthesize()
-        audio_data = np.int16(
-            audio_data / np.max(np.abs(audio_data)) * 32767 * 0.9
-        )  # -- Normalize for 16 bit audio https://github.com/jkanner/streamlit-audio/blob/main/helper.py
-        virtualfile = io.BytesIO()
-        wavfile.write(virtualfile, 44100, audio_data)
-
-    st.audio(virtualfile)
-    st.markdown("Download the audio by right-clicking on the media player")
diff --git a/spaces/zhan66/vits-simple-api/bert_vits2/text/symbols.py b/spaces/zhan66/vits-simple-api/bert_vits2/text/symbols.py
deleted file mode 100644
index 0639a3e07665f9fb0e26f7002c811b987f161ba1..0000000000000000000000000000000000000000
--- a/spaces/zhan66/vits-simple-api/bert_vits2/text/symbols.py
+++ /dev/null
@@ -1,198 +0,0 @@
-punctuation = ["!", "?", "…", ",", ".", "'", "-"]
-pu_symbols = punctuation + ["SP", "UNK"]
-pad = "_"
-
-# chinese
-zh_symbols = [
-    "E",
-    "En",
-    "a",
-    "ai",
-    "an",
-    "ang",
-    "ao",
-    "b",
-    "c",
-    "ch",
-    "d",
-    "e",
-    "ei",
-    "en",
-    "eng",
-    "er",
-    "f",
-    "g",
-    "h",
-    "i",
-    "i0",
-    "ia",
-    "ian",
-    "iang",
-    "iao",
-    "ie",
-    "in",
-    "ing",
-    "iong",
-    "ir",
-    "iu",
-    "j",
-    "k",
-    "l",
-    "m",
-    "n",
-    "o",
-    "ong",
-    "ou",
-    "p",
-    "q",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "u",
-    "ua",
-    "uai",
-    "uan",
-    "uang",
-    "ui",
-    "un",
-    "uo",
-    "v",
-    "van",
-    "ve",
-    "vn",
-    "w",
-    "x",
-    "y",
-    "z",
-    "zh",
-    "AA",
-    "EE",
-    "OO",
-]
-num_zh_tones = 6
-
-# japanese
-ja_symbols_legacy = ['I', 'N', 'U', 'a', 'b', 'by', 'ch', 'cl', 'd', 'dy', 'e', 'f', 'g', 'gy', 'h', 'hy', 'i', 'j',
-                     'k', 'ky',
-                     'm', 'my', 'n', 'ny', 'o', 'p', 'py', 'r', 'ry', 's', 'sh', 't', 'ts', 'u', 'V', 'w', 'y', 'z']
-ja_symbols = [
-    "N",
-    "a",
-    "a:",
-    "b",
-    "by",
-    "ch",
-    "d",
-    "dy",
-    "e",
-    "e:",
-    "f",
-    "g",
-    "gy",
-    "h",
-    "hy",
-    "i",
-    "i:",
-    "j",
-    "k",
-    "ky",
-    "m",
-    "my",
-    "n",
-    "ny",
-    "o",
-    "o:",
-    "p",
-    "py",
-    "q",
-    "r",
-    "ry",
-    "s",
-    "sh",
-    "t",
-    "ts",
-    "ty",
-    "u",
-    "u:",
-    "w",
-    "y",
-    "z",
-    "zy",
-]
-num_ja_tones = 1
-
-# English
-en_symbols = [
-    "aa",
-    "ae",
-    "ah",
-    "ao",
-    "aw",
-    "ay",
-    "b",
-    "ch",
-    "d",
-    "dh",
-    "eh",
-    "er",
-    "ey",
-    "f",
-    "g",
-    "hh",
-    "ih",
-    "iy",
-    "jh",
-    "k",
-    "l",
-    "m",
-    "n",
-    "ng",
-    "ow",
-    "oy",
-    "p",
-    "r",
-    "s",
-    "sh",
-    "t",
-    "th",
-    "uh",
-    "uw",
-    "V",
-    "w",
-    "y",
-    "z",
-    "zh",
-]
-num_en_tones = 4
-
-normal_symbols = sorted(set(zh_symbols + ja_symbols + en_symbols))
-symbols = [pad] + normal_symbols + pu_symbols
-sil_phonemes_ids = [symbols.index(i) for i in pu_symbols]
-
-# legacy
-normal_symbols_legacy = sorted(set(zh_symbols + ja_symbols_legacy + en_symbols))
-symbols_legacy = [pad] + normal_symbols_legacy + pu_symbols
-sil_phonemes_ids_legacy = [symbols_legacy.index(i) for i in pu_symbols]
-
-# combine all tones
-num_tones = num_zh_tones + num_ja_tones + num_en_tones
-
-# language maps
-language_id_map = {"zh": 0, "ja": 1, "en": 2}
-num_languages = len(language_id_map.keys())
-
-language_tone_start_map = {
-    "zh": 0,
-    "ja": num_zh_tones,
-    "en": num_zh_tones + num_ja_tones,
-}
-
-if __name__ == "__main__":
-    zh = set(zh_symbols)
-    en = set(en_symbols)
-    ja = set(ja_symbols)
-    print(zh)
-    print(en)
-    print(ja)
-    print(sorted(zh & en))
diff --git a/spaces/zhaoys/wfms-kuiwenc/src/components/chat-progress.tsx b/spaces/zhaoys/wfms-kuiwenc/src/components/chat-progress.tsx
deleted file mode 100644
index 58101fd7e640b82ea00a54235ad25686dbe36864..0000000000000000000000000000000000000000
--- a/spaces/zhaoys/wfms-kuiwenc/src/components/chat-progress.tsx
+++ /dev/null
@@ -1,22 +0,0 @@
-import { MemoizedReactMarkdown } from "./markdown"
-import { SVG } from "./ui/svg"
-import CheckMarkIcon from '@/assets/images/check-mark.svg'
-
-interface ChatProgressProps {
-  progress?: string[]
-}
-
-export function ChatProgress({ progress = [] }: ChatProgressProps) {
-  return progress?.length ? (
-    <div className="chat-progress my-3">
-      {progress.map((item, index) => (
-        <div key={index} className="chat-progress__item">
-          <SVG src={CheckMarkIcon} width={28} fill="#13a10e" />
-          <div className="body-1 meta-text">
-            <MemoizedReactMarkdown>{item}</MemoizedReactMarkdown>
-          </div>
-        </div>
-      ))}
-    </div>
-  ) : null
-}
diff --git a/spaces/zhuowen999/vits_chinese/modules.py b/spaces/zhuowen999/vits_chinese/modules.py
deleted file mode 100644
index 289f4e3bdc7e1c783766b4c20bdf4475e65c932b..0000000000000000000000000000000000000000
--- a/spaces/zhuowen999/vits_chinese/modules.py
+++ /dev/null
@@ -1,522 +0,0 @@
-import copy
-import math
-import numpy as np
-import scipy
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
-from torch.nn.utils import weight_norm, remove_weight_norm
-
-import commons
-from commons import init_weights, get_padding
-from transforms import piecewise_rational_quadratic_transform
-
-
-LRELU_SLOPE = 0.1
-
-
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super().__init__()
-        self.channels = channels
-        self.eps = eps
-
-        self.gamma = nn.Parameter(torch.ones(channels))
-        self.beta = nn.Parameter(torch.zeros(channels))
-
-    def forward(self, x):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.hidden_channels = hidden_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-        assert n_layers > 1, "Number of layers should be larger than 0."
-
-        self.conv_layers = nn.ModuleList()
-        self.norm_layers = nn.ModuleList()
-        self.conv_layers.append(
-            nn.Conv1d(
-                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
-            )
-        )
-        self.norm_layers.append(LayerNorm(hidden_channels))
-        self.relu_drop = nn.Sequential(nn.ReLU(), nn.Dropout(p_dropout))
-        for _ in range(n_layers - 1):
-            self.conv_layers.append(
-                nn.Conv1d(
-                    hidden_channels,
-                    hidden_channels,
-                    kernel_size,
-                    padding=kernel_size // 2,
-                )
-            )
-            self.norm_layers.append(LayerNorm(hidden_channels))
-        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask):
-        x_org = x
-        for i in range(self.n_layers):
-            x = self.conv_layers[i](x * x_mask)
-            x = self.norm_layers[i](x)
-            x = self.relu_drop(x)
-        x = x_org + self.proj(x)
-        return x * x_mask
-
-
-class DDSConv(nn.Module):
-    """
-    Dialted and Depth-Separable Convolution
-    """
-
-    def __init__(self, channels, kernel_size, n_layers, p_dropout=0.0):
-        super().__init__()
-        self.channels = channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.p_dropout = p_dropout
-
-        self.drop = nn.Dropout(p_dropout)
-        self.convs_sep = nn.ModuleList()
-        self.convs_1x1 = nn.ModuleList()
-        self.norms_1 = nn.ModuleList()
-        self.norms_2 = nn.ModuleList()
-        for i in range(n_layers):
-            dilation = kernel_size**i
-            padding = (kernel_size * dilation - dilation) // 2
-            self.convs_sep.append(
-                nn.Conv1d(
-                    channels,
-                    channels,
-                    kernel_size,
-                    groups=channels,
-                    dilation=dilation,
-                    padding=padding,
-                )
-            )
-            self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
-            self.norms_1.append(LayerNorm(channels))
-            self.norms_2.append(LayerNorm(channels))
-
-    def forward(self, x, x_mask, g=None):
-        if g is not None:
-            x = x + g
-        for i in range(self.n_layers):
-            y = self.convs_sep[i](x * x_mask)
-            y = self.norms_1[i](y)
-            y = F.gelu(y)
-            y = self.convs_1x1[i](y)
-            y = self.norms_2[i](y)
-            y = F.gelu(y)
-            y = self.drop(y)
-            x = x + y
-        return x * x_mask
-
-
-class WN(torch.nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        gin_channels=0,
-        p_dropout=0,
-    ):
-        super(WN, self).__init__()
-        assert kernel_size % 2 == 1
-        self.hidden_channels = hidden_channels
-        self.kernel_size = (kernel_size,)
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.gin_channels = gin_channels
-        self.p_dropout = p_dropout
-
-        self.in_layers = torch.nn.ModuleList()
-        self.res_skip_layers = torch.nn.ModuleList()
-        self.drop = nn.Dropout(p_dropout)
-
-        if gin_channels != 0:
-            cond_layer = torch.nn.Conv1d(
-                gin_channels, 2 * hidden_channels * n_layers, 1
-            )
-            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
-
-        for i in range(n_layers):
-            dilation = dilation_rate**i
-            padding = int((kernel_size * dilation - dilation) / 2)
-            in_layer = torch.nn.Conv1d(
-                hidden_channels,
-                2 * hidden_channels,
-                kernel_size,
-                dilation=dilation,
-                padding=padding,
-            )
-            in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
-            self.in_layers.append(in_layer)
-
-            # last one is not necessary
-            if i < n_layers - 1:
-                res_skip_channels = 2 * hidden_channels
-            else:
-                res_skip_channels = hidden_channels
-
-            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
-            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name="weight")
-            self.res_skip_layers.append(res_skip_layer)
-
-    def forward(self, x, x_mask, g=None, **kwargs):
-        output = torch.zeros_like(x)
-        n_channels_tensor = torch.IntTensor([self.hidden_channels])
-
-        if g is not None:
-            g = self.cond_layer(g)
-
-        for i in range(self.n_layers):
-            x_in = self.in_layers[i](x)
-            if g is not None:
-                cond_offset = i * 2 * self.hidden_channels
-                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
-            else:
-                g_l = torch.zeros_like(x_in)
-
-            acts = commons.fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
-            acts = self.drop(acts)
-
-            res_skip_acts = self.res_skip_layers[i](acts)
-            if i < self.n_layers - 1:
-                res_acts = res_skip_acts[:, : self.hidden_channels, :]
-                x = (x + res_acts) * x_mask
-                output = output + res_skip_acts[:, self.hidden_channels :, :]
-            else:
-                output = output + res_skip_acts
-        return output * x_mask
-
-    def remove_weight_norm(self):
-        if self.gin_channels != 0:
-            torch.nn.utils.remove_weight_norm(self.cond_layer)
-        for l in self.in_layers:
-            torch.nn.utils.remove_weight_norm(l)
-        for l in self.res_skip_layers:
-            torch.nn.utils.remove_weight_norm(l)
-
-
-class ResBlock1(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super(ResBlock1, self).__init__()
-        self.convs1 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[2],
-                        padding=get_padding(kernel_size, dilation[2]),
-                    )
-                ),
-            ]
-        )
-        self.convs1.apply(init_weights)
-
-        self.convs2 = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=1,
-                        padding=get_padding(kernel_size, 1),
-                    )
-                ),
-            ]
-        )
-        self.convs2.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c1, c2 in zip(self.convs1, self.convs2):
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c1(xt)
-            xt = F.leaky_relu(xt, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c2(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs1:
-            remove_weight_norm(l)
-        for l in self.convs2:
-            remove_weight_norm(l)
-
-
-class ResBlock2(torch.nn.Module):
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super(ResBlock2, self).__init__()
-        self.convs = nn.ModuleList(
-            [
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[0],
-                        padding=get_padding(kernel_size, dilation[0]),
-                    )
-                ),
-                weight_norm(
-                    Conv1d(
-                        channels,
-                        channels,
-                        kernel_size,
-                        1,
-                        dilation=dilation[1],
-                        padding=get_padding(kernel_size, dilation[1]),
-                    )
-                ),
-            ]
-        )
-        self.convs.apply(init_weights)
-
-    def forward(self, x, x_mask=None):
-        for c in self.convs:
-            xt = F.leaky_relu(x, LRELU_SLOPE)
-            if x_mask is not None:
-                xt = xt * x_mask
-            xt = c(xt)
-            x = xt + x
-        if x_mask is not None:
-            x = x * x_mask
-        return x
-
-    def remove_weight_norm(self):
-        for l in self.convs:
-            remove_weight_norm(l)
-
-
-class Log(nn.Module):
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
-            logdet = torch.sum(-y, [1, 2])
-            return y, logdet
-        else:
-            x = torch.exp(x) * x_mask
-            return x
-
-
-class Flip(nn.Module):
-    def forward(self, x, *args, reverse=False, **kwargs):
-        x = torch.flip(x, [1])
-        if not reverse:
-            logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
-            return x, logdet
-        else:
-            return x
-
-
-class ElementwiseAffine(nn.Module):
-    def __init__(self, channels):
-        super().__init__()
-        self.channels = channels
-        self.m = nn.Parameter(torch.zeros(channels, 1))
-        self.logs = nn.Parameter(torch.zeros(channels, 1))
-
-    def forward(self, x, x_mask, reverse=False, **kwargs):
-        if not reverse:
-            y = self.m + torch.exp(self.logs) * x
-            y = y * x_mask
-            logdet = torch.sum(self.logs * x_mask, [1, 2])
-            return y, logdet
-        else:
-            x = (x - self.m) * torch.exp(-self.logs) * x_mask
-            return x
-
-
-class ResidualCouplingLayer(nn.Module):
-    def __init__(
-        self,
-        channels,
-        hidden_channels,
-        kernel_size,
-        dilation_rate,
-        n_layers,
-        p_dropout=0,
-        gin_channels=0,
-        mean_only=False,
-    ):
-        assert channels % 2 == 0, "channels should be divisible by 2"
-        super().__init__()
-        self.channels = channels
-        self.hidden_channels = hidden_channels
-        self.kernel_size = kernel_size
-        self.dilation_rate = dilation_rate
-        self.n_layers = n_layers
-        self.half_channels = channels // 2
-        self.mean_only = mean_only
-
-        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
-        self.enc = WN(
-            hidden_channels,
-            kernel_size,
-            dilation_rate,
-            n_layers,
-            p_dropout=p_dropout,
-            gin_channels=gin_channels,
-        )
-        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
-        self.post.weight.data.zero_()
-        self.post.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0) * x_mask
-        h = self.enc(h, x_mask, g=g)
-        stats = self.post(h) * x_mask
-        if not self.mean_only:
-            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
-        else:
-            m = stats
-            logs = torch.zeros_like(m)
-
-        if not reverse:
-            x1 = m + x1 * torch.exp(logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            logdet = torch.sum(logs, [1, 2])
-            return x, logdet
-        else:
-            x1 = (x1 - m) * torch.exp(-logs) * x_mask
-            x = torch.cat([x0, x1], 1)
-            return x
-
-    def remove_weight_norm(self):
-        self.enc.remove_weight_norm()
-
-
-class ConvFlow(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        filter_channels,
-        kernel_size,
-        n_layers,
-        num_bins=10,
-        tail_bound=5.0,
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        self.filter_channels = filter_channels
-        self.kernel_size = kernel_size
-        self.n_layers = n_layers
-        self.num_bins = num_bins
-        self.tail_bound = tail_bound
-        self.half_channels = in_channels // 2
-
-        self.pre = nn.Conv1d(self.half_channels, filter_channels, 1)
-        self.convs = DDSConv(filter_channels, kernel_size, n_layers, p_dropout=0.0)
-        self.proj = nn.Conv1d(
-            filter_channels, self.half_channels * (num_bins * 3 - 1), 1
-        )
-        self.proj.weight.data.zero_()
-        self.proj.bias.data.zero_()
-
-    def forward(self, x, x_mask, g=None, reverse=False):
-        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
-        h = self.pre(x0)
-        h = self.convs(h, x_mask, g=g)
-        h = self.proj(h) * x_mask
-
-        b, c, t = x0.shape
-        h = h.reshape(b, c, -1, t).permute(0, 1, 3, 2)  # [b, cx?, t] -> [b, c, t, ?]
-
-        unnormalized_widths = h[..., : self.num_bins] / math.sqrt(self.filter_channels)
-        unnormalized_heights = h[..., self.num_bins : 2 * self.num_bins] / math.sqrt(
-            self.filter_channels
-        )
-        unnormalized_derivatives = h[..., 2 * self.num_bins :]
-
-        x1, logabsdet = piecewise_rational_quadratic_transform(
-            x1,
-            unnormalized_widths,
-            unnormalized_heights,
-            unnormalized_derivatives,
-            inverse=reverse,
-            tails="linear",
-            tail_bound=self.tail_bound,
-        )
-
-        x = torch.cat([x0, x1], 1) * x_mask
-        logdet = torch.sum(logabsdet * x_mask, [1, 2])
-        if not reverse:
-            return x, logdet
-        else:
-            return x